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import torch
import torchvision
from torchvision.models.detection import FasterRCNN_ResNet50_FPN_Weights
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import gradio as gr
# Load the pre-trained model once
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT)
model.eval()
# COCO class names
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'
]
# Gradio-compatible detection function
def detect_objects(image, threshold=0.5):
transform = FasterRCNN_ResNet50_FPN_Weights.DEFAULT.transforms()
image_tensor = transform(image).unsqueeze(0)
with torch.no_grad():
prediction = model(image_tensor)[0]
boxes = prediction['boxes'].cpu().numpy()
labels = prediction['labels'].cpu().numpy()
scores = prediction['scores'].cpu().numpy()
image_np = np.array(image)
plt.figure(figsize=(10, 10))
plt.imshow(image_np)
ax = plt.gca()
for box, label, score in zip(boxes, labels, scores):
if score >= threshold:
x1, y1, x2, y2 = box
ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1,
fill=False, color='red', linewidth=2))
class_name = COCO_INSTANCE_CATEGORY_NAMES[label]
ax.text(x1, y1, f'{class_name}: {score:.2f}', bbox=dict(facecolor='yellow', alpha=0.5),
fontsize=12, color='black')
plt.axis('off')
plt.tight_layout()
# Save the figure to return
plt.savefig("output.png")
plt.close()
return "output.png"
# Create Gradio interface
gr.Interface(
fn=detect_objects,
inputs=[
gr.Image(type="pil"),
gr.Slider(0, 1, value=0.5, label="Confidence Threshold")
],
outputs=gr.Image(type="filepath"),
title="Faster R-CNN Object Detection",
description="Upload an image to detect objects using a pretrained Faster R-CNN model."
).launch()
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