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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)
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