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Drop old python samples and tests

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This commit is contained in:
Andrey Kamaev
2013-04-12 17:39:16 +04:00
parent b2ba8b9969
commit f886651cf0
55 changed files with 163 additions and 5436 deletions
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5
samples/python2/_coverage.py
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@@ -24,8 +24,3 @@ if __name__ == '__main__':
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

2
samples/python2/browse.py
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@@ -37,7 +37,7 @@ if __name__ == '__main__':
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)
cv2.polylines(img, [track], 0, 255, 1, cv2.LINE_AA)
small = img

4
samples/python2/common.py
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@@ -71,8 +71,8 @@ def mtx2rvec(R):
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)
cv2.putText(dst, s, (x+1, y+1), cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 0), thickness = 2, lineType=cv2.LINE_AA)
cv2.putText(dst, s, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (255, 255, 255), lineType=cv2.LINE_AA)
class Sketcher:
def __init__(self, windowname, dests, colors_func):

2
samples/python2/contours.py
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@@ -53,7 +53,7 @@ if __name__ == '__main__':
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) )
3, cv2.LINE_AA, hierarchy, abs(levels) )
cv2.imshow('contours', vis)
update(3)
cv2.createTrackbar( "levels+3", "contours", 3, 7, update )

6
samples/python2/deconvolution.py
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@@ -57,7 +57,7 @@ def motion_kernel(angle, d, sz=65):
def defocus_kernel(d, sz=65):
kern = np.zeros((sz, sz), np.uint8)
cv2.circle(kern, (sz, sz), d, 255, -1, cv2.CV_AA, shift=1)
cv2.circle(kern, (sz, sz), d, 255, -1, cv2.LINE_AA, shift=1)
kern = np.float32(kern) / 255.0
return kern
@@ -69,7 +69,7 @@ if __name__ == '__main__':
opts = dict(opts)
try:
fn = args[0]
except:
except:
fn = 'data/licenseplate_motion.jpg'
win = 'deconvolution'
@@ -78,7 +78,7 @@ if __name__ == '__main__':
if img is None:
print 'Failed to load fn1:', fn1
sys.exit(1)
img = np.float32(img)/255.0
cv2.imshow('input', img)

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

@@ -93,7 +93,7 @@ if __name__ == "__main__":
shift_dft(log_spectrum, log_spectrum)
# normalize and display the results as rgb
cv2.normalize(log_spectrum, log_spectrum, 0.0, 1.0, cv2.cv.CV_MINMAX)
cv2.normalize(log_spectrum, log_spectrum, 0.0, 1.0, cv2.NORM_MINMAX)
cv2.imshow("magnitude", log_spectrum)
cv2.waitKey(0)

5
samples/python2/distrans.py
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@@ -14,7 +14,6 @@ Keys:
import numpy as np
import cv2
import cv2.cv as cv
from common import make_cmap
@@ -30,7 +29,7 @@ if __name__ == '__main__':
if img is None:
print 'Failed to load fn:', fn
sys.exit(1)
cm = make_cmap('jet')
need_update = True
voronoi = False
@@ -40,7 +39,7 @@ if __name__ == '__main__':
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)
dist, labels = cv2.distanceTransformWithLabels(~mark, cv2.DIST_L2, 5)
if voronoi:
vis = cm[np.uint8(labels)]
else:

3
samples/python2/facedetect.py
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@@ -2,7 +2,6 @@
import numpy as np
import cv2
import cv2.cv as cv
# local modules
from video import create_capture
@@ -13,7 +12,7 @@ USAGE: facedetect.py [--cascade <cascade_fn>] [--nested-cascade <cascade_fn>] [<
'''
def detect(img, cascade):
rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30), flags = cv.CV_HAAR_SCALE_IMAGE)
rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30), flags = cv2.CASCADE_SCALE_IMAGE)
if len(rects) == 0:
return []
rects[:,2:] += rects[:,:2]

4
samples/python2/fitline.py
View File

@@ -42,7 +42,7 @@ def sample_line(p1, p2, n, noise=0.0):
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())
dist_func_names = it.cycle('DIST_L2 DIST_L1 DIST_L12 DIST_FAIR DIST_WELSCH DIST_HUBER'.split())
cur_func_name = dist_func_names.next()
def update(_=None):
@@ -63,7 +63,7 @@ def update(_=None):
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)
func = getattr(cv2, 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))

