opencv/samples/python2/obj_detect.py

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import numpy as np
import cv2
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def detect(img, cascade):
rects = cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
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if len(rects) == 0:
return []
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rects[:,2:] += rects[:,:2]
return rects
def detect_turned(img, cascade):
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.equalizeHist(img)
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img_t = cv2.transpose(img)
img_cw = cv2.flip(img_t, 1)
img_ccw = cv2.flip(img_t, 0)
r = detect(img, cascade)
r_cw = detect(img_cw, cascade)
r_ccw = detect(img_ccw, cascade)
h, w = img.shape[:2]
rects = []
rects += [(x1, y1, x2, y2, 1, 0) for x1, y1, x2, y2 in r]
rects += [(y1, h-x1-1, y2, h-x2-1, 0, -1) for x1, y1, x2, y2 in r_cw]
rects += [(w-y1-1, x1, w-y2-1, x2, 0, 1) for x1, y1, x2, y2 in r_ccw]
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return rects
def process_image(fn, cascade, extract_faces=True):
img = cv2.imread(fn)
h, w = img.shape[:2]
scale = max(h, w) / 512.0
small = cv2.resize(img, (int(w/scale), int(h/scale)), interpolation=cv2.INTER_AREA)
rects = detect_turned(small, cascade)
for i, (x1, y1, x2, y2, vx, vy) in enumerate(rects):
cv2.rectangle(small, (x1, y1), (x2, y2), (0, 255, 0))
cv2.circle(small, (x1, y1), 2, (0, 0, 255), -1)
cv2.putText(small, str(i), ((x1+x2)/2, (y1+y2)/2), cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 255, 0))
rects = np.float32(rects).reshape(-1,6)
rects[:,:4] = np.around(rects[:,:4]*scale)
faces = []
if extract_faces:
path, name, ext = splitfn(fn)
face_sz = 256
for i, r in enumerate(rects):
p1, p2, u = r.reshape(3, 2)
v = np.float32( [-u[1], u[0]] )
w = np.abs(p2-p1).max()
fscale = w / face_sz
p0 = 0.5*(p1+p2 - w*(u+v))
M = np.float32([u*fscale, v*fscale, p0]).T
face = cv2.warpAffine(img, M, (face_sz, face_sz), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_AREA)
faces.append(face)
return small, rects, faces
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if __name__ == '__main__':
import sys
import getopt
from glob import glob
from common import splitfn, image_extensions
args, img_args = getopt.getopt(sys.argv[1:], '', ['cascade=', 'outdir='])
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args = dict(args)
cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml")
outdir = args.get('--outdir')
img_list = []
if len(img_args) == 0:
img_list = ['../cpp/lena.jpg']
else:
for mask in img_args:
img_list.extend(glob(mask))
img_list = [fn for fn in img_list if splitfn(fn)[-1].lower() in image_extensions]
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cascade = cv2.CascadeClassifier(cascade_fn)
for i, fn in enumerate(img_list):
print '%d / %d %s' % (i+1, len(img_list), fn),
vis, rects, faces = process_image(fn, cascade)
if len(faces) > 0 and outdir is not None:
path, name, ext = splitfn(fn)
cv2.imwrite('%s/%s_all.bmp' % (outdir, name), vis)
for face_i, face in enumerate(faces):
cv2.imwrite('%s/%s_obj%02d.bmp' % (outdir, name, face_i), face)
print ' - %d object(s) found' % len(faces)
cv2.imshow('img', vis)
cv2.waitKey(50)
cv2.waitKey()