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Normalize line endings and whitespace

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This commit is contained in:
OpenCV Buildbot
2012-10-17 03:18:30 +04:00
committed by Andrey Kamaev
parent 69020da607
commit 04384a71e4
1516 changed files with 258846 additions and 258162 deletions
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58
samples/python2/_coverage.py Normal file → Executable file
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'''
Utility for measuring python opencv API coverage by samples.
'''
from glob import glob
import cv2
import re
if __name__ == '__main__':
cv2_callable = set(['cv2.'+name for name in dir(cv2) if callable( getattr(cv2, name) )])
found = set()
for fn in glob('*.py'):
print ' --- ', fn
code = open(fn).read()
found |= set(re.findall('cv2?\.\w+', code))
cv2_used = found & cv2_callable
cv2_unused = cv2_callable - cv2_used
with open('unused_api.txt', 'w') as f:
f.write('\n'.join(sorted(cv2_unused)))
r = 1.0 * len(cv2_used) / len(cv2_callable)
print '\ncv2 api coverage: %d / %d (%.1f%%)' % ( len(cv2_used), len(cv2_callable), r*100 )
print '\nold (cv) symbols:'
for s in found:
if s.startswith('cv.'):
print s
'''
Utility for measuring python opencv API coverage by samples.
'''
from glob import glob
import cv2
import re
if __name__ == '__main__':
cv2_callable = set(['cv2.'+name for name in dir(cv2) if callable( getattr(cv2, name) )])
found = set()
for fn in glob('*.py'):
print ' --- ', fn
code = open(fn).read()
found |= set(re.findall('cv2?\.\w+', code))
cv2_used = found & cv2_callable
cv2_unused = cv2_callable - cv2_used
with open('unused_api.txt', 'w') as f:
f.write('\n'.join(sorted(cv2_unused)))
r = 1.0 * len(cv2_used) / len(cv2_callable)
print '\ncv2 api coverage: %d / %d (%.1f%%)' % ( len(cv2_used), len(cv2_callable), r*100 )
print '\nold (cv) symbols:'
for s in found:
if s.startswith('cv.'):
print s

0
samples/python2/_doc.py Normal file → Executable file
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286
samples/python2/asift.py Normal file → Executable file
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'''
Affine invariant feature-based image matching sample.
This sample is similar to find_obj.py, but uses the affine transformation
space sampling technique, called ASIFT [1]. While the original implementation
is based on SIFT, you can try to use SURF or ORB detectors instead. Homography RANSAC
is used to reject outliers. Threading is used for faster affine sampling.
[1] http://www.ipol.im/pub/algo/my_affine_sift/
USAGE
asift.py [--feature=<sift|surf|orb>[-flann]] [ <image1> <image2> ]
--feature - Feature to use. Can be sift, surf of orb. Append '-flann' to feature name
to use Flann-based matcher instead bruteforce.
Press left mouse button on a feature point to see its mathcing point.
'''
import numpy as np
import cv2
import itertools as it
from multiprocessing.pool import ThreadPool
from common import Timer
from find_obj import init_feature, filter_matches, explore_match
def affine_skew(tilt, phi, img, mask=None):
'''
affine_skew(tilt, phi, img, mask=None) -> skew_img, skew_mask, Ai
Ai - is an affine transform matrix from skew_img to img
'''
h, w = img.shape[:2]
if mask is None:
mask = np.zeros((h, w), np.uint8)
mask[:] = 255
A = np.float32([[1, 0, 0], [0, 1, 0]])
if phi != 0.0:
phi = np.deg2rad(phi)
s, c = np.sin(phi), np.cos(phi)
A = np.float32([[c,-s], [ s, c]])
corners = [[0, 0], [w, 0], [w, h], [0, h]]
tcorners = np.int32( np.dot(corners, A.T) )
x, y, w, h = cv2.boundingRect(tcorners.reshape(1,-1,2))
A = np.hstack([A, [[-x], [-y]]])
img = cv2.warpAffine(img, A, (w, h), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
if tilt != 1.0:
s = 0.8*np.sqrt(tilt*tilt-1)
img = cv2.GaussianBlur(img, (0, 0), sigmaX=s, sigmaY=0.01)
img = cv2.resize(img, (0, 0), fx=1.0/tilt, fy=1.0, interpolation=cv2.INTER_NEAREST)
A[0] /= tilt
if phi != 0.0 or tilt != 1.0:
h, w = img.shape[:2]
mask = cv2.warpAffine(mask, A, (w, h), flags=cv2.INTER_NEAREST)
Ai = cv2.invertAffineTransform(A)
return img, mask, Ai
def affine_detect(detector, img, mask=None, pool=None):
'''
affine_detect(detector, img, mask=None, pool=None) -> keypoints, descrs
Apply a set of affine transormations to the image, detect keypoints and
reproject them into initial image coordinates.
See http://www.ipol.im/pub/algo/my_affine_sift/ for the details.
ThreadPool object may be passed to speedup the computation.
'''
params = [(1.0, 0.0)]
for t in 2**(0.5*np.arange(1,6)):
for phi in np.arange(0, 180, 72.0 / t):
params.append((t, phi))
def f(p):
t, phi = p
timg, tmask, Ai = affine_skew(t, phi, img)
keypoints, descrs = detector.detectAndCompute(timg, tmask)
for kp in keypoints:
x, y = kp.pt
kp.pt = tuple( np.dot(Ai, (x, y, 1)) )
if descrs is None:
descrs = []
return keypoints, descrs
keypoints, descrs = [], []
if pool is None:
ires = it.imap(f, params)
else:
ires = pool.imap(f, params)
for i, (k, d) in enumerate(ires):
print 'affine sampling: %d / %d\r' % (i+1, len(params)),
keypoints.extend(k)
descrs.extend(d)
print
return keypoints, np.array(descrs)
if __name__ == '__main__':
print __doc__
import sys, getopt
opts, args = getopt.getopt(sys.argv[1:], '', ['feature='])
opts = dict(opts)
feature_name = opts.get('--feature', 'sift-flann')
try: fn1, fn2 = args
except:
fn1 = 'data/aero1.jpg'
fn2 = 'data/aero3.jpg'
img1 = cv2.imread(fn1, 0)
img2 = cv2.imread(fn2, 0)
detector, matcher = init_feature(feature_name)
if detector != None:
print 'using', feature_name
else:
print 'unknown feature:', feature_name
sys.exit(1)
pool=ThreadPool(processes = cv2.getNumberOfCPUs())
kp1, desc1 = affine_detect(detector, img1, pool=pool)
kp2, desc2 = affine_detect(detector, img2, pool=pool)
print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))
def match_and_draw(win):
with Timer('matching'):
raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2
p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
if len(p1) >= 4:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
print '%d / %d inliers/matched' % (np.sum(status), len(status))
# do not draw outliers (there will be a lot of them)
kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag]
else:
H, status = None, None
print '%d matches found, not enough for homography estimation' % len(p1)
vis = explore_match(win, img1, img2, kp_pairs, None, H)
match_and_draw('affine find_obj')
cv2.waitKey()
cv2.destroyAllWindows()
'''
Affine invariant feature-based image matching sample.
This sample is similar to find_obj.py, but uses the affine transformation
space sampling technique, called ASIFT [1]. While the original implementation
is based on SIFT, you can try to use SURF or ORB detectors instead. Homography RANSAC
is used to reject outliers. Threading is used for faster affine sampling.
[1] http://www.ipol.im/pub/algo/my_affine_sift/
USAGE
asift.py [--feature=<sift|surf|orb>[-flann]] [ <image1> <image2> ]
--feature - Feature to use. Can be sift, surf of orb. Append '-flann' to feature name
to use Flann-based matcher instead bruteforce.
Press left mouse button on a feature point to see its mathcing point.
'''
import numpy as np
import cv2
import itertools as it
from multiprocessing.pool import ThreadPool
from common import Timer
from find_obj import init_feature, filter_matches, explore_match
def affine_skew(tilt, phi, img, mask=None):
'''
affine_skew(tilt, phi, img, mask=None) -> skew_img, skew_mask, Ai
Ai - is an affine transform matrix from skew_img to img
'''
h, w = img.shape[:2]
if mask is None:
mask = np.zeros((h, w), np.uint8)
mask[:] = 255
A = np.float32([[1, 0, 0], [0, 1, 0]])
if phi != 0.0:
phi = np.deg2rad(phi)
s, c = np.sin(phi), np.cos(phi)
A = np.float32([[c,-s], [ s, c]])
corners = [[0, 0], [w, 0], [w, h], [0, h]]
tcorners = np.int32( np.dot(corners, A.T) )
x, y, w, h = cv2.boundingRect(tcorners.reshape(1,-1,2))
A = np.hstack([A, [[-x], [-y]]])
img = cv2.warpAffine(img, A, (w, h), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
if tilt != 1.0:
s = 0.8*np.sqrt(tilt*tilt-1)
img = cv2.GaussianBlur(img, (0, 0), sigmaX=s, sigmaY=0.01)
img = cv2.resize(img, (0, 0), fx=1.0/tilt, fy=1.0, interpolation=cv2.INTER_NEAREST)
A[0] /= tilt
if phi != 0.0 or tilt != 1.0:
h, w = img.shape[:2]
mask = cv2.warpAffine(mask, A, (w, h), flags=cv2.INTER_NEAREST)
Ai = cv2.invertAffineTransform(A)
return img, mask, Ai
def affine_detect(detector, img, mask=None, pool=None):
'''
affine_detect(detector, img, mask=None, pool=None) -> keypoints, descrs
Apply a set of affine transormations to the image, detect keypoints and
reproject them into initial image coordinates.
See http://www.ipol.im/pub/algo/my_affine_sift/ for the details.
ThreadPool object may be passed to speedup the computation.
'''
params = [(1.0, 0.0)]
for t in 2**(0.5*np.arange(1,6)):
for phi in np.arange(0, 180, 72.0 / t):
params.append((t, phi))
def f(p):
t, phi = p
timg, tmask, Ai = affine_skew(t, phi, img)
keypoints, descrs = detector.detectAndCompute(timg, tmask)
for kp in keypoints:
x, y = kp.pt
kp.pt = tuple( np.dot(Ai, (x, y, 1)) )
if descrs is None:
descrs = []
return keypoints, descrs
keypoints, descrs = [], []
if pool is None:
ires = it.imap(f, params)
else:
ires = pool.imap(f, params)
for i, (k, d) in enumerate(ires):
print 'affine sampling: %d / %d\r' % (i+1, len(params)),
keypoints.extend(k)
descrs.extend(d)
print
return keypoints, np.array(descrs)
if __name__ == '__main__':
print __doc__
import sys, getopt
opts, args = getopt.getopt(sys.argv[1:], '', ['feature='])
opts = dict(opts)
feature_name = opts.get('--feature', 'sift-flann')
try: fn1, fn2 = args
except:
fn1 = 'data/aero1.jpg'
fn2 = 'data/aero3.jpg'
img1 = cv2.imread(fn1, 0)
img2 = cv2.imread(fn2, 0)
detector, matcher = init_feature(feature_name)
if detector != None:
print 'using', feature_name
else:
print 'unknown feature:', feature_name
sys.exit(1)
pool=ThreadPool(processes = cv2.getNumberOfCPUs())
kp1, desc1 = affine_detect(detector, img1, pool=pool)
kp2, desc2 = affine_detect(detector, img2, pool=pool)
print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))
def match_and_draw(win):
with Timer('matching'):
raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2
p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
if len(p1) >= 4:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
print '%d / %d inliers/matched' % (np.sum(status), len(status))
# do not draw outliers (there will be a lot of them)
kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag]
else:
H, status = None, None
print '%d matches found, not enough for homography estimation' % len(p1)
vis = explore_match(win, img1, img2, kp_pairs, None, H)
match_and_draw('affine find_obj')
cv2.waitKey()
cv2.destroyAllWindows()

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samples/python2/browse.py Normal file → Executable file
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@@ -1,48 +1,48 @@
'''
browse.py
=========
Sample shows how to implement a simple hi resolution image navigation
Usage
-----
browse.py [image filename]
'''
import numpy as np
import cv2
import sys
if __name__ == '__main__':
print 'This sample shows how to implement a simple hi resolution image navigation.'
print 'USAGE: browse.py [image filename]'
print
if len(sys.argv) > 1:
fn = sys.argv[1]
print 'loading %s ...' % fn
img = cv2.imread(fn)
else:
sz = 4096
print 'generating %dx%d procedural image ...' % (sz, sz)
img = np.zeros((sz, sz), np.uint8)
track = np.cumsum(np.random.rand(500000, 2)-0.5, axis=0)
track = np.int32(track*10 + (sz/2, sz/2))
cv2.polylines(img, [track], 0, 255, 1, cv2.CV_AA)
small = img
for i in xrange(3):
small = cv2.pyrDown(small)
def onmouse(event, x, y, flags, param):
h, w = img.shape[:2]
h1, w1 = small.shape[:2]
x, y = 1.0*x*h/h1, 1.0*y*h/h1
zoom = cv2.getRectSubPix(img, (800, 600), (x+0.5, y+0.5))
cv2.imshow('zoom', zoom)
cv2.imshow('preview', small)
cv2.setMouseCallback('preview', onmouse)
cv2.waitKey()
cv2.destroyAllWindows()
'''
browse.py
=========
Sample shows how to implement a simple hi resolution image navigation
Usage
-----
browse.py [image filename]
'''
import numpy as np
import cv2
import sys
if __name__ == '__main__':
print 'This sample shows how to implement a simple hi resolution image navigation.'
print 'USAGE: browse.py [image filename]'
print
if len(sys.argv) > 1:
fn = sys.argv[1]
print 'loading %s ...' % fn
img = cv2.imread(fn)
else:
sz = 4096
print 'generating %dx%d procedural image ...' % (sz, sz)
img = np.zeros((sz, sz), np.uint8)
track = np.cumsum(np.random.rand(500000, 2)-0.5, axis=0)
track = np.int32(track*10 + (sz/2, sz/2))
cv2.polylines(img, [track], 0, 255, 1, cv2.CV_AA)
small = img
for i in xrange(3):
small = cv2.pyrDown(small)
def onmouse(event, x, y, flags, param):
h, w = img.shape[:2]
h1, w1 = small.shape[:2]
x, y = 1.0*x*h/h1, 1.0*y*h/h1
zoom = cv2.getRectSubPix(img, (800, 600), (x+0.5, y+0.5))
cv2.imshow('zoom', zoom)
cv2.imshow('preview', small)
cv2.setMouseCallback('preview', onmouse)
cv2.waitKey()
cv2.destroyAllWindows()

116
samples/python2/calibrate.py Normal file → Executable file
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@@ -1,58 +1,58 @@
import numpy as np
import cv2
import os
from common import splitfn
USAGE = '''
USAGE: calib.py [--save <filename>] [--debug <output path>] [--square_size] [<image mask>]
'''
if __name__ == '__main__':
import sys, getopt
from glob import glob
args, img_mask = getopt.getopt(sys.argv[1:], '', ['save=', 'debug=', 'square_size='])
args = dict(args)
try: img_mask = img_mask[0]
except: img_mask = '../cpp/left*.jpg'
img_names = glob(img_mask)
debug_dir = args.get('--debug')
square_size = float(args.get('--square_size', 1.0))
pattern_size = (9, 6)
pattern_points = np.zeros( (np.prod(pattern_size), 3), np.float32 )
pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2)
pattern_points *= square_size
obj_points = []
img_points = []
h, w = 0, 0
for fn in img_names:
print 'processing %s...' % fn,
img = cv2.imread(fn, 0)
h, w = img.shape[:2]
found, corners = cv2.findChessboardCorners(img, pattern_size)
if found:
term = ( cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1 )
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if debug_dir:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
path, name, ext = splitfn(fn)
cv2.imwrite('%s/%s_chess.bmp' % (debug_dir, name), vis)
if not found:
print 'chessboard not found'
continue
img_points.append(corners.reshape(-1, 2))
obj_points.append(pattern_points)
print 'ok'
rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h))
print "RMS:", rms
print "camera matrix:\n", camera_matrix
print "distortion coefficients: ", dist_coefs.ravel()
cv2.destroyAllWindows()
import numpy as np
import cv2
import os
from common import splitfn
USAGE = '''
USAGE: calib.py [--save <filename>] [--debug <output path>] [--square_size] [<image mask>]
'''
if __name__ == '__main__':
import sys, getopt
from glob import glob
args, img_mask = getopt.getopt(sys.argv[1:], '', ['save=', 'debug=', 'square_size='])
args = dict(args)
try: img_mask = img_mask[0]
except: img_mask = '../cpp/left*.jpg'
img_names = glob(img_mask)
debug_dir = args.get('--debug')
square_size = float(args.get('--square_size', 1.0))
pattern_size = (9, 6)
pattern_points = np.zeros( (np.prod(pattern_size), 3), np.float32 )
pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2)
pattern_points *= square_size
obj_points = []
img_points = []
h, w = 0, 0
for fn in img_names:
print 'processing %s...' % fn,
img = cv2.imread(fn, 0)
h, w = img.shape[:2]
found, corners = cv2.findChessboardCorners(img, pattern_size)
if found:
term = ( cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1 )
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if debug_dir:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
path, name, ext = splitfn(fn)
cv2.imwrite('%s/%s_chess.bmp' % (debug_dir, name), vis)
if not found:
print 'chessboard not found'
continue
img_points.append(corners.reshape(-1, 2))
obj_points.append(pattern_points)
print 'ok'
rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h))
print "RMS:", rms
print "camera matrix:\n", camera_matrix
print "distortion coefficients: ", dist_coefs.ravel()
cv2.destroyAllWindows()

236
samples/python2/camshift.py Normal file → Executable file
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@@ -1,118 +1,118 @@
'''
Camshift tracker
================
This is a demo that shows mean-shift based tracking
You select a color objects such as your face and it tracks it.
This reads from video camera (0 by default, or the camera number the user enters)
http://www.robinhewitt.com/research/track/camshift.html
Usage:
------
camshift.py [<video source>]
To initialize tracking, select the object with mouse
Keys:
-----
ESC - exit
b - toggle back-projected probability visualization
'''
import numpy as np
import cv2
import video
class App(object):
def __init__(self, video_src):
self.cam = video.create_capture(video_src)
ret, self.frame = self.cam.read()
cv2.namedWindow('camshift')
cv2.setMouseCallback('camshift', self.onmouse)
self.selection = None
self.drag_start = None
self.tracking_state = 0
self.show_backproj = False
def onmouse(self, event, x, y, flags, param):
x, y = np.int16([x, y]) # BUG
if event == cv2.EVENT_LBUTTONDOWN:
self.drag_start = (x, y)
self.tracking_state = 0
if self.drag_start:
if flags & cv2.EVENT_FLAG_LBUTTON:
h, w = self.frame.shape[:2]
xo, yo = self.drag_start
x0, y0 = np.maximum(0, np.minimum([xo, yo], [x, y]))
x1, y1 = np.minimum([w, h], np.maximum([xo, yo], [x, y]))
self.selection = None
if x1-x0 > 0 and y1-y0 > 0:
self.selection = (x0, y0, x1, y1)
else:
self.drag_start = None
if self.selection is not None:
self.tracking_state = 1
def show_hist(self):
bin_count = self.hist.shape[0]
bin_w = 24
img = np.zeros((256, bin_count*bin_w, 3), np.uint8)
for i in xrange(bin_count):
h = int(self.hist[i])
cv2.rectangle(img, (i*bin_w+2, 255), ((i+1)*bin_w-2, 255-h), (int(180.0*i/bin_count), 255, 255), -1)
img = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)
cv2.imshow('hist', img)
def run(self):
while True:
ret, self.frame = self.cam.read()
vis = self.frame.copy()
hsv = cv2.cvtColor(self.frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, np.array((0., 60., 32.)), np.array((180., 255., 255.)))
if self.selection:
x0, y0, x1, y1 = self.selection
self.track_window = (x0, y0, x1-x0, y1-y0)
hsv_roi = hsv[y0:y1, x0:x1]
mask_roi = mask[y0:y1, x0:x1]
hist = cv2.calcHist( [hsv_roi], [0], mask_roi, [16], [0, 180] )
cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX);
self.hist = hist.reshape(-1)
self.show_hist()
vis_roi = vis[y0:y1, x0:x1]
cv2.bitwise_not(vis_roi, vis_roi)
vis[mask == 0] = 0
if self.tracking_state == 1:
self.selection = None
prob = cv2.calcBackProject([hsv], [0], self.hist, [0, 180], 1)
prob &= mask
term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
track_box, self.track_window = cv2.CamShift(prob, self.track_window, term_crit)
if self.show_backproj:
vis[:] = prob[...,np.newaxis]
try: cv2.ellipse(vis, track_box, (0, 0, 255), 2)
except: print track_box
cv2.imshow('camshift', vis)
ch = 0xFF & cv2.waitKey(5)
if ch == 27:
break
if ch == ord('b'):
self.show_backproj = not self.show_backproj
cv2.destroyAllWindows()
if __name__ == '__main__':
import sys
try: video_src = sys.argv[1]
except: video_src = 0
print __doc__
App(video_src).run()
'''
Camshift tracker
================
This is a demo that shows mean-shift based tracking
You select a color objects such as your face and it tracks it.
This reads from video camera (0 by default, or the camera number the user enters)
http://www.robinhewitt.com/research/track/camshift.html
Usage:
------
camshift.py [<video source>]
To initialize tracking, select the object with mouse
Keys:
-----
ESC - exit
b - toggle back-projected probability visualization
'''
import numpy as np
import cv2
import video
class App(object):
def __init__(self, video_src):
self.cam = video.create_capture(video_src)
ret, self.frame = self.cam.read()
cv2.namedWindow('camshift')
cv2.setMouseCallback('camshift', self.onmouse)
self.selection = None
self.drag_start = None
self.tracking_state = 0
self.show_backproj = False
def onmouse(self, event, x, y, flags, param):
x, y = np.int16([x, y]) # BUG
if event == cv2.EVENT_LBUTTONDOWN:
self.drag_start = (x, y)
self.tracking_state = 0
if self.drag_start:
if flags & cv2.EVENT_FLAG_LBUTTON:
h, w = self.frame.shape[:2]
xo, yo = self.drag_start
x0, y0 = np.maximum(0, np.minimum([xo, yo], [x, y]))
x1, y1 = np.minimum([w, h], np.maximum([xo, yo], [x, y]))
self.selection = None
if x1-x0 > 0 and y1-y0 > 0:
self.selection = (x0, y0, x1, y1)
else:
self.drag_start = None
if self.selection is not None:
self.tracking_state = 1
def show_hist(self):
bin_count = self.hist.shape[0]
bin_w = 24
img = np.zeros((256, bin_count*bin_w, 3), np.uint8)
for i in xrange(bin_count):
h = int(self.hist[i])
cv2.rectangle(img, (i*bin_w+2, 255), ((i+1)*bin_w-2, 255-h), (int(180.0*i/bin_count), 255, 255), -1)
img = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)
cv2.imshow('hist', img)
def run(self):
while True:
ret, self.frame = self.cam.read()
vis = self.frame.copy()
hsv = cv2.cvtColor(self.frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, np.array((0., 60., 32.)), np.array((180., 255., 255.)))
if self.selection:
x0, y0, x1, y1 = self.selection
self.track_window = (x0, y0, x1-x0, y1-y0)
hsv_roi = hsv[y0:y1, x0:x1]
mask_roi = mask[y0:y1, x0:x1]
hist = cv2.calcHist( [hsv_roi], [0], mask_roi, [16], [0, 180] )
cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX);
self.hist = hist.reshape(-1)
self.show_hist()
vis_roi = vis[y0:y1, x0:x1]
cv2.bitwise_not(vis_roi, vis_roi)
vis[mask == 0] = 0
if self.tracking_state == 1:
self.selection = None
prob = cv2.calcBackProject([hsv], [0], self.hist, [0, 180], 1)
prob &= mask
term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
track_box, self.track_window = cv2.CamShift(prob, self.track_window, term_crit)
if self.show_backproj:
vis[:] = prob[...,np.newaxis]
try: cv2.ellipse(vis, track_box, (0, 0, 255), 2)
except: print track_box
cv2.imshow('camshift', vis)
ch = 0xFF & cv2.waitKey(5)
if ch == 27:
break
if ch == ord('b'):
self.show_backproj = not self.show_backproj
cv2.destroyAllWindows()
if __name__ == '__main__':
import sys
try: video_src = sys.argv[1]
except: video_src = 0
print __doc__
App(video_src).run()

146
samples/python2/coherence.py Normal file → Executable file
View File

@@ -1,73 +1,73 @@
'''
Coherence-enhancing filtering example
=====================================
inspired by
Joachim Weickert "Coherence-Enhancing Shock Filters"
http://www.mia.uni-saarland.de/Publications/weickert-dagm03.pdf
'''
import numpy as np
import cv2
def coherence_filter(img, sigma = 11, str_sigma = 11, blend = 0.5, iter_n = 4):
h, w = img.shape[:2]
for i in xrange(iter_n):
print i,
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
eigen = cv2.cornerEigenValsAndVecs(gray, str_sigma, 3)
eigen = eigen.reshape(h, w, 3, 2) # [[e1, e2], v1, v2]
x, y = eigen[:,:,1,0], eigen[:,:,1,1]
gxx = cv2.Sobel(gray, cv2.CV_32F, 2, 0, ksize=sigma)
gxy = cv2.Sobel(gray, cv2.CV_32F, 1, 1, ksize=sigma)
gyy = cv2.Sobel(gray, cv2.CV_32F, 0, 2, ksize=sigma)
gvv = x*x*gxx + 2*x*y*gxy + y*y*gyy
m = gvv < 0
ero = cv2.erode(img, None)
dil = cv2.dilate(img, None)
img1 = ero
img1[m] = dil[m]
img = np.uint8(img*(1.0 - blend) + img1*blend)
print 'done'
return img
if __name__ == '__main__':
import sys
try: fn = sys.argv[1]
except: fn = '../cpp/baboon.jpg'
src = cv2.imread(fn)
def nothing(*argv):
pass
def update():
sigma = cv2.getTrackbarPos('sigma', 'control')*2+1
str_sigma = cv2.getTrackbarPos('str_sigma', 'control')*2+1
blend = cv2.getTrackbarPos('blend', 'control') / 10.0
print 'sigma: %d str_sigma: %d blend_coef: %f' % (sigma, str_sigma, blend)
dst = coherence_filter(src, sigma=sigma, str_sigma = str_sigma, blend = blend)
cv2.imshow('dst', dst)
cv2.namedWindow('control', 0)
cv2.createTrackbar('sigma', 'control', 9, 15, nothing)
cv2.createTrackbar('blend', 'control', 7, 10, nothing)
cv2.createTrackbar('str_sigma', 'control', 9, 15, nothing)
print 'Press SPACE to update the image\n'
cv2.imshow('src', src)
update()
while True:
ch = 0xFF & cv2.waitKey()
if ch == ord(' '):
update()
if ch == 27:
break
cv2.destroyAllWindows()
'''
Coherence-enhancing filtering example
=====================================
inspired by
Joachim Weickert "Coherence-Enhancing Shock Filters"
http://www.mia.uni-saarland.de/Publications/weickert-dagm03.pdf
'''
import numpy as np
import cv2
def coherence_filter(img, sigma = 11, str_sigma = 11, blend = 0.5, iter_n = 4):
h, w = img.shape[:2]
for i in xrange(iter_n):
print i,
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
eigen = cv2.cornerEigenValsAndVecs(gray, str_sigma, 3)
eigen = eigen.reshape(h, w, 3, 2) # [[e1, e2], v1, v2]
x, y = eigen[:,:,1,0], eigen[:,:,1,1]
gxx = cv2.Sobel(gray, cv2.CV_32F, 2, 0, ksize=sigma)
gxy = cv2.Sobel(gray, cv2.CV_32F, 1, 1, ksize=sigma)
gyy = cv2.Sobel(gray, cv2.CV_32F, 0, 2, ksize=sigma)
gvv = x*x*gxx + 2*x*y*gxy + y*y*gyy
m = gvv < 0
ero = cv2.erode(img, None)
dil = cv2.dilate(img, None)
img1 = ero
img1[m] = dil[m]
img = np.uint8(img*(1.0 - blend) + img1*blend)
print 'done'
return img
if __name__ == '__main__':
import sys
try: fn = sys.argv[1]
except: fn = '../cpp/baboon.jpg'
src = cv2.imread(fn)
def nothing(*argv):
pass
def update():
sigma = cv2.getTrackbarPos('sigma', 'control')*2+1
str_sigma = cv2.getTrackbarPos('str_sigma', 'control')*2+1
blend = cv2.getTrackbarPos('blend', 'control') / 10.0
print 'sigma: %d str_sigma: %d blend_coef: %f' % (sigma, str_sigma, blend)
dst = coherence_filter(src, sigma=sigma, str_sigma = str_sigma, blend = blend)
cv2.imshow('dst', dst)
cv2.namedWindow('control', 0)
cv2.createTrackbar('sigma', 'control', 9, 15, nothing)
cv2.createTrackbar('blend', 'control', 7, 10, nothing)
cv2.createTrackbar('str_sigma', 'control', 9, 15, nothing)
print 'Press SPACE to update the image\n'
cv2.imshow('src', src)
update()
while True:
ch = 0xFF & cv2.waitKey()
if ch == ord(' '):
update()
if ch == 27:
break
cv2.destroyAllWindows()

