import cv2
import numpy as np

# Assume objectPoints (3D points of the object in world coordinates)
# and imagePoints (corresponding 2D points in the image) are already defined.
# cameraMatrix and distCoeffs are obtained from camera calibration.

# Example placeholder data (replace with actual calibrated values)
objectPoints = np.array([(0.0, 0.0, 0.0), (1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (1.0, 1.0, 0.0)], dtype=np.float32)
imagePoints = np.array([(100, 100), (200, 100), (100, 200), (200, 200)], dtype=np.float32) # Example image points
cameraMatrix = np.array([[800, 0, 320], [0, 800, 240], [0, 0, 1]], dtype=np.float32) # Example camera matrix
distCoeffs = np.zeros((4, 1)) # Assuming no distortion for simplicity

success, rotation_vector, translation_vector = cv2.solvePnP(
    objectPoints, imagePoints, cameraMatrix, distCoeffs
)

if success:
    print("Pose estimation successful.")
    # rotation_vector and translation_vector define the pose of the object relative to the camera.
else:
    print("Pose estimation failed.")

import cv2
# Assume 'model' is a loaded YOLO model and 'frame' is the current camera feed frame.
# Example placeholder for model and frame
# model = ... # Load your YOLO model
# frame = ... # Capture a frame from the camera

# Dummy frame for demonstration
frame = np.zeros((480, 640, 3), dtype=np.uint8)

# Dummy results for demonstration
# results = model(frame)

# Example of processing dummy results
results_dummy = {'xyxy': [[(100, 100, 200, 200, 0.95, 0), (300, 150, 400, 300, 0.90, 1)]]} # format: [x1, y1, x2, y2, confidence, class_id]
model_names_dummy = {0: 'object1', 1: 'object2'}

# Iterate over detected objects
for *box, conf, cls in results_dummy['xyxy'][0]:
    label = model_names_dummy[int(cls)]
    x1, y1, x2, y2 = map(int, box)
    cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
    cv2.putText(frame, f"{label}: {conf:.2f}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

# cv2.imshow("AR with YOLO", frame) # In a real app, you would display the frame.

import cv2
import numpy as np

# --- Configuration ---
OVERLAY_IMAGE_PATH = "overlay.png" # Path to the image you want to overlay
CAMERA_SOURCE = 0 # 0 for default webcam, or provide a video file path

# --- Load Resources ---
try:
    overlay_img = cv2.imread(OVERLAY_IMAGE_PATH)
    if overlay_img is None:
        raise FileNotFoundError(f"Overlay image not found at {OVERLAY_IMAGE_PATH}")
    h_overlay, w_overlay = overlay_img.shape[:2]
except Exception as e:
    print(f"Error loading overlay image: {e}")
    exit()

# Initialize webcam
cap = cv2.VideoCapture(CAMERA_SOURCE)
if not cap.isOpened():
    print("Error: Could not open webcam.")
    exit()

# Load ArUco dictionary and detector parameters
aruco_dict = cv2.aruco.Dictionary_get(cv2.aruco.DICT_4X4_50) # Choose a suitable dictionary
parameters = cv2.aruco.DetectorParameters_create()

print("Starting AR overlay. Press 'q' to exit.")

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

    # Detect ArUco markers in the frame
    corners, ids, _ = cv2.aruco.detectMarkers(frame, aruco_dict, parameters=parameters)

    # If markers are detected, process them
    if ids is not None:
        for i in range(len(ids)):
            # Get the corner points of the detected marker
            marker_corners = corners[i][0].astype(np.float32)

            # Define the source points for the overlay image (its corners)
            # These correspond to the order of corners returned by detectMarkers
            pts_src = np.array([[0, 0], [w_overlay, 0], [w_overlay, h_overlay], [0, h_overlay]], dtype=np.float32)

            # Compute the perspective transformation matrix (homography)
            # This matrix maps the overlay image's corners to the marker's corners
            matrix, _ = cv2.findHomography(pts_src, marker_corners)

            # Warp the overlay image to match the marker's perspective
            # The output image size is the same as the input frame
            warped = cv2.warpPerspective(overlay_img, matrix, (frame.shape[1], frame.shape[0]))

            # Create a mask for the warped image to avoid blending with black areas
            # We consider pixels > 0 in the warped image as part of the overlay
            mask = np.zeros((frame.shape[0], frame.shape[1]), dtype=np.uint8)
            cv2.fillConvexPoly(mask, np.int32(marker_corners), (255)) # Use marker corners for mask shape

            # Apply the mask:
            # 1. Invert the mask to get areas *not* covered by the overlay.
            # 2. Bitwise AND the frame with the inverted mask to remove existing content where the overlay will be.
            # 3. Bitwise OR the result with the warped image to place the overlay.
            mask_inv = cv2.bitwise_not(mask)
            frame_bg = cv2.bitwise_and(frame, frame, mask=mask_inv)
            frame_fg = cv2.bitwise_and(warped, warped, mask=mask)
            frame = cv2.add(frame_bg, frame_fg)

    # Display the resulting frame
    cv2.imshow("AR Overlay", frame)

    # Exit if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# --- Cleanup ---
cap.release()
cv2.destroyAllWindows()
print("AR session ended.")