| | 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))) |
| |
|
| | |
| | |
| | |
| | |