96
samples/python2/color_histogram.py Normal file → Executable file
View File

@@ -1,48 +1,48 @@
import numpy as np
import cv2
from time import clock
import sys
import video
if __name__ == '__main__':
hsv_map = np.zeros((180, 256, 3), np.uint8)
h, s = np.indices(hsv_map.shape[:2])
hsv_map[:,:,0] = h
hsv_map[:,:,1] = s
hsv_map[:,:,2] = 255
hsv_map = cv2.cvtColor(hsv_map, cv2.COLOR_HSV2BGR)
cv2.imshow('hsv_map', hsv_map)
cv2.namedWindow('hist', 0)
hist_scale = 10
def set_scale(val):
global hist_scale
hist_scale = val
cv2.createTrackbar('scale', 'hist', hist_scale, 32, set_scale)
try: fn = sys.argv[1]
except: fn = 0
cam = video.create_capture(fn, fallback='synth:bg=../cpp/baboon.jpg:class=chess:noise=0.05')
while True:
flag, frame = cam.read()
cv2.imshow('camera', frame)
small = cv2.pyrDown(frame)
hsv = cv2.cvtColor(small, cv2.COLOR_BGR2HSV)
dark = hsv[...,2] < 32
hsv[dark] = 0
h = cv2.calcHist( [hsv], [0, 1], None, [180, 256], [0, 180, 0, 256] )
h = np.clip(h*0.005*hist_scale, 0, 1)
vis = hsv_map*h[:,:,np.newaxis] / 255.0
cv2.imshow('hist', vis)
ch = 0xFF & cv2.waitKey(1)
if ch == 27:
break
cv2.destroyAllWindows()
import numpy as np
import cv2
from time import clock
import sys
import video
if __name__ == '__main__':
hsv_map = np.zeros((180, 256, 3), np.uint8)
h, s = np.indices(hsv_map.shape[:2])
hsv_map[:,:,0] = h
hsv_map[:,:,1] = s
hsv_map[:,:,2] = 255
hsv_map = cv2.cvtColor(hsv_map, cv2.COLOR_HSV2BGR)
cv2.imshow('hsv_map', hsv_map)
cv2.namedWindow('hist', 0)
hist_scale = 10
def set_scale(val):
global hist_scale
hist_scale = val
cv2.createTrackbar('scale', 'hist', hist_scale, 32, set_scale)
try: fn = sys.argv[1]
except: fn = 0
cam = video.create_capture(fn, fallback='synth:bg=../cpp/baboon.jpg:class=chess:noise=0.05')
while True:
flag, frame = cam.read()
cv2.imshow('camera', frame)
small = cv2.pyrDown(frame)
hsv = cv2.cvtColor(small, cv2.COLOR_BGR2HSV)
dark = hsv[...,2] < 32
hsv[dark] = 0
h = cv2.calcHist( [hsv], [0, 1], None, [180, 256], [0, 180, 0, 256] )
h = np.clip(h*0.005*hist_scale, 0, 1)
vis = hsv_map*h[:,:,np.newaxis] / 255.0
cv2.imshow('hist', vis)
ch = 0xFF & cv2.waitKey(1)
if ch == 27:
break
cv2.destroyAllWindows()

432
samples/python2/common.py Normal file → Executable file
View File

@@ -1,216 +1,216 @@
'''
This module contais some common routines used by other samples.
'''
import numpy as np
import cv2
import os
from contextlib import contextmanager
import itertools as it
image_extensions = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.pbm', '.pgm', '.ppm']
class Bunch(object):
def __init__(self, **kw):
self.__dict__.update(kw)
def __str__(self):
return str(self.__dict__)
def splitfn(fn):
path, fn = os.path.split(fn)
name, ext = os.path.splitext(fn)
return path, name, ext
def anorm2(a):
return (a*a).sum(-1)
def anorm(a):
return np.sqrt( anorm2(a) )
def homotrans(H, x, y):
xs = H[0, 0]*x + H[0, 1]*y + H[0, 2]
ys = H[1, 0]*x + H[1, 1]*y + H[1, 2]
s = H[2, 0]*x + H[2, 1]*y + H[2, 2]
return xs/s, ys/s
def to_rect(a):
a = np.ravel(a)
if len(a) == 2:
a = (0, 0, a[0], a[1])
return np.array(a, np.float64).reshape(2, 2)
def rect2rect_mtx(src, dst):
src, dst = to_rect(src), to_rect(dst)
cx, cy = (dst[1] - dst[0]) / (src[1] - src[0])
tx, ty = dst[0] - src[0] * (cx, cy)
M = np.float64([[ cx, 0, tx],
[ 0, cy, ty],
[ 0, 0, 1]])
return M
def lookat(eye, target, up = (0, 0, 1)):
fwd = np.asarray(target, np.float64) - eye
fwd /= anorm(fwd)
right = np.cross(fwd, up)
right /= anorm(right)
down = np.cross(fwd, right)
R = np.float64([right, down, fwd])
tvec = -np.dot(R, eye)
return R, tvec
def mtx2rvec(R):
w, u, vt = cv2.SVDecomp(R - np.eye(3))
p = vt[0] + u[:,0]*w[0] # same as np.dot(R, vt[0])
c = np.dot(vt[0], p)
s = np.dot(vt[1], p)
axis = np.cross(vt[0], vt[1])
return axis * np.arctan2(s, c)
def draw_str(dst, (x, y), s):
cv2.putText(dst, s, (x+1, y+1), cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 0), thickness = 2, lineType=cv2.CV_AA)
cv2.putText(dst, s, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (255, 255, 255), lineType=cv2.CV_AA)
class Sketcher:
def __init__(self, windowname, dests, colors_func):
self.prev_pt = None
self.windowname = windowname
self.dests = dests
self.colors_func = colors_func
self.dirty = False
self.show()
cv2.setMouseCallback(self.windowname, self.on_mouse)
def show(self):
cv2.imshow(self.windowname, self.dests[0])
def on_mouse(self, event, x, y, flags, param):
pt = (x, y)
if event == cv2.EVENT_LBUTTONDOWN:
self.prev_pt = pt
if self.prev_pt and flags & cv2.EVENT_FLAG_LBUTTON:
for dst, color in zip(self.dests, self.colors_func()):
cv2.line(dst, self.prev_pt, pt, color, 5)
self.dirty = True
self.prev_pt = pt
self.show()
else:
self.prev_pt = None
# palette data from matplotlib/_cm.py
_jet_data = {'red': ((0., 0, 0), (0.35, 0, 0), (0.66, 1, 1), (0.89,1, 1),
(1, 0.5, 0.5)),
'green': ((0., 0, 0), (0.125,0, 0), (0.375,1, 1), (0.64,1, 1),
(0.91,0,0), (1, 0, 0)),
'blue': ((0., 0.5, 0.5), (0.11, 1, 1), (0.34, 1, 1), (0.65,0, 0),
(1, 0, 0))}
cmap_data = { 'jet' : _jet_data }
def make_cmap(name, n=256):
data = cmap_data[name]
xs = np.linspace(0.0, 1.0, n)
channels = []
eps = 1e-6
for ch_name in ['blue', 'green', 'red']:
ch_data = data[ch_name]
xp, yp = [], []
for x, y1, y2 in ch_data:
xp += [x, x+eps]
yp += [y1, y2]
ch = np.interp(xs, xp, yp)
channels.append(ch)
return np.uint8(np.array(channels).T*255)
def nothing(*arg, **kw):
pass
def clock():
return cv2.getTickCount() / cv2.getTickFrequency()
@contextmanager
def Timer(msg):
print msg, '...',
start = clock()
try:
yield
finally:
print "%.2f ms" % ((clock()-start)*1000)
class StatValue:
def __init__(self, smooth_coef = 0.5):
self.value = None
self.smooth_coef = smooth_coef
def update(self, v):
if self.value is None:
self.value = v
else:
c = self.smooth_coef
self.value = c * self.value + (1.0-c) * v
class RectSelector:
def __init__(self, win, callback):
self.win = win
self.callback = callback
cv2.setMouseCallback(win, self.onmouse)
self.drag_start = None
self.drag_rect = None
def onmouse(self, event, x, y, flags, param):
x, y = np.int16([x, y]) # BUG
if event == cv2.EVENT_LBUTTONDOWN:
self.drag_start = (x, y)
if self.drag_start:
if flags & cv2.EVENT_FLAG_LBUTTON:
xo, yo = self.drag_start
x0, y0 = np.minimum([xo, yo], [x, y])
x1, y1 = np.maximum([xo, yo], [x, y])
self.drag_rect = None
if x1-x0 > 0 and y1-y0 > 0:
self.drag_rect = (x0, y0, x1, y1)
else:
rect = self.drag_rect
self.drag_start = None
self.drag_rect = None
if rect:
self.callback(rect)
def draw(self, vis):
if not self.drag_rect:
return False
x0, y0, x1, y1 = self.drag_rect
cv2.rectangle(vis, (x0, y0), (x1, y1), (0, 255, 0), 2)
return True
@property
def dragging(self):
return self.drag_rect is not None
def grouper(n, iterable, fillvalue=None):
'''grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx'''
args = [iter(iterable)] * n
return it.izip_longest(fillvalue=fillvalue, *args)
def mosaic(w, imgs):
'''Make a grid from images.
w -- number of grid columns
imgs -- images (must have same size and format)
'''
imgs = iter(imgs)
img0 = imgs.next()
pad = np.zeros_like(img0)
imgs = it.chain([img0], imgs)
rows = grouper(w, imgs, pad)
return np.vstack(map(np.hstack, rows))
def getsize(img):
h, w = img.shape[:2]
return w, h
def mdot(*args):
return reduce(np.dot, args)
def draw_keypoints(vis, keypoints, color = (0, 255, 255)):
for kp in keypoints:
x, y = kp.pt
cv2.circle(vis, (int(x), int(y)), 2, color)
'''
This module contais some common routines used by other samples.
'''
import numpy as np
import cv2
import os
from contextlib import contextmanager
import itertools as it
image_extensions = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.pbm', '.pgm', '.ppm']
class Bunch(object):
def __init__(self, **kw):
self.__dict__.update(kw)
def __str__(self):
return str(self.__dict__)
def splitfn(fn):
path, fn = os.path.split(fn)
name, ext = os.path.splitext(fn)
return path, name, ext
def anorm2(a):
return (a*a).sum(-1)
def anorm(a):
return np.sqrt( anorm2(a) )
def homotrans(H, x, y):
xs = H[0, 0]*x + H[0, 1]*y + H[0, 2]
ys = H[1, 0]*x + H[1, 1]*y + H[1, 2]
s = H[2, 0]*x + H[2, 1]*y + H[2, 2]
return xs/s, ys/s
def to_rect(a):
a = np.ravel(a)
if len(a) == 2:
a = (0, 0, a[0], a[1])
return np.array(a, np.float64).reshape(2, 2)
def rect2rect_mtx(src, dst):
src, dst = to_rect(src), to_rect(dst)
cx, cy = (dst[1] - dst[0]) / (src[1] - src[0])
tx, ty = dst[0] - src[0] * (cx, cy)
M = np.float64([[ cx, 0, tx],
[ 0, cy, ty],
[ 0, 0, 1]])
return M
def lookat(eye, target, up = (0, 0, 1)):
fwd = np.asarray(target, np.float64) - eye
fwd /= anorm(fwd)
right = np.cross(fwd, up)
right /= anorm(right)
down = np.cross(fwd, right)
R = np.float64([right, down, fwd])
tvec = -np.dot(R, eye)
return R, tvec
def mtx2rvec(R):
w, u, vt = cv2.SVDecomp(R - np.eye(3))
p = vt[0] + u[:,0]*w[0] # same as np.dot(R, vt[0])
c = np.dot(vt[0], p)
s = np.dot(vt[1], p)
axis = np.cross(vt[0], vt[1])
return axis * np.arctan2(s, c)
def draw_str(dst, (x, y), s):
cv2.putText(dst, s, (x+1, y+1), cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 0), thickness = 2, lineType=cv2.CV_AA)
cv2.putText(dst, s, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (255, 255, 255), lineType=cv2.CV_AA)
class Sketcher:
def __init__(self, windowname, dests, colors_func):
self.prev_pt = None
self.windowname = windowname
self.dests = dests
self.colors_func = colors_func
self.dirty = False
self.show()
cv2.setMouseCallback(self.windowname, self.on_mouse)
def show(self):
cv2.imshow(self.windowname, self.dests[0])
def on_mouse(self, event, x, y, flags, param):
pt = (x, y)
if event == cv2.EVENT_LBUTTONDOWN:
self.prev_pt = pt
if self.prev_pt and flags & cv2.EVENT_FLAG_LBUTTON:
for dst, color in zip(self.dests, self.colors_func()):
cv2.line(dst, self.prev_pt, pt, color, 5)
self.dirty = True
self.prev_pt = pt
self.show()
else:
self.prev_pt = None
# palette data from matplotlib/_cm.py
_jet_data = {'red': ((0., 0, 0), (0.35, 0, 0), (0.66, 1, 1), (0.89,1, 1),
(1, 0.5, 0.5)),
'green': ((0., 0, 0), (0.125,0, 0), (0.375,1, 1), (0.64,1, 1),
(0.91,0,0), (1, 0, 0)),
'blue': ((0., 0.5, 0.5), (0.11, 1, 1), (0.34, 1, 1), (0.65,0, 0),
(1, 0, 0))}
cmap_data = { 'jet' : _jet_data }
def make_cmap(name, n=256):
data = cmap_data[name]
xs = np.linspace(0.0, 1.0, n)
channels = []
eps = 1e-6
for ch_name in ['blue', 'green', 'red']:
ch_data = data[ch_name]
xp, yp = [], []
for x, y1, y2 in ch_data:
xp += [x, x+eps]
yp += [y1, y2]
ch = np.interp(xs, xp, yp)
channels.append(ch)
return np.uint8(np.array(channels).T*255)
def nothing(*arg, **kw):
pass
def clock():
return cv2.getTickCount() / cv2.getTickFrequency()
@contextmanager
def Timer(msg):
print msg, '...',
start = clock()
try:
yield
finally:
print "%.2f ms" % ((clock()-start)*1000)
class StatValue:
def __init__(self, smooth_coef = 0.5):
self.value = None
self.smooth_coef = smooth_coef
def update(self, v):
if self.value is None:
self.value = v
else:
c = self.smooth_coef
self.value = c * self.value + (1.0-c) * v
class RectSelector:
def __init__(self, win, callback):
self.win = win
self.callback = callback
cv2.setMouseCallback(win, self.onmouse)
self.drag_start = None
self.drag_rect = None
def onmouse(self, event, x, y, flags, param):
x, y = np.int16([x, y]) # BUG
if event == cv2.EVENT_LBUTTONDOWN:
self.drag_start = (x, y)
if self.drag_start:
if flags & cv2.EVENT_FLAG_LBUTTON:
xo, yo = self.drag_start
x0, y0 = np.minimum([xo, yo], [x, y])
x1, y1 = np.maximum([xo, yo], [x, y])
self.drag_rect = None
if x1-x0 > 0 and y1-y0 > 0:
self.drag_rect = (x0, y0, x1, y1)
else:
rect = self.drag_rect
self.drag_start = None
self.drag_rect = None
if rect:
self.callback(rect)
def draw(self, vis):
if not self.drag_rect:
return False
x0, y0, x1, y1 = self.drag_rect
cv2.rectangle(vis, (x0, y0), (x1, y1), (0, 255, 0), 2)
return True
@property
def dragging(self):
return self.drag_rect is not None
def grouper(n, iterable, fillvalue=None):
'''grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx'''
args = [iter(iterable)] * n
return it.izip_longest(fillvalue=fillvalue, *args)
def mosaic(w, imgs):
'''Make a grid from images.
w -- number of grid columns
imgs -- images (must have same size and format)
'''
imgs = iter(imgs)
img0 = imgs.next()
pad = np.zeros_like(img0)
imgs = it.chain([img0], imgs)
rows = grouper(w, imgs, pad)
return np.vstack(map(np.hstack, rows))
def getsize(img):
h, w = img.shape[:2]
return w, h
def mdot(*args):
return reduce(np.dot, args)
def draw_keypoints(vis, keypoints, color = (0, 255, 255)):
for kp in keypoints:
x, y = kp.pt
cv2.circle(vis, (int(x), int(y)), 2, color)

120
samples/python2/contours.py Normal file → Executable file
View File

@@ -1,60 +1,60 @@
'''
This program illustrates the use of findContours and drawContours.
The original image is put up along with the image of drawn contours.
Usage:
contours.py
A trackbar is put up which controls the contour level from -3 to 3
'''
import numpy as np
import cv2
def make_image():
img = np.zeros((500, 500), np.uint8)
black, white = 0, 255
for i in xrange(6):
dx = (i%2)*250 - 30
dy = (i/2)*150
if i == 0:
for j in xrange(11):
angle = (j+5)*np.pi/21
c, s = np.cos(angle), np.sin(angle)
x1, y1 = np.int32([dx+100+j*10-80*c, dy+100-90*s])
x2, y2 = np.int32([dx+100+j*10-30*c, dy+100-30*s])
cv2.line(img, (x1, y1), (x2, y2), white)
cv2.ellipse( img, (dx+150, dy+100), (100,70), 0, 0, 360, white, -1 )
cv2.ellipse( img, (dx+115, dy+70), (30,20), 0, 0, 360, black, -1 )
cv2.ellipse( img, (dx+185, dy+70), (30,20), 0, 0, 360, black, -1 )
cv2.ellipse( img, (dx+115, dy+70), (15,15), 0, 0, 360, white, -1 )
cv2.ellipse( img, (dx+185, dy+70), (15,15), 0, 0, 360, white, -1 )
cv2.ellipse( img, (dx+115, dy+70), (5,5), 0, 0, 360, black, -1 )
cv2.ellipse( img, (dx+185, dy+70), (5,5), 0, 0, 360, black, -1 )
cv2.ellipse( img, (dx+150, dy+100), (10,5), 0, 0, 360, black, -1 )
cv2.ellipse( img, (dx+150, dy+150), (40,10), 0, 0, 360, black, -1 )
cv2.ellipse( img, (dx+27, dy+100), (20,35), 0, 0, 360, white, -1 )
cv2.ellipse( img, (dx+273, dy+100), (20,35), 0, 0, 360, white, -1 )
return img
if __name__ == '__main__':
print __doc__
img = make_image()
h, w = img.shape[:2]
contours0, hierarchy = cv2.findContours( img.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = [cv2.approxPolyDP(cnt, 3, True) for cnt in contours0]
def update(levels):
vis = np.zeros((h, w, 3), np.uint8)
levels = levels - 3
cv2.drawContours( vis, contours, (-1, 3)[levels <= 0], (128,255,255),
3, cv2.CV_AA, hierarchy, abs(levels) )
cv2.imshow('contours', vis)
update(3)
cv2.createTrackbar( "levels+3", "contours", 3, 7, update )
cv2.imshow('image', img)
0xFF & cv2.waitKey()
cv2.destroyAllWindows()
'''
This program illustrates the use of findContours and drawContours.
The original image is put up along with the image of drawn contours.
Usage:
contours.py
A trackbar is put up which controls the contour level from -3 to 3
'''
import numpy as np
import cv2
def make_image():
img = np.zeros((500, 500), np.uint8)
black, white = 0, 255
for i in xrange(6):
dx = (i%2)*250 - 30
dy = (i/2)*150
if i == 0:
for j in xrange(11):
angle = (j+5)*np.pi/21
c, s = np.cos(angle), np.sin(angle)
x1, y1 = np.int32([dx+100+j*10-80*c, dy+100-90*s])
x2, y2 = np.int32([dx+100+j*10-30*c, dy+100-30*s])
cv2.line(img, (x1, y1), (x2, y2), white)
cv2.ellipse( img, (dx+150, dy+100), (100,70), 0, 0, 360, white, -1 )
cv2.ellipse( img, (dx+115, dy+70), (30,20), 0, 0, 360, black, -1 )
cv2.ellipse( img, (dx+185, dy+70), (30,20), 0, 0, 360, black, -1 )
cv2.ellipse( img, (dx+115, dy+70), (15,15), 0, 0, 360, white, -1 )
cv2.ellipse( img, (dx+185, dy+70), (15,15), 0, 0, 360, white, -1 )
cv2.ellipse( img, (dx+115, dy+70), (5,5), 0, 0, 360, black, -1 )
cv2.ellipse( img, (dx+185, dy+70), (5,5), 0, 0, 360, black, -1 )
cv2.ellipse( img, (dx+150, dy+100), (10,5), 0, 0, 360, black, -1 )
cv2.ellipse( img, (dx+150, dy+150), (40,10), 0, 0, 360, black, -1 )
cv2.ellipse( img, (dx+27, dy+100), (20,35), 0, 0, 360, white, -1 )
cv2.ellipse( img, (dx+273, dy+100), (20,35), 0, 0, 360, white, -1 )
return img
if __name__ == '__main__':
print __doc__
img = make_image()
h, w = img.shape[:2]
contours0, hierarchy = cv2.findContours( img.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = [cv2.approxPolyDP(cnt, 3, True) for cnt in contours0]
def update(levels):
vis = np.zeros((h, w, 3), np.uint8)
levels = levels - 3
cv2.drawContours( vis, contours, (-1, 3)[levels <= 0], (128,255,255),
3, cv2.CV_AA, hierarchy, abs(levels) )
cv2.imshow('contours', vis)
update(3)
cv2.createTrackbar( "levels+3", "contours", 3, 7, update )
cv2.imshow('image', img)
0xFF & cv2.waitKey()
cv2.destroyAllWindows()

14
samples/python2/deconvolution.py Normal file → Executable file
View File

@@ -5,9 +5,9 @@ Sample shows how DFT can be used to perform Weiner deconvolution [1]
of an image with user-defined point spread function (PSF)
Usage:
deconvolution.py [--circle]
[--angle <degrees>]
[--d <diameter>]
deconvolution.py [--circle]
[--angle <degrees>]
[--d <diameter>]
[--snr <signal/noise ratio in db>]
[<input image>]
@@ -19,11 +19,11 @@ Usage:
Examples:
deconvolution.py --angle 135 --d 22 data/licenseplate_motion.jpg
(image source: http://www.topazlabs.com/infocus/_images/licenseplate_compare.jpg)
deconvolution.py --angle 86 --d 31 data/text_motion.jpg
deconvolution.py --circle --d 19 data/text_defocus.jpg
(image source: compact digital photo camera, no artificial distortion)
[1] http://en.wikipedia.org/wiki/Wiener_deconvolution
'''
@@ -56,7 +56,7 @@ def defocus_kernel(d, sz=65):
cv2.circle(kern, (sz, sz), d, 255, -1, cv2.CV_AA, shift=1)
kern = np.float32(kern) / 255.0
return kern
if __name__ == '__main__':
print __doc__
@@ -67,7 +67,7 @@ if __name__ == '__main__':
except: fn = 'data/licenseplate_motion.jpg'
win = 'deconvolution'
img = cv2.imread(fn, 0)
img = np.float32(img)/255.0
cv2.imshow('input', img)

