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Update app.py
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app.py
CHANGED
@@ -7,35 +7,23 @@ from huggingface_hub import hf_shiny as hf
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def process_image(image):
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# Convert image to numpy array
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img_array = np.array(image)
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# Dummy sidewalk segmentation (replace with your actual segmentation algorithm)
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patch_size = 128
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step = 128
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all_img_patches = []
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for i in range(0, img_array.shape[0] - patch_size + 1, step):
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for j in range(0, img_array.shape[1] - patch_size + 1, step):
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single_patch_img = img_array[i:i + patch_size, j:j + patch_size]
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all_img_patches.append(single_patch_img)
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images = np.array(all_img_patches)
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# Perform your actual image processing here
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# Replace the code below with your segmentation algorithm or any other processing you need
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# This is just a dummy example to show how to process the image
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processed_images = []
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for img in images:
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processed_image = np.mean(img, axis=-1) # Example: Convert to grayscale
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processed_images.append(processed_image)
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processed_images = np.array(processed_images)
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return processed_images
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# Define Shiny app
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app = hf.start()
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@app.streamlit_app(
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title="Sidewalk Segmentation App",
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uploaders={"Upload an image": ["jpg", "jpeg", "png"]}
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def process_image(image):
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# Convert image to numpy array
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img_array = np.array(image)
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patch_size = 128
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step = 128
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all_img_patches = []
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for i in range(0, img_array.shape[0] - patch_size + 1, step):
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for j in range(0, img_array.shape[1] - patch_size + 1, step):
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single_patch_img = img_array[i:i + patch_size, j:j + patch_size]
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all_img_patches.append(single_patch_img)
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images = np.array(all_img_patches)
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# This is just a example to process the image
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processed_images = []
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for img in images:
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processed_image = np.mean(img, axis=-1) # Example: Convert to grayscale
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processed_images.append(processed_image)
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processed_images = np.array(processed_images)
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return processed_images
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# Define Shiny app
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app = hf.start()
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@app.streamlit_app(
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title="Sidewalk Segmentation App",
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uploaders={"Upload an image": ["jpg", "jpeg", "png"]}
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