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import argparse |
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import cv2 |
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import numpy as np |
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import axengine as axe |
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def parse_args() -> argparse.Namespace: |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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"--img", |
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type=str, |
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required=True, |
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help="Path to input image.", |
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) |
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parser.add_argument( |
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"--model", |
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type=str, |
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required=True, |
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help="Path to axmodel model.", |
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) |
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return parser.parse_args() |
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def infer(img: str, model: str, viz: bool = False): |
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img_raw = cv2.imread(img) |
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image = cv2.cvtColor(img_raw, cv2.COLOR_BGR2RGB) |
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orig_h, orig_w = image.shape[:2] |
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image = cv2.resize(image, (518,518)) |
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image = image[None] |
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session = axe.InferenceSession(model) |
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depth = session.run(None, {"input": image})[0] |
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depth = cv2.resize(depth[0, 0], (orig_w, orig_h)) |
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depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 |
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depth = depth.astype(np.uint8) |
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depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO) |
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combined_result = cv2.hconcat([img_raw, depth_color]) |
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cv2.imwrite("output-ax.png", combined_result) |
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return depth |
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if __name__ == "__main__": |
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args = parse_args() |
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infer(**vars(args)) |
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