312
samples/python2/demo.py Normal file → Executable file
View File

@@ -1,156 +1,156 @@
'''
Sample-launcher application.
'''
import Tkinter as tk
from ScrolledText import ScrolledText
from glob import glob
from common import splitfn
import webbrowser
from subprocess import Popen
#from IPython.Shell import IPShellEmbed
#ipshell = IPShellEmbed()
exclude_list = ['demo', 'common']
class LinkManager:
def __init__(self, text, url_callback = None):
self.text = text
self.text.tag_config("link", foreground="blue", underline=1)
self.text.tag_bind("link", "<Enter>", self._enter)
self.text.tag_bind("link", "<Leave>", self._leave)
self.text.tag_bind("link", "<Button-1>", self._click)
self.url_callback = url_callback
self.reset()
def reset(self):
self.links = {}
def add(self, action):
# add an action to the manager. returns tags to use in
# associated text widget
tag = "link-%d" % len(self.links)
self.links[tag] = action
return "link", tag
def _enter(self, event):
self.text.config(cursor="hand2")
def _leave(self, event):
self.text.config(cursor="")
def _click(self, event):
for tag in self.text.tag_names(tk.CURRENT):
if tag.startswith("link-"):
proc = self.links[tag]
if callable(proc):
proc()
else:
if self.url_callback:
self.url_callback(proc)
class App:
def __init__(self):
root = tk.Tk()
root.title('OpenCV Demo')
self.win = win = tk.PanedWindow(root, orient=tk.HORIZONTAL, sashrelief=tk.RAISED, sashwidth=4)
self.win.pack(fill=tk.BOTH, expand=1)
left = tk.Frame(win)
right = tk.Frame(win)
win.add(left)
win.add(right)
scrollbar = tk.Scrollbar(left, orient=tk.VERTICAL)
self.demos_lb = demos_lb = tk.Listbox(left, yscrollcommand=scrollbar.set)
scrollbar.config(command=demos_lb.yview)
scrollbar.pack(side=tk.RIGHT, fill=tk.Y)
demos_lb.pack(side=tk.LEFT, fill=tk.BOTH, expand=1)
self.samples = {}
for fn in glob('*.py'):
name = splitfn(fn)[1]
if fn[0] != '_' and name not in exclude_list:
demos_lb.insert(tk.END, name)
self.samples[name] = fn
demos_lb.bind('<<ListboxSelect>>', self.on_demo_select)
self.cmd_entry = cmd_entry = tk.Entry(right)
cmd_entry.bind('<Return>', self.on_run)
run_btn = tk.Button(right, command=self.on_run, text='Run', width=8)
self.text = text = ScrolledText(right, font=('arial', 12, 'normal'), width = 30, wrap='word')
self.linker = linker = LinkManager(text, self.on_link)
self.text.tag_config("header1", font=('arial', 14, 'bold'))
self.text.tag_config("header2", font=('arial', 12, 'bold'))
text.config(state='disabled')
text.pack(fill='both', expand=1, side=tk.BOTTOM)
cmd_entry.pack(fill='x', side='left' , expand=1)
run_btn.pack()
def on_link(self, url):
print url
webbrowser.open(url)
def on_demo_select(self, evt):
name = self.demos_lb.get( self.demos_lb.curselection()[0] )
fn = self.samples[name]
loc = {}
execfile(fn, loc)
descr = loc.get('__doc__', 'no-description')
self.linker.reset()
self.text.config(state='normal')
self.text.delete(1.0, tk.END)
self.format_text(descr)
self.text.config(state='disabled')
self.cmd_entry.delete(0, tk.END)
self.cmd_entry.insert(0, fn)
def format_text(self, s):
text = self.text
lines = s.splitlines()
for i, s in enumerate(lines):
s = s.rstrip()
if i == 0 and not s:
continue
if s and s == '='*len(s):
text.tag_add('header1', 'end-2l', 'end-1l')
elif s and s == '-'*len(s):
text.tag_add('header2', 'end-2l', 'end-1l')
else:
text.insert('end', s+'\n')
def add_link(start, end, url):
for tag in self.linker.add(url):
text.tag_add(tag, start, end)
self.match_text(r'http://\S+', add_link)
def match_text(self, pattern, tag_proc, regexp=True):
text = self.text
text.mark_set('matchPos', '1.0')
count = tk.IntVar()
while True:
match_index = text.search(pattern, 'matchPos', count=count, regexp=regexp, stopindex='end')
if not match_index: break
end_index = text.index( "%s+%sc" % (match_index, count.get()) )
text.mark_set('matchPos', end_index)
if callable(tag_proc):
tag_proc(match_index, end_index, text.get(match_index, end_index))
else:
text.tag_add(tag_proc, match_index, end_index)
def on_run(self, *args):
cmd = self.cmd_entry.get()
print 'running:', cmd
Popen("python " + cmd, shell=True)
def run(self):
tk.mainloop()
if __name__ == '__main__':
App().run()
'''
Sample-launcher application.
'''
import Tkinter as tk
from ScrolledText import ScrolledText
from glob import glob
from common import splitfn
import webbrowser
from subprocess import Popen
#from IPython.Shell import IPShellEmbed
#ipshell = IPShellEmbed()
exclude_list = ['demo', 'common']
class LinkManager:
def __init__(self, text, url_callback = None):
self.text = text
self.text.tag_config("link", foreground="blue", underline=1)
self.text.tag_bind("link", "<Enter>", self._enter)
self.text.tag_bind("link", "<Leave>", self._leave)
self.text.tag_bind("link", "<Button-1>", self._click)
self.url_callback = url_callback
self.reset()
def reset(self):
self.links = {}
def add(self, action):
# add an action to the manager. returns tags to use in
# associated text widget
tag = "link-%d" % len(self.links)
self.links[tag] = action
return "link", tag
def _enter(self, event):
self.text.config(cursor="hand2")
def _leave(self, event):
self.text.config(cursor="")
def _click(self, event):
for tag in self.text.tag_names(tk.CURRENT):
if tag.startswith("link-"):
proc = self.links[tag]
if callable(proc):
proc()
else:
if self.url_callback:
self.url_callback(proc)
class App:
def __init__(self):
root = tk.Tk()
root.title('OpenCV Demo')
self.win = win = tk.PanedWindow(root, orient=tk.HORIZONTAL, sashrelief=tk.RAISED, sashwidth=4)
self.win.pack(fill=tk.BOTH, expand=1)
left = tk.Frame(win)
right = tk.Frame(win)
win.add(left)
win.add(right)
scrollbar = tk.Scrollbar(left, orient=tk.VERTICAL)
self.demos_lb = demos_lb = tk.Listbox(left, yscrollcommand=scrollbar.set)
scrollbar.config(command=demos_lb.yview)
scrollbar.pack(side=tk.RIGHT, fill=tk.Y)
demos_lb.pack(side=tk.LEFT, fill=tk.BOTH, expand=1)
self.samples = {}
for fn in glob('*.py'):
name = splitfn(fn)[1]
if fn[0] != '_' and name not in exclude_list:
demos_lb.insert(tk.END, name)
self.samples[name] = fn
demos_lb.bind('<<ListboxSelect>>', self.on_demo_select)
self.cmd_entry = cmd_entry = tk.Entry(right)
cmd_entry.bind('<Return>', self.on_run)
run_btn = tk.Button(right, command=self.on_run, text='Run', width=8)
self.text = text = ScrolledText(right, font=('arial', 12, 'normal'), width = 30, wrap='word')
self.linker = linker = LinkManager(text, self.on_link)
self.text.tag_config("header1", font=('arial', 14, 'bold'))
self.text.tag_config("header2", font=('arial', 12, 'bold'))
text.config(state='disabled')
text.pack(fill='both', expand=1, side=tk.BOTTOM)
cmd_entry.pack(fill='x', side='left' , expand=1)
run_btn.pack()
def on_link(self, url):
print url
webbrowser.open(url)
def on_demo_select(self, evt):
name = self.demos_lb.get( self.demos_lb.curselection()[0] )
fn = self.samples[name]
loc = {}
execfile(fn, loc)
descr = loc.get('__doc__', 'no-description')
self.linker.reset()
self.text.config(state='normal')
self.text.delete(1.0, tk.END)
self.format_text(descr)
self.text.config(state='disabled')
self.cmd_entry.delete(0, tk.END)
self.cmd_entry.insert(0, fn)
def format_text(self, s):
text = self.text
lines = s.splitlines()
for i, s in enumerate(lines):
s = s.rstrip()
if i == 0 and not s:
continue
if s and s == '='*len(s):
text.tag_add('header1', 'end-2l', 'end-1l')
elif s and s == '-'*len(s):
text.tag_add('header2', 'end-2l', 'end-1l')
else:
text.insert('end', s+'\n')
def add_link(start, end, url):
for tag in self.linker.add(url):
text.tag_add(tag, start, end)
self.match_text(r'http://\S+', add_link)
def match_text(self, pattern, tag_proc, regexp=True):
text = self.text
text.mark_set('matchPos', '1.0')
count = tk.IntVar()
while True:
match_index = text.search(pattern, 'matchPos', count=count, regexp=regexp, stopindex='end')
if not match_index: break
end_index = text.index( "%s+%sc" % (match_index, count.get()) )
text.mark_set('matchPos', end_index)
if callable(tag_proc):
tag_proc(match_index, end_index, text.get(match_index, end_index))
else:
text.tag_add(tag_proc, match_index, end_index)
def on_run(self, *args):
cmd = self.cmd_entry.get()
print 'running:', cmd
Popen("python " + cmd, shell=True)
def run(self):
tk.mainloop()
if __name__ == '__main__':
App().run()

350
samples/python2/digits.py Normal file → Executable file
View File

@@ -1,175 +1,175 @@
'''
SVM and KNearest digit recognition.
Sample loads a dataset of handwritten digits from 'digits.png'.
Then it trains a SVM and KNearest classifiers on it and evaluates
their accuracy.
Following preprocessing is applied to the dataset:
- Moment-based image deskew (see deskew())
- Digit images are split into 4 10x10 cells and 16-bin
histogram of oriented gradients is computed for each
cell
- Transform histograms to space with Hellinger metric (see [1] (RootSIFT))
[1] R. Arandjelovic, A. Zisserman
"Three things everyone should know to improve object retrieval"
http://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12/arandjelovic12.pdf
Usage:
digits.py
'''
import numpy as np
import cv2
from multiprocessing.pool import ThreadPool
from common import clock, mosaic
from numpy.linalg import norm
SZ = 20 # size of each digit is SZ x SZ
CLASS_N = 10
DIGITS_FN = 'data/digits.png'
def split2d(img, cell_size, flatten=True):
h, w = img.shape[:2]
sx, sy = cell_size
cells = [np.hsplit(row, w//sx) for row in np.vsplit(img, h//sy)]
cells = np.array(cells)
if flatten:
cells = cells.reshape(-1, sy, sx)
return cells
def load_digits(fn):
print 'loading "%s" ...' % fn
digits_img = cv2.imread(fn, 0)
digits = split2d(digits_img, (SZ, SZ))
labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N)
return digits, labels
def deskew(img):
m = cv2.moments(img)
if abs(m['mu02']) < 1e-2:
return img.copy()
skew = m['mu11']/m['mu02']
M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
return img
class StatModel(object):
def load(self, fn):
self.model.load(fn)
def save(self, fn):
self.model.save(fn)
class KNearest(StatModel):
def __init__(self, k = 3):
self.k = k
self.model = cv2.KNearest()
def train(self, samples, responses):
self.model = cv2.KNearest()
self.model.train(samples, responses)
def predict(self, samples):
retval, results, neigh_resp, dists = self.model.find_nearest(samples, self.k)
return results.ravel()
class SVM(StatModel):
def __init__(self, C = 1, gamma = 0.5):
self.params = dict( kernel_type = cv2.SVM_RBF,
svm_type = cv2.SVM_C_SVC,
C = C,
gamma = gamma )
self.model = cv2.SVM()
def train(self, samples, responses):
self.model = cv2.SVM()
self.model.train(samples, responses, params = self.params)
def predict(self, samples):
return self.model.predict_all(samples).ravel()
def evaluate_model(model, digits, samples, labels):
resp = model.predict(samples)
err = (labels != resp).mean()
print 'error: %.2f %%' % (err*100)
confusion = np.zeros((10, 10), np.int32)
for i, j in zip(labels, resp):
confusion[i, j] += 1
print 'confusion matrix:'
print confusion
print
vis = []
for img, flag in zip(digits, resp == labels):
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
if not flag:
img[...,:2] = 0
vis.append(img)
return mosaic(25, vis)
def preprocess_simple(digits):
return np.float32(digits).reshape(-1, SZ*SZ) / 255.0
def preprocess_hog(digits):
samples = []
for img in digits:
gx = cv2.Sobel(img, cv2.CV_32F, 1, 0)
gy = cv2.Sobel(img, cv2.CV_32F, 0, 1)
mag, ang = cv2.cartToPolar(gx, gy)
bin_n = 16
bin = np.int32(bin_n*ang/(2*np.pi))
bin_cells = bin[:10,:10], bin[10:,:10], bin[:10,10:], bin[10:,10:]
mag_cells = mag[:10,:10], mag[10:,:10], mag[:10,10:], mag[10:,10:]
hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]
hist = np.hstack(hists)
# transform to Hellinger kernel
eps = 1e-7
hist /= hist.sum() + eps
hist = np.sqrt(hist)
hist /= norm(hist) + eps
samples.append(hist)
return np.float32(samples)
if __name__ == '__main__':
print __doc__
digits, labels = load_digits(DIGITS_FN)
print 'preprocessing...'
# shuffle digits
rand = np.random.RandomState(321)
shuffle = rand.permutation(len(digits))
digits, labels = digits[shuffle], labels[shuffle]
digits2 = map(deskew, digits)
samples = preprocess_hog(digits2)
train_n = int(0.9*len(samples))
cv2.imshow('test set', mosaic(25, digits[train_n:]))
digits_train, digits_test = np.split(digits2, [train_n])
samples_train, samples_test = np.split(samples, [train_n])
labels_train, labels_test = np.split(labels, [train_n])
print 'training KNearest...'
model = KNearest(k=4)
model.train(samples_train, labels_train)
vis = evaluate_model(model, digits_test, samples_test, labels_test)
cv2.imshow('KNearest test', vis)
print 'training SVM...'
model = SVM(C=2.67, gamma=5.383)
model.train(samples_train, labels_train)
vis = evaluate_model(model, digits_test, samples_test, labels_test)
cv2.imshow('SVM test', vis)
print 'saving SVM as "digits_svm.dat"...'
model.save('digits_svm.dat')
cv2.waitKey(0)
'''
SVM and KNearest digit recognition.
Sample loads a dataset of handwritten digits from 'digits.png'.
Then it trains a SVM and KNearest classifiers on it and evaluates
their accuracy.
Following preprocessing is applied to the dataset:
- Moment-based image deskew (see deskew())
- Digit images are split into 4 10x10 cells and 16-bin
histogram of oriented gradients is computed for each
cell
- Transform histograms to space with Hellinger metric (see [1] (RootSIFT))
[1] R. Arandjelovic, A. Zisserman
"Three things everyone should know to improve object retrieval"
http://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12/arandjelovic12.pdf
Usage:
digits.py
'''
import numpy as np
import cv2
from multiprocessing.pool import ThreadPool
from common import clock, mosaic
from numpy.linalg import norm
SZ = 20 # size of each digit is SZ x SZ
CLASS_N = 10
DIGITS_FN = 'data/digits.png'
def split2d(img, cell_size, flatten=True):
h, w = img.shape[:2]
sx, sy = cell_size
cells = [np.hsplit(row, w//sx) for row in np.vsplit(img, h//sy)]
cells = np.array(cells)
if flatten:
cells = cells.reshape(-1, sy, sx)
return cells
def load_digits(fn):
print 'loading "%s" ...' % fn
digits_img = cv2.imread(fn, 0)
digits = split2d(digits_img, (SZ, SZ))
labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N)
return digits, labels
def deskew(img):
m = cv2.moments(img)
if abs(m['mu02']) < 1e-2:
return img.copy()
skew = m['mu11']/m['mu02']
M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
return img
class StatModel(object):
def load(self, fn):
self.model.load(fn)
def save(self, fn):
self.model.save(fn)
class KNearest(StatModel):
def __init__(self, k = 3):
self.k = k
self.model = cv2.KNearest()
def train(self, samples, responses):
self.model = cv2.KNearest()
self.model.train(samples, responses)
def predict(self, samples):
retval, results, neigh_resp, dists = self.model.find_nearest(samples, self.k)
return results.ravel()
class SVM(StatModel):
def __init__(self, C = 1, gamma = 0.5):
self.params = dict( kernel_type = cv2.SVM_RBF,
svm_type = cv2.SVM_C_SVC,
C = C,
gamma = gamma )
self.model = cv2.SVM()
def train(self, samples, responses):
self.model = cv2.SVM()
self.model.train(samples, responses, params = self.params)
def predict(self, samples):
return self.model.predict_all(samples).ravel()
def evaluate_model(model, digits, samples, labels):
resp = model.predict(samples)
err = (labels != resp).mean()
print 'error: %.2f %%' % (err*100)
confusion = np.zeros((10, 10), np.int32)
for i, j in zip(labels, resp):
confusion[i, j] += 1
print 'confusion matrix:'
print confusion
print
vis = []
for img, flag in zip(digits, resp == labels):
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
if not flag:
img[...,:2] = 0
vis.append(img)
return mosaic(25, vis)
def preprocess_simple(digits):
return np.float32(digits).reshape(-1, SZ*SZ) / 255.0
def preprocess_hog(digits):
samples = []
for img in digits:
gx = cv2.Sobel(img, cv2.CV_32F, 1, 0)
gy = cv2.Sobel(img, cv2.CV_32F, 0, 1)
mag, ang = cv2.cartToPolar(gx, gy)
bin_n = 16
bin = np.int32(bin_n*ang/(2*np.pi))
bin_cells = bin[:10,:10], bin[10:,:10], bin[:10,10:], bin[10:,10:]
mag_cells = mag[:10,:10], mag[10:,:10], mag[:10,10:], mag[10:,10:]
hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]
hist = np.hstack(hists)
# transform to Hellinger kernel
eps = 1e-7
hist /= hist.sum() + eps
hist = np.sqrt(hist)
hist /= norm(hist) + eps
samples.append(hist)
return np.float32(samples)
if __name__ == '__main__':
print __doc__
digits, labels = load_digits(DIGITS_FN)
print 'preprocessing...'
# shuffle digits
rand = np.random.RandomState(321)
shuffle = rand.permutation(len(digits))
digits, labels = digits[shuffle], labels[shuffle]
digits2 = map(deskew, digits)
samples = preprocess_hog(digits2)
train_n = int(0.9*len(samples))
cv2.imshow('test set', mosaic(25, digits[train_n:]))
digits_train, digits_test = np.split(digits2, [train_n])
samples_train, samples_test = np.split(samples, [train_n])
labels_train, labels_test = np.split(labels, [train_n])
print 'training KNearest...'
model = KNearest(k=4)
model.train(samples_train, labels_train)
vis = evaluate_model(model, digits_test, samples_test, labels_test)
cv2.imshow('KNearest test', vis)
print 'training SVM...'
model = SVM(C=2.67, gamma=5.383)
model.train(samples_train, labels_train)
vis = evaluate_model(model, digits_test, samples_test, labels_test)
cv2.imshow('SVM test', vis)
print 'saving SVM as "digits_svm.dat"...'
model.save('digits_svm.dat')
cv2.waitKey(0)

324
samples/python2/digits_adjust.py Normal file → Executable file
View File

@@ -1,162 +1,162 @@
'''
Digit recognition adjustment.
Grid search is used to find the best parameters for SVM and KNearest classifiers.
SVM adjustment follows the guidelines given in
http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
Threading or cloud computing (with http://www.picloud.com/)) may be used
to speedup the computation.
Usage:
digits_adjust.py [--model {svm|knearest}] [--cloud] [--env <PiCloud environment>]
--model {svm|knearest} - select the classifier (SVM is the default)
--cloud - use PiCloud computing platform
--env - cloud environment name
'''
# TODO cloud env setup tutorial
import numpy as np
import cv2
from multiprocessing.pool import ThreadPool
from digits import *
try:
import cloud
have_cloud = True
except ImportError:
have_cloud = False
def cross_validate(model_class, params, samples, labels, kfold = 3, pool = None):
n = len(samples)
folds = np.array_split(np.arange(n), kfold)
def f(i):
model = model_class(**params)
test_idx = folds[i]
train_idx = list(folds)
train_idx.pop(i)
train_idx = np.hstack(train_idx)
train_samples, train_labels = samples[train_idx], labels[train_idx]
test_samples, test_labels = samples[test_idx], labels[test_idx]
model.train(train_samples, train_labels)
resp = model.predict(test_samples)
score = (resp != test_labels).mean()
print ".",
return score
if pool is None:
scores = map(f, xrange(kfold))
else:
scores = pool.map(f, xrange(kfold))
return np.mean(scores)
class App(object):
def __init__(self, usecloud=False, cloud_env=''):
if usecloud and not have_cloud:
print 'warning: cloud module is not installed, running locally'
usecloud = False
self.usecloud = usecloud
self.cloud_env = cloud_env
if self.usecloud:
print 'uploading dataset to cloud...'
cloud.files.put(DIGITS_FN)
self.preprocess_job = cloud.call(self.preprocess, _env=self.cloud_env)
else:
self._samples, self._labels = self.preprocess()
def preprocess(self):
if self.usecloud:
cloud.files.get(DIGITS_FN)
digits, labels = load_digits(DIGITS_FN)
shuffle = np.random.permutation(len(digits))
digits, labels = digits[shuffle], labels[shuffle]
digits2 = map(deskew, digits)
samples = preprocess_hog(digits2)
return samples, labels
def get_dataset(self):
if self.usecloud:
return cloud.result(self.preprocess_job)
else:
return self._samples, self._labels
def run_jobs(self, f, jobs):
if self.usecloud:
jids = cloud.map(f, jobs, _env=self.cloud_env, _profile=True, _depends_on=self.preprocess_job)
ires = cloud.iresult(jids)
else:
pool = ThreadPool(processes=cv2.getNumberOfCPUs())
ires = pool.imap_unordered(f, jobs)
return ires
def adjust_SVM(self):
Cs = np.logspace(0, 10, 15, base=2)
gammas = np.logspace(-7, 4, 15, base=2)
scores = np.zeros((len(Cs), len(gammas)))
scores[:] = np.nan
print 'adjusting SVM (may take a long time) ...'
def f(job):
i, j = job
samples, labels = self.get_dataset()
params = dict(C = Cs[i], gamma=gammas[j])
score = cross_validate(SVM, params, samples, labels)
return i, j, score
ires = self.run_jobs(f, np.ndindex(*scores.shape))
for count, (i, j, score) in enumerate(ires):
scores[i, j] = score
print '%d / %d (best error: %.2f %%, last: %.2f %%)' % (count+1, scores.size, np.nanmin(scores)*100, score*100)
print scores
print 'writing score table to "svm_scores.npz"'
np.savez('svm_scores.npz', scores=scores, Cs=Cs, gammas=gammas)
i, j = np.unravel_index(scores.argmin(), scores.shape)
best_params = dict(C = Cs[i], gamma=gammas[j])
print 'best params:', best_params
print 'best error: %.2f %%' % (scores.min()*100)
return best_params
def adjust_KNearest(self):
print 'adjusting KNearest ...'
def f(k):
samples, labels = self.get_dataset()
err = cross_validate(KNearest, dict(k=k), samples, labels)
return k, err
best_err, best_k = np.inf, -1
for k, err in self.run_jobs(f, xrange(1, 9)):
if err < best_err:
best_err, best_k = err, k
print 'k = %d, error: %.2f %%' % (k, err*100)
best_params = dict(k=best_k)
print 'best params:', best_params, 'err: %.2f' % (best_err*100)
return best_params
if __name__ == '__main__':
import getopt
import sys
print __doc__
args, _ = getopt.getopt(sys.argv[1:], '', ['model=', 'cloud', 'env='])
args = dict(args)
args.setdefault('--model', 'svm')
args.setdefault('--env', '')
if args['--model'] not in ['svm', 'knearest']:
print 'unknown model "%s"' % args['--model']
sys.exit(1)
t = clock()
app = App(usecloud='--cloud' in args, cloud_env = args['--env'])
if args['--model'] == 'knearest':
app.adjust_KNearest()
else:
app.adjust_SVM()
print 'work time: %f s' % (clock() - t)
'''
Digit recognition adjustment.
Grid search is used to find the best parameters for SVM and KNearest classifiers.
SVM adjustment follows the guidelines given in
http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
Threading or cloud computing (with http://www.picloud.com/)) may be used
to speedup the computation.
Usage:
digits_adjust.py [--model {svm|knearest}] [--cloud] [--env <PiCloud environment>]
--model {svm|knearest} - select the classifier (SVM is the default)
--cloud - use PiCloud computing platform
--env - cloud environment name
'''
# TODO cloud env setup tutorial
import numpy as np
import cv2
from multiprocessing.pool import ThreadPool
from digits import *
try:
import cloud
have_cloud = True
except ImportError:
have_cloud = False
def cross_validate(model_class, params, samples, labels, kfold = 3, pool = None):
n = len(samples)
folds = np.array_split(np.arange(n), kfold)
def f(i):
model = model_class(**params)
test_idx = folds[i]
train_idx = list(folds)
train_idx.pop(i)
train_idx = np.hstack(train_idx)
train_samples, train_labels = samples[train_idx], labels[train_idx]
test_samples, test_labels = samples[test_idx], labels[test_idx]
model.train(train_samples, train_labels)
resp = model.predict(test_samples)
score = (resp != test_labels).mean()
print ".",
return score
if pool is None:
scores = map(f, xrange(kfold))
else:
scores = pool.map(f, xrange(kfold))
return np.mean(scores)
class App(object):
def __init__(self, usecloud=False, cloud_env=''):
if usecloud and not have_cloud:
print 'warning: cloud module is not installed, running locally'
usecloud = False
self.usecloud = usecloud
self.cloud_env = cloud_env
if self.usecloud:
print 'uploading dataset to cloud...'
cloud.files.put(DIGITS_FN)
self.preprocess_job = cloud.call(self.preprocess, _env=self.cloud_env)
else:
self._samples, self._labels = self.preprocess()
def preprocess(self):
if self.usecloud:
cloud.files.get(DIGITS_FN)
digits, labels = load_digits(DIGITS_FN)
shuffle = np.random.permutation(len(digits))
digits, labels = digits[shuffle], labels[shuffle]
digits2 = map(deskew, digits)
samples = preprocess_hog(digits2)
return samples, labels
def get_dataset(self):
if self.usecloud:
return cloud.result(self.preprocess_job)
else:
return self._samples, self._labels
def run_jobs(self, f, jobs):
if self.usecloud:
jids = cloud.map(f, jobs, _env=self.cloud_env, _profile=True, _depends_on=self.preprocess_job)
ires = cloud.iresult(jids)
else:
pool = ThreadPool(processes=cv2.getNumberOfCPUs())
ires = pool.imap_unordered(f, jobs)
return ires
def adjust_SVM(self):
Cs = np.logspace(0, 10, 15, base=2)
gammas = np.logspace(-7, 4, 15, base=2)
scores = np.zeros((len(Cs), len(gammas)))
scores[:] = np.nan
print 'adjusting SVM (may take a long time) ...'
def f(job):
i, j = job
samples, labels = self.get_dataset()
params = dict(C = Cs[i], gamma=gammas[j])
score = cross_validate(SVM, params, samples, labels)
return i, j, score
ires = self.run_jobs(f, np.ndindex(*scores.shape))
for count, (i, j, score) in enumerate(ires):
scores[i, j] = score
print '%d / %d (best error: %.2f %%, last: %.2f %%)' % (count+1, scores.size, np.nanmin(scores)*100, score*100)
print scores
print 'writing score table to "svm_scores.npz"'
np.savez('svm_scores.npz', scores=scores, Cs=Cs, gammas=gammas)
i, j = np.unravel_index(scores.argmin(), scores.shape)
best_params = dict(C = Cs[i], gamma=gammas[j])
print 'best params:', best_params
print 'best error: %.2f %%' % (scores.min()*100)
return best_params
def adjust_KNearest(self):
print 'adjusting KNearest ...'
def f(k):
samples, labels = self.get_dataset()
err = cross_validate(KNearest, dict(k=k), samples, labels)
return k, err
best_err, best_k = np.inf, -1
for k, err in self.run_jobs(f, xrange(1, 9)):
if err < best_err:
best_err, best_k = err, k
print 'k = %d, error: %.2f %%' % (k, err*100)
best_params = dict(k=best_k)
print 'best params:', best_params, 'err: %.2f' % (best_err*100)
return best_params
if __name__ == '__main__':
import getopt
import sys
print __doc__
args, _ = getopt.getopt(sys.argv[1:], '', ['model=', 'cloud', 'env='])
args = dict(args)
args.setdefault('--model', 'svm')
args.setdefault('--env', '')
if args['--model'] not in ['svm', 'knearest']:
print 'unknown model "%s"' % args['--model']
sys.exit(1)
t = clock()
app = App(usecloud='--cloud' in args, cloud_env = args['--env'])
if args['--model'] == 'knearest':
app.adjust_KNearest()
else:
app.adjust_SVM()
print 'work time: %f s' % (clock() - t)

