| | |
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| | | if not os.path.exists(args.video_path): |
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| | | sys.exit('Video does not exist') |
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| | | |
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| | | # ResNet50 |
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| | | # ResNet50 structure |
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| | | model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66) |
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| | | |
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| | | print 'Loading snapshot.' |
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| | |
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| | | img = img.view(1, img_shape[0], img_shape[1], img_shape[2]) |
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| | | img = Variable(img).cuda(gpu) |
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| | | |
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| | | yaw, pitch, roll, angles = model(img) |
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| | | yaw, pitch, roll = model(img) |
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| | | |
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| | | yaw_predicted = angles[:,0].data[0].cpu() |
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| | | pitch_predicted = angles[:,1].data[0].cpu() |
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| | | roll_predicted = angles[:,2].data[0].cpu() |
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| | | yaw_predicted = F.softmax(yaw) |
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| | | pitch_predicted = F.softmax(pitch) |
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| | | roll_predicted = F.softmax(roll) |
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| | | # Get continuous predictions in degrees. |
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| | | yaw_predicted = torch.sum(yaw_predicted.data[0] * idx_tensor) * 3 - 99 |
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| | | pitch_predicted = torch.sum(pitch_predicted.data[0] * idx_tensor) * 3 - 99 |
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| | | roll_predicted = torch.sum(roll_predicted.data[0] * idx_tensor) * 3 - 99 |
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| | | |
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| | | # Print new frame with cube and axis |
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| | | txt_out.write(str(frame_num) + ' %f %f %f\n' % (yaw_predicted, pitch_predicted, roll_predicted)) |
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| | | # utils.plot_pose_cube(frame, yaw_predicted, pitch_predicted, roll_predicted, (x_min + x_max) / 2, (y_min + y_max) / 2, size = bbox_width) |
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