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Browse files- app.py +53 -0
- requirements.txt +0 -0
app.py
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import gradio as gr
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import cv2
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import numpy as np
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# Function to process the image and extract contours
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def extract_contours(image, min_contour_area=100):
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# Convert the uploaded image from RGB to BGR format for OpenCV processing
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image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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# Step 1: Convert to grayscale
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gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY)
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# Step 2: Apply Gaussian blur to reduce noise
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blurred = cv2.GaussianBlur(gray, (5, 5), 0)
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# Step 3: Apply Canny edge detection with low thresholds for finer edges
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edges = cv2.Canny(blurred, 30, 100) # Adjust thresholds as needed
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# Step 4: Apply morphological operations to refine edges
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kernel = np.ones((3, 3), np.uint8)
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edges_dilated = cv2.dilate(edges, kernel, iterations=1) # Dilation to emphasize edges
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edges_eroded = cv2.erode(edges_dilated, kernel, iterations=1) # Erosion to refine
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# Step 5: Find contours
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contours, _ = cv2.findContours(edges_eroded, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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# Step 6: Create a blank white background
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white_background = np.ones_like(image_bgr) * 255 # White background
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# Step 7: Draw contours on the white background, excluding small contours
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for contour in contours:
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if cv2.contourArea(contour) > min_contour_area:
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cv2.drawContours(white_background, [contour], -1, (0, 0, 0), thickness=1) # Thinner lines
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# Convert the result back to RGB for displaying
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result_rgb = cv2.cvtColor(white_background, cv2.COLOR_BGR2RGB)
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return result_rgb
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# Gradio interface
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interface = gr.Interface(
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fn=extract_contours,
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inputs=[
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gr.Image(type="numpy", label="Upload Image"),
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gr.Slider(50, 500, step=10, value=100, label="Minimum Contour Area") # Use 'value' instead of 'default'
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],
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outputs=gr.Image(type="numpy", label="Processed Image"),
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title="Edge Detection and Contour Extraction",
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description="Upload an image to extract contours, excluding small areas like text labels. Adjust the minimum contour area using the slider."
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)
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# Launch the Gradio app
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interface.launch()
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requirements.txt
ADDED
Binary file (1.97 kB). View file
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