168
samples/python2/digits_video.py Normal file → Executable file
View File

@@ -1,84 +1,84 @@
import numpy as np
import cv2
import os
import sys
import video
from common import mosaic
from digits import *
def main():
try: src = sys.argv[1]
except: src = 0
cap = video.create_capture(src)
classifier_fn = 'digits_svm.dat'
if not os.path.exists(classifier_fn):
print '"%s" not found, run digits.py first' % classifier_fn
return
model = SVM()
model.load(classifier_fn)
while True:
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
bin = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 31, 10)
bin = cv2.medianBlur(bin, 3)
contours, heirs = cv2.findContours( bin.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
try: heirs = heirs[0]
except: heirs = []
for cnt, heir in zip(contours, heirs):
_, _, _, outer_i = heir
if outer_i >= 0:
continue
x, y, w, h = cv2.boundingRect(cnt)
if not (16 <= h <= 64 and w <= 1.2*h):
continue
pad = max(h-w, 0)
x, w = x-pad/2, w+pad
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0))
bin_roi = bin[y:,x:][:h,:w]
gray_roi = gray[y:,x:][:h,:w]
m = bin_roi != 0
if not 0.1 < m.mean() < 0.4:
continue
'''
v_in, v_out = gray_roi[m], gray_roi[~m]
if v_out.std() > 10.0:
continue
s = "%f, %f" % (abs(v_in.mean() - v_out.mean()), v_out.std())
cv2.putText(frame, s, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
'''
s = 1.5*float(h)/SZ
m = cv2.moments(bin_roi)
c1 = np.float32([m['m10'], m['m01']]) / m['m00']
c0 = np.float32([SZ/2, SZ/2])
t = c1 - s*c0
A = np.zeros((2, 3), np.float32)
A[:,:2] = np.eye(2)*s
A[:,2] = t
bin_norm = cv2.warpAffine(bin_roi, A, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
bin_norm = deskew(bin_norm)
if x+w+SZ < frame.shape[1] and y+SZ < frame.shape[0]:
frame[y:,x+w:][:SZ, :SZ] = bin_norm[...,np.newaxis]
sample = preprocess_hog([bin_norm])
digit = model.predict(sample)[0]
cv2.putText(frame, '%d'%digit, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
cv2.imshow('frame', frame)
cv2.imshow('bin', bin)
ch = cv2.waitKey(1)
if ch == 27:
break
if __name__ == '__main__':
main()
import numpy as np
import cv2
import os
import sys
import video
from common import mosaic
from digits import *
def main():
try: src = sys.argv[1]
except: src = 0
cap = video.create_capture(src)
classifier_fn = 'digits_svm.dat'
if not os.path.exists(classifier_fn):
print '"%s" not found, run digits.py first' % classifier_fn
return
model = SVM()
model.load(classifier_fn)
while True:
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
bin = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 31, 10)
bin = cv2.medianBlur(bin, 3)
contours, heirs = cv2.findContours( bin.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
try: heirs = heirs[0]
except: heirs = []
for cnt, heir in zip(contours, heirs):
_, _, _, outer_i = heir
if outer_i >= 0:
continue
x, y, w, h = cv2.boundingRect(cnt)
if not (16 <= h <= 64 and w <= 1.2*h):
continue
pad = max(h-w, 0)
x, w = x-pad/2, w+pad
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0))
bin_roi = bin[y:,x:][:h,:w]
gray_roi = gray[y:,x:][:h,:w]
m = bin_roi != 0
if not 0.1 < m.mean() < 0.4:
continue
'''
v_in, v_out = gray_roi[m], gray_roi[~m]
if v_out.std() > 10.0:
continue
s = "%f, %f" % (abs(v_in.mean() - v_out.mean()), v_out.std())
cv2.putText(frame, s, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
'''
s = 1.5*float(h)/SZ
m = cv2.moments(bin_roi)
c1 = np.float32([m['m10'], m['m01']]) / m['m00']
c0 = np.float32([SZ/2, SZ/2])
t = c1 - s*c0
A = np.zeros((2, 3), np.float32)
A[:,:2] = np.eye(2)*s
A[:,2] = t
bin_norm = cv2.warpAffine(bin_roi, A, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
bin_norm = deskew(bin_norm)
if x+w+SZ < frame.shape[1] and y+SZ < frame.shape[0]:
frame[y:,x+w:][:SZ, :SZ] = bin_norm[...,np.newaxis]
sample = preprocess_hog([bin_norm])
digit = model.predict(sample)[0]
cv2.putText(frame, '%d'%digit, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
cv2.imshow('frame', frame)
cv2.imshow('bin', bin)
ch = cv2.waitKey(1)
if ch == 27:
break
if __name__ == '__main__':
main()

124
samples/python2/distrans.py Normal file → Executable file
View File

@@ -1,62 +1,62 @@
'''
Distance transform sample.
Usage:
distrans.py [<image>]
Keys:
ESC - exit
v - toggle voronoi mode
'''
import numpy as np
import cv2
import cv2.cv as cv
from common import make_cmap
if __name__ == '__main__':
import sys
try: fn = sys.argv[1]
except: fn = '../cpp/fruits.jpg'
print __doc__
img = cv2.imread(fn, 0)
cm = make_cmap('jet')
need_update = True
voronoi = False
def update(dummy=None):
global need_update
need_update = False
thrs = cv2.getTrackbarPos('threshold', 'distrans')
mark = cv2.Canny(img, thrs, 3*thrs)
dist, labels = cv2.distanceTransformWithLabels(~mark, cv.CV_DIST_L2, 5)
if voronoi:
vis = cm[np.uint8(labels)]
else:
vis = cm[np.uint8(dist*2)]
vis[mark != 0] = 255
cv2.imshow('distrans', vis)
def invalidate(dummy=None):
global need_update
need_update = True
cv2.namedWindow('distrans')
cv2.createTrackbar('threshold', 'distrans', 60, 255, invalidate)
update()
while True:
ch = 0xFF & cv2.waitKey(50)
if ch == 27:
break
if ch == ord('v'):
voronoi = not voronoi
print 'showing', ['distance', 'voronoi'][voronoi]
update()
if need_update:
update()
cv2.destroyAllWindows()
'''
Distance transform sample.
Usage:
distrans.py [<image>]
Keys:
ESC - exit
v - toggle voronoi mode
'''
import numpy as np
import cv2
import cv2.cv as cv
from common import make_cmap
if __name__ == '__main__':
import sys
try: fn = sys.argv[1]
except: fn = '../cpp/fruits.jpg'
print __doc__
img = cv2.imread(fn, 0)
cm = make_cmap('jet')
need_update = True
voronoi = False
def update(dummy=None):
global need_update
need_update = False
thrs = cv2.getTrackbarPos('threshold', 'distrans')
mark = cv2.Canny(img, thrs, 3*thrs)
dist, labels = cv2.distanceTransformWithLabels(~mark, cv.CV_DIST_L2, 5)
if voronoi:
vis = cm[np.uint8(labels)]
else:
vis = cm[np.uint8(dist*2)]
vis[mark != 0] = 255
cv2.imshow('distrans', vis)
def invalidate(dummy=None):
global need_update
need_update = True
cv2.namedWindow('distrans')
cv2.createTrackbar('threshold', 'distrans', 60, 255, invalidate)
update()
while True:
ch = 0xFF & cv2.waitKey(50)
if ch == 27:
break
if ch == ord('v'):
voronoi = not voronoi
print 'showing', ['distance', 'voronoi'][voronoi]
update()
if need_update:
update()
cv2.destroyAllWindows()

88
samples/python2/edge.py Normal file → Executable file
View File

@@ -1,44 +1,44 @@
'''
This sample demonstrates Canny edge detection.
Usage:
edge.py [<video source>]
Trackbars control edge thresholds.
'''
import cv2
import video
import sys
if __name__ == '__main__':
print __doc__
try: fn = sys.argv[1]
except: fn = 0
def nothing(*arg):
pass
cv2.namedWindow('edge')
cv2.createTrackbar('thrs1', 'edge', 2000, 5000, nothing)
cv2.createTrackbar('thrs2', 'edge', 4000, 5000, nothing)
cap = video.create_capture(fn)
while True:
flag, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thrs1 = cv2.getTrackbarPos('thrs1', 'edge')
thrs2 = cv2.getTrackbarPos('thrs2', 'edge')
edge = cv2.Canny(gray, thrs1, thrs2, apertureSize=5)
vis = img.copy()
vis /= 2
vis[edge != 0] = (0, 255, 0)
cv2.imshow('edge', vis)
ch = cv2.waitKey(5)
if ch == 27:
break
cv2.destroyAllWindows()
'''
This sample demonstrates Canny edge detection.
Usage:
edge.py [<video source>]
Trackbars control edge thresholds.
'''
import cv2
import video
import sys
if __name__ == '__main__':
print __doc__
try: fn = sys.argv[1]
except: fn = 0
def nothing(*arg):
pass
cv2.namedWindow('edge')
cv2.createTrackbar('thrs1', 'edge', 2000, 5000, nothing)
cv2.createTrackbar('thrs2', 'edge', 4000, 5000, nothing)
cap = video.create_capture(fn)
while True:
flag, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thrs1 = cv2.getTrackbarPos('thrs1', 'edge')
thrs2 = cv2.getTrackbarPos('thrs2', 'edge')
edge = cv2.Canny(gray, thrs1, thrs2, apertureSize=5)
vis = img.copy()
vis /= 2
vis[edge != 0] = (0, 255, 0)
cv2.imshow('edge', vis)
ch = cv2.waitKey(5)
if ch == 27:
break
cv2.destroyAllWindows()

120
samples/python2/facedetect.py Normal file → Executable file
View File

@@ -1,60 +1,60 @@
import numpy as np
import cv2
import cv2.cv as cv
from video import create_capture
from common import clock, draw_str
help_message = '''
USAGE: facedetect.py [--cascade <cascade_fn>] [--nested-cascade <cascade_fn>] [<video_source>]
'''
def detect(img, cascade):
rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30), flags = cv.CV_HAAR_SCALE_IMAGE)
if len(rects) == 0:
return []
rects[:,2:] += rects[:,:2]
return rects
def draw_rects(img, rects, color):
for x1, y1, x2, y2 in rects:
cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
if __name__ == '__main__':
import sys, getopt
print help_message
args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
try: video_src = video_src[0]
except: video_src = 0
args = dict(args)
cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml")
nested_fn = args.get('--nested-cascade', "../../data/haarcascades/haarcascade_eye.xml")
cascade = cv2.CascadeClassifier(cascade_fn)
nested = cv2.CascadeClassifier(nested_fn)
cam = create_capture(video_src, fallback='synth:bg=../cpp/lena.jpg:noise=0.05')
while True:
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.equalizeHist(gray)
t = clock()
rects = detect(gray, cascade)
vis = img.copy()
draw_rects(vis, rects, (0, 255, 0))
for x1, y1, x2, y2 in rects:
roi = gray[y1:y2, x1:x2]
vis_roi = vis[y1:y2, x1:x2]
subrects = detect(roi.copy(), nested)
draw_rects(vis_roi, subrects, (255, 0, 0))
dt = clock() - t
draw_str(vis, (20, 20), 'time: %.1f ms' % (dt*1000))
cv2.imshow('facedetect', vis)
if 0xFF & cv2.waitKey(5) == 27:
break
cv2.destroyAllWindows()
import numpy as np
import cv2
import cv2.cv as cv
from video import create_capture
from common import clock, draw_str
help_message = '''
USAGE: facedetect.py [--cascade <cascade_fn>] [--nested-cascade <cascade_fn>] [<video_source>]
'''
def detect(img, cascade):
rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30), flags = cv.CV_HAAR_SCALE_IMAGE)
if len(rects) == 0:
return []
rects[:,2:] += rects[:,:2]
return rects
def draw_rects(img, rects, color):
for x1, y1, x2, y2 in rects:
cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
if __name__ == '__main__':
import sys, getopt
print help_message
args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
try: video_src = video_src[0]
except: video_src = 0
args = dict(args)
cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml")
nested_fn = args.get('--nested-cascade', "../../data/haarcascades/haarcascade_eye.xml")
cascade = cv2.CascadeClassifier(cascade_fn)
nested = cv2.CascadeClassifier(nested_fn)
cam = create_capture(video_src, fallback='synth:bg=../cpp/lena.jpg:noise=0.05')
while True:
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.equalizeHist(gray)
t = clock()
rects = detect(gray, cascade)
vis = img.copy()
draw_rects(vis, rects, (0, 255, 0))
for x1, y1, x2, y2 in rects:
roi = gray[y1:y2, x1:x2]
vis_roi = vis[y1:y2, x1:x2]
subrects = detect(roi.copy(), nested)
draw_rects(vis_roi, subrects, (255, 0, 0))
dt = clock() - t
draw_str(vis, (20, 20), 'time: %.1f ms' % (dt*1000))
cv2.imshow('facedetect', vis)
if 0xFF & cv2.waitKey(5) == 27:
break
cv2.destroyAllWindows()

28
samples/python2/facerec_demo.py Normal file → Executable file
View File

@@ -41,7 +41,7 @@ def normalize(X, low, high, dtype=None):
"""Normalizes a given array in X to a value between low and high."""
X = np.asarray(X)
minX, maxX = np.min(X), np.max(X)
# normalize to [0...1].
# normalize to [0...1].
X = X - float(minX)
X = X / float((maxX - minX))
# scale to [low...high].
@@ -54,14 +54,14 @@ def normalize(X, low, high, dtype=None):
def read_images(path, sz=None):
"""Reads the images in a given folder, resizes images on the fly if size is given.
Args:
path: Path to a folder with subfolders representing the subjects (persons).
sz: A tuple with the size Resizes
sz: A tuple with the size Resizes
Returns:
A list [X,y]
X: The images, which is a Python list of numpy arrays.
y: The corresponding labels (the unique number of the subject, person) in a Python list.
"""
@@ -85,7 +85,7 @@ def read_images(path, sz=None):
raise
c = c+1
return [X,y]
if __name__ == "__main__":
# This is where we write the images, if an output_dir is given
# in command line:
@@ -99,7 +99,7 @@ if __name__ == "__main__":
# Now read in the image data. This must be a valid path!
[X,y] = read_images(sys.argv[1])
# Convert labels to 32bit integers. This is a workaround for 64bit machines,
# because the labels will truncated else. This will be fixed in code as
# because the labels will truncated else. This will be fixed in code as
# soon as possible, so Python users don't need to know about this.
# Thanks to Leo Dirac for reporting:
y = np.asarray(y, dtype=np.int32)
@@ -115,10 +115,10 @@ if __name__ == "__main__":
# so we use np.asarray to turn them into NumPy lists to make
# the OpenCV wrapper happy:
model.train(np.asarray(X), np.asarray(y))
# We now get a prediction from the model! In reality you
# should always use unseen images for testing your model.
# But so many people were confused, when I sliced an image
# off in the C++ version, so I am just using an image we
# We now get a prediction from the model! In reality you
# should always use unseen images for testing your model.
# But so many people were confused, when I sliced an image
# off in the C++ version, so I am just using an image we
# have trained with.
#
# model.predict is going to return the predicted label and
@@ -126,7 +126,7 @@ if __name__ == "__main__":
[p_label, p_confidence] = model.predict(np.asarray(X[0]))
# Print it:
print "Predicted label = %d (confidence=%.2f)" % (p_label, p_confidence)
# Cool! Finally we'll plot the Eigenfaces, because that's
# Cool! Finally we'll plot the Eigenfaces, because that's
# what most people read in the papers are keen to see.
#
# Just like in C++ you have access to all model internal
@@ -144,9 +144,9 @@ if __name__ == "__main__":
cv2.imshow("mean", mean_resized)
else:
cv2.imwrite("%s/mean.png" % (out_dir), mean_resized)
# Turn the first (at most) 16 eigenvectors into grayscale
# Turn the first (at most) 16 eigenvectors into grayscale
# images. You could also use cv::normalize here, but sticking
# to NumPy is much easier for now.
# to NumPy is much easier for now.
# Note: eigenvectors are stored by column:
for i in xrange(min(len(X), 16)):
eigenvector_i = eigenvectors[:,i].reshape(X[0].shape)

176
samples/python2/feature_homography.py Normal file → Executable file
View File

@@ -1,88 +1,88 @@
'''
Feature homography
==================
Example of using features2d framework for interactive video homography matching.
ORB features and FLANN matcher are used. The actual tracking is implemented by
PlaneTracker class in plane_tracker.py
Inspired by http://www.youtube.com/watch?v=-ZNYoL8rzPY
video: http://www.youtube.com/watch?v=FirtmYcC0Vc
Usage
-----
feature_homography.py [<video source>]
Keys:
SPACE - pause video
Select a textured planar object to track by drawing a box with a mouse.
'''
import numpy as np
import cv2
import video
import common
from common import getsize, draw_keypoints
from plane_tracker import PlaneTracker
class App:
def __init__(self, src):
self.cap = video.create_capture(src)
self.frame = None
self.paused = False
self.tracker = PlaneTracker()
cv2.namedWindow('plane')
self.rect_sel = common.RectSelector('plane', self.on_rect)
def on_rect(self, rect):
self.tracker.clear()
self.tracker.add_target(self.frame, rect)
def run(self):
while True:
playing = not self.paused and not self.rect_sel.dragging
if playing or self.frame is None:
ret, frame = self.cap.read()
if not ret:
break
self.frame = frame.copy()
w, h = getsize(self.frame)
vis = np.zeros((h, w*2, 3), np.uint8)
vis[:h,:w] = self.frame
if len(self.tracker.targets) > 0:
target = self.tracker.targets[0]
vis[:,w:] = target.image
draw_keypoints(vis[:,w:], target.keypoints)
x0, y0, x1, y1 = target.rect
cv2.rectangle(vis, (x0+w, y0), (x1+w, y1), (0, 255, 0), 2)
if playing:
tracked = self.tracker.track(self.frame)
if len(tracked) > 0:
tracked = tracked[0]
cv2.polylines(vis, [np.int32(tracked.quad)], True, (255, 255, 255), 2)
for (x0, y0), (x1, y1) in zip(np.int32(tracked.p0), np.int32(tracked.p1)):
cv2.line(vis, (x0+w, y0), (x1, y1), (0, 255, 0))
draw_keypoints(vis, self.tracker.frame_points)
self.rect_sel.draw(vis)
cv2.imshow('plane', vis)
ch = cv2.waitKey(1)
if ch == ord(' '):
self.paused = not self.paused
if ch == 27:
break
if __name__ == '__main__':
print __doc__
import sys
try: video_src = sys.argv[1]
except: video_src = 0
App(video_src).run()
'''
Feature homography
==================
Example of using features2d framework for interactive video homography matching.
ORB features and FLANN matcher are used. The actual tracking is implemented by
PlaneTracker class in plane_tracker.py
Inspired by http://www.youtube.com/watch?v=-ZNYoL8rzPY
video: http://www.youtube.com/watch?v=FirtmYcC0Vc
Usage
-----
feature_homography.py [<video source>]
Keys:
SPACE - pause video
Select a textured planar object to track by drawing a box with a mouse.
'''
import numpy as np
import cv2
import video
import common
from common import getsize, draw_keypoints
from plane_tracker import PlaneTracker
class App:
def __init__(self, src):
self.cap = video.create_capture(src)
self.frame = None
self.paused = False
self.tracker = PlaneTracker()
cv2.namedWindow('plane')
self.rect_sel = common.RectSelector('plane', self.on_rect)
def on_rect(self, rect):
self.tracker.clear()
self.tracker.add_target(self.frame, rect)
def run(self):
while True:
playing = not self.paused and not self.rect_sel.dragging
if playing or self.frame is None:
ret, frame = self.cap.read()
if not ret:
break
self.frame = frame.copy()
w, h = getsize(self.frame)
vis = np.zeros((h, w*2, 3), np.uint8)
vis[:h,:w] = self.frame
if len(self.tracker.targets) > 0:
target = self.tracker.targets[0]
vis[:,w:] = target.image
draw_keypoints(vis[:,w:], target.keypoints)
x0, y0, x1, y1 = target.rect
cv2.rectangle(vis, (x0+w, y0), (x1+w, y1), (0, 255, 0), 2)
if playing:
tracked = self.tracker.track(self.frame)
if len(tracked) > 0:
tracked = tracked[0]
cv2.polylines(vis, [np.int32(tracked.quad)], True, (255, 255, 255), 2)
for (x0, y0), (x1, y1) in zip(np.int32(tracked.p0), np.int32(tracked.p1)):
cv2.line(vis, (x0+w, y0), (x1, y1), (0, 255, 0))
draw_keypoints(vis, self.tracker.frame_points)
self.rect_sel.draw(vis)
cv2.imshow('plane', vis)
ch = cv2.waitKey(1)
if ch == ord(' '):
self.paused = not self.paused
if ch == 27:
break
if __name__ == '__main__':
print __doc__
import sys
try: video_src = sys.argv[1]
except: video_src = 0
App(video_src).run()

330
samples/python2/find_obj.py Normal file → Executable file
View File

@@ -1,165 +1,165 @@
'''
Feature-based image matching sample.
USAGE
find_obj.py [--feature=<sift|surf|orb>[-flann]] [ <image1> <image2> ]
--feature - Feature to use. Can be sift, surf of orb. Append '-flann' to feature name
to use Flann-based matcher instead bruteforce.
Press left mouse button on a feature point to see its mathcing point.
'''
import numpy as np
import cv2
from common import anorm, getsize
FLANN_INDEX_KDTREE = 1 # bug: flann enums are missing
FLANN_INDEX_LSH = 6
def init_feature(name):
chunks = name.split('-')
if chunks[0] == 'sift':
detector = cv2.SIFT()
norm = cv2.NORM_L2
elif chunks[0] == 'surf':
detector = cv2.SURF(800)
norm = cv2.NORM_L2
elif chunks[0] == 'orb':
detector = cv2.ORB(400)
norm = cv2.NORM_HAMMING
else:
return None, None
if 'flann' in chunks:
if norm == cv2.NORM_L2:
flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
else:
flann_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
else:
matcher = cv2.BFMatcher(norm)
return detector, matcher
def filter_matches(kp1, kp2, matches, ratio = 0.75):
mkp1, mkp2 = [], []
for m in matches:
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
m = m[0]
mkp1.append( kp1[m.queryIdx] )
mkp2.append( kp2[m.trainIdx] )
p1 = np.float32([kp.pt for kp in mkp1])
p2 = np.float32([kp.pt for kp in mkp2])
kp_pairs = zip(mkp1, mkp2)
return p1, p2, kp_pairs
def explore_match(win, img1, img2, kp_pairs, status = None, H = None):
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
vis = np.zeros((max(h1, h2), w1+w2), np.uint8)
vis[:h1, :w1] = img1
vis[:h2, w1:w1+w2] = img2
vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
if H is not None:
corners = np.float32([[0, 0], [w1, 0], [w1, h1], [0, h1]])
corners = np.int32( cv2.perspectiveTransform(corners.reshape(1, -1, 2), H).reshape(-1, 2) + (w1, 0) )
cv2.polylines(vis, [corners], True, (255, 255, 255))
if status is None:
status = np.ones(len(kp_pairs), np.bool_)
p1 = np.int32([kpp[0].pt for kpp in kp_pairs])
p2 = np.int32([kpp[1].pt for kpp in kp_pairs]) + (w1, 0)
green = (0, 255, 0)
red = (0, 0, 255)
white = (255, 255, 255)
kp_color = (51, 103, 236)
for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
if inlier:
col = green
cv2.circle(vis, (x1, y1), 2, col, -1)
cv2.circle(vis, (x2, y2), 2, col, -1)
else:
col = red
r = 2
thickness = 3
cv2.line(vis, (x1-r, y1-r), (x1+r, y1+r), col, thickness)
cv2.line(vis, (x1-r, y1+r), (x1+r, y1-r), col, thickness)
cv2.line(vis, (x2-r, y2-r), (x2+r, y2+r), col, thickness)
cv2.line(vis, (x2-r, y2+r), (x2+r, y2-r), col, thickness)
vis0 = vis.copy()
for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
if inlier:
cv2.line(vis, (x1, y1), (x2, y2), green)
cv2.imshow(win, vis)
def onmouse(event, x, y, flags, param):
cur_vis = vis
if flags & cv2.EVENT_FLAG_LBUTTON:
cur_vis = vis0.copy()
r = 8
m = (anorm(p1 - (x, y)) < r) | (anorm(p2 - (x, y)) < r)
idxs = np.where(m)[0]
kp1s, kp2s = [], []
for i in idxs:
(x1, y1), (x2, y2) = p1[i], p2[i]
col = (red, green)[status[i]]
cv2.line(cur_vis, (x1, y1), (x2, y2), col)
kp1, kp2 = kp_pairs[i]
kp1s.append(kp1)
kp2s.append(kp2)
cur_vis = cv2.drawKeypoints(cur_vis, kp1s, flags=4, color=kp_color)
cur_vis[:,w1:] = cv2.drawKeypoints(cur_vis[:,w1:], kp2s, flags=4, color=kp_color)
cv2.imshow(win, cur_vis)
cv2.setMouseCallback(win, onmouse)
return vis
if __name__ == '__main__':
print __doc__
import sys, getopt
opts, args = getopt.getopt(sys.argv[1:], '', ['feature='])
opts = dict(opts)
feature_name = opts.get('--feature', 'sift')
try: fn1, fn2 = args
except:
fn1 = '../c/box.png'
fn2 = '../c/box_in_scene.png'
img1 = cv2.imread(fn1, 0)
img2 = cv2.imread(fn2, 0)
detector, matcher = init_feature(feature_name)
if detector != None:
print 'using', feature_name
else:
print 'unknown feature:', feature_name
sys.exit(1)
kp1, desc1 = detector.detectAndCompute(img1, None)
kp2, desc2 = detector.detectAndCompute(img2, None)
print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))
def match_and_draw(win):
print 'matching...'
raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2
p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
if len(p1) >= 4:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
print '%d / %d inliers/matched' % (np.sum(status), len(status))
else:
H, status = None, None
print '%d matches found, not enough for homography estimation' % len(p1)
vis = explore_match(win, img1, img2, kp_pairs, status, H)
match_and_draw('find_obj')
cv2.waitKey()
cv2.destroyAllWindows()
'''
Feature-based image matching sample.
USAGE
find_obj.py [--feature=<sift|surf|orb>[-flann]] [ <image1> <image2> ]
--feature - Feature to use. Can be sift, surf of orb. Append '-flann' to feature name
to use Flann-based matcher instead bruteforce.
Press left mouse button on a feature point to see its mathcing point.
'''
import numpy as np
import cv2
from common import anorm, getsize
FLANN_INDEX_KDTREE = 1 # bug: flann enums are missing
FLANN_INDEX_LSH = 6
def init_feature(name):
chunks = name.split('-')
if chunks[0] == 'sift':
detector = cv2.SIFT()
norm = cv2.NORM_L2
elif chunks[0] == 'surf':
detector = cv2.SURF(800)
norm = cv2.NORM_L2
elif chunks[0] == 'orb':
detector = cv2.ORB(400)
norm = cv2.NORM_HAMMING
else:
return None, None
if 'flann' in chunks:
if norm == cv2.NORM_L2:
flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
else:
flann_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
else:
matcher = cv2.BFMatcher(norm)
return detector, matcher
def filter_matches(kp1, kp2, matches, ratio = 0.75):
mkp1, mkp2 = [], []
for m in matches:
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
m = m[0]
mkp1.append( kp1[m.queryIdx] )
mkp2.append( kp2[m.trainIdx] )
p1 = np.float32([kp.pt for kp in mkp1])
p2 = np.float32([kp.pt for kp in mkp2])
kp_pairs = zip(mkp1, mkp2)
return p1, p2, kp_pairs
def explore_match(win, img1, img2, kp_pairs, status = None, H = None):
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
vis = np.zeros((max(h1, h2), w1+w2), np.uint8)
vis[:h1, :w1] = img1
vis[:h2, w1:w1+w2] = img2
vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
if H is not None:
corners = np.float32([[0, 0], [w1, 0], [w1, h1], [0, h1]])
corners = np.int32( cv2.perspectiveTransform(corners.reshape(1, -1, 2), H).reshape(-1, 2) + (w1, 0) )
cv2.polylines(vis, [corners], True, (255, 255, 255))
if status is None:
status = np.ones(len(kp_pairs), np.bool_)
p1 = np.int32([kpp[0].pt for kpp in kp_pairs])
p2 = np.int32([kpp[1].pt for kpp in kp_pairs]) + (w1, 0)
green = (0, 255, 0)
red = (0, 0, 255)
white = (255, 255, 255)
kp_color = (51, 103, 236)
for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
if inlier:
col = green
cv2.circle(vis, (x1, y1), 2, col, -1)
cv2.circle(vis, (x2, y2), 2, col, -1)
else:
col = red
r = 2
thickness = 3
cv2.line(vis, (x1-r, y1-r), (x1+r, y1+r), col, thickness)
cv2.line(vis, (x1-r, y1+r), (x1+r, y1-r), col, thickness)
cv2.line(vis, (x2-r, y2-r), (x2+r, y2+r), col, thickness)
cv2.line(vis, (x2-r, y2+r), (x2+r, y2-r), col, thickness)
vis0 = vis.copy()
for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
if inlier:
cv2.line(vis, (x1, y1), (x2, y2), green)
cv2.imshow(win, vis)
def onmouse(event, x, y, flags, param):
cur_vis = vis
if flags & cv2.EVENT_FLAG_LBUTTON:
cur_vis = vis0.copy()
r = 8
m = (anorm(p1 - (x, y)) < r) | (anorm(p2 - (x, y)) < r)
idxs = np.where(m)[0]
kp1s, kp2s = [], []
for i in idxs:
(x1, y1), (x2, y2) = p1[i], p2[i]
col = (red, green)[status[i]]
cv2.line(cur_vis, (x1, y1), (x2, y2), col)
kp1, kp2 = kp_pairs[i]
kp1s.append(kp1)
kp2s.append(kp2)
cur_vis = cv2.drawKeypoints(cur_vis, kp1s, flags=4, color=kp_color)
cur_vis[:,w1:] = cv2.drawKeypoints(cur_vis[:,w1:], kp2s, flags=4, color=kp_color)
cv2.imshow(win, cur_vis)
cv2.setMouseCallback(win, onmouse)
return vis
if __name__ == '__main__':
print __doc__
import sys, getopt
opts, args = getopt.getopt(sys.argv[1:], '', ['feature='])
opts = dict(opts)
feature_name = opts.get('--feature', 'sift')
try: fn1, fn2 = args
except:
fn1 = '../c/box.png'
fn2 = '../c/box_in_scene.png'
img1 = cv2.imread(fn1, 0)
img2 = cv2.imread(fn2, 0)
detector, matcher = init_feature(feature_name)
if detector != None:
print 'using', feature_name
else:
print 'unknown feature:', feature_name
sys.exit(1)
kp1, desc1 = detector.detectAndCompute(img1, None)
kp2, desc2 = detector.detectAndCompute(img2, None)
print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))
def match_and_draw(win):
print 'matching...'
raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2
p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
if len(p1) >= 4:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
print '%d / %d inliers/matched' % (np.sum(status), len(status))
else:
H, status = None, None
print '%d matches found, not enough for homography estimation' % len(p1)
vis = explore_match(win, img1, img2, kp_pairs, status, H)
match_and_draw('find_obj')
cv2.waitKey()
cv2.destroyAllWindows()

