Create sam_api.py

sam_api.py

@@ -0,0 +1,49 @@
+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)))
+
+# Example usage
+# image_url = 'https://example.com/path/to/image.jpg'
+# output_file = segment_image_from_url(image_url)
+# print(f"Segmented image saved to {output_file}")