Create app.py

app.py

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+import torch
+import torchvision
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+
+# Load pretrained Mask R-CNN model
+model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)
+model.eval()
+
+# COCO labels
+COCO_INSTANCE_CATEGORY_NAMES = [
+    '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign',
+    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+    'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella',
+    'N/A', 'N/A', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
+    'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
+    'surfboard', 'tennis racket', 'bottle', 'N/A', 'wine glass', 'cup', 'fork',
+    'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli',
+    'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant',
+    'bed', 'N/A', 'dining table', 'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop',
+    'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',
+    'sink', 'refrigerator', 'N/A', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
+    'hair drier', 'toothbrush'
+]
+
+# Detection and segmentation function
+def segment_objects(image, threshold=0.5):
+    transform = torchvision.transforms.ToTensor()
+    img_tensor = transform(image).unsqueeze(0)
+
+    with torch.no_grad():
+        output = model(img_tensor)[0]
+
+    masks = output['masks']  # shape: [N, 1, H, W]
+    boxes = output['boxes']
+    labels = output['labels']
+    scores = output['scores']
+
+    image_np = np.array(image).copy()
+    fig, ax = plt.subplots(1, figsize=(10, 10))
+    ax.imshow(image_np)
+
+    for i in range(len(masks)):
+        if scores[i] >= threshold:
+            mask = masks[i, 0].cpu().numpy()
+            mask = mask > 0.5  # convert to binary mask
+
+            # Random color for each mask
+            color = np.random.rand(3)
+            colored_mask = np.zeros_like(image_np, dtype=np.uint8)
+            for c in range(3):
+                colored_mask[:, :, c] = mask * int(color[c] * 255)
+
+            # Blend the mask onto the image
+            image_np = np.where(mask[:, :, None], 0.5 * image_np + 0.5 * colored_mask, image_np).astype(np.uint8)
+
+            # Draw bounding box
+            x1, y1, x2, y2 = boxes[i].cpu().numpy()
+            ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1,
+                                       fill=False, color=color, linewidth=2))
+            label = COCO_INSTANCE_CATEGORY_NAMES[labels[i].item()]
+            ax.text(x1, y1, f"{label}: {scores[i]:.2f}",
+                    bbox=dict(facecolor='yellow', alpha=0.5), fontsize=10)
+
+    ax.imshow(image_np)
+    ax.axis('off')
+    output_path = "output_maskrcnn_with_masks.png"
+    plt.savefig(output_path, bbox_inches='tight', pad_inches=0)
+    plt.close()
+    return output_path
+
+# Gradio interface
+interface = gr.Interface(
+    fn=segment_objects,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Slider(0.0, 1.0, value=0.5, step=0.05, label="Confidence Threshold")
+    ],
+    outputs=gr.Image(type="filepath", label="Segmented Output"),
+    title="Mask R-CNN Instance Segmentation",
+    description="Upload an image to detect and segment objects using a pretrained Mask R-CNN model (TorchVision)."
+)
+
+if __name__ == "__main__":
+    interface.launch(debug=True)