if __name__ == '__main__':
url = sys.argv[1]
cf = Classifier('../models/deploy.prototxt',
'../models/pretrained.caffemodel',
'../models/mean.binaryproto', '../models/labels.txt')
quit = False
def cb_image(img):
global quit, cf
if img is None:
print 'end'
quit = True
else:
pred = cf.predicate(img)
f = cv2.resize(img, (480, 270))
cv2.imshow('show', f)
key = cv2.waitKey(1)
print pred[0][1].decode('utf-8').encode(
'gbk') + ' score:', pred[0][2]
vc = VideoCap(url, (256, 256), "1/3")
vc.start(cb_image)
while not quit:
time.sleep(1.0)
# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
if image is None:
quit = True
return
if stamp_begin < 0.0:
stamp_begin = time.time()
pred = cf.predicate(image)
stamp = time.time() - stamp_begin
# FIXME: 使用 'CR:' 作为一条分析结果的前缀，解析起来更方便 :)
print "CR:", stamp, pr(pred[0][1]), pred[0][2], pr(pred[1][1]), pred[1][2], pr(pred[2][1]), pred[2][2]
sys.stdout.flush()
def pr(s):
global curr_encoding
return s.decode('UTF-8').encode(curr_encoding)
quit = False
vc.start(cb_image)
while not quit:
time.sleep(0.5)
vc.stop()
# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
