app.py

app.py

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+import gradio as gr
+import requests
+import base64
+import numpy as np
+import cv2
+
+# Hugging Face SAM API 配置
+HF_API_URL = "https://api-inference.huggingface.co/models/facebook/sam-vit-base"
+HF_TOKEN = "YOUR_HUGGINGFACE_TOKEN"  # 替换成你自己的 token
+headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+
+def segment_and_crop(image):
+    # 将输入图像转为 PNG bytes
+    _, buffer = cv2.imencode(".png", cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
+    img_bytes = buffer.tobytes()
+
+    # 调用 Hugging Face SAM API
+    response = requests.post(HF_API_URL, headers=headers, files={"image": img_bytes})
+    if response.status_code != 200:
+        return None, f"API error: {response.status_code}\n{response.text}"
+
+    result = response.json()
+
+    # SAM 输出 mask（有的模型返回 'masks' base64）
+    masks = result.get("masks", [])
+    if not masks:
+        return None, "No masks returned"
+
+    cropped_images = []
+    for idx, mask_b64 in enumerate(masks):
+        mask_bytes = base64.b64decode(mask_b64)
+        mask_array = cv2.imdecode(np.frombuffer(mask_bytes, np.uint8), cv2.IMREAD_GRAYSCALE)
+        ys, xs = np.where(mask_array > 127)
+        if len(xs) == 0 or len(ys) == 0:
+            continue
+
+        # 取边界框并裁剪
+        x1, x2 = xs.min(), xs.max()
+        y1, y2 = ys.min(), ys.max()
+        cropped = image[y1:y2, x1:x2]
+        cropped_images.append(cropped)
+
+    if not cropped_images:
+        return None, "No valid crops"
+
+    # 输出多张裁剪图
+    return cropped_images, f"Detected {len(cropped_images)} object(s)."
+
+
+demo = gr.Interface(
+    fn=segment_and_crop,
+    inputs=gr.Image(type="numpy", label="Upload or Capture an Image"),
+    outputs=[gr.Gallery(label="Cropped Objects"), gr.Textbox(label="Status")],
+    title="Smart Object Cropper with SAM",
+    description="Upload or capture an image. The app will use the Segment Anything Model (SAM) on Hugging Face to detect and crop main objects.",
+)
+
+if __name__ == "__main__":
+    demo.launch()