160
samples/python2/fitline.py Normal file → Executable file
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@@ -1,80 +1,80 @@
'''
Robust line fitting.
==================
Example of using cv2.fitLine function for fitting line
to points in presence of outliers.
Usage
-----
fitline.py
Switch through different M-estimator functions and see,
how well the robust functions fit the line even
in case of ~50% of outliers.
Keys
----
SPACE - generaty random points
f - change distance function
ESC - exit
'''
import numpy as np
import cv2
import itertools as it
from common import draw_str
w, h = 512, 256
def toint(p):
return tuple(map(int, p))
def sample_line(p1, p2, n, noise=0.0):
p1 = np.float32(p1)
t = np.random.rand(n,1)
return p1 + (p2-p1)*t + np.random.normal(size=(n, 2))*noise
dist_func_names = it.cycle('CV_DIST_L2 CV_DIST_L1 CV_DIST_L12 CV_DIST_FAIR CV_DIST_WELSCH CV_DIST_HUBER'.split())
cur_func_name = dist_func_names.next()
def update(_=None):
noise = cv2.getTrackbarPos('noise', 'fit line')
n = cv2.getTrackbarPos('point n', 'fit line')
r = cv2.getTrackbarPos('outlier %', 'fit line') / 100.0
outn = int(n*r)
p0, p1 = (90, 80), (w-90, h-80)
img = np.zeros((h, w, 3), np.uint8)
cv2.line(img, toint(p0), toint(p1), (0, 255, 0))
if n > 0:
line_points = sample_line(p0, p1, n-outn, noise)
outliers = np.random.rand(outn, 2) * (w, h)
points = np.vstack([line_points, outliers])
for p in line_points:
cv2.circle(img, toint(p), 2, (255, 255, 255), -1)
for p in outliers:
cv2.circle(img, toint(p), 2, (64, 64, 255), -1)
func = getattr(cv2.cv, cur_func_name)
vx, vy, cx, cy = cv2.fitLine(np.float32(points), func, 0, 0.01, 0.01)
cv2.line(img, (int(cx-vx*w), int(cy-vy*w)), (int(cx+vx*w), int(cy+vy*w)), (0, 0, 255))
draw_str(img, (20, 20), cur_func_name)
cv2.imshow('fit line', img)
if __name__ == '__main__':
print __doc__
cv2.namedWindow('fit line')
cv2.createTrackbar('noise', 'fit line', 3, 50, update)
cv2.createTrackbar('point n', 'fit line', 100, 500, update)
cv2.createTrackbar('outlier %', 'fit line', 30, 100, update)
while True:
update()
ch = cv2.waitKey(0)
if ch == ord('f'):
cur_func_name = dist_func_names.next()
if ch == 27:
break
'''
Robust line fitting.
==================
Example of using cv2.fitLine function for fitting line
to points in presence of outliers.
Usage
-----
fitline.py
Switch through different M-estimator functions and see,
how well the robust functions fit the line even
in case of ~50% of outliers.
Keys
----
SPACE - generaty random points
f - change distance function
ESC - exit
'''
import numpy as np
import cv2
import itertools as it
from common import draw_str
w, h = 512, 256
def toint(p):
return tuple(map(int, p))
def sample_line(p1, p2, n, noise=0.0):
p1 = np.float32(p1)
t = np.random.rand(n,1)
return p1 + (p2-p1)*t + np.random.normal(size=(n, 2))*noise
dist_func_names = it.cycle('CV_DIST_L2 CV_DIST_L1 CV_DIST_L12 CV_DIST_FAIR CV_DIST_WELSCH CV_DIST_HUBER'.split())
cur_func_name = dist_func_names.next()
def update(_=None):
noise = cv2.getTrackbarPos('noise', 'fit line')
n = cv2.getTrackbarPos('point n', 'fit line')
r = cv2.getTrackbarPos('outlier %', 'fit line') / 100.0
outn = int(n*r)
p0, p1 = (90, 80), (w-90, h-80)
img = np.zeros((h, w, 3), np.uint8)
cv2.line(img, toint(p0), toint(p1), (0, 255, 0))
if n > 0:
line_points = sample_line(p0, p1, n-outn, noise)
outliers = np.random.rand(outn, 2) * (w, h)
points = np.vstack([line_points, outliers])
for p in line_points:
cv2.circle(img, toint(p), 2, (255, 255, 255), -1)
for p in outliers:
cv2.circle(img, toint(p), 2, (64, 64, 255), -1)
func = getattr(cv2.cv, cur_func_name)
vx, vy, cx, cy = cv2.fitLine(np.float32(points), func, 0, 0.01, 0.01)
cv2.line(img, (int(cx-vx*w), int(cy-vy*w)), (int(cx+vx*w), int(cy+vy*w)), (0, 0, 255))
draw_str(img, (20, 20), cur_func_name)
cv2.imshow('fit line', img)
if __name__ == '__main__':
print __doc__
cv2.namedWindow('fit line')
cv2.createTrackbar('noise', 'fit line', 3, 50, update)
cv2.createTrackbar('point n', 'fit line', 100, 500, update)
cv2.createTrackbar('outlier %', 'fit line', 30, 100, update)
while True:
update()
ch = cv2.waitKey(0)
if ch == ord('f'):
cur_func_name = dist_func_names.next()
if ch == 27:
break

138
samples/python2/floodfill.py Normal file → Executable file
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@@ -1,69 +1,69 @@
'''
Floodfill sample.
Usage:
floodfill.py [<image>]
Click on the image to set seed point
Keys:
f - toggle floating range
c - toggle 4/8 connectivity
ESC - exit
'''
import numpy as np
import cv2
if __name__ == '__main__':
import sys
try: fn = sys.argv[1]
except: fn = '../cpp/fruits.jpg'
print __doc__
img = cv2.imread(fn, True)
h, w = img.shape[:2]
mask = np.zeros((h+2, w+2), np.uint8)
seed_pt = None
fixed_range = True
connectivity = 4
def update(dummy=None):
if seed_pt is None:
cv2.imshow('floodfill', img)
return
flooded = img.copy()
mask[:] = 0
lo = cv2.getTrackbarPos('lo', 'floodfill')
hi = cv2.getTrackbarPos('hi', 'floodfill')
flags = connectivity
if fixed_range:
flags |= cv2.FLOODFILL_FIXED_RANGE
cv2.floodFill(flooded, mask, seed_pt, (255, 255, 255), (lo,)*3, (hi,)*3, flags)
cv2.circle(flooded, seed_pt, 2, (0, 0, 255), -1)
cv2.imshow('floodfill', flooded)
def onmouse(event, x, y, flags, param):
global seed_pt
if flags & cv2.EVENT_FLAG_LBUTTON:
seed_pt = x, y
update()
update()
cv2.setMouseCallback('floodfill', onmouse)
cv2.createTrackbar('lo', 'floodfill', 20, 255, update)
cv2.createTrackbar('hi', 'floodfill', 20, 255, update)
while True:
ch = 0xFF & cv2.waitKey()
if ch == 27:
break
if ch == ord('f'):
fixed_range = not fixed_range
print 'using %s range' % ('floating', 'fixed')[fixed_range]
update()
if ch == ord('c'):
connectivity = 12-connectivity
print 'connectivity =', connectivity
update()
cv2.destroyAllWindows()
'''
Floodfill sample.
Usage:
floodfill.py [<image>]
Click on the image to set seed point
Keys:
f - toggle floating range
c - toggle 4/8 connectivity
ESC - exit
'''
import numpy as np
import cv2
if __name__ == '__main__':
import sys
try: fn = sys.argv[1]
except: fn = '../cpp/fruits.jpg'
print __doc__
img = cv2.imread(fn, True)
h, w = img.shape[:2]
mask = np.zeros((h+2, w+2), np.uint8)
seed_pt = None
fixed_range = True
connectivity = 4
def update(dummy=None):
if seed_pt is None:
cv2.imshow('floodfill', img)
return
flooded = img.copy()
mask[:] = 0
lo = cv2.getTrackbarPos('lo', 'floodfill')
hi = cv2.getTrackbarPos('hi', 'floodfill')
flags = connectivity
if fixed_range:
flags |= cv2.FLOODFILL_FIXED_RANGE
cv2.floodFill(flooded, mask, seed_pt, (255, 255, 255), (lo,)*3, (hi,)*3, flags)
cv2.circle(flooded, seed_pt, 2, (0, 0, 255), -1)
cv2.imshow('floodfill', flooded)
def onmouse(event, x, y, flags, param):
global seed_pt
if flags & cv2.EVENT_FLAG_LBUTTON:
seed_pt = x, y
update()
update()
cv2.setMouseCallback('floodfill', onmouse)
cv2.createTrackbar('lo', 'floodfill', 20, 255, update)
cv2.createTrackbar('hi', 'floodfill', 20, 255, update)
while True:
ch = 0xFF & cv2.waitKey()
if ch == 27:
break
if ch == ord('f'):
fixed_range = not fixed_range
print 'using %s range' % ('floating', 'fixed')[fixed_range]
update()
if ch == ord('c'):
connectivity = 12-connectivity
print 'connectivity =', connectivity
update()
cv2.destroyAllWindows()

130
samples/python2/gabor_threads.py Normal file → Executable file
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@@ -1,65 +1,65 @@
'''
gabor_threads.py
=========
Sample demonstrates:
- use of multiple Gabor filter convolutions to get Fractalius-like image effect (http://www.redfieldplugins.com/filterFractalius.htm)
- use of python threading to accelerate the computation
Usage
-----
gabor_threads.py [image filename]
'''
import numpy as np
import cv2
from multiprocessing.pool import ThreadPool
def build_filters():
filters = []
ksize = 31
for theta in np.arange(0, np.pi, np.pi / 16):
kern = cv2.getGaborKernel((ksize, ksize), 4.0, theta, 10.0, 0.5, 0, ktype=cv2.CV_32F)
kern /= 1.5*kern.sum()
filters.append(kern)
return filters
def process(img, filters):
accum = np.zeros_like(img)
for kern in filters:
fimg = cv2.filter2D(img, cv2.CV_8UC3, kern)
np.maximum(accum, fimg, accum)
return accum
def process_threaded(img, filters, threadn = 8):
accum = np.zeros_like(img)
def f(kern):
return cv2.filter2D(img, cv2.CV_8UC3, kern)
pool = ThreadPool(processes=threadn)
for fimg in pool.imap_unordered(f, filters):
np.maximum(accum, fimg, accum)
return accum
if __name__ == '__main__':
import sys
from common import Timer
print __doc__
try: img_fn = sys.argv[1]
except: img_fn = '../cpp/baboon.jpg'
img = cv2.imread(img_fn)
filters = build_filters()
with Timer('running single-threaded'):
res1 = process(img, filters)
with Timer('running multi-threaded'):
res2 = process_threaded(img, filters)
print 'res1 == res2: ', (res1 == res2).all()
cv2.imshow('img', img)
cv2.imshow('result', res2)
cv2.waitKey()
cv2.destroyAllWindows()
'''
gabor_threads.py
=========
Sample demonstrates:
- use of multiple Gabor filter convolutions to get Fractalius-like image effect (http://www.redfieldplugins.com/filterFractalius.htm)
- use of python threading to accelerate the computation
Usage
-----
gabor_threads.py [image filename]
'''
import numpy as np
import cv2
from multiprocessing.pool import ThreadPool
def build_filters():
filters = []
ksize = 31
for theta in np.arange(0, np.pi, np.pi / 16):
kern = cv2.getGaborKernel((ksize, ksize), 4.0, theta, 10.0, 0.5, 0, ktype=cv2.CV_32F)
kern /= 1.5*kern.sum()
filters.append(kern)
return filters
def process(img, filters):
accum = np.zeros_like(img)
for kern in filters:
fimg = cv2.filter2D(img, cv2.CV_8UC3, kern)
np.maximum(accum, fimg, accum)
return accum
def process_threaded(img, filters, threadn = 8):
accum = np.zeros_like(img)
def f(kern):
return cv2.filter2D(img, cv2.CV_8UC3, kern)
pool = ThreadPool(processes=threadn)
for fimg in pool.imap_unordered(f, filters):
np.maximum(accum, fimg, accum)
return accum
if __name__ == '__main__':
import sys
from common import Timer
print __doc__
try: img_fn = sys.argv[1]
except: img_fn = '../cpp/baboon.jpg'
img = cv2.imread(img_fn)
filters = build_filters()
with Timer('running single-threaded'):
res1 = process(img, filters)
with Timer('running multi-threaded'):
res2 = process_threaded(img, filters)
print 'res1 == res2: ', (res1 == res2).all()
cv2.imshow('img', img)
cv2.imshow('result', res2)
cv2.waitKey()
cv2.destroyAllWindows()

114
samples/python2/gaussian_mix.py Normal file → Executable file
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@@ -1,57 +1,57 @@
import numpy as np
from numpy import random
import cv2
def make_gaussians(cluster_n, img_size):
points = []
ref_distrs = []
for i in xrange(cluster_n):
mean = (0.1 + 0.8*random.rand(2)) * img_size
a = (random.rand(2, 2)-0.5)*img_size*0.1
cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
n = 100 + random.randint(900)
pts = random.multivariate_normal(mean, cov, n)
points.append( pts )
ref_distrs.append( (mean, cov) )
points = np.float32( np.vstack(points) )
return points, ref_distrs
def draw_gaussain(img, mean, cov, color):
x, y = np.int32(mean)
w, u, vt = cv2.SVDecomp(cov)
ang = np.arctan2(u[1, 0], u[0, 0])*(180/np.pi)
s1, s2 = np.sqrt(w)*3.0
cv2.ellipse(img, (x, y), (s1, s2), ang, 0, 360, color, 1, cv2.CV_AA)
if __name__ == '__main__':
cluster_n = 5
img_size = 512
print 'press any key to update distributions, ESC - exit\n'
while True:
print 'sampling distributions...'
points, ref_distrs = make_gaussians(cluster_n, img_size)
print 'EM (opencv) ...'
em = cv2.EM(cluster_n, cv2.EM_COV_MAT_GENERIC)
em.train(points)
means = em.getMat('means')
covs = em.getMatVector('covs')
found_distrs = zip(means, covs)
print 'ready!\n'
img = np.zeros((img_size, img_size, 3), np.uint8)
for x, y in np.int32(points):
cv2.circle(img, (x, y), 1, (255, 255, 255), -1)
for m, cov in ref_distrs:
draw_gaussain(img, m, cov, (0, 255, 0))
for m, cov in found_distrs:
draw_gaussain(img, m, cov, (0, 0, 255))
cv2.imshow('gaussian mixture', img)
ch = 0xFF & cv2.waitKey(0)
if ch == 27:
break
cv2.destroyAllWindows()
import numpy as np
from numpy import random
import cv2
def make_gaussians(cluster_n, img_size):
points = []
ref_distrs = []
for i in xrange(cluster_n):
mean = (0.1 + 0.8*random.rand(2)) * img_size
a = (random.rand(2, 2)-0.5)*img_size*0.1
cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
n = 100 + random.randint(900)
pts = random.multivariate_normal(mean, cov, n)
points.append( pts )
ref_distrs.append( (mean, cov) )
points = np.float32( np.vstack(points) )
return points, ref_distrs
def draw_gaussain(img, mean, cov, color):
x, y = np.int32(mean)
w, u, vt = cv2.SVDecomp(cov)
ang = np.arctan2(u[1, 0], u[0, 0])*(180/np.pi)
s1, s2 = np.sqrt(w)*3.0
cv2.ellipse(img, (x, y), (s1, s2), ang, 0, 360, color, 1, cv2.CV_AA)
if __name__ == '__main__':
cluster_n = 5
img_size = 512
print 'press any key to update distributions, ESC - exit\n'
while True:
print 'sampling distributions...'
points, ref_distrs = make_gaussians(cluster_n, img_size)
print 'EM (opencv) ...'
em = cv2.EM(cluster_n, cv2.EM_COV_MAT_GENERIC)
em.train(points)
means = em.getMat('means')
covs = em.getMatVector('covs')
found_distrs = zip(means, covs)
print 'ready!\n'
img = np.zeros((img_size, img_size, 3), np.uint8)
for x, y in np.int32(points):
cv2.circle(img, (x, y), 1, (255, 255, 255), -1)
for m, cov in ref_distrs:
draw_gaussain(img, m, cov, (0, 255, 0))
for m, cov in found_distrs:
draw_gaussain(img, m, cov, (0, 0, 255))
cv2.imshow('gaussian mixture', img)
ch = 0xFF & cv2.waitKey(0)
if ch == 27:
break
cv2.destroyAllWindows()

220
samples/python2/hist.py
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@@ -1,110 +1,110 @@
''' This is a sample for histogram plotting for RGB images and grayscale images for better understanding of colour distribution
Benefit : Learn how to draw histogram of images
Get familier with cv2.calcHist, cv2.equalizeHist,cv2.normalize and some drawing functions
Level : Beginner or Intermediate
Functions : 1) hist_curve : returns histogram of an image drawn as curves
2) hist_lines : return histogram of an image drawn as bins ( only for grayscale images )
Usage : python hist.py <image_file>
Abid Rahman 3/14/12 debug Gary Bradski
'''
import cv2
import numpy as np
bins = np.arange(256).reshape(256,1)
def hist_curve(im):
h = np.zeros((300,256,3))
if len(im.shape) == 2:
color = [(255,255,255)]
elif im.shape[2] == 3:
color = [ (255,0,0),(0,255,0),(0,0,255) ]
for ch, col in enumerate(color):
hist_item = cv2.calcHist([im],[ch],None,[256],[0,255])
cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX)
hist=np.int32(np.around(hist_item))
pts = np.int32(np.column_stack((bins,hist)))
cv2.polylines(h,[pts],False,col)
y=np.flipud(h)
return y
def hist_lines(im):
h = np.zeros((300,256,3))
if len(im.shape)!=2:
print "hist_lines applicable only for grayscale images"
#print "so converting image to grayscale for representation"
im = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
hist_item = cv2.calcHist([im],[0],None,[256],[0,255])
cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX)
hist=np.int32(np.around(hist_item))
for x,y in enumerate(hist):
cv2.line(h,(x,0),(x,y),(255,255,255))
y = np.flipud(h)
return y
if __name__ == '__main__':
import sys
if len(sys.argv)>1:
im = cv2.imread(sys.argv[1])
else :
im = cv2.imread('../cpp/lena.jpg')
print "usage : python hist.py <image_file>"
gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
print ''' Histogram plotting \n
Keymap :\n
a - show histogram for color image in curve mode \n
b - show histogram in bin mode \n
c - show equalized histogram (always in bin mode) \n
d - show histogram for color image in curve mode \n
e - show histogram for a normalized image in curve mode \n
Esc - exit \n
'''
cv2.imshow('image',im)
while True:
k = cv2.waitKey(0)&0xFF
if k == ord('a'):
curve = hist_curve(im)
cv2.imshow('histogram',curve)
cv2.imshow('image',im)
print 'a'
elif k == ord('b'):
print 'b'
lines = hist_lines(im)
cv2.imshow('histogram',lines)
cv2.imshow('image',gray)
elif k == ord('c'):
print 'c'
equ = cv2.equalizeHist(gray)
lines = hist_lines(equ)
cv2.imshow('histogram',lines)
cv2.imshow('image',equ)
elif k == ord('d'):
print 'd'
curve = hist_curve(gray)
cv2.imshow('histogram',curve)
cv2.imshow('image',gray)
elif k == ord('e'):
print 'e'
norm = cv2.normalize(gray,alpha = 0,beta = 255,norm_type = cv2.NORM_MINMAX)
lines = hist_lines(norm)
cv2.imshow('histogram',lines)
cv2.imshow('image',norm)
elif k == 27:
print 'ESC'
cv2.destroyAllWindows()
break
cv2.destroyAllWindows()
''' This is a sample for histogram plotting for RGB images and grayscale images for better understanding of colour distribution
Benefit : Learn how to draw histogram of images
Get familier with cv2.calcHist, cv2.equalizeHist,cv2.normalize and some drawing functions
Level : Beginner or Intermediate
Functions : 1) hist_curve : returns histogram of an image drawn as curves
2) hist_lines : return histogram of an image drawn as bins ( only for grayscale images )
Usage : python hist.py <image_file>
Abid Rahman 3/14/12 debug Gary Bradski
'''
import cv2
import numpy as np
bins = np.arange(256).reshape(256,1)
def hist_curve(im):
h = np.zeros((300,256,3))
if len(im.shape) == 2:
color = [(255,255,255)]
elif im.shape[2] == 3:
color = [ (255,0,0),(0,255,0),(0,0,255) ]
for ch, col in enumerate(color):
hist_item = cv2.calcHist([im],[ch],None,[256],[0,255])
cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX)
hist=np.int32(np.around(hist_item))
pts = np.int32(np.column_stack((bins,hist)))
cv2.polylines(h,[pts],False,col)
y=np.flipud(h)
return y
def hist_lines(im):
h = np.zeros((300,256,3))
if len(im.shape)!=2:
print "hist_lines applicable only for grayscale images"
#print "so converting image to grayscale for representation"
im = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
hist_item = cv2.calcHist([im],[0],None,[256],[0,255])
cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX)
hist=np.int32(np.around(hist_item))
for x,y in enumerate(hist):
cv2.line(h,(x,0),(x,y),(255,255,255))
y = np.flipud(h)
return y
if __name__ == '__main__':
import sys
if len(sys.argv)>1:
im = cv2.imread(sys.argv[1])
else :
im = cv2.imread('../cpp/lena.jpg')
print "usage : python hist.py <image_file>"
gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
print ''' Histogram plotting \n
Keymap :\n
a - show histogram for color image in curve mode \n
b - show histogram in bin mode \n
c - show equalized histogram (always in bin mode) \n
d - show histogram for color image in curve mode \n
e - show histogram for a normalized image in curve mode \n
Esc - exit \n
'''
cv2.imshow('image',im)
while True:
k = cv2.waitKey(0)&0xFF
if k == ord('a'):
curve = hist_curve(im)
cv2.imshow('histogram',curve)
cv2.imshow('image',im)
print 'a'
elif k == ord('b'):
print 'b'
lines = hist_lines(im)
cv2.imshow('histogram',lines)
cv2.imshow('image',gray)
elif k == ord('c'):
print 'c'
equ = cv2.equalizeHist(gray)
lines = hist_lines(equ)
cv2.imshow('histogram',lines)
cv2.imshow('image',equ)
elif k == ord('d'):
print 'd'
curve = hist_curve(gray)
cv2.imshow('histogram',curve)
cv2.imshow('image',gray)
elif k == ord('e'):
print 'e'
norm = cv2.normalize(gray,alpha = 0,beta = 255,norm_type = cv2.NORM_MINMAX)
lines = hist_lines(norm)
cv2.imshow('histogram',lines)
cv2.imshow('image',norm)
elif k == 27:
print 'ESC'
cv2.destroyAllWindows()
break
cv2.destroyAllWindows()

86
samples/python2/inpaint.py Normal file → Executable file
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@@ -1,43 +1,43 @@
'''
Inpainting sample.
Inpainting repairs damage to images by floodfilling
the damage with surrounding image areas.
Usage:
inpaint.py [<image>]
Keys:
SPACE - inpaint
r - reset the inpainting mask
ESC - exit
'''
import numpy as np
import cv2
from common import Sketcher
if __name__ == '__main__':
import sys
try: fn = sys.argv[1]
except: fn = '../cpp/fruits.jpg'
print __doc__
img = cv2.imread(fn)
img_mark = img.copy()
mark = np.zeros(img.shape[:2], np.uint8)
sketch = Sketcher('img', [img_mark, mark], lambda : ((255, 255, 255), 255))
while True:
ch = 0xFF & cv2.waitKey()
if ch == 27:
break
if ch == ord(' '):
res = cv2.inpaint(img_mark, mark, 3, cv2.INPAINT_TELEA)
cv2.imshow('inpaint', res)
if ch == ord('r'):
img_mark[:] = img
mark[:] = 0
sketch.show()
cv2.destroyAllWindows()
'''
Inpainting sample.
Inpainting repairs damage to images by floodfilling
the damage with surrounding image areas.
Usage:
inpaint.py [<image>]
Keys:
SPACE - inpaint
r - reset the inpainting mask
ESC - exit
'''
import numpy as np
import cv2
from common import Sketcher
if __name__ == '__main__':
import sys
try: fn = sys.argv[1]
except: fn = '../cpp/fruits.jpg'
print __doc__
img = cv2.imread(fn)
img_mark = img.copy()
mark = np.zeros(img.shape[:2], np.uint8)
sketch = Sketcher('img', [img_mark, mark], lambda : ((255, 255, 255), 255))
while True:
ch = 0xFF & cv2.waitKey()
if ch == 27:
break
if ch == ord(' '):
res = cv2.inpaint(img_mark, mark, 3, cv2.INPAINT_TELEA)
cv2.imshow('inpaint', res)
if ch == ord('r'):
img_mark[:] = img
mark[:] = 0
sketch.show()
cv2.destroyAllWindows()

88
samples/python2/kmeans.py Normal file → Executable file
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@@ -1,44 +1,44 @@
'''
K-means clusterization sample.
Usage:
kmeans.py
Keyboard shortcuts:
ESC - exit
space - generate new distribution
'''
import numpy as np
import cv2
from gaussian_mix import make_gaussians
if __name__ == '__main__':
cluster_n = 5
img_size = 512
print __doc__
# generating bright palette
colors = np.zeros((1, cluster_n, 3), np.uint8)
colors[0,:] = 255
colors[0,:,0] = np.arange(0, 180, 180.0/cluster_n)
colors = cv2.cvtColor(colors, cv2.COLOR_HSV2BGR)[0]
while True:
print 'sampling distributions...'
points, _ = make_gaussians(cluster_n, img_size)
term_crit = (cv2.TERM_CRITERIA_EPS, 30, 0.1)
ret, labels, centers = cv2.kmeans(points, cluster_n, term_crit, 10, 0)
img = np.zeros((img_size, img_size, 3), np.uint8)
for (x, y), label in zip(np.int32(points), labels.ravel()):
c = map(int, colors[label])
cv2.circle(img, (x, y), 1, c, -1)
cv2.imshow('gaussian mixture', img)
ch = 0xFF & cv2.waitKey(0)
if ch == 27:
break
cv2.destroyAllWindows()
'''
K-means clusterization sample.
Usage:
kmeans.py
Keyboard shortcuts:
ESC - exit
space - generate new distribution
'''
import numpy as np
import cv2
from gaussian_mix import make_gaussians
if __name__ == '__main__':
cluster_n = 5
img_size = 512
print __doc__
# generating bright palette
colors = np.zeros((1, cluster_n, 3), np.uint8)
colors[0,:] = 255
colors[0,:,0] = np.arange(0, 180, 180.0/cluster_n)
colors = cv2.cvtColor(colors, cv2.COLOR_HSV2BGR)[0]
while True:
print 'sampling distributions...'
points, _ = make_gaussians(cluster_n, img_size)
term_crit = (cv2.TERM_CRITERIA_EPS, 30, 0.1)
ret, labels, centers = cv2.kmeans(points, cluster_n, term_crit, 10, 0)
img = np.zeros((img_size, img_size, 3), np.uint8)
for (x, y), label in zip(np.int32(points), labels.ravel()):
c = map(int, colors[label])
cv2.circle(img, (x, y), 1, c, -1)
cv2.imshow('gaussian mixture', img)
ch = 0xFF & cv2.waitKey(0)
if ch == 27:
break
cv2.destroyAllWindows()

