def detect(only_snms=True):
# load image
load_begin = time.time()
pkllistfile = open(os.path.join('video', 'pkllist.txt'))
pkllist = pkllistfile.readlines()
pkllistfile.close()
pkllist = [pkl.strip() for pkl in pkllist]
load_end = time.time()
print('load: {:.4f}s'.format(load_end - load_begin))
# detection
detect_begin = time.time()
res = yolo_detection.detect_imgs(pkllist, nms=0, thresh=0.25)
detect_end = time.time()
print('total detect: {:.4f}s'.format(detect_end - detect_begin))
print('-----------------------------------')
if only_snms:
save_results(res, pkllist, True)
else: # Run both methods
save_results(res, pkllist, False)
save_results(res, pkllist, True)
if __name__ == "__main__":
# load image
load_begin = time.time()
pkllistfile = open(os.path.join('video', 'pkllist.txt'))
pkllist = pkllistfile.readlines()
pkllistfile.close()
pkllist = [pkl.strip() for pkl in pkllist]
load_end = time.time()
print('load: {:.4f}s'.format(load_end - load_begin))
# detection
detect_begin = time.time()
if len(sys.argv) > 1 and sys.argv[1] == 'tiny':
res = yolo_detection.detect_imgs(pkllist,
cfg="cfg/yolov2-tiny-voc.cfg",
weights="yolov2-tiny-voc.weights",
data="cfg/voc.data",
nms=0,
thresh=0.1)
elif len(sys.argv) > 1 and sys.argv[1] == 'v2':
res = yolo_detection.detect_imgs(pkllist,
cfg="cfg/yolov2.cfg",
weights="yolov2.weights",
nms=0,
thresh=0.1)
elif len(sys.argv) > 1 and sys.argv[1] == 'face':
res = yolo_detection.detect_imgs(pkllist,
cfg="cfg/yolov3-wider.cfg",
weights="yolov3-wider.weights",
data="cfg/wider.data",
nms=0,
thresh=0.1)
category_index)
return image_process
if __name__ == "__main__":
# load image
load_begin=time.time()
pkllistfile=open(os.path.join('video', 'pkllist.txt'))
pkllist=pkllistfile.readlines()
pkllistfile.close()
pkllist=[pkl.strip() for pkl in pkllist]
load_end=time.time()
print('load: {:.4f}s'.format(load_end - load_begin))
# detection
detect_begin=time.time()
res = yolo_detection.detect_imgs(pkllist, nms=0, thresh=0.25)
detect_end=time.time()
print('total detect: {:.4f}s'.format(detect_end - detect_begin))
# nms
nms_begin=time.time()
boxes, classes, scores = dsnms(res)
nms_end=time.time()
print('total nms: {:.4f}s'.format(nms_end - nms_begin))
# save&visualization
save_begin=time.time()
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 80
if not os.path.exists('video/output'):
os.makedirs('video/output')
if __name__ == "__main__":
# load image
load_begin = time.time()
pkllistfile = open(os.path.join('video', 'pkllist.txt'))
pkllist = pkllistfile.readlines()
pkllistfile.close()
pkllist = [pkl.strip() for pkl in pkllist]
load_end = time.time()
print('load: {:.4f}s'.format(load_end - load_begin))
# detection
detect_begin = time.time()
if len(sys.argv) > 1 and sys.argv[1] == 'tiny':
res = yolo_detection.detect_imgs(pkllist,
cfg="cfg/tiny-yolo.cfg",
weights="tiny-yolo.weights",
nms=0,
thresh=0.25)
else:
res = yolo_detection.detect_imgs(pkllist, nms=0, thresh=0.25)
detect_end = time.time()
print('total detect: {:.4f}s'.format(detect_end - detect_begin))
print('average detect: {:.4f}s'.format(
(detect_end - detect_begin) / len(pkllist)))
# nms
nms_begin = time.time()
if len(sys.argv) > 1 and sys.argv[1] == 'only_person':
boxes, classes, scores = dsnms(res, only_person=True)
else:
boxes, classes, scores = dsnms(res)