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