creating app.py file

app.py

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+import gradio as gr
+import cv2
+import torch
+from torchvision import transforms
+from PIL import Image
+
+# Load the pre-trained object detection model (replace with your own model)
+# For example, using a torchvision model for demonstration purposes
+model = torch.hub.load('pytorch/vision:v0.10.0', 'fasterrcnn_resnet50_fpn', pretrained=True)
+model.eval()
+
+# Define the transformations for the input image
+transform = transforms.Compose([
+    transforms.ToTensor(),
+])
+
+# Function to perform object detection on an image
+def detect_objects(image):
+    # Convert image to tensor
+    input_tensor = transform(image).unsqueeze(0)
+
+    # Perform object detection
+    with torch.no_grad():
+        predictions = model(input_tensor)
+
+    # Extract bounding boxes and labels from predictions
+    boxes = predictions[0]['boxes'].numpy()
+    labels = predictions[0]['labels'].numpy()
+
+    return boxes, labels
+
+# Function for live object detection from the camera
+def live_object_detection():
+    # Open a connection to the camera (replace with your own camera setup)
+    cap = cv2.VideoCapture(0)
+
+    while True:
+        # Capture frame-by-frame
+        ret, frame = cap.read()
+
+        # Convert the frame to PIL Image
+        frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+
+        # Perform object detection
+        boxes, labels = detect_objects(frame_pil)
+
+        # Draw bounding boxes on the frame
+        for box, label in zip(boxes, labels):
+            box = [int(coord) for coord in box]
+            cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
+            cv2.putText(frame, f"Label: {label}", (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+        # Display the resulting frame
+        cv2.imshow('Object Detection', frame)
+
+        # Break the loop when 'q' key is pressed
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    # Release the camera and close all windows
+    cap.release()
+    cv2.destroyAllWindows()
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=[detect_objects, live_object_detection],
+    inputs=[
+        gr.Image(type="pil", label="Upload a photo for object detection"),
+        "webcam",
+    ],
+    outputs="image",
+    live=True,
+)
+
+# Launch the Gradio interface
+iface.launch()