"""
#frame_resize = mx.nd.array(cv2.resize(frame, (self.data_shape[0], self.data_shape[1])))
#frame_resize = mx.img.imresize(frame, self.data_shape[0], self.data_shape[1], cv2.INTER_LINEAR)
# Change dimensions from (w,h,channels) to (channels, w, h)
#frame_t = mx.nd.transpose(frame_resize, axes=(2,0,1))
#frame_norm = frame_t - self.mean_pixels_nd
print(y_gen[0].asnumpy().shape)
"""
print(y_gen[0].asnumpy().flatten()[:20])
result = Detector.filter_positive_detections(y_gen[0].asnumpy())
for k, det in enumerate(result):
#img = cv2.imread(im_list[k])
#img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
visualize_detection(frame, det, classes, 0.6)
end = time.time()
#print(time.process_time() - start)
