import cv2
import cvzone
import numpy as np
import os
import gradio as gr

# Load the YuNet model
model_path = 'face_detection_yunet_2023mar.onnx'
face_detector = cv2.FaceDetectorYN.create(model_path, "", (320, 320))

# Initialize the glass number
num = 1
overlay = cv2.imread(f'glasses/glass{num}.png', cv2.IMREAD_UNCHANGED)

# Count glasses files
def count_files_in_directory(directory):
    file_count = 0
    for root, dirs, files in os.walk(directory):
        file_count += len(files)
    return file_count

directory_path = 'glasses'
total_glass_num = count_files_in_directory(directory_path)

# Change glasses
def change_glasses():
    global num, overlay
    num += 1
    if num > total_glass_num:
        num = 1
    overlay = cv2.imread(f'glasses/glass{num}.png', cv2.IMREAD_UNCHANGED)
    return overlay

# Process frame for overlay
def process_frame(frame):
    global overlay
    # Ensure the frame is writable
    frame = np.array(frame, copy=True)

    height, width = frame.shape[:2]
    face_detector.setInputSize((width, height))
    _, faces = face_detector.detect(frame)

    if faces is not None:
        for face in faces:
            x, y, w, h = face[:4].astype(int)
            face_landmarks = face[4:14].reshape(5, 2).astype(int)  # Facial landmarks

            # Get the nose position
            nose_x, nose_y = face_landmarks[2].astype(int)
            # Left and right eye positions
            left_eye_x, left_eye_y = face_landmarks[0].astype(int)
            right_eye_x, right_eye_y = face_landmarks[1].astype(int)

            # Calculate the midpoint between the eyes
            eye_center_x = (left_eye_x + right_eye_x) // 2
            eye_center_y = (left_eye_y + right_eye_y) // 2

            # Calculate the angle of rotation
            delta_x = right_eye_x - left_eye_x
            delta_y = right_eye_y - left_eye_y
            angle = np.degrees(np.arctan2(delta_y, delta_x))

            # Negate the angle to rotate in the opposite direction
            angle = -angle

            # Resize the overlay
            overlay_resize = cv2.resize(overlay, (int(w * 1.15), int(h * 0.8)))

            # Rotate the overlay
            overlay_center = (overlay_resize.shape[1] // 2, overlay_resize.shape[0] // 2)
            rotation_matrix = cv2.getRotationMatrix2D(overlay_center, angle, 1.0)
            overlay_rotated = cv2.warpAffine(
                overlay_resize, rotation_matrix, 
                (overlay_resize.shape[1], overlay_resize.shape[0]),
                flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0, 0)
            )

            # Calculate the position to center the glasses on the eyes
            overlay_x = eye_center_x - overlay_rotated.shape[1] // 2
            overlay_y = eye_center_y - overlay_rotated.shape[0] // 2

            # Overlay the glasses
            try:
                frame = cvzone.overlayPNG(frame, overlay_rotated, [overlay_x, overlay_y])
            except Exception as e:
                print(f"Error overlaying glasses: {e}")
                
    return frame

# Transform function
def transform_cv2(frame, transform):
    if transform == "cartoon":
        # prepare color
        img_color = cv2.pyrDown(cv2.pyrDown(frame))
        for _ in range(6):
            img_color = cv2.bilateralFilter(img_color, 9, 9, 7)
        img_color = cv2.pyrUp(cv2.pyrUp(img_color))

        # prepare edges
        img_edges = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
        img_edges = cv2.adaptiveThreshold(
            cv2.medianBlur(img_edges, 7),
            255,
            cv2.ADAPTIVE_THRESH_MEAN_C,
            cv2.THRESH_BINARY,
            9,
            2,
        )
        img_edges = cv2.cvtColor(img_edges, cv2.COLOR_GRAY2RGB)
        # combine color and edges
        img = cv2.bitwise_and(img_color, img_edges)
        return img
    elif transform == "edges":
        # perform edge detection
        img = cv2.cvtColor(cv2.Canny(frame, 100, 200), cv2.COLOR_GRAY2BGR)
        return img
    else:
        return frame
    


# Gradio webcam input
def webcam_input(frame, transform):
    frame = process_frame(frame)
    frame = transform_cv2(frame, transform)
    return frame

# Gradio Interface
with gr.Blocks() as demo:
    with gr.Column(elem_classes=["my-column"]):
        with gr.Group(elem_classes=["my-group"]):
            transform = gr.Dropdown(choices=["cartoon", "edges", "none"],
                                    value="none", label="Transformation")
            input_img = gr.Image(sources=["webcam"], type="numpy", streaming=True)
            next_button = gr.Button("Next Glasses")
    input_img.stream(webcam_input, [input_img, transform], [input_img], time_limit=30, stream_every=0.1)

    next_button.click(change_glasses, [], [])
if __name__ == "__main__":
    demo.launch(share=True)



# # Gradio Interface
# with gr.Blocks() as demo:
#     with gr.Row():
#         with gr.Column():
#             input_img = gr.Image(label="Input", sources="webcam", streaming=True)
#             next_button = gr.Button("Next Glasses")
#     input_img.stream(webcam_input, [input_img], [input_img], stream_every=0.1, concurrency_limit=30)
#     next_button.click(change_glasses, [], [])
# if __name__ == "__main__":
#     demo.launch(share=True)