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44 lines
1.5 KiB
44 lines
1.5 KiB
#!/usr/bin/env python3
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import sys
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import glob
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import head_estimator_python3 as he
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import cv2
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import deep_learning_model_options as do
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def pix_xy(x_float, y_float):
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return (int(round(x_float)), int(round(y_float)))
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if __name__ == '__main__':
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print('cv2.__version__ =', cv2.__version__)
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print('Python version =', sys.version)
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assert(int(sys.version[0]) >= 3)
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models_directory = do.get_directory()
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print('Using the following directory for deep learning models:', models_directory)
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use_neural_compute_stick = do.use_neural_compute_stick()
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if use_neural_compute_stick:
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print('Attempt to use an Intel Neural Compute Stick 2.')
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else:
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print('Not attempting to use an Intel Neural Compute Stick 2.')
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only_display_result_images = True
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input_dir = './test_images/'
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output_dir = './output_images/'
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filenames = glob.glob(input_dir + '*')
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filenames.sort()
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print('Will attempt to load the following files:')
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for f in filenames:
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print(f)
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estimator = he.HeadPoseEstimator(models_directory,
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use_neural_compute_stick=use_neural_compute_stick)
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for i, f in enumerate(filenames):
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rgb_image = cv2.imread(f)
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if rgb_image is not None:
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heads, output_image = estimator.apply_to_image(rgb_image, draw_output=True)
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cv2.imwrite(output_dir + 'face_detection_and_pose_estimation_' + str(i) + '.png', output_image)
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