2
samples/python2/gaussian_mix.py
View File

@@ -23,7 +23,7 @@ def draw_gaussain(img, mean, cov, color):
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)
cv2.ellipse(img, (x, y), (s1, s2), ang, 0, 360, color, 1, cv2.LINE_AA)
if __name__ == '__main__':

6
samples/python2/houghcircles.py
View File

@@ -22,11 +22,11 @@ img = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
img = cv2.medianBlur(img, 5)
cimg = src.copy() # numpy function
circles = cv2.HoughCircles(img, cv2.cv.CV_HOUGH_GRADIENT, 1, 10, np.array([]), 100, 30, 1, 30)
circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 10, np.array([]), 100, 30, 1, 30)
a, b, c = circles.shape
for i in range(b):
cv2.circle(cimg, (circles[0][i][0], circles[0][i][1]), circles[0][i][2], (0, 0, 255), 3, cv2.cv.CV_AA)
cv2.circle(cimg, (circles[0][i][0], circles[0][i][1]), 2, (0, 255, 0), 3, cv2.cv.CV_AA) # draw center of circle
cv2.circle(cimg, (circles[0][i][0], circles[0][i][1]), circles[0][i][2], (0, 0, 255), 3, cv2.LINE_AA)
cv2.circle(cimg, (circles[0][i][0], circles[0][i][1]), 2, (0, 255, 0), 3, cv2.LINE_AA) # draw center of circle
cv2.imshow("source", src)
cv2.imshow("detected circles", cimg)

26
samples/python2/houghlines.py
View File

@@ -10,31 +10,31 @@ import sys
import math
try:
fn = sys.argv[1]
fn = sys.argv[1]
except:
fn = "../cpp/pic1.png"
fn = "../cpp/pic1.png"
print __doc__
src = cv2.imread(fn)
dst = cv2.Canny(src, 50, 200)
cdst = cv2.cvtColor(dst, cv2.COLOR_GRAY2BGR)
# HoughLines()
# lines = cv2.HoughLines(dst, 1, cv2.cv.CV_PI/180.0, 50, np.array([]), 0, 0)
# lines = cv2.HoughLines(dst, 1, math.pi/180.0, 50, np.array([]), 0, 0)
# a,b,c = lines.shape
# for i in range(b):
# rho = lines[0][i][0]
# theta = lines[0][i][1]
# a = math.cos(theta)
# b = math.sin(theta)
# x0, y0 = a*rho, b*rho
# pt1 = ( int(x0+1000*(-b)), int(y0+1000*(a)) )
# pt2 = ( int(x0-1000*(-b)), int(y0-1000*(a)) )
# cv2.line(cdst, pt1, pt2, (0, 0, 255), 3, cv2.cv.CV_AA)
# rho = lines[0][i][0]
# theta = lines[0][i][1]
# a = math.cos(theta)
# b = math.sin(theta)
# x0, y0 = a*rho, b*rho
# pt1 = ( int(x0+1000*(-b)), int(y0+1000*(a)) )
# pt2 = ( int(x0-1000*(-b)), int(y0-1000*(a)) )
# cv2.line(cdst, pt1, pt2, (0, 0, 255), 3, cv2.LINE_AA)
lines = cv2.HoughLinesP(dst, 1, cv2.cv.CV_PI/180.0, 50, np.array([]), 50, 10)
lines = cv2.HoughLinesP(dst, 1, math.pi/180.0, 50, np.array([]), 50, 10)
a,b,c = lines.shape
for i in range(b):
cv2.line(cdst, (lines[0][i][0], lines[0][i][1]), (lines[0][i][2], lines[0][i][3]), (0, 0, 255), 3, cv2.cv.CV_AA)
cv2.line(cdst, (lines[0][i][0], lines[0][i][1]), (lines[0][i][2], lines[0][i][3]), (0, 0, 255), 3, cv2.LINE_AA)
cv2.imshow("source", src)
cv2.imshow("detected lines", cdst)

