scripts/inference.py

import torch
import torchvision.transforms as transforms
import torchvision.models.detection as detection
from PIL import Image
import cv2
import numpy as np

# Define class names
CLASS_NAMES = {
    0: "vehicle", 1: "bicycle", 2: "bus", 3: "car", 4: "lorry"
}
NUM_CLASSES = len(CLASS_NAMES) + 1  # Include background class

# Global flag to stop detection when clicking the window
stop_detection = False

# Load Faster R-CNN model
def load_model(model_path, num_classes):
    model = detection.fasterrcnn_resnet50_fpn(weights=None, num_classes=num_classes)

    # Load the trained model
    checkpoint = torch.load(model_path, map_location="cpu")
    model.load_state_dict(checkpoint)

    model.eval()
    return model

# Transform input image
def preprocess_image(image):
    transform = transforms.Compose([transforms.ToTensor()])
    return transform(image).unsqueeze(0)

# Run inference on an image
def run_inference_on_image(image_path, model):
    img = Image.open(image_path).convert("RGB")
    img_tensor = preprocess_image(img)

    with torch.no_grad():
        prediction = model(img_tensor)[0]

    draw_predictions(image_path, prediction)

# Draw predictions on an image
def draw_predictions(image_path, prediction):
    image = cv2.imread(image_path)
    boxes, scores, labels = prediction['boxes'], prediction['scores'], prediction['labels']

    for i in range(len(boxes)):
        if scores[i] > 0.5:  # Confidence threshold
            x1, y1, x2, y2 = map(int, boxes[i].tolist())
            class_id = labels[i].item()
            class_name = CLASS_NAMES.get(class_id, f"Class {class_id}")
            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.putText(image, f"{class_name}: {scores[i]:.2f}", (x1, y1 - 10),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    cv2.imshow("Detection Result", image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# Mouse click event function to stop real-time detection
def stop_real_time_detection(event, x, y, flags, param):
    global stop_detection
    if event == cv2.EVENT_LBUTTONDOWN:  # Detect left mouse button click
        stop_detection = True

# Real-time detection with webcam
def real_time_detection(model):
    global stop_detection
    stop_detection = False  # Reset flag before starting detection

    cap = cv2.VideoCapture(0)
    if not cap.isOpened():
        print("❌ Error: Could not open webcam.")
        return

    print("🎥 Starting real-time object detection. Click the window to close.")

    cv2.namedWindow("Real-time Detection")  # Name the OpenCV window
    cv2.setMouseCallback("Real-time Detection", stop_real_time_detection)  # Set mouse click callback

    while True:
        ret, frame = cap.read()
        if not ret:
            print("❌ Error: Failed to capture frame.")
            break

        img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
        img_tensor = preprocess_image(img)

        with torch.no_grad():
            prediction = model(img_tensor)[0]

        draw_predictions_live(frame, prediction)

        cv2.imshow("Real-time Detection", frame)

        # FIX: Process window events so OpenCV does not freeze
        if cv2.waitKey(1) & 0xFF == ord('q'):  # Press 'q' to exit (optional)
            break

        if stop_detection:  # If window is clicked, stop detection
            print("🛑 Stopping real-time detection...")
            break

    cap.release()
    cv2.destroyAllWindows()  # FIX: Close OpenCV windows properly
    cv2.waitKey(1)  # FIX: Ensure window is destroyed

# Draw predictions on live video feed
def draw_predictions_live(frame, prediction):
    boxes, scores, labels = prediction['boxes'], prediction['scores'], prediction['labels']

    for i in range(len(boxes)):
        if scores[i] > 0.5:
            x1, y1, x2, y2 = map(int, boxes[i].tolist())
            class_id = labels[i].item()
            class_name = CLASS_NAMES.get(class_id, f"Class {class_id}")
            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.putText(frame, f"{class_name}: {scores[i]:.2f}", (x1, y1 - 10),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

if __name__ == "__main__":
    model = load_model("models/fasterrcnn_model.pth", NUM_CLASSES)

    while True:
        print("\nOptions:")
        print("1 - Run object detection on an image")
        print("2 - Run real-time object detection (Click to exit)")
        print("q - Quit")

        choice = input("Enter your choice: ").strip().lower()

        if choice == "1":
            image_path = input("Enter the path of the test image: ").strip()
            run_inference_on_image(image_path, model)
        elif choice == "2":
            real_time_detection(model)
        elif choice == "q":
            print("👋 Exiting program.")
            break
        else:
            print("⚠️ Invalid choice! Please enter '1', '2', or 'q'.")