128
samples/python2/lappyr.py Normal file → Executable file
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@@ -1,64 +1,64 @@
''' An example of Laplacian Pyramid construction and merging.
Level : Intermediate
Usage : python lappyr.py [<video source>]
References:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.299
Alexander Mordvintsev 6/10/12
'''
import numpy as np
import cv2
import video
from common import nothing, getsize
def build_lappyr(img, leveln=6, dtype=np.int16):
img = dtype(img)
levels = []
for i in xrange(leveln-1):
next_img = cv2.pyrDown(img)
img1 = cv2.pyrUp(next_img, dstsize=getsize(img))
levels.append(img-img1)
img = next_img
levels.append(img)
return levels
def merge_lappyr(levels):
img = levels[-1]
for lev_img in levels[-2::-1]:
img = cv2.pyrUp(img, dstsize=getsize(lev_img))
img += lev_img
return np.uint8(np.clip(img, 0, 255))
if __name__ == '__main__':
import sys
print __doc__
try: fn = sys.argv[1]
except: fn = 0
cap = video.create_capture(fn)
leveln = 6
cv2.namedWindow('level control')
for i in xrange(leveln):
cv2.createTrackbar('%d'%i, 'level control', 5, 50, nothing)
while True:
ret, frame = cap.read()
pyr = build_lappyr(frame, leveln)
for i in xrange(leveln):
v = cv2.getTrackbarPos('%d'%i, 'level control') / 5
pyr[i] *= v
res = merge_lappyr(pyr)
cv2.imshow('laplacian pyramid filter', res)
if cv2.waitKey(1) == 27:
break
''' An example of Laplacian Pyramid construction and merging.
Level : Intermediate
Usage : python lappyr.py [<video source>]
References:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.299
Alexander Mordvintsev 6/10/12
'''
import numpy as np
import cv2
import video
from common import nothing, getsize
def build_lappyr(img, leveln=6, dtype=np.int16):
img = dtype(img)
levels = []
for i in xrange(leveln-1):
next_img = cv2.pyrDown(img)
img1 = cv2.pyrUp(next_img, dstsize=getsize(img))
levels.append(img-img1)
img = next_img
levels.append(img)
return levels
def merge_lappyr(levels):
img = levels[-1]
for lev_img in levels[-2::-1]:
img = cv2.pyrUp(img, dstsize=getsize(lev_img))
img += lev_img
return np.uint8(np.clip(img, 0, 255))
if __name__ == '__main__':
import sys
print __doc__
try: fn = sys.argv[1]
except: fn = 0
cap = video.create_capture(fn)
leveln = 6
cv2.namedWindow('level control')
for i in xrange(leveln):
cv2.createTrackbar('%d'%i, 'level control', 5, 50, nothing)
while True:
ret, frame = cap.read()
pyr = build_lappyr(frame, leveln)
for i in xrange(leveln):
v = cv2.getTrackbarPos('%d'%i, 'level control') / 5
pyr[i] *= v
res = merge_lappyr(pyr)
cv2.imshow('laplacian pyramid filter', res)
if cv2.waitKey(1) == 27:
break

360
samples/python2/letter_recog.py Normal file → Executable file
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@@ -1,180 +1,180 @@
'''
The sample demonstrates how to train Random Trees classifier
(or Boosting classifier, or MLP, or Knearest, or Support Vector Machines) using the provided dataset.
We use the sample database letter-recognition.data
from UCI Repository, here is the link:
Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).
UCI Repository of machine learning databases
[http://www.ics.uci.edu/~mlearn/MLRepository.html].
Irvine, CA: University of California, Department of Information and Computer Science.
The dataset consists of 20000 feature vectors along with the
responses - capital latin letters A..Z.
The first 10000 samples are used for training
and the remaining 10000 - to test the classifier.
======================================================
USAGE:
letter_recog.py [--model <model>]
[--data <data fn>]
[--load <model fn>] [--save <model fn>]
Models: RTrees, KNearest, Boost, SVM, MLP
'''
import numpy as np
import cv2
def load_base(fn):
a = np.loadtxt(fn, np.float32, delimiter=',', converters={ 0 : lambda ch : ord(ch)-ord('A') })
samples, responses = a[:,1:], a[:,0]
return samples, responses
class LetterStatModel(object):
class_n = 26
train_ratio = 0.5
def load(self, fn):
self.model.load(fn)
def save(self, fn):
self.model.save(fn)
def unroll_samples(self, samples):
sample_n, var_n = samples.shape
new_samples = np.zeros((sample_n * self.class_n, var_n+1), np.float32)
new_samples[:,:-1] = np.repeat(samples, self.class_n, axis=0)
new_samples[:,-1] = np.tile(np.arange(self.class_n), sample_n)
return new_samples
def unroll_responses(self, responses):
sample_n = len(responses)
new_responses = np.zeros(sample_n*self.class_n, np.int32)
resp_idx = np.int32( responses + np.arange(sample_n)*self.class_n )
new_responses[resp_idx] = 1
return new_responses
class RTrees(LetterStatModel):
def __init__(self):
self.model = cv2.RTrees()
def train(self, samples, responses):
sample_n, var_n = samples.shape
var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL], np.uint8)
#CvRTParams(10,10,0,false,15,0,true,4,100,0.01f,CV_TERMCRIT_ITER));
params = dict(max_depth=10 )
self.model.train(samples, cv2.CV_ROW_SAMPLE, responses, varType = var_types, params = params)
def predict(self, samples):
return np.float32( [self.model.predict(s) for s in samples] )
class KNearest(LetterStatModel):
def __init__(self):
self.model = cv2.KNearest()
def train(self, samples, responses):
self.model.train(samples, responses)
def predict(self, samples):
retval, results, neigh_resp, dists = self.model.find_nearest(samples, k = 10)
return results.ravel()
class Boost(LetterStatModel):
def __init__(self):
self.model = cv2.Boost()
def train(self, samples, responses):
sample_n, var_n = samples.shape
new_samples = self.unroll_samples(samples)
new_responses = self.unroll_responses(responses)
var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL, cv2.CV_VAR_CATEGORICAL], np.uint8)
#CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0 )
params = dict(max_depth=5) #, use_surrogates=False)
self.model.train(new_samples, cv2.CV_ROW_SAMPLE, new_responses, varType = var_types, params=params)
def predict(self, samples):
new_samples = self.unroll_samples(samples)
pred = np.array( [self.model.predict(s, returnSum = True) for s in new_samples] )
pred = pred.reshape(-1, self.class_n).argmax(1)
return pred
class SVM(LetterStatModel):
def __init__(self):
self.model = cv2.SVM()
def train(self, samples, responses):
params = dict( kernel_type = cv2.SVM_LINEAR,
svm_type = cv2.SVM_C_SVC,
C = 1 )
self.model.train(samples, responses, params = params)
def predict(self, samples):
return self.model.predict_all(samples).ravel()
class MLP(LetterStatModel):
def __init__(self):
self.model = cv2.ANN_MLP()
def train(self, samples, responses):
sample_n, var_n = samples.shape
new_responses = self.unroll_responses(responses).reshape(-1, self.class_n)
layer_sizes = np.int32([var_n, 100, 100, self.class_n])
self.model.create(layer_sizes)
# CvANN_MLP_TrainParams::BACKPROP,0.001
params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 300, 0.01),
train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,
bp_dw_scale = 0.001,
bp_moment_scale = 0.0 )
self.model.train(samples, np.float32(new_responses), None, params = params)
def predict(self, samples):
ret, resp = self.model.predict(samples)
return resp.argmax(-1)
if __name__ == '__main__':
import getopt
import sys
print __doc__
models = [RTrees, KNearest, Boost, SVM, MLP] # NBayes
models = dict( [(cls.__name__.lower(), cls) for cls in models] )
args, dummy = getopt.getopt(sys.argv[1:], '', ['model=', 'data=', 'load=', 'save='])
args = dict(args)
args.setdefault('--model', 'rtrees')
args.setdefault('--data', '../cpp/letter-recognition.data')
print 'loading data %s ...' % args['--data']
samples, responses = load_base(args['--data'])
Model = models[args['--model']]
model = Model()
train_n = int(len(samples)*model.train_ratio)
if '--load' in args:
fn = args['--load']
print 'loading model from %s ...' % fn
model.load(fn)
else:
print 'training %s ...' % Model.__name__
model.train(samples[:train_n], responses[:train_n])
print 'testing...'
train_rate = np.mean(model.predict(samples[:train_n]) == responses[:train_n])
test_rate = np.mean(model.predict(samples[train_n:]) == responses[train_n:])
print 'train rate: %f test rate: %f' % (train_rate*100, test_rate*100)
if '--save' in args:
fn = args['--save']
print 'saving model to %s ...' % fn
model.save(fn)
cv2.destroyAllWindows()
'''
The sample demonstrates how to train Random Trees classifier
(or Boosting classifier, or MLP, or Knearest, or Support Vector Machines) using the provided dataset.
We use the sample database letter-recognition.data
from UCI Repository, here is the link:
Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).
UCI Repository of machine learning databases
[http://www.ics.uci.edu/~mlearn/MLRepository.html].
Irvine, CA: University of California, Department of Information and Computer Science.
The dataset consists of 20000 feature vectors along with the
responses - capital latin letters A..Z.
The first 10000 samples are used for training
and the remaining 10000 - to test the classifier.
======================================================
USAGE:
letter_recog.py [--model <model>]
[--data <data fn>]
[--load <model fn>] [--save <model fn>]
Models: RTrees, KNearest, Boost, SVM, MLP
'''
import numpy as np
import cv2
def load_base(fn):
a = np.loadtxt(fn, np.float32, delimiter=',', converters={ 0 : lambda ch : ord(ch)-ord('A') })
samples, responses = a[:,1:], a[:,0]
return samples, responses
class LetterStatModel(object):
class_n = 26
train_ratio = 0.5
def load(self, fn):
self.model.load(fn)
def save(self, fn):
self.model.save(fn)
def unroll_samples(self, samples):
sample_n, var_n = samples.shape
new_samples = np.zeros((sample_n * self.class_n, var_n+1), np.float32)
new_samples[:,:-1] = np.repeat(samples, self.class_n, axis=0)
new_samples[:,-1] = np.tile(np.arange(self.class_n), sample_n)
return new_samples
def unroll_responses(self, responses):
sample_n = len(responses)
new_responses = np.zeros(sample_n*self.class_n, np.int32)
resp_idx = np.int32( responses + np.arange(sample_n)*self.class_n )
new_responses[resp_idx] = 1
return new_responses
class RTrees(LetterStatModel):
def __init__(self):
self.model = cv2.RTrees()
def train(self, samples, responses):
sample_n, var_n = samples.shape
var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL], np.uint8)
#CvRTParams(10,10,0,false,15,0,true,4,100,0.01f,CV_TERMCRIT_ITER));
params = dict(max_depth=10 )
self.model.train(samples, cv2.CV_ROW_SAMPLE, responses, varType = var_types, params = params)
def predict(self, samples):
return np.float32( [self.model.predict(s) for s in samples] )
class KNearest(LetterStatModel):
def __init__(self):
self.model = cv2.KNearest()
def train(self, samples, responses):
self.model.train(samples, responses)
def predict(self, samples):
retval, results, neigh_resp, dists = self.model.find_nearest(samples, k = 10)
return results.ravel()
class Boost(LetterStatModel):
def __init__(self):
self.model = cv2.Boost()
def train(self, samples, responses):
sample_n, var_n = samples.shape
new_samples = self.unroll_samples(samples)
new_responses = self.unroll_responses(responses)
var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL, cv2.CV_VAR_CATEGORICAL], np.uint8)
#CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0 )
params = dict(max_depth=5) #, use_surrogates=False)
self.model.train(new_samples, cv2.CV_ROW_SAMPLE, new_responses, varType = var_types, params=params)
def predict(self, samples):
new_samples = self.unroll_samples(samples)
pred = np.array( [self.model.predict(s, returnSum = True) for s in new_samples] )
pred = pred.reshape(-1, self.class_n).argmax(1)
return pred
class SVM(LetterStatModel):
def __init__(self):
self.model = cv2.SVM()
def train(self, samples, responses):
params = dict( kernel_type = cv2.SVM_LINEAR,
svm_type = cv2.SVM_C_SVC,
C = 1 )
self.model.train(samples, responses, params = params)
def predict(self, samples):
return self.model.predict_all(samples).ravel()
class MLP(LetterStatModel):
def __init__(self):
self.model = cv2.ANN_MLP()
def train(self, samples, responses):
sample_n, var_n = samples.shape
new_responses = self.unroll_responses(responses).reshape(-1, self.class_n)
layer_sizes = np.int32([var_n, 100, 100, self.class_n])
self.model.create(layer_sizes)
# CvANN_MLP_TrainParams::BACKPROP,0.001
params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 300, 0.01),
train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,
bp_dw_scale = 0.001,
bp_moment_scale = 0.0 )
self.model.train(samples, np.float32(new_responses), None, params = params)
def predict(self, samples):
ret, resp = self.model.predict(samples)
return resp.argmax(-1)
if __name__ == '__main__':
import getopt
import sys
print __doc__
models = [RTrees, KNearest, Boost, SVM, MLP] # NBayes
models = dict( [(cls.__name__.lower(), cls) for cls in models] )
args, dummy = getopt.getopt(sys.argv[1:], '', ['model=', 'data=', 'load=', 'save='])
args = dict(args)
args.setdefault('--model', 'rtrees')
args.setdefault('--data', '../cpp/letter-recognition.data')
print 'loading data %s ...' % args['--data']
samples, responses = load_base(args['--data'])
Model = models[args['--model']]
model = Model()
train_n = int(len(samples)*model.train_ratio)
if '--load' in args:
fn = args['--load']
print 'loading model from %s ...' % fn
model.load(fn)
else:
print 'training %s ...' % Model.__name__
model.train(samples[:train_n], responses[:train_n])
print 'testing...'
train_rate = np.mean(model.predict(samples[:train_n]) == responses[:train_n])
test_rate = np.mean(model.predict(samples[train_n:]) == responses[train_n:])
print 'train rate: %f test rate: %f' % (train_rate*100, test_rate*100)
if '--save' in args:
fn = args['--save']
print 'saving model to %s ...' % fn
model.save(fn)
cv2.destroyAllWindows()

224
samples/python2/lk_homography.py Normal file → Executable file
View File

@@ -1,112 +1,112 @@
'''
Lucas-Kanade homography tracker
===============================
Lucas-Kanade sparse optical flow demo. Uses goodFeaturesToTrack
for track initialization and back-tracking for match verification
between frames. Finds homography between reference and current views.
Usage
-----
lk_homography.py [<video_source>]
Keys
----
ESC - exit
SPACE - start tracking
r - toggle RANSAC
'''
import numpy as np
import cv2
import video
from common import draw_str
lk_params = dict( winSize = (19, 19),
maxLevel = 2,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
feature_params = dict( maxCorners = 1000,
qualityLevel = 0.01,
minDistance = 8,
blockSize = 19 )
def checkedTrace(img0, img1, p0, back_threshold = 1.0):
p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
d = abs(p0-p0r).reshape(-1, 2).max(-1)
status = d < back_threshold
return p1, status
green = (0, 255, 0)
red = (0, 0, 255)
class App:
def __init__(self, video_src):
self.cam = video.create_capture(video_src)
self.p0 = None
self.use_ransac = True
def run(self):
while True:
ret, frame = self.cam.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
vis = frame.copy()
if self.p0 is not None:
p2, trace_status = checkedTrace(self.gray1, frame_gray, self.p1)
self.p1 = p2[trace_status].copy()
self.p0 = self.p0[trace_status].copy()
self.gray1 = frame_gray
if len(self.p0) < 4:
self.p0 = None
continue
H, status = cv2.findHomography(self.p0, self.p1, (0, cv2.RANSAC)[self.use_ransac], 10.0)
h, w = frame.shape[:2]
overlay = cv2.warpPerspective(self.frame0, H, (w, h))
vis = cv2.addWeighted(vis, 0.5, overlay, 0.5, 0.0)
for (x0, y0), (x1, y1), good in zip(self.p0[:,0], self.p1[:,0], status[:,0]):
if good:
cv2.line(vis, (x0, y0), (x1, y1), (0, 128, 0))
cv2.circle(vis, (x1, y1), 2, (red, green)[good], -1)
draw_str(vis, (20, 20), 'track count: %d' % len(self.p1))
if self.use_ransac:
draw_str(vis, (20, 40), 'RANSAC')
else:
p = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
if p is not None:
for x, y in p[:,0]:
cv2.circle(vis, (x, y), 2, green, -1)
draw_str(vis, (20, 20), 'feature count: %d' % len(p))
cv2.imshow('lk_homography', vis)
ch = 0xFF & cv2.waitKey(1)
if ch == 27:
break
if ch == ord(' '):
self.frame0 = frame.copy()
self.p0 = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
if self.p0 is not None:
self.p1 = self.p0
self.gray0 = frame_gray
self.gray1 = frame_gray
if ch == ord('r'):
self.use_ransac = not self.use_ransac
def main():
import sys
try: video_src = sys.argv[1]
except: video_src = 0
print __doc__
App(video_src).run()
cv2.destroyAllWindows()
if __name__ == '__main__':
main()
'''
Lucas-Kanade homography tracker
===============================
Lucas-Kanade sparse optical flow demo. Uses goodFeaturesToTrack
for track initialization and back-tracking for match verification
between frames. Finds homography between reference and current views.
Usage
-----
lk_homography.py [<video_source>]
Keys
----
ESC - exit
SPACE - start tracking
r - toggle RANSAC
'''
import numpy as np
import cv2
import video
from common import draw_str
lk_params = dict( winSize = (19, 19),
maxLevel = 2,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
feature_params = dict( maxCorners = 1000,
qualityLevel = 0.01,
minDistance = 8,
blockSize = 19 )
def checkedTrace(img0, img1, p0, back_threshold = 1.0):
p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
d = abs(p0-p0r).reshape(-1, 2).max(-1)
status = d < back_threshold
return p1, status
green = (0, 255, 0)
red = (0, 0, 255)
class App:
def __init__(self, video_src):
self.cam = video.create_capture(video_src)
self.p0 = None
self.use_ransac = True
def run(self):
while True:
ret, frame = self.cam.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
vis = frame.copy()
if self.p0 is not None:
p2, trace_status = checkedTrace(self.gray1, frame_gray, self.p1)
self.p1 = p2[trace_status].copy()
self.p0 = self.p0[trace_status].copy()
self.gray1 = frame_gray
if len(self.p0) < 4:
self.p0 = None
continue
H, status = cv2.findHomography(self.p0, self.p1, (0, cv2.RANSAC)[self.use_ransac], 10.0)
h, w = frame.shape[:2]
overlay = cv2.warpPerspective(self.frame0, H, (w, h))
vis = cv2.addWeighted(vis, 0.5, overlay, 0.5, 0.0)
for (x0, y0), (x1, y1), good in zip(self.p0[:,0], self.p1[:,0], status[:,0]):
if good:
cv2.line(vis, (x0, y0), (x1, y1), (0, 128, 0))
cv2.circle(vis, (x1, y1), 2, (red, green)[good], -1)
draw_str(vis, (20, 20), 'track count: %d' % len(self.p1))
if self.use_ransac:
draw_str(vis, (20, 40), 'RANSAC')
else:
p = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
if p is not None:
for x, y in p[:,0]:
cv2.circle(vis, (x, y), 2, green, -1)
draw_str(vis, (20, 20), 'feature count: %d' % len(p))
cv2.imshow('lk_homography', vis)
ch = 0xFF & cv2.waitKey(1)
if ch == 27:
break
if ch == ord(' '):
self.frame0 = frame.copy()
self.p0 = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
if self.p0 is not None:
self.p1 = self.p0
self.gray0 = frame_gray
self.gray1 = frame_gray
if ch == ord('r'):
self.use_ransac = not self.use_ransac
def main():
import sys
try: video_src = sys.argv[1]
except: video_src = 0
print __doc__
App(video_src).run()
cv2.destroyAllWindows()
if __name__ == '__main__':
main()

194
samples/python2/lk_track.py Normal file → Executable file
View File

@@ -1,97 +1,97 @@
'''
Lucas-Kanade tracker
====================
Lucas-Kanade sparse optical flow demo. Uses goodFeaturesToTrack
for track initialization and back-tracking for match verification
between frames.
Usage
-----
lk_track.py [<video_source>]
Keys
----
ESC - exit
'''
import numpy as np
import cv2
import video
from common import anorm2, draw_str
from time import clock
lk_params = dict( winSize = (15, 15),
maxLevel = 2,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
feature_params = dict( maxCorners = 500,
qualityLevel = 0.3,
minDistance = 7,
blockSize = 7 )
class App:
def __init__(self, video_src):
self.track_len = 10
self.detect_interval = 5
self.tracks = []
self.cam = video.create_capture(video_src)
self.frame_idx = 0
def run(self):
while True:
ret, frame = self.cam.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
vis = frame.copy()
if len(self.tracks) > 0:
img0, img1 = self.prev_gray, frame_gray
p0 = np.float32([tr[-1] for tr in self.tracks]).reshape(-1, 1, 2)
p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
d = abs(p0-p0r).reshape(-1, 2).max(-1)
good = d < 1
new_tracks = []
for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good):
if not good_flag:
continue
tr.append((x, y))
if len(tr) > self.track_len:
del tr[0]
new_tracks.append(tr)
cv2.circle(vis, (x, y), 2, (0, 255, 0), -1)
self.tracks = new_tracks
cv2.polylines(vis, [np.int32(tr) for tr in self.tracks], False, (0, 255, 0))
draw_str(vis, (20, 20), 'track count: %d' % len(self.tracks))
if self.frame_idx % self.detect_interval == 0:
mask = np.zeros_like(frame_gray)
mask[:] = 255
for x, y in [np.int32(tr[-1]) for tr in self.tracks]:
cv2.circle(mask, (x, y), 5, 0, -1)
p = cv2.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params)
if p is not None:
for x, y in np.float32(p).reshape(-1, 2):
self.tracks.append([(x, y)])
self.frame_idx += 1
self.prev_gray = frame_gray
cv2.imshow('lk_track', vis)
ch = 0xFF & cv2.waitKey(1)
if ch == 27:
break
def main():
import sys
try: video_src = sys.argv[1]
except: video_src = 0
print __doc__
App(video_src).run()
cv2.destroyAllWindows()
if __name__ == '__main__':
main()
'''
Lucas-Kanade tracker
====================
Lucas-Kanade sparse optical flow demo. Uses goodFeaturesToTrack
for track initialization and back-tracking for match verification
between frames.
Usage
-----
lk_track.py [<video_source>]
Keys
----
ESC - exit
'''
import numpy as np
import cv2
import video
from common import anorm2, draw_str
from time import clock
lk_params = dict( winSize = (15, 15),
maxLevel = 2,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
feature_params = dict( maxCorners = 500,
qualityLevel = 0.3,
minDistance = 7,
blockSize = 7 )
class App:
def __init__(self, video_src):
self.track_len = 10
self.detect_interval = 5
self.tracks = []
self.cam = video.create_capture(video_src)
self.frame_idx = 0
def run(self):
while True:
ret, frame = self.cam.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
vis = frame.copy()
if len(self.tracks) > 0:
img0, img1 = self.prev_gray, frame_gray
p0 = np.float32([tr[-1] for tr in self.tracks]).reshape(-1, 1, 2)
p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
d = abs(p0-p0r).reshape(-1, 2).max(-1)
good = d < 1
new_tracks = []
for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good):
if not good_flag:
continue
tr.append((x, y))
if len(tr) > self.track_len:
del tr[0]
new_tracks.append(tr)
cv2.circle(vis, (x, y), 2, (0, 255, 0), -1)
self.tracks = new_tracks
cv2.polylines(vis, [np.int32(tr) for tr in self.tracks], False, (0, 255, 0))
draw_str(vis, (20, 20), 'track count: %d' % len(self.tracks))
if self.frame_idx % self.detect_interval == 0:
mask = np.zeros_like(frame_gray)
mask[:] = 255
for x, y in [np.int32(tr[-1]) for tr in self.tracks]:
cv2.circle(mask, (x, y), 5, 0, -1)
p = cv2.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params)
if p is not None:
for x, y in np.float32(p).reshape(-1, 2):
self.tracks.append([(x, y)])
self.frame_idx += 1
self.prev_gray = frame_gray
cv2.imshow('lk_track', vis)
ch = 0xFF & cv2.waitKey(1)
if ch == 27:
break
def main():
import sys
try: video_src = sys.argv[1]
except: video_src = 0
print __doc__
App(video_src).run()
cv2.destroyAllWindows()
if __name__ == '__main__':
main()

140
samples/python2/morphology.py Normal file → Executable file
View File

@@ -1,70 +1,70 @@
'''
Morphology operations.
Usage:
morphology.py [<image>]
Keys:
1 - change operation
2 - change structure element shape
ESC - exit
'''
import numpy as np
import cv2
if __name__ == '__main__':
print __doc__
import sys
from itertools import cycle
from common import draw_str
try: fn = sys.argv[1]
except: fn = '../cpp/baboon.jpg'
img = cv2.imread(fn)
cv2.imshow('original', img)
modes = cycle(['erode/dilate', 'open/close', 'blackhat/tophat', 'gradient'])
str_modes = cycle(['ellipse', 'rect', 'cross'])
cur_mode = modes.next()
cur_str_mode = str_modes.next()
def update(dummy=None):
sz = cv2.getTrackbarPos('op/size', 'morphology')
iters = cv2.getTrackbarPos('iters', 'morphology')
opers = cur_mode.split('/')
if len(opers) > 1:
sz = sz - 10
op = opers[sz > 0]
sz = abs(sz)
else:
op = opers[0]
sz = sz*2+1
str_name = 'MORPH_' + cur_str_mode.upper()
oper_name = 'MORPH_' + op.upper()
st = cv2.getStructuringElement(getattr(cv2, str_name), (sz, sz))
res = cv2.morphologyEx(img, getattr(cv2, oper_name), st, iterations=iters)
draw_str(res, (10, 20), 'mode: ' + cur_mode)
draw_str(res, (10, 40), 'operation: ' + oper_name)
draw_str(res, (10, 60), 'structure: ' + str_name)
draw_str(res, (10, 80), 'ksize: %d iters: %d' % (sz, iters))
cv2.imshow('morphology', res)
cv2.namedWindow('morphology')
cv2.createTrackbar('op/size', 'morphology', 12, 20, update)
cv2.createTrackbar('iters', 'morphology', 1, 10, update)
update()
while True:
ch = 0xFF & cv2.waitKey()
if ch == 27:
break
if ch == ord('1'):
cur_mode = modes.next()
if ch == ord('2'):
cur_str_mode = str_modes.next()
update()
cv2.destroyAllWindows()
'''
Morphology operations.
Usage:
morphology.py [<image>]
Keys:
1 - change operation
2 - change structure element shape
ESC - exit
'''
import numpy as np
import cv2
if __name__ == '__main__':
print __doc__
import sys
from itertools import cycle
from common import draw_str
try: fn = sys.argv[1]
except: fn = '../cpp/baboon.jpg'
img = cv2.imread(fn)
cv2.imshow('original', img)
modes = cycle(['erode/dilate', 'open/close', 'blackhat/tophat', 'gradient'])
str_modes = cycle(['ellipse', 'rect', 'cross'])
cur_mode = modes.next()
cur_str_mode = str_modes.next()
def update(dummy=None):
sz = cv2.getTrackbarPos('op/size', 'morphology')
iters = cv2.getTrackbarPos('iters', 'morphology')
opers = cur_mode.split('/')
if len(opers) > 1:
sz = sz - 10
op = opers[sz > 0]
sz = abs(sz)
else:
op = opers[0]
sz = sz*2+1
str_name = 'MORPH_' + cur_str_mode.upper()
oper_name = 'MORPH_' + op.upper()
st = cv2.getStructuringElement(getattr(cv2, str_name), (sz, sz))
res = cv2.morphologyEx(img, getattr(cv2, oper_name), st, iterations=iters)
draw_str(res, (10, 20), 'mode: ' + cur_mode)
draw_str(res, (10, 40), 'operation: ' + oper_name)
draw_str(res, (10, 60), 'structure: ' + str_name)
draw_str(res, (10, 80), 'ksize: %d iters: %d' % (sz, iters))
cv2.imshow('morphology', res)
cv2.namedWindow('morphology')
cv2.createTrackbar('op/size', 'morphology', 12, 20, update)
cv2.createTrackbar('iters', 'morphology', 1, 10, update)
update()
while True:
ch = 0xFF & cv2.waitKey()
if ch == 27:
break
if ch == ord('1'):
cur_mode = modes.next()
if ch == ord('2'):
cur_str_mode = str_modes.next()
update()
cv2.destroyAllWindows()

