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Update app.py
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app.py
CHANGED
@@ -7,7 +7,7 @@ from typing import Union
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from pathlib import Path
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import os
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def predict_depth(image: Image.Image, auto_rotate: bool, remove_alpha: bool, model, transform):
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# Convert the PIL image to a temporary file path if needed
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image_path = "temp_image.jpg"
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image.save(image_path)
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@@ -30,14 +30,20 @@ def predict_depth(image: Image.Image, auto_rotate: bool, remove_alpha: bool, mod
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focallength = prediction["focallength_px"].cpu().numpy()
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# Clean up temporary image
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os.remove(image_path)
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return
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def main():
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# Load model and preprocessing transform
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@@ -46,11 +52,12 @@ def main():
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# Set up Gradio interface
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iface = gr.Interface(
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fn=lambda image, auto_rotate, remove_alpha: predict_depth(image, auto_rotate, remove_alpha, model, transform),
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inputs=[
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gr.Image(type="pil", label="Upload Image"), # Use image browser for input
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gr.Checkbox(label="Auto Rotate", value=True), # Checkbox for auto_rotate
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gr.Checkbox(label="Remove Alpha", value=True)
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],
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outputs=[
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gr.Image(label="Depth Map"), # Use PIL image output
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@@ -65,4 +72,4 @@ def main():
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iface.launch()
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if __name__ == "__main__":
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main()
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from pathlib import Path
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import os
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def predict_depth(image: Image.Image, auto_rotate: bool, remove_alpha: bool, grayscale: bool, model, transform):
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# Convert the PIL image to a temporary file path if needed
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image_path = "temp_image.jpg"
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image.save(image_path)
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focallength = prediction["focallength_px"].cpu().numpy()
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if grayscale:
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# Normalize the inverse depth map to 0-255 and convert to grayscale
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grayscale_depth = (inverse_depth_normalized * 255).astype(np.uint8)
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depth_image = Image.fromarray(grayscale_depth, mode="L")
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else:
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# Normalize and colorize depth map
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cmap = plt.get_cmap("turbo_r")
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color_depth = (cmap(inverse_depth_normalized)[..., :3] * 255).astype(np.uint8)
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depth_image = Image.fromarray(color_depth)
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# Clean up temporary image
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os.remove(image_path)
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return depth_image, focallength # Return depth map and f_px
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def main():
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# Load model and preprocessing transform
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# Set up Gradio interface
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iface = gr.Interface(
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fn=lambda image, auto_rotate, remove_alpha, grayscale: predict_depth(image, auto_rotate, remove_alpha, grayscale, model, transform),
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inputs=[
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gr.Image(type="pil", label="Upload Image"), # Use image browser for input
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gr.Checkbox(label="Auto Rotate", value=True), # Checkbox for auto_rotate
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gr.Checkbox(label="Remove Alpha", value=True), # Checkbox for remove_alpha
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gr.Checkbox(label="Grayscale Depth", value=False) # Checkbox for grayscale
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],
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outputs=[
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gr.Image(label="Depth Map"), # Use PIL image output
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iface.launch()
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if __name__ == "__main__":
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main()
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