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856189d
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Parent(s):
7b8cabf
Update app.py
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app.py
CHANGED
@@ -1,15 +1,55 @@
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import gradio as gr
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from transformers import pipeline
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gr.Interface(
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predict,
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inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),
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outputs=gr.outputs.Label(num_top_classes=2),
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title="Hot Dog? Or Not?",
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).launch()
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import cv2
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import numpy as np
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import gradio as gr
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def watershed_segmentation(input_image):
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# Convert the image to grayscale
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gray = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)
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# Apply adaptive thresholding
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thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 2)
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# Morphological operations to remove small noise - use morphologyEx
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kernel = np.ones((3, 3), np.uint8)
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opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
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# Identify sure background area
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sure_bg = cv2.dilate(opening, kernel, iterations=3)
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# Find sure foreground area using distance transform
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dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
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ret, sure_fg = cv2.threshold(dist_transform, 0.7 * dist_transform.max(), 255, 0)
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# Find unknown region
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sure_fg = np.uint8(sure_fg)
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unknown = cv2.subtract(sure_bg, sure_fg)
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# Marker labeling
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ret, markers = cv2.connectedComponents(sure_fg)
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# Add one to all labels so that sure background is not 0, but 1
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markers = markers + 1
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# Mark the unknown region with zero
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markers[unknown == 255] = 0
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# Apply watershed
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cv2.watershed(input_image, markers)
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input_image[markers == -1] = [0, 0, 255] # boundary region
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return input_image
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def ui_interface(input_image):
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segmented = watershed_segmentation(input_image)
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return segmented
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iface = gr.Interface(
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fn=ui_interface,
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inputs=gr.inputs.Image(),
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outputs=gr.outputs.Image()
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)
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iface.launch()
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