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import cv2 |
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import requests |
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from io import BytesIO |
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from PIL import Image |
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from segment_anything import SamPredictor, sam_model_registry |
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from segment_anything import SamAutomaticMaskGenerator, sam_model_registry |
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import matplotlib.pyplot as plt |
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import numpy as np |
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def segment_image_from_url(image_url): |
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sam = sam_model_registry["vit_h"](checkpoint="sam_vit_h_4b8939.pth") |
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mask_generator = SamAutomaticMaskGenerator(sam) |
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response = requests.get(image_url) |
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img = Image.open(BytesIO(response.content)) |
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image = np.array(img) |
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masks = mask_generator.generate(image) |
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plt.figure(figsize=(20,20)) |
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plt.imshow(image) |
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show_anns(masks) |
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plt.axis('off') |
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output_file = 'segmented_image.png' |
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plt.savefig(output_file) |
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plt.close() |
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return output_file |
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def show_anns(anns): |
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if len(anns) == 0: |
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return |
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sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True) |
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ax = plt.gca() |
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ax.set_autoscale_on(False) |
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polygons = [] |
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color = [] |
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for ann in sorted_anns: |
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m = ann['segmentation'] |
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img = np.ones((m.shape[0], m.shape[1], 3)) |
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color_mask = np.random.random((1, 3)).tolist()[0] |
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for i in range(3): |
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img[:,:,i] = color_mask[i] |
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ax.imshow(np.dstack((img, m*0.35))) |
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