Algorithm/deepHeadPose.git

			@@ -51,12 +51,12 @@
			# ResNet50 structure
			model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)

			print 'Loading snapshot.'
			print('Loading snapshot.')
			# Load snapshot
			saved_state_dict = torch.load(snapshot_path)
			model.load_state_dict(saved_state_dict)

			print 'Loading data.'
			print('Loading data.')

			transformations = transforms.Compose([transforms.Scale(224),
			transforms.CenterCrop(224), transforms.ToTensor(),
			@@ -64,7 +64,7 @@

			model.cuda(gpu)

			print 'Ready to test network.'
			print('Ready to test network.')

			# Test the Model
			model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
			@@ -105,7 +105,7 @@
			line = line.split(' ')
			det_frame_num = int(line[0])

			print frame_num
			print(frame_num)

			# Stop at a certain frame number
			if frame_num > args.n_frames:
			@@ -166,7 +166,7 @@
			pitch_predicted = torch.sum(pitch_predicted.data[0] * idx_tensor) * 3 - 99
			roll_predicted = torch.sum(roll_predicted.data[0] * idx_tensor) * 3 - 99

			# Print new frame with cube and axis
			# print(new frame with cube and axis
			txt_out.write(str(frame_num) + ' %f %f %f\n' % (yaw_predicted, pitch_predicted, roll_predicted))
			# utils.plot_pose_cube(frame, yaw_predicted, pitch_predicted, roll_predicted, (x_min + x_max) / 2, (y_min + y_max) / 2, size = bbox_width)
			utils.draw_axis(frame, yaw_predicted, pitch_predicted, roll_predicted, tdx = (x_min + x_max) / 2, tdy= (y_min + y_max) / 2, size = bbox_height/2)
			@@ -175,7 +175,7 @@

			# Peek next frame detection
			next_frame_num = int(bbox_line_list[idx+1].strip('\n').split(' ')[0])
			# print 'next_frame_num ', next_frame_num
			# print('next_frame_num ', next_frame_num
			if next_frame_num == det_frame_num:
			idx += 1
			line = bbox_line_list[idx].strip('\n').split(' ')