app.py

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()