Create app.py

app.py

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+# Install required libraries if not already installed
+# !pip install gradio opencv-python torch torchvision
+
+import gradio as gr
+import cv2
+import torch
+from torchvision import models, transforms
+from PIL import Image
+
+# Load the pre-trained Faster R-CNN model
+model = models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
+model.eval()
+
+# Define the transformation for the input image
+transform = transforms.Compose([
+    transforms.ToTensor()
+])
+
+# Function to perform object detection
+def detect_objects(input_image):
+    # Convert the Gradio image to PIL Image
+    image_pil = Image.fromarray(input_image.astype('uint8'), 'RGB')
+    
+    # Apply transformations
+    image = transform(image_pil)
+    image = image.unsqueeze(0)  # Add batch dimension
+    
+    # Get predictions
+    with torch.no_grad():
+        predictions = model(image)
+    
+    # Process predictions
+    boxes = predictions[0]['boxes'].detach().numpy()
+    labels = predictions[0]['labels'].detach().numpy()
+    scores = predictions[0]['scores'].detach().numpy()
+    
+    # Convert PIL Image to OpenCV format
+    image_cv = cv2.cvtColor(np.array(image_pil), cv2.COLOR_RGB2BGR)
+    
+    # Draw bounding boxes on the image
+    for box, label, score in zip(boxes, labels, scores):
+        if score < 0.5:
+            continue  # Skip detections with low confidence
+        x1, y1, x2, y2 = box.astype(int)
+        cv2.rectangle(image_cv, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        cv2.putText(image_cv, f'{label}: {score:.2f}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+    
+    # Convert back to RGB for Gradio
+    image_rgb = cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB)
+    return image_rgb
+
+# Create the Gradio interface
+app = gr.Interface(
+    fn=detect_objects,
+    inputs="image",
+    outputs="image",
+    title="Object Detection using Faster R-CNN",
+    description="Upload an image and the model will detect objects and draw bounding boxes around them."
+)
+
+# Launch the app
+app.launch()