frontend/__init__.py: allow for data transfer minimization
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922bd3ab80
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@ -44,7 +44,7 @@ def extract_data(data):
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# get result and rotate 90 deg
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pred_disp = cv2.transpose(np.asarray(data['disp'], dtype='uint8'))
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if input not in data:
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if 'input' not in data:
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return pred_disp, duration
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in_img = np.asarray(data['input'], dtype='uint8').transpose((2, 0, 1))
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@ -72,6 +72,12 @@ def put_image(img_path):
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return data
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def change_minimal_data(enabled):
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r = requests.post(f'{API_URL}/params/minimal_data/{not enabled}')
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cv2.destroyWindow('Input Image')
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cv2.destroyWindow('Reference Image')
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if __name__ == '__main__':
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while True:
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for img in os.scandir(img_dir):
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@ -83,14 +89,18 @@ if __name__ == '__main__':
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downsize_input_img()
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data = put_image('buffer.png')
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pred_disp, in_img, ref_pat, duration = extract_data(data)
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if 'input' in data:
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pred_disp, in_img, ref_pat, duration = extract_data(data)
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else:
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pred_disp, duration = extract_data(data)
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print(f'inference took {duration:1.4f}s')
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print(f'pipeline and transfer took another {(datetime.now() - start).total_seconds() - float(duration):1.4f}s')
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print(f"Pred. Disparity: \n\t{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}\n")
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cv2.imshow('Input Image', in_img)
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# cv2.imshow('Reference Image', ref_pat)
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if 'input' in data:
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cv2.imshow('Input Image', in_img)
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cv2.imshow('Reference Image', ref_pat)
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cv2.imshow('Normalized Predicted Disparity', normalize_and_colormap(pred_disp))
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cv2.imshow('Predicted Disparity', pred_disp)
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key = cv2.waitKey()
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@ -99,3 +109,5 @@ if __name__ == '__main__':
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quit()
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elif key == 101:
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change_epoch()
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elif key == 109:
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change_minimal_data('input' not in data)
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