16
samples/python2/mosse.py Normal file → Executable file
View File

@@ -53,13 +53,13 @@ class MOSSE:
self.pos = x, y = x1+0.5*(w-1), y1+0.5*(h-1)
self.size = w, h
img = cv2.getRectSubPix(frame, (w, h), (x, y))
self.win = cv2.createHanningWindow((w, h), cv2.CV_32F)
self.win = cv2.createHanningWindow((w, h), cv2.CV_32F)
g = np.zeros((h, w), np.float32)
g[h//2, w//2] = 1
g = cv2.GaussianBlur(g, (-1, -1), 2.0)
g /= g.max()
self.G = cv2.dft(g, flags=cv2.DFT_COMPLEX_OUTPUT)
self.H1 = np.zeros_like(self.G)
self.H2 = np.zeros_like(self.G)
@@ -79,7 +79,7 @@ class MOSSE:
self.good = self.psr > 8.0
if not self.good:
return
self.pos = x+dx, y+dy
self.last_img = img = cv2.getRectSubPix(frame, (w, h), self.pos)
img = self.preprocess(img)
@@ -147,7 +147,7 @@ class App:
frame_gray = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY)
tracker = MOSSE(frame_gray, rect)
self.trackers.append(tracker)
def run(self):
while True:
if not self.paused:
@@ -157,14 +157,14 @@ class App:
frame_gray = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY)
for tracker in self.trackers:
tracker.update(frame_gray)
vis = self.frame.copy()
for tracker in self.trackers:
tracker.draw_state(vis)
if len(self.trackers) > 0:
cv2.imshow('tracker state', self.trackers[-1].state_vis)
self.rect_sel.draw(vis)
cv2.imshow('frame', vis)
ch = cv2.waitKey(10)
if ch == 27:
@@ -174,7 +174,7 @@ class App:
if ch == ord('c'):
self.trackers = []
if __name__ == '__main__':
print __doc__
import sys, getopt

162
samples/python2/motempl.py Normal file → Executable file
View File

@@ -1,81 +1,81 @@
import numpy as np
import cv2
import video
from common import nothing, clock, draw_str
MHI_DURATION = 0.5
DEFAULT_THRESHOLD = 32
MAX_TIME_DELTA = 0.25
MIN_TIME_DELTA = 0.05
def draw_motion_comp(vis, (x, y, w, h), angle, color):
cv2.rectangle(vis, (x, y), (x+w, y+h), (0, 255, 0))
r = min(w/2, h/2)
cx, cy = x+w/2, y+h/2
angle = angle*np.pi/180
cv2.circle(vis, (cx, cy), r, color, 3)
cv2.line(vis, (cx, cy), (int(cx+np.cos(angle)*r), int(cy+np.sin(angle)*r)), color, 3)
if __name__ == '__main__':
import sys
try: video_src = sys.argv[1]
except: video_src = 0
cv2.namedWindow('motempl')
visuals = ['input', 'frame_diff', 'motion_hist', 'grad_orient']
cv2.createTrackbar('visual', 'motempl', 2, len(visuals)-1, nothing)
cv2.createTrackbar('threshold', 'motempl', DEFAULT_THRESHOLD, 255, nothing)
cam = video.create_capture(video_src, fallback='synth:class=chess:bg=../cpp/lena.jpg:noise=0.01')
ret, frame = cam.read()
h, w = frame.shape[:2]
prev_frame = frame.copy()
motion_history = np.zeros((h, w), np.float32)
hsv = np.zeros((h, w, 3), np.uint8)
hsv[:,:,1] = 255
while True:
ret, frame = cam.read()
frame_diff = cv2.absdiff(frame, prev_frame)
gray_diff = cv2.cvtColor(frame_diff, cv2.COLOR_BGR2GRAY)
thrs = cv2.getTrackbarPos('threshold', 'motempl')
ret, motion_mask = cv2.threshold(gray_diff, thrs, 1, cv2.THRESH_BINARY)
timestamp = clock()
cv2.updateMotionHistory(motion_mask, motion_history, timestamp, MHI_DURATION)
mg_mask, mg_orient = cv2.calcMotionGradient( motion_history, MAX_TIME_DELTA, MIN_TIME_DELTA, apertureSize=5 )
seg_mask, seg_bounds = cv2.segmentMotion(motion_history, timestamp, MAX_TIME_DELTA)
visual_name = visuals[cv2.getTrackbarPos('visual', 'motempl')]
if visual_name == 'input':
vis = frame.copy()
elif visual_name == 'frame_diff':
vis = frame_diff.copy()
elif visual_name == 'motion_hist':
vis = np.uint8(np.clip((motion_history-(timestamp-MHI_DURATION)) / MHI_DURATION, 0, 1)*255)
vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
elif visual_name == 'grad_orient':
hsv[:,:,0] = mg_orient/2
hsv[:,:,2] = mg_mask*255
vis = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
for i, rect in enumerate([(0, 0, w, h)] + list(seg_bounds)):
x, y, rw, rh = rect
area = rw*rh
if area < 64**2:
continue
silh_roi = motion_mask [y:y+rh,x:x+rw]
orient_roi = mg_orient [y:y+rh,x:x+rw]
mask_roi = mg_mask [y:y+rh,x:x+rw]
mhi_roi = motion_history[y:y+rh,x:x+rw]
if cv2.norm(silh_roi, cv2.NORM_L1) < area*0.05:
continue
angle = cv2.calcGlobalOrientation(orient_roi, mask_roi, mhi_roi, timestamp, MHI_DURATION)
color = ((255, 0, 0), (0, 0, 255))[i == 0]
draw_motion_comp(vis, rect, angle, color)
draw_str(vis, (20, 20), visual_name)
cv2.imshow('motempl', vis)
prev_frame = frame.copy()
if 0xFF & cv2.waitKey(5) == 27:
break
cv2.destroyAllWindows()
import numpy as np
import cv2
import video
from common import nothing, clock, draw_str
MHI_DURATION = 0.5
DEFAULT_THRESHOLD = 32
MAX_TIME_DELTA = 0.25
MIN_TIME_DELTA = 0.05
def draw_motion_comp(vis, (x, y, w, h), angle, color):
cv2.rectangle(vis, (x, y), (x+w, y+h), (0, 255, 0))
r = min(w/2, h/2)
cx, cy = x+w/2, y+h/2
angle = angle*np.pi/180
cv2.circle(vis, (cx, cy), r, color, 3)
cv2.line(vis, (cx, cy), (int(cx+np.cos(angle)*r), int(cy+np.sin(angle)*r)), color, 3)
if __name__ == '__main__':
import sys
try: video_src = sys.argv[1]
except: video_src = 0
cv2.namedWindow('motempl')
visuals = ['input', 'frame_diff', 'motion_hist', 'grad_orient']
cv2.createTrackbar('visual', 'motempl', 2, len(visuals)-1, nothing)
cv2.createTrackbar('threshold', 'motempl', DEFAULT_THRESHOLD, 255, nothing)
cam = video.create_capture(video_src, fallback='synth:class=chess:bg=../cpp/lena.jpg:noise=0.01')
ret, frame = cam.read()
h, w = frame.shape[:2]
prev_frame = frame.copy()
motion_history = np.zeros((h, w), np.float32)
hsv = np.zeros((h, w, 3), np.uint8)
hsv[:,:,1] = 255
while True:
ret, frame = cam.read()
frame_diff = cv2.absdiff(frame, prev_frame)
gray_diff = cv2.cvtColor(frame_diff, cv2.COLOR_BGR2GRAY)
thrs = cv2.getTrackbarPos('threshold', 'motempl')
ret, motion_mask = cv2.threshold(gray_diff, thrs, 1, cv2.THRESH_BINARY)
timestamp = clock()
cv2.updateMotionHistory(motion_mask, motion_history, timestamp, MHI_DURATION)
mg_mask, mg_orient = cv2.calcMotionGradient( motion_history, MAX_TIME_DELTA, MIN_TIME_DELTA, apertureSize=5 )
seg_mask, seg_bounds = cv2.segmentMotion(motion_history, timestamp, MAX_TIME_DELTA)
visual_name = visuals[cv2.getTrackbarPos('visual', 'motempl')]
if visual_name == 'input':
vis = frame.copy()
elif visual_name == 'frame_diff':
vis = frame_diff.copy()
elif visual_name == 'motion_hist':
vis = np.uint8(np.clip((motion_history-(timestamp-MHI_DURATION)) / MHI_DURATION, 0, 1)*255)
vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
elif visual_name == 'grad_orient':
hsv[:,:,0] = mg_orient/2
hsv[:,:,2] = mg_mask*255
vis = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
for i, rect in enumerate([(0, 0, w, h)] + list(seg_bounds)):
x, y, rw, rh = rect
area = rw*rh
if area < 64**2:
continue
silh_roi = motion_mask [y:y+rh,x:x+rw]
orient_roi = mg_orient [y:y+rh,x:x+rw]
mask_roi = mg_mask [y:y+rh,x:x+rw]
mhi_roi = motion_history[y:y+rh,x:x+rw]
if cv2.norm(silh_roi, cv2.NORM_L1) < area*0.05:
continue
angle = cv2.calcGlobalOrientation(orient_roi, mask_roi, mhi_roi, timestamp, MHI_DURATION)
color = ((255, 0, 0), (0, 0, 255))[i == 0]
draw_motion_comp(vis, rect, angle, color)
draw_str(vis, (20, 20), visual_name)
cv2.imshow('motempl', vis)
prev_frame = frame.copy()
if 0xFF & cv2.waitKey(5) == 27:
break
cv2.destroyAllWindows()

10
samples/python2/mouse_and_match.py
View File

@@ -1,5 +1,5 @@
#!/usr/bin/env python
'''
'''
mouse_and_match.py [-i path | --input path: default ./]
Demonstrate using a mouse to interact with an image:
@@ -45,13 +45,13 @@ def onmouse(event, x, y, flags, param):
else:
print "selection is complete"
drag_start = None
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Demonstrate mouse interaction with images')
parser.add_argument("-i","--input", default='./', help="Input directory.")
args = parser.parse_args()
path = args.input
cv.namedWindow("gray",1)
cv.setMouseCallback("gray", onmouse)
'''Loop through all the images in the directory'''
@@ -59,7 +59,7 @@ if __name__ == '__main__':
ext = os.path.splitext(infile)[1][1:] #get the filename extenstion
if ext == "png" or ext == "jpg" or ext == "bmp" or ext == "tiff" or ext == "pbm":
print infile
img=cv.imread(infile,1)
if img == None:
continue
@@ -69,4 +69,4 @@ if __name__ == '__main__':
cv.imshow("gray",gray)
if (cv.waitKey() & 255) == 27:
break
cv.destroyAllWindows()
cv.destroyAllWindows()

76
samples/python2/mser.py Normal file → Executable file
View File

@@ -1,38 +1,38 @@
'''
MSER detector demo
==================
Usage:
------
mser.py [<video source>]
Keys:
-----
ESC - exit
'''
import numpy as np
import cv2
import video
if __name__ == '__main__':
import sys
try: video_src = sys.argv[1]
except: video_src = 0
cam = video.create_capture(video_src)
mser = cv2.MSER()
while True:
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
vis = img.copy()
regions = mser.detect(gray, None)
hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in regions]
cv2.polylines(vis, hulls, 1, (0, 255, 0))
cv2.imshow('img', vis)
if 0xFF & cv2.waitKey(5) == 27:
break
cv2.destroyAllWindows()
'''
MSER detector demo
==================
Usage:
------
mser.py [<video source>]
Keys:
-----
ESC - exit
'''
import numpy as np
import cv2
import video
if __name__ == '__main__':
import sys
try: video_src = sys.argv[1]
except: video_src = 0
cam = video.create_capture(video_src)
mser = cv2.MSER()
while True:
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
vis = img.copy()
regions = mser.detect(gray, None)
hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in regions]
cv2.polylines(vis, hulls, 1, (0, 255, 0))
cv2.imshow('img', vis)
if 0xFF & cv2.waitKey(5) == 27:
break
cv2.destroyAllWindows()

168
samples/python2/opt_flow.py Normal file → Executable file
View File

@@ -1,84 +1,84 @@
import numpy as np
import cv2
import video
help_message = '''
USAGE: opt_flow.py [<video_source>]
Keys:
1 - toggle HSV flow visualization
2 - toggle glitch
'''
def draw_flow(img, flow, step=16):
h, w = img.shape[:2]
y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1)
fx, fy = flow[y,x].T
lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)
lines = np.int32(lines + 0.5)
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.polylines(vis, lines, 0, (0, 255, 0))
for (x1, y1), (x2, y2) in lines:
cv2.circle(vis, (x1, y1), 1, (0, 255, 0), -1)
return vis
def draw_hsv(flow):
h, w = flow.shape[:2]
fx, fy = flow[:,:,0], flow[:,:,1]
ang = np.arctan2(fy, fx) + np.pi
v = np.sqrt(fx*fx+fy*fy)
hsv = np.zeros((h, w, 3), np.uint8)
hsv[...,0] = ang*(180/np.pi/2)
hsv[...,1] = 255
hsv[...,2] = np.minimum(v*4, 255)
bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
return bgr
def warp_flow(img, flow):
h, w = flow.shape[:2]
flow = -flow
flow[:,:,0] += np.arange(w)
flow[:,:,1] += np.arange(h)[:,np.newaxis]
res = cv2.remap(img, flow, None, cv2.INTER_LINEAR)
return res
if __name__ == '__main__':
import sys
print help_message
try: fn = sys.argv[1]
except: fn = 0
cam = video.create_capture(fn)
ret, prev = cam.read()
prevgray = cv2.cvtColor(prev, cv2.COLOR_BGR2GRAY)
show_hsv = False
show_glitch = False
cur_glitch = prev.copy()
while True:
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
flow = cv2.calcOpticalFlowFarneback(prevgray, gray, 0.5, 3, 15, 3, 5, 1.2, 0)
prevgray = gray
cv2.imshow('flow', draw_flow(gray, flow))
if show_hsv:
cv2.imshow('flow HSV', draw_hsv(flow))
if show_glitch:
cur_glitch = warp_flow(cur_glitch, flow)
cv2.imshow('glitch', cur_glitch)
ch = 0xFF & cv2.waitKey(5)
if ch == 27:
break
if ch == ord('1'):
show_hsv = not show_hsv
print 'HSV flow visualization is', ['off', 'on'][show_hsv]
if ch == ord('2'):
show_glitch = not show_glitch
if show_glitch:
cur_glitch = img.copy()
print 'glitch is', ['off', 'on'][show_glitch]
cv2.destroyAllWindows()
import numpy as np
import cv2
import video
help_message = '''
USAGE: opt_flow.py [<video_source>]
Keys:
1 - toggle HSV flow visualization
2 - toggle glitch
'''
def draw_flow(img, flow, step=16):
h, w = img.shape[:2]
y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1)
fx, fy = flow[y,x].T
lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)
lines = np.int32(lines + 0.5)
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.polylines(vis, lines, 0, (0, 255, 0))
for (x1, y1), (x2, y2) in lines:
cv2.circle(vis, (x1, y1), 1, (0, 255, 0), -1)
return vis
def draw_hsv(flow):
h, w = flow.shape[:2]
fx, fy = flow[:,:,0], flow[:,:,1]
ang = np.arctan2(fy, fx) + np.pi
v = np.sqrt(fx*fx+fy*fy)
hsv = np.zeros((h, w, 3), np.uint8)
hsv[...,0] = ang*(180/np.pi/2)
hsv[...,1] = 255
hsv[...,2] = np.minimum(v*4, 255)
bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
return bgr
def warp_flow(img, flow):
h, w = flow.shape[:2]
flow = -flow
flow[:,:,0] += np.arange(w)
flow[:,:,1] += np.arange(h)[:,np.newaxis]
res = cv2.remap(img, flow, None, cv2.INTER_LINEAR)
return res
if __name__ == '__main__':
import sys
print help_message
try: fn = sys.argv[1]
except: fn = 0
cam = video.create_capture(fn)
ret, prev = cam.read()
prevgray = cv2.cvtColor(prev, cv2.COLOR_BGR2GRAY)
show_hsv = False
show_glitch = False
cur_glitch = prev.copy()
while True:
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
flow = cv2.calcOpticalFlowFarneback(prevgray, gray, 0.5, 3, 15, 3, 5, 1.2, 0)
prevgray = gray
cv2.imshow('flow', draw_flow(gray, flow))
if show_hsv:
cv2.imshow('flow HSV', draw_hsv(flow))
if show_glitch:
cur_glitch = warp_flow(cur_glitch, flow)
cv2.imshow('glitch', cur_glitch)
ch = 0xFF & cv2.waitKey(5)
if ch == 27:
break
if ch == ord('1'):
show_hsv = not show_hsv
print 'HSV flow visualization is', ['off', 'on'][show_hsv]
if ch == ord('2'):
show_glitch = not show_glitch
if show_glitch:
cur_glitch = img.copy()
print 'glitch is', ['off', 'on'][show_glitch]
cv2.destroyAllWindows()

112
samples/python2/peopledetect.py Normal file → Executable file
View File

@@ -1,56 +1,56 @@
import numpy as np
import cv2
help_message = '''
USAGE: peopledetect.py <image_names> ...
Press any key to continue, ESC to stop.
'''
def inside(r, q):
rx, ry, rw, rh = r
qx, qy, qw, qh = q
return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh
def draw_detections(img, rects, thickness = 1):
for x, y, w, h in rects:
# the HOG detector returns slightly larger rectangles than the real objects.
# so we slightly shrink the rectangles to get a nicer output.
pad_w, pad_h = int(0.15*w), int(0.05*h)
cv2.rectangle(img, (x+pad_w, y+pad_h), (x+w-pad_w, y+h-pad_h), (0, 255, 0), thickness)
if __name__ == '__main__':
import sys
from glob import glob
import itertools as it
print help_message
hog = cv2.HOGDescriptor()
hog.setSVMDetector( cv2.HOGDescriptor_getDefaultPeopleDetector() )
for fn in it.chain(*map(glob, sys.argv[1:])):
print fn, ' - ',
try:
img = cv2.imread(fn)
except:
print 'loading error'
continue
found, w = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05)
found_filtered = []
for ri, r in enumerate(found):
for qi, q in enumerate(found):
if ri != qi and inside(r, q):
break
else:
found_filtered.append(r)
draw_detections(img, found)
draw_detections(img, found_filtered, 3)
print '%d (%d) found' % (len(found_filtered), len(found))
cv2.imshow('img', img)
ch = 0xFF & cv2.waitKey()
if ch == 27:
break
cv2.destroyAllWindows()
import numpy as np
import cv2
help_message = '''
USAGE: peopledetect.py <image_names> ...
Press any key to continue, ESC to stop.
'''
def inside(r, q):
rx, ry, rw, rh = r
qx, qy, qw, qh = q
return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh
def draw_detections(img, rects, thickness = 1):
for x, y, w, h in rects:
# the HOG detector returns slightly larger rectangles than the real objects.
# so we slightly shrink the rectangles to get a nicer output.
pad_w, pad_h = int(0.15*w), int(0.05*h)
cv2.rectangle(img, (x+pad_w, y+pad_h), (x+w-pad_w, y+h-pad_h), (0, 255, 0), thickness)
if __name__ == '__main__':
import sys
from glob import glob
import itertools as it
print help_message
hog = cv2.HOGDescriptor()
hog.setSVMDetector( cv2.HOGDescriptor_getDefaultPeopleDetector() )
for fn in it.chain(*map(glob, sys.argv[1:])):
print fn, ' - ',
try:
img = cv2.imread(fn)
except:
print 'loading error'
continue
found, w = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05)
found_filtered = []
for ri, r in enumerate(found):
for qi, q in enumerate(found):
if ri != qi and inside(r, q):
break
else:
found_filtered.append(r)
draw_detections(img, found)
draw_detections(img, found_filtered, 3)
print '%d (%d) found' % (len(found_filtered), len(found))
cv2.imshow('img', img)
ch = 0xFF & cv2.waitKey()
if ch == 27:
break
cv2.destroyAllWindows()

12
samples/python2/plane_ar.py Normal file → Executable file
View File

@@ -26,13 +26,13 @@ import video
import common
from plane_tracker import PlaneTracker
ar_verts = np.float32([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0],
[0, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0, 1],
[0, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0, 1],
[0, 0.5, 2], [1, 0.5, 2]])
ar_edges = [(0, 1), (1, 2), (2, 3), (3, 0),
ar_edges = [(0, 1), (1, 2), (2, 3), (3, 0),
(4, 5), (5, 6), (6, 7), (7, 4),
(0, 4), (1, 5), (2, 6), (3, 7),
(0, 4), (1, 5), (2, 6), (3, 7),
(4, 8), (5, 8), (6, 9), (7, 9), (8, 9)]
class App:
@@ -45,7 +45,7 @@ class App:
cv2.namedWindow('plane')
cv2.createTrackbar('focal', 'plane', 25, 50, common.nothing)
self.rect_sel = common.RectSelector('plane', self.on_rect)
def on_rect(self, rect):
self.tracker.add_target(self.frame, rect)
@@ -57,7 +57,7 @@ class App:
if not ret:
break
self.frame = frame.copy()
vis = self.frame.copy()
if playing:
tracked = self.tracker.track(self.frame)

6
samples/python2/plane_tracker.py Normal file → Executable file
View File

@@ -104,7 +104,7 @@ class PlaneTracker:
if status.sum() < MIN_MATCH_COUNT:
continue
p0, p1 = p0[status], p1[status]
x0, y0, x1, y1 = target.rect
quad = np.float32([[x0, y0], [x1, y0], [x1, y1], [x0, y1]])
quad = cv2.perspectiveTransform(quad.reshape(1, -1, 2), H).reshape(-1, 2)
@@ -131,7 +131,7 @@ class App:
cv2.namedWindow('plane')
self.rect_sel = common.RectSelector('plane', self.on_rect)
def on_rect(self, rect):
self.tracker.add_target(self.frame, rect)
@@ -143,7 +143,7 @@ class App:
if not ret:
break
self.frame = frame.copy()
vis = self.frame.copy()
if playing:
tracked = self.tracker.track(self.frame)

92
samples/python2/squares.py Normal file → Executable file
View File

@@ -1,46 +1,46 @@
'''
Simple "Square Detector" program.
Loads several images sequentially and tries to find squares in each image.
'''
import numpy as np
import cv2
def angle_cos(p0, p1, p2):
d1, d2 = (p0-p1).astype('float'), (p2-p1).astype('float')
return abs( np.dot(d1, d2) / np.sqrt( np.dot(d1, d1)*np.dot(d2, d2) ) )
def find_squares(img):
img = cv2.GaussianBlur(img, (5, 5), 0)
squares = []
for gray in cv2.split(img):
for thrs in xrange(0, 255, 26):
if thrs == 0:
bin = cv2.Canny(gray, 0, 50, apertureSize=5)
bin = cv2.dilate(bin, None)
else:
retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
cnt_len = cv2.arcLength(cnt, True)
cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True)
if len(cnt) == 4 and cv2.contourArea(cnt) > 1000 and cv2.isContourConvex(cnt):
cnt = cnt.reshape(-1, 2)
max_cos = np.max([angle_cos( cnt[i], cnt[(i+1) % 4], cnt[(i+2) % 4] ) for i in xrange(4)])
if max_cos < 0.1:
squares.append(cnt)
return squares
if __name__ == '__main__':
from glob import glob
for fn in glob('../cpp/pic*.png'):
img = cv2.imread(fn)
squares = find_squares(img)
cv2.drawContours( img, squares, -1, (0, 255, 0), 3 )
cv2.imshow('squares', img)
ch = 0xFF & cv2.waitKey()
if ch == 27:
break
cv2.destroyAllWindows()
'''
Simple "Square Detector" program.
Loads several images sequentially and tries to find squares in each image.
'''
import numpy as np
import cv2
def angle_cos(p0, p1, p2):
d1, d2 = (p0-p1).astype('float'), (p2-p1).astype('float')
return abs( np.dot(d1, d2) / np.sqrt( np.dot(d1, d1)*np.dot(d2, d2) ) )
def find_squares(img):
img = cv2.GaussianBlur(img, (5, 5), 0)
squares = []
for gray in cv2.split(img):
for thrs in xrange(0, 255, 26):
if thrs == 0:
bin = cv2.Canny(gray, 0, 50, apertureSize=5)
bin = cv2.dilate(bin, None)
else:
retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
cnt_len = cv2.arcLength(cnt, True)
cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True)
if len(cnt) == 4 and cv2.contourArea(cnt) > 1000 and cv2.isContourConvex(cnt):
cnt = cnt.reshape(-1, 2)
max_cos = np.max([angle_cos( cnt[i], cnt[(i+1) % 4], cnt[(i+2) % 4] ) for i in xrange(4)])
if max_cos < 0.1:
squares.append(cnt)
return squares
if __name__ == '__main__':
from glob import glob
for fn in glob('../cpp/pic*.png'):
img = cv2.imread(fn)
squares = find_squares(img)
cv2.drawContours( img, squares, -1, (0, 255, 0), 3 )
cv2.imshow('squares', img)
ch = 0xFF & cv2.waitKey()
if ch == 27:
break
cv2.destroyAllWindows()