35
samples/python2/mouse_and_match.py
View File

@@ -9,7 +9,6 @@ Demonstrate using a mouse to interact with an image:
ESC to exit
'''
import numpy as np
import cv2 as cv
# built-in modules
import os
@@ -24,27 +23,27 @@ sel = (0,0,0,0)
def onmouse(event, x, y, flags, param):
global drag_start, sel
if event == cv.EVENT_LBUTTONDOWN:
if event == cv2.EVENT_LBUTTONDOWN:
drag_start = x, y
sel = 0,0,0,0
elif event == cv.EVENT_LBUTTONUP:
elif event == cv2.EVENT_LBUTTONUP:
if sel[2] > sel[0] and sel[3] > sel[1]:
patch = gray[sel[1]:sel[3],sel[0]:sel[2]]
result = cv.matchTemplate(gray,patch,cv.TM_CCOEFF_NORMED)
result = cv2.matchTemplate(gray,patch,cv2.TM_CCOEFF_NORMED)
result = np.abs(result)**3
val, result = cv.threshold(result, 0.01, 0, cv.THRESH_TOZERO)
result8 = cv.normalize(result,None,0,255,cv.NORM_MINMAX,cv.CV_8U)
cv.imshow("result", result8)
val, result = cv2.threshold(result, 0.01, 0, cv2.THRESH_TOZERO)
result8 = cv2.normalize(result,None,0,255,cv2.NORM_MINMAX,cv2.CV_8U)
cv2.imshow("result", result8)
drag_start = None
elif drag_start:
#print flags
if flags & cv.EVENT_FLAG_LBUTTON:
if flags & cv2.EVENT_FLAG_LBUTTON:
minpos = min(drag_start[0], x), min(drag_start[1], y)
maxpos = max(drag_start[0], x), max(drag_start[1], y)
sel = minpos[0], minpos[1], maxpos[0], maxpos[1]
img = cv.cvtColor(gray, cv.COLOR_GRAY2BGR)
cv.rectangle(img, (sel[0], sel[1]), (sel[2], sel[3]), (0,255,255), 1)
cv.imshow("gray", img)
img = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
cv2.rectangle(img, (sel[0], sel[1]), (sel[2], sel[3]), (0,255,255), 1)
cv2.imshow("gray", img)
else:
print "selection is complete"
drag_start = None
@@ -55,21 +54,21 @@ if __name__ == '__main__':
args = parser.parse_args()
path = args.input
cv.namedWindow("gray",1)
cv.setMouseCallback("gray", onmouse)
cv2.namedWindow("gray",1)
cv2.setMouseCallback("gray", onmouse)
'''Loop through all the images in the directory'''
for infile in glob.glob( os.path.join(path, '*.*') ):
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)
img=cv2.imread(infile,1)
if img == None:
continue
sel = (0,0,0,0)
drag_start = None
gray=cv.cvtColor(img, cv.COLOR_BGR2GRAY)
cv.imshow("gray",gray)
if (cv.waitKey() & 255) == 27:
gray=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow("gray",gray)
if (cv2.waitKey() & 255) == 27:
break
cv.destroyAllWindows()
cv2.destroyAllWindows()

4
samples/python2/texture_flow.py
View File

@@ -24,7 +24,7 @@ if __name__ == '__main__':
if img is None:
print 'Failed to load image file:', fn
sys.exit(1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
h, w = img.shape[:2]
@@ -38,7 +38,7 @@ if __name__ == '__main__':
points = np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)
for x, y in points:
vx, vy = np.int32(flow[y, x]*d)
cv2.line(vis, (x-vx, y-vy), (x+vx, y+vy), (0, 0, 0), 1, cv2.CV_AA)
cv2.line(vis, (x-vx, y-vy), (x+vx, y+vy), (0, 0, 0), 1, cv2.LINE_AA)
cv2.imshow('input', img)
cv2.imshow('flow', vis)
cv2.waitKey()

3
samples/python2/turing.py
View File

@@ -9,7 +9,6 @@ Inspired by http://www.jonathanmccabe.com/Cyclic_Symmetric_Multi-Scale_Turing_Pa
import numpy as np
import cv2
import cv2.cv as cv
from common import draw_str
import getopt, sys
from itertools import count
@@ -30,7 +29,7 @@ if __name__ == '__main__':
out = None
if '-o' in args:
fn = args['-o']
out = cv2.VideoWriter(args['-o'], cv.CV_FOURCC(*'DIB '), 30.0, (w, h), False)
out = cv2.VideoWriter(args['-o'], cv2.VideoWriter_fourcc(*'DIB '), 30.0, (w, h), False)
print 'writing %s ...' % fn
a = np.zeros((h, w), np.float32)

6
samples/python2/video.py
View File

@@ -105,7 +105,7 @@ class Chess(VideoSynthBase):
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)
cv2.fillConvexPoly(img, np.int32(q*4), color, cv2.LINE_AA, shift=2)
def render(self, dst):
t = self.t
@@ -159,8 +159,8 @@ def create_capture(source = 0, fallback = presets['chess']):
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)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, w)
cap.set(cv2.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: