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from PIL import Image
import numpy as np
import io
from huggingface_hub import hf_shiny as hf
# Define function to process uploaded image
def process_image(image):
# Convert image to numpy array
img_array = np.array(image)
patch_size = 128
step = 128
all_img_patches = []
for i in range(0, img_array.shape[0] - patch_size + 1, step):
for j in range(0, img_array.shape[1] - patch_size + 1, step):
single_patch_img = img_array[i:i + patch_size, j:j + patch_size]
all_img_patches.append(single_patch_img)
images = np.array(all_img_patches)
# This is just a example to process the image
processed_images = []
for img in images:
processed_image = np.mean(img, axis=-1) # Example: Convert to grayscale
processed_images.append(processed_image)
processed_images = np.array(processed_images)
return processed_images
# Define Shiny app
app = hf.start()
@app.streamlit_app(
title="Sidewalk Segmentation App",
uploaders={"Upload an image": ["jpg", "jpeg", "png"]}
)
def main(uploaded_image):
# Convert uploaded image to PIL Image
image = Image.open(io.BytesIO(uploaded_image.read()))
# Display uploaded image
app.image(image, caption="Uploaded Image")
# Process uploaded image
with app.spinner("Processing..."):
segmentation_result = process_image(image)
# Display segmentation result
app.image(segmentation_result, caption="Sidewalk Segmentation Result")
if __name__ == "__main__":
app.run()
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