148
samples/python2/stereo_match.py Normal file → Executable file
View File

@@ -1,74 +1,74 @@
'''
Simple example of stereo image matching and point cloud generation.
Resulting .ply file cam be easily viewed using MeshLab ( http://meshlab.sourceforge.net/ )
'''
import numpy as np
import cv2
ply_header = '''ply
format ascii 1.0
element vertex %(vert_num)d
property float x
property float y
property float z
property uchar red
property uchar green
property uchar blue
end_header
'''
def write_ply(fn, verts, colors):
verts = verts.reshape(-1, 3)
colors = colors.reshape(-1, 3)
verts = np.hstack([verts, colors])
with open(fn, 'w') as f:
f.write(ply_header % dict(vert_num=len(verts)))
np.savetxt(f, verts, '%f %f %f %d %d %d')
if __name__ == '__main__':
print 'loading images...'
imgL = cv2.pyrDown( cv2.imread('../gpu/aloeL.jpg') ) # downscale images for faster processing
imgR = cv2.pyrDown( cv2.imread('../gpu/aloeR.jpg') )
# disparity range is tuned for 'aloe' image pair
window_size = 3
min_disp = 16
num_disp = 112-min_disp
stereo = cv2.StereoSGBM(minDisparity = min_disp,
numDisparities = num_disp,
SADWindowSize = window_size,
uniquenessRatio = 10,
speckleWindowSize = 100,
speckleRange = 32,
disp12MaxDiff = 1,
P1 = 8*3*window_size**2,
P2 = 32*3*window_size**2,
fullDP = False
)
print 'computing disparity...'
disp = stereo.compute(imgL, imgR).astype(np.float32) / 16.0
print 'generating 3d point cloud...',
h, w = imgL.shape[:2]
f = 0.8*w # guess for focal length
Q = np.float32([[1, 0, 0, -0.5*w],
[0,-1, 0, 0.5*h], # turn points 180 deg around x-axis,
[0, 0, 0, -f], # so that y-axis looks up
[0, 0, 1, 0]])
points = cv2.reprojectImageTo3D(disp, Q)
colors = cv2.cvtColor(imgL, cv2.COLOR_BGR2RGB)
mask = disp > disp.min()
out_points = points[mask]
out_colors = colors[mask]
out_fn = 'out.ply'
write_ply('out.ply', out_points, out_colors)
print '%s saved' % 'out.ply'
cv2.imshow('left', imgL)
cv2.imshow('disparity', (disp-min_disp)/num_disp)
cv2.waitKey()
cv2.destroyAllWindows()
'''
Simple example of stereo image matching and point cloud generation.
Resulting .ply file cam be easily viewed using MeshLab ( http://meshlab.sourceforge.net/ )
'''
import numpy as np
import cv2
ply_header = '''ply
format ascii 1.0
element vertex %(vert_num)d
property float x
property float y
property float z
property uchar red
property uchar green
property uchar blue
end_header
'''
def write_ply(fn, verts, colors):
verts = verts.reshape(-1, 3)
colors = colors.reshape(-1, 3)
verts = np.hstack([verts, colors])
with open(fn, 'w') as f:
f.write(ply_header % dict(vert_num=len(verts)))
np.savetxt(f, verts, '%f %f %f %d %d %d')
if __name__ == '__main__':
print 'loading images...'
imgL = cv2.pyrDown( cv2.imread('../gpu/aloeL.jpg') ) # downscale images for faster processing
imgR = cv2.pyrDown( cv2.imread('../gpu/aloeR.jpg') )
# disparity range is tuned for 'aloe' image pair
window_size = 3
min_disp = 16
num_disp = 112-min_disp
stereo = cv2.StereoSGBM(minDisparity = min_disp,
numDisparities = num_disp,
SADWindowSize = window_size,
uniquenessRatio = 10,
speckleWindowSize = 100,
speckleRange = 32,
disp12MaxDiff = 1,
P1 = 8*3*window_size**2,
P2 = 32*3*window_size**2,
fullDP = False
)
print 'computing disparity...'
disp = stereo.compute(imgL, imgR).astype(np.float32) / 16.0
print 'generating 3d point cloud...',
h, w = imgL.shape[:2]
f = 0.8*w # guess for focal length
Q = np.float32([[1, 0, 0, -0.5*w],
[0,-1, 0, 0.5*h], # turn points 180 deg around x-axis,
[0, 0, 0, -f], # so that y-axis looks up
[0, 0, 1, 0]])
points = cv2.reprojectImageTo3D(disp, Q)
colors = cv2.cvtColor(imgL, cv2.COLOR_BGR2RGB)
mask = disp > disp.min()
out_points = points[mask]
out_colors = colors[mask]
out_fn = 'out.ply'
write_ply('out.ply', out_points, out_colors)
print '%s saved' % 'out.ply'
cv2.imshow('left', imgL)
cv2.imshow('disparity', (disp-min_disp)/num_disp)
cv2.waitKey()
cv2.destroyAllWindows()

2
samples/python2/texture_flow.py Normal file → Executable file
View File

@@ -1,7 +1,7 @@
'''
Texture flow direction estimation.
Sample shows how cv2.cornerEigenValsAndVecs function can be used
Sample shows how cv2.cornerEigenValsAndVecs function can be used
to estimate image texture flow direction.
Usage:

130
samples/python2/turing.py Normal file → Executable file
View File

@@ -1,65 +1,65 @@
'''
Multiscale Turing Patterns generator
====================================
Inspired by http://www.jonathanmccabe.com/Cyclic_Symmetric_Multi-Scale_Turing_Patterns.pdf
'''
import numpy as np
import cv2
import cv2.cv as cv
from common import draw_str
import getopt, sys
from itertools import count
help_message = '''
USAGE: turing.py [-o <output.avi>]
Press ESC to stop.
'''
if __name__ == '__main__':
print help_message
w, h = 512, 512
args, args_list = getopt.getopt(sys.argv[1:], 'o:', [])
args = dict(args)
out = None
if '-o' in args:
fn = args['-o']
out = cv2.VideoWriter(args['-o'], cv.CV_FOURCC(*'DIB '), 30.0, (w, h), False)
print 'writing %s ...' % fn
a = np.zeros((h, w), np.float32)
cv2.randu(a, np.array([0]), np.array([1]))
def process_scale(a_lods, lod):
d = a_lods[lod] - cv2.pyrUp(a_lods[lod+1])
for i in xrange(lod):
d = cv2.pyrUp(d)
v = cv2.GaussianBlur(d*d, (3, 3), 0)
return np.sign(d), v
scale_num = 6
for frame_i in count():
a_lods = [a]
for i in xrange(scale_num):
a_lods.append(cv2.pyrDown(a_lods[-1]))
ms, vs = [], []
for i in xrange(1, scale_num):
m, v = process_scale(a_lods, i)
ms.append(m)
vs.append(v)
mi = np.argmin(vs, 0)
a += np.choose(mi, ms) * 0.025
a = (a-a.min()) / a.ptp()
if out:
out.write(a)
vis = a.copy()
draw_str(vis, (20, 20), 'frame %d' % frame_i)
cv2.imshow('a', vis)
if 0xFF & cv2.waitKey(5) == 27:
break
cv2.destroyAllWindows()
'''
Multiscale Turing Patterns generator
====================================
Inspired by http://www.jonathanmccabe.com/Cyclic_Symmetric_Multi-Scale_Turing_Patterns.pdf
'''
import numpy as np
import cv2
import cv2.cv as cv
from common import draw_str
import getopt, sys
from itertools import count
help_message = '''
USAGE: turing.py [-o <output.avi>]
Press ESC to stop.
'''
if __name__ == '__main__':
print help_message
w, h = 512, 512
args, args_list = getopt.getopt(sys.argv[1:], 'o:', [])
args = dict(args)
out = None
if '-o' in args:
fn = args['-o']
out = cv2.VideoWriter(args['-o'], cv.CV_FOURCC(*'DIB '), 30.0, (w, h), False)
print 'writing %s ...' % fn
a = np.zeros((h, w), np.float32)
cv2.randu(a, np.array([0]), np.array([1]))
def process_scale(a_lods, lod):
d = a_lods[lod] - cv2.pyrUp(a_lods[lod+1])
for i in xrange(lod):
d = cv2.pyrUp(d)
v = cv2.GaussianBlur(d*d, (3, 3), 0)
return np.sign(d), v
scale_num = 6
for frame_i in count():
a_lods = [a]
for i in xrange(scale_num):
a_lods.append(cv2.pyrDown(a_lods[-1]))
ms, vs = [], []
for i in xrange(1, scale_num):
m, v = process_scale(a_lods, i)
ms.append(m)
vs.append(v)
mi = np.argmin(vs, 0)
a += np.choose(mi, ms) * 0.025
a = (a-a.min()) / a.ptp()
if out:
out.write(a)
vis = a.copy()
draw_str(vis, (20, 20), 'frame %d' % frame_i)
cv2.imshow('a', vis)
if 0xFF & cv2.waitKey(5) == 27:
break
cv2.destroyAllWindows()

384
samples/python2/video.py Normal file → Executable file
View File

@@ -1,192 +1,192 @@
'''
Video capture sample.
Sample shows how VideoCapture class can be used to acquire video
frames from a camera of a movie file. Also the sample provides
an example of procedural video generation by an object, mimicking
the VideoCapture interface (see Chess class).
'create_capture' is a convinience function for capture creation,
falling back to procedural video in case of error.
Usage:
video.py [--shotdir <shot path>] [source0] [source1] ...'
sourceN is an
- integer number for camera capture
- name of video file
- synth:<params> for procedural video
Synth examples:
synth:bg=../cpp/lena.jpg:noise=0.1
synth:class=chess:bg=../cpp/lena.jpg:noise=0.1:size=640x480
Keys:
ESC - exit
SPACE - save current frame to <shot path> directory
'''
import numpy as np
import cv2
from time import clock
from numpy import pi, sin, cos
import common
class VideoSynthBase(object):
def __init__(self, size=None, noise=0.0, bg = None, **params):
self.bg = None
self.frame_size = (640, 480)
if bg is not None:
self.bg = cv2.imread(bg, 1)
h, w = self.bg.shape[:2]
self.frame_size = (w, h)
if size is not None:
w, h = map(int, size.split('x'))
self.frame_size = (w, h)
self.bg = cv2.resize(self.bg, self.frame_size)
self.noise = float(noise)
def render(self, dst):
pass
def read(self, dst=None):
w, h = self.frame_size
if self.bg is None:
buf = np.zeros((h, w, 3), np.uint8)
else:
buf = self.bg.copy()
self.render(buf)
if self.noise > 0.0:
noise = np.zeros((h, w, 3), np.int8)
cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise)
buf = cv2.add(buf, noise, dtype=cv2.CV_8UC3)
return True, buf
def isOpened(self):
return True
class Chess(VideoSynthBase):
def __init__(self, **kw):
super(Chess, self).__init__(**kw)
w, h = self.frame_size
self.grid_size = sx, sy = 10, 7
white_quads = []
black_quads = []
for i, j in np.ndindex(sy, sx):
q = [[j, i, 0], [j+1, i, 0], [j+1, i+1, 0], [j, i+1, 0]]
[white_quads, black_quads][(i + j) % 2].append(q)
self.white_quads = np.float32(white_quads)
self.black_quads = np.float32(black_quads)
fx = 0.9
self.K = np.float64([[fx*w, 0, 0.5*(w-1)],
[0, fx*w, 0.5*(h-1)],
[0.0,0.0, 1.0]])
self.dist_coef = np.float64([-0.2, 0.1, 0, 0])
self.t = 0
def draw_quads(self, img, quads, color = (0, 255, 0)):
img_quads = cv2.projectPoints(quads.reshape(-1, 3), self.rvec, self.tvec, self.K, self.dist_coef) [0]
img_quads.shape = quads.shape[:2] + (2,)
for q in img_quads:
cv2.fillConvexPoly(img, np.int32(q*4), color, cv2.CV_AA, shift=2)
def render(self, dst):
t = self.t
self.t += 1.0/30.0
sx, sy = self.grid_size
center = np.array([0.5*sx, 0.5*sy, 0.0])
phi = pi/3 + sin(t*3)*pi/8
c, s = cos(phi), sin(phi)
ofs = np.array([sin(1.2*t), cos(1.8*t), 0]) * sx * 0.2
eye_pos = center + np.array([cos(t)*c, sin(t)*c, s]) * 15.0 + ofs
target_pos = center + ofs
R, self.tvec = common.lookat(eye_pos, target_pos)
self.rvec = common.mtx2rvec(R)
self.draw_quads(dst, self.white_quads, (245, 245, 245))
self.draw_quads(dst, self.black_quads, (10, 10, 10))
classes = dict(chess=Chess)
presets = dict(
empty = 'synth:',
lena = 'synth:bg=../cpp/lena.jpg:noise=0.1',
chess = 'synth:class=chess:bg=../cpp/lena.jpg:noise=0.1:size=640x480'
)
def create_capture(source = 0, fallback = presets['chess']):
'''source: <int> or '<int>|<filename>|synth [:<param_name>=<value> [:...]]'
'''
source = str(source).strip()
chunks = source.split(':')
# hanlde drive letter ('c:', ...)
if len(chunks) > 1 and len(chunks[0]) == 1 and chunks[0].isalpha():
chunks[1] = chunks[0] + ':' + chunks[1]
del chunks[0]
source = chunks[0]
try: source = int(source)
except ValueError: pass
params = dict( s.split('=') for s in chunks[1:] )
cap = None
if source == 'synth':
Class = classes.get(params.get('class', None), VideoSynthBase)
try: cap = Class(**params)
except: pass
else:
cap = cv2.VideoCapture(source)
if 'size' in params:
w, h = map(int, params['size'].split('x'))
cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, w)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, h)
if cap is None or not cap.isOpened():
print 'Warning: unable to open video source: ', source
if fallback is not None:
return create_capture(fallback, None)
return cap
if __name__ == '__main__':
import sys
import getopt
print __doc__
args, sources = getopt.getopt(sys.argv[1:], '', 'shotdir=')
args = dict(args)
shotdir = args.get('--shotdir', '.')
if len(sources) == 0:
sources = [ 0 ]
caps = map(create_capture, sources)
shot_idx = 0
while True:
imgs = []
for i, cap in enumerate(caps):
ret, img = cap.read()
imgs.append(img)
cv2.imshow('capture %d' % i, img)
ch = 0xFF & cv2.waitKey(1)
if ch == 27:
break
if ch == ord(' '):
for i, img in enumerate(imgs):
fn = '%s/shot_%d_%03d.bmp' % (shotdir, i, shot_idx)
cv2.imwrite(fn, img)
print fn, 'saved'
shot_idx += 1
cv2.destroyAllWindows()
'''
Video capture sample.
Sample shows how VideoCapture class can be used to acquire video
frames from a camera of a movie file. Also the sample provides
an example of procedural video generation by an object, mimicking
the VideoCapture interface (see Chess class).
'create_capture' is a convinience function for capture creation,
falling back to procedural video in case of error.
Usage:
video.py [--shotdir <shot path>] [source0] [source1] ...'
sourceN is an
- integer number for camera capture
- name of video file
- synth:<params> for procedural video
Synth examples:
synth:bg=../cpp/lena.jpg:noise=0.1
synth:class=chess:bg=../cpp/lena.jpg:noise=0.1:size=640x480
Keys:
ESC - exit
SPACE - save current frame to <shot path> directory
'''
import numpy as np
import cv2
from time import clock
from numpy import pi, sin, cos
import common
class VideoSynthBase(object):
def __init__(self, size=None, noise=0.0, bg = None, **params):
self.bg = None
self.frame_size = (640, 480)
if bg is not None:
self.bg = cv2.imread(bg, 1)
h, w = self.bg.shape[:2]
self.frame_size = (w, h)
if size is not None:
w, h = map(int, size.split('x'))
self.frame_size = (w, h)
self.bg = cv2.resize(self.bg, self.frame_size)
self.noise = float(noise)
def render(self, dst):
pass
def read(self, dst=None):
w, h = self.frame_size
if self.bg is None:
buf = np.zeros((h, w, 3), np.uint8)
else:
buf = self.bg.copy()
self.render(buf)
if self.noise > 0.0:
noise = np.zeros((h, w, 3), np.int8)
cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise)
buf = cv2.add(buf, noise, dtype=cv2.CV_8UC3)
return True, buf
def isOpened(self):
return True
class Chess(VideoSynthBase):
def __init__(self, **kw):
super(Chess, self).__init__(**kw)
w, h = self.frame_size
self.grid_size = sx, sy = 10, 7
white_quads = []
black_quads = []
for i, j in np.ndindex(sy, sx):
q = [[j, i, 0], [j+1, i, 0], [j+1, i+1, 0], [j, i+1, 0]]
[white_quads, black_quads][(i + j) % 2].append(q)
self.white_quads = np.float32(white_quads)
self.black_quads = np.float32(black_quads)
fx = 0.9
self.K = np.float64([[fx*w, 0, 0.5*(w-1)],
[0, fx*w, 0.5*(h-1)],
[0.0,0.0, 1.0]])
self.dist_coef = np.float64([-0.2, 0.1, 0, 0])
self.t = 0
def draw_quads(self, img, quads, color = (0, 255, 0)):
img_quads = cv2.projectPoints(quads.reshape(-1, 3), self.rvec, self.tvec, self.K, self.dist_coef) [0]
img_quads.shape = quads.shape[:2] + (2,)
for q in img_quads:
cv2.fillConvexPoly(img, np.int32(q*4), color, cv2.CV_AA, shift=2)
def render(self, dst):
t = self.t
self.t += 1.0/30.0
sx, sy = self.grid_size
center = np.array([0.5*sx, 0.5*sy, 0.0])
phi = pi/3 + sin(t*3)*pi/8
c, s = cos(phi), sin(phi)
ofs = np.array([sin(1.2*t), cos(1.8*t), 0]) * sx * 0.2
eye_pos = center + np.array([cos(t)*c, sin(t)*c, s]) * 15.0 + ofs
target_pos = center + ofs
R, self.tvec = common.lookat(eye_pos, target_pos)
self.rvec = common.mtx2rvec(R)
self.draw_quads(dst, self.white_quads, (245, 245, 245))
self.draw_quads(dst, self.black_quads, (10, 10, 10))
classes = dict(chess=Chess)
presets = dict(
empty = 'synth:',
lena = 'synth:bg=../cpp/lena.jpg:noise=0.1',
chess = 'synth:class=chess:bg=../cpp/lena.jpg:noise=0.1:size=640x480'
)
def create_capture(source = 0, fallback = presets['chess']):
'''source: <int> or '<int>|<filename>|synth [:<param_name>=<value> [:...]]'
'''
source = str(source).strip()
chunks = source.split(':')
# hanlde drive letter ('c:', ...)
if len(chunks) > 1 and len(chunks[0]) == 1 and chunks[0].isalpha():
chunks[1] = chunks[0] + ':' + chunks[1]
del chunks[0]
source = chunks[0]
try: source = int(source)
except ValueError: pass
params = dict( s.split('=') for s in chunks[1:] )
cap = None
if source == 'synth':
Class = classes.get(params.get('class', None), VideoSynthBase)
try: cap = Class(**params)
except: pass
else:
cap = cv2.VideoCapture(source)
if 'size' in params:
w, h = map(int, params['size'].split('x'))
cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, w)
cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, h)
if cap is None or not cap.isOpened():
print 'Warning: unable to open video source: ', source
if fallback is not None:
return create_capture(fallback, None)
return cap
if __name__ == '__main__':
import sys
import getopt
print __doc__
args, sources = getopt.getopt(sys.argv[1:], '', 'shotdir=')
args = dict(args)
shotdir = args.get('--shotdir', '.')
if len(sources) == 0:
sources = [ 0 ]
caps = map(create_capture, sources)
shot_idx = 0
while True:
imgs = []
for i, cap in enumerate(caps):
ret, img = cap.read()
imgs.append(img)
cv2.imshow('capture %d' % i, img)
ch = 0xFF & cv2.waitKey(1)
if ch == 27:
break
if ch == ord(' '):
for i, img in enumerate(imgs):
fn = '%s/shot_%d_%03d.bmp' % (shotdir, i, shot_idx)
cv2.imwrite(fn, img)
print fn, 'saved'
shot_idx += 1
cv2.destroyAllWindows()

20
samples/python2/video_dmtx.py Normal file → Executable file
View File

@@ -7,7 +7,7 @@ Usage:
NOTE: This only handles data matrices, generated for text strings of max 3 characters
Resize the screen to be large enough for your camera to see, and it should find an read it.
Keyboard shortcuts:
q or ESC - exit
@@ -22,32 +22,32 @@ def data_matrix_demo(cap):
window_name = "Data Matrix Detector"
frame_number = 0
need_to_save = False
while 1:
ret, frame = cap.read()
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
codes, corners, dmtx = cv2.findDataMatrix(gray)
cv2.drawDataMatrixCodes(frame, codes, corners)
cv2.imshow(window_name, frame)
key = cv2.waitKey(30)
c = chr(key & 255)
if c in ['q', 'Q', chr(27)]:
break
if c == ' ':
need_to_save = True
if need_to_save and codes:
filename = ("datamatrix%03d.jpg" % frame_number)
cv2.imwrite(filename, frame)
print "Saved frame to " + filename
need_to_save = False
frame_number += 1
@@ -60,9 +60,9 @@ if __name__ == '__main__':
cap = cv2.VideoCapture(sys.argv[1])
if not cap.isOpened():
cap = cv2.VideoCapture(int(sys.argv[1]))
if not cap.isOpened():
print 'Cannot initialize video capture'
sys.exit(-1)
data_matrix_demo(cap)

166
samples/python2/video_threaded.py Normal file → Executable file
View File

@@ -1,83 +1,83 @@
'''
Multithreaded video processing sample.
Usage:
video_threaded.py {<video device number>|<video file name>}
Shows how python threading capabilities can be used
to organize parallel captured frame processing pipeline
for smoother playback.
Keyboard shortcuts:
ESC - exit
space - switch between multi and single threaded processing
'''
import numpy as np
import cv2
from multiprocessing.pool import ThreadPool
from collections import deque
from common import clock, draw_str, StatValue
import video
class DummyTask:
def __init__(self, data):
self.data = data
def ready(self):
return True
def get(self):
return self.data
if __name__ == '__main__':
import sys
print __doc__
try: fn = sys.argv[1]
except: fn = 0
cap = video.create_capture(fn)
def process_frame(frame, t0):
# some intensive computation...
frame = cv2.medianBlur(frame, 19)
frame = cv2.medianBlur(frame, 19)
return frame, t0
threadn = cv2.getNumberOfCPUs()
pool = ThreadPool(processes = threadn)
pending = deque()
threaded_mode = True
latency = StatValue()
frame_interval = StatValue()
last_frame_time = clock()
while True:
while len(pending) > 0 and pending[0].ready():
res, t0 = pending.popleft().get()
latency.update(clock() - t0)
draw_str(res, (20, 20), "threaded : " + str(threaded_mode))
draw_str(res, (20, 40), "latency : %.1f ms" % (latency.value*1000))
draw_str(res, (20, 60), "frame interval : %.1f ms" % (frame_interval.value*1000))
cv2.imshow('threaded video', res)
if len(pending) < threadn:
ret, frame = cap.read()
t = clock()
frame_interval.update(t - last_frame_time)
last_frame_time = t
if threaded_mode:
task = pool.apply_async(process_frame, (frame.copy(), t))
else:
task = DummyTask(process_frame(frame, t))
pending.append(task)
ch = 0xFF & cv2.waitKey(1)
if ch == ord(' '):
threaded_mode = not threaded_mode
if ch == 27:
break
cv2.destroyAllWindows()
'''
Multithreaded video processing sample.
Usage:
video_threaded.py {<video device number>|<video file name>}
Shows how python threading capabilities can be used
to organize parallel captured frame processing pipeline
for smoother playback.
Keyboard shortcuts:
ESC - exit
space - switch between multi and single threaded processing
'''
import numpy as np
import cv2
from multiprocessing.pool import ThreadPool
from collections import deque
from common import clock, draw_str, StatValue
import video
class DummyTask:
def __init__(self, data):
self.data = data
def ready(self):
return True
def get(self):
return self.data
if __name__ == '__main__':
import sys
print __doc__
try: fn = sys.argv[1]
except: fn = 0
cap = video.create_capture(fn)
def process_frame(frame, t0):
# some intensive computation...
frame = cv2.medianBlur(frame, 19)
frame = cv2.medianBlur(frame, 19)
return frame, t0
threadn = cv2.getNumberOfCPUs()
pool = ThreadPool(processes = threadn)
pending = deque()
threaded_mode = True
latency = StatValue()
frame_interval = StatValue()
last_frame_time = clock()
while True:
while len(pending) > 0 and pending[0].ready():
res, t0 = pending.popleft().get()
latency.update(clock() - t0)
draw_str(res, (20, 20), "threaded : " + str(threaded_mode))
draw_str(res, (20, 40), "latency : %.1f ms" % (latency.value*1000))
draw_str(res, (20, 60), "frame interval : %.1f ms" % (frame_interval.value*1000))
cv2.imshow('threaded video', res)
if len(pending) < threadn:
ret, frame = cap.read()
t = clock()
frame_interval.update(t - last_frame_time)
last_frame_time = t
if threaded_mode:
task = pool.apply_async(process_frame, (frame.copy(), t))
else:
task = DummyTask(process_frame(frame, t))
pending.append(task)
ch = 0xFF & cv2.waitKey(1)
if ch == ord(' '):
threaded_mode = not threaded_mode
if ch == 27:
break
cv2.destroyAllWindows()

154
samples/python2/watershed.py Normal file → Executable file
View File

@@ -1,77 +1,77 @@
'''
Watershed segmentation
=========
This program demonstrates the watershed segmentation algorithm
in OpenCV: watershed().
Usage
-----
watershed.py [image filename]
Keys
----
1-7 - switch marker color
SPACE - update segmentation
r - reset
a - toggle autoupdate
ESC - exit
'''
import numpy as np
import cv2
from common import Sketcher
class App:
def __init__(self, fn):
self.img = cv2.imread(fn)
h, w = self.img.shape[:2]
self.markers = np.zeros((h, w), np.int32)
self.markers_vis = self.img.copy()
self.cur_marker = 1
self.colors = np.int32( list(np.ndindex(2, 2, 2)) ) * 255
self.auto_update = True
self.sketch = Sketcher('img', [self.markers_vis, self.markers], self.get_colors)
def get_colors(self):
return map(int, self.colors[self.cur_marker]), self.cur_marker
def watershed(self):
m = self.markers.copy()
cv2.watershed(self.img, m)
overlay = self.colors[np.maximum(m, 0)]
vis = cv2.addWeighted(self.img, 0.5, overlay, 0.5, 0.0, dtype=cv2.CV_8UC3)
cv2.imshow('watershed', vis)
def run(self):
while True:
ch = 0xFF & cv2.waitKey(50)
if ch == 27:
break
if ch >= ord('1') and ch <= ord('7'):
self.cur_marker = ch - ord('0')
print 'marker: ', self.cur_marker
if ch == ord(' ') or (self.sketch.dirty and self.auto_update):
self.watershed()
self.sketch.dirty = False
if ch in [ord('a'), ord('A')]:
self.auto_update = not self.auto_update
print 'auto_update if', ['off', 'on'][self.auto_update]
if ch in [ord('r'), ord('R')]:
self.markers[:] = 0
self.markers_vis[:] = self.img
self.sketch.show()
cv2.destroyAllWindows()
if __name__ == '__main__':
import sys
try: fn = sys.argv[1]
except: fn = '../cpp/fruits.jpg'
print __doc__
App(fn).run()
'''
Watershed segmentation
=========
This program demonstrates the watershed segmentation algorithm
in OpenCV: watershed().
Usage
-----
watershed.py [image filename]
Keys
----
1-7 - switch marker color
SPACE - update segmentation
r - reset
a - toggle autoupdate
ESC - exit
'''
import numpy as np
import cv2
from common import Sketcher
class App:
def __init__(self, fn):
self.img = cv2.imread(fn)
h, w = self.img.shape[:2]
self.markers = np.zeros((h, w), np.int32)
self.markers_vis = self.img.copy()
self.cur_marker = 1
self.colors = np.int32( list(np.ndindex(2, 2, 2)) ) * 255
self.auto_update = True
self.sketch = Sketcher('img', [self.markers_vis, self.markers], self.get_colors)
def get_colors(self):
return map(int, self.colors[self.cur_marker]), self.cur_marker
def watershed(self):
m = self.markers.copy()
cv2.watershed(self.img, m)
overlay = self.colors[np.maximum(m, 0)]
vis = cv2.addWeighted(self.img, 0.5, overlay, 0.5, 0.0, dtype=cv2.CV_8UC3)
cv2.imshow('watershed', vis)
def run(self):
while True:
ch = 0xFF & cv2.waitKey(50)
if ch == 27:
break
if ch >= ord('1') and ch <= ord('7'):
self.cur_marker = ch - ord('0')
print 'marker: ', self.cur_marker
if ch == ord(' ') or (self.sketch.dirty and self.auto_update):
self.watershed()
self.sketch.dirty = False
if ch in [ord('a'), ord('A')]:
self.auto_update = not self.auto_update
print 'auto_update if', ['off', 'on'][self.auto_update]
if ch in [ord('r'), ord('R')]:
self.markers[:] = 0
self.markers_vis[:] = self.img
self.sketch.show()
cv2.destroyAllWindows()
if __name__ == '__main__':
import sys
try: fn = sys.argv[1]
except: fn = '../cpp/fruits.jpg'
print __doc__
App(fn).run()