def generator_val(): while True: for it in range(0, len(val_data_names), batch_size): val_data_X, val_data_Y = load_images( val_data_names[it:it + batch_size], division=8, crop=b_crop, rescale=b_rescale, scale=b_scale, b_debug=b_debug, normcv2=b_normcv2, rows=rows, fulldepth=fulldepth, cols=cols, removeBackground=removeBackground, equalize=equalize) yield val_data_X, val_data_Y
def generator(): random.shuffle(train_data_names) while True: for it in range(0, len(train_data_names), batch_size): X, Y = load_images(train_data_names[it:it + batch_size], division=8, b_debug=b_debug, crop=b_crop, rescale=b_rescale, scale=b_scale, normcv2=b_normcv2, fulldepth=fulldepth, rows=rows, cols=cols, removeBackground=removeBackground, equalize=equalize) yield X, Y
seq = test_data_names[image]['image'].split('\\')[-3] frame = 0 if checkBadFrame(seq, frame): print 'skipped', seq, frame continue if test_data_names[image]['face'] == 0: print 'SKIP NO GT', seq, frame continue t = time.time() test_data_X, _ = load_images(test_data_names[image:image + 1], crop=b_crop, rescale=b_rescale, scale=b_scale, b_debug=False, normcv2=b_normcv2, rows=rows, fulldepth=False, cols=cols, equalize=True, removeBackground=True, division=4) pred = model.predict(x=test_data_X, batch_size=batch_size, verbose=0) for index, i in enumerate(pred): gt_head = bool(test_data_names[image + index]['face']) if gt_head: gt_coord = test_data_names[image + index]['facecord'] head = False img = cv2.imread(test_data_names[image + index]['image'], cv2.IMREAD_ANYDEPTH) img = cv2.resize(img, (cols, rows))