from ultralytics import YOLO
import streamlit as st
import cv2
import pafy

import settings
import tracker


def load_model(model_path):
    """
    Loads a YOLO object detection model from the specified model_path.

    Parameters:
        model_path (str): The path to the YOLO model file.

    Returns:
        A YOLO object detection model.
    """
    model = YOLO(model_path)
    return model


def display_tracker_option():
    display_tracker = st.radio("Display Tracker", ('Yes', 'No'))
    is_display_tracker = True if display_tracker == 'Yes' else False
    return is_display_tracker


def _display_detected_frames(conf, model, st_frame, image, is_display_tracking=None):
    """
    Display the detected objects on a video frame using the YOLOv8 model.

    Args:
    - conf (float): Confidence threshold for object detection.
    - model (YoloV8): A YOLOv8 object detection model.
    - st_frame (Streamlit object): A Streamlit object to display the detected video.
    - image (numpy array): A numpy array representing the video frame.
    - is_display_tracking (bool): A flag indicating whether to display object tracking (default=None).

    Returns:
    None
    """

    # Resize the image to a standard size
    image = cv2.resize(image, (720, int(720*(9/16))))

    # Predict the objects in the image using the YOLOv8 model
    res = model.predict(image, conf=conf)
    result_tensor = res[0].boxes

    # Display object tracking, if specified
    if is_display_tracking:
        tracker._display_detected_tracks(result_tensor.data, image)

    # # Plot the detected objects on the video frame
    res_plotted = res[0].plot()
    st_frame.image(res_plotted,
                   caption='Detected Video',
                   channels="BGR",
                   use_column_width=True
                   )


def play_youtube_video(conf, model):
    """
    Plays a webcam stream. Detects Objects in real-time using the YOLOv8 object detection model.

    Parameters:
        conf: Confidence of YOLOv8 model.
        model: An instance of the `YOLOv8` class containing the YOLOv8 model.

    Returns:
        None

    Raises:
        None
    """
    source_youtube = st.sidebar.text_input("YouTube Video url")

    is_display_tracker = display_tracker_option()

    if st.sidebar.button('Detect Objects'):
        try:
            video = pafy.new(source_youtube)
            best = video.getbest(preftype="mp4")
            vid_cap = cv2.VideoCapture(best.url)
            st_frame = st.empty()
            while (vid_cap.isOpened()):
                success, image = vid_cap.read()
                if success:
                    _display_detected_frames(conf,
                                             model,
                                             st_frame,
                                             image,
                                             is_display_tracker
                                             )
                else:
                    vid_cap.release()
                    break
        except Exception as e:
            st.sidebar.error("Error loading video: " + str(e))


def play_rtsp_stream(conf, model):
    """
    Plays an rtsp stream. Detects Objects in real-time using the YOLOv8 object detection model.

    Parameters:
        conf: Confidence of YOLOv8 model.
        model: An instance of the `YOLOv8` class containing the YOLOv8 model.

    Returns:
        None

    Raises:
        None
    """
    source_rtsp = st.sidebar.text_input("rtsp stream url")
    is_display_tracker = display_tracker_option()
    if st.sidebar.button('Detect Objects'):
        try:
            vid_cap = cv2.VideoCapture(source_rtsp)
            st_frame = st.empty()
            while (vid_cap.isOpened()):
                success, image = vid_cap.read()
                if success:
                    _display_detected_frames(conf,
                                             model,
                                             st_frame,
                                             image,
                                             is_display_tracker
                                             )
                else:
                    vid_cap.release()
                    break
        except Exception as e:
            st.sidebar.error("Error loading RTSP stream: " + str(e))


def play_webcam(conf, model):
    """
    Plays a webcam stream. Detects Objects in real-time using the YOLOv8 object detection model.

    Parameters:
        conf: Confidence of YOLOv8 model.
        model: An instance of the `YOLOv8` class containing the YOLOv8 model.

    Returns:
        None

    Raises:
        None
    """
    source_webcam = settings.WEBCAM_PATH
    is_display_tracker = display_tracker_option()
    if st.sidebar.button('Detect Objects'):
        try:
            vid_cap = cv2.VideoCapture(source_webcam)
            st_frame = st.empty()
            while (vid_cap.isOpened()):
                success, image = vid_cap.read()
                if success:
                    _display_detected_frames(conf,
                                             model,
                                             st_frame,
                                             image,
                                             is_display_tracker
                                             )
                else:
                    vid_cap.release()
                    break
        except Exception as e:
            st.sidebar.error("Error loading video: " + str(e))


def play_stored_video(conf, model):
    """
    Plays a stored video file. Tracks and detects objects in real-time using the YOLOv8 object detection model.

    Parameters:
        conf: Confidence of YOLOv8 model.
        model: An instance of the `YOLOv8` class containing the YOLOv8 model.

    Returns:
        None

    Raises:
        None
    """
    source_vid = st.sidebar.selectbox(
        "Choose a video...", settings.VIDEOS_DICT.keys())

    is_display_tracker = display_tracker_option()

    with open(settings.VIDEOS_DICT.get(source_vid), 'rb') as video_file:
        video_bytes = video_file.read()
    if video_bytes:
        st.video(video_bytes)

    if st.sidebar.button('Detect Video Objects'):
        try:
            vid_cap = cv2.VideoCapture(
                str(settings.VIDEOS_DICT.get(source_vid)))
            st_frame = st.empty()
            while (vid_cap.isOpened()):
                success, image = vid_cap.read()
                if success:
                    _display_detected_frames(conf,
                                             model,
                                             st_frame,
                                             image,
                                             is_display_tracker
                                             )
                else:
                    vid_cap.release()
                    break
        except Exception as e:
            st.sidebar.error("Error loading video: " + str(e))