import cv2
import numpy as np

from dora import DoraStatus


CAMERA_WIDTH = 960
CAMERA_HEIGHT = 540

FONT = cv2.FONT_HERSHEY_SIMPLEX

writer = cv2.VideoWriter(
    "output01.avi",
    cv2.VideoWriter_fourcc(*"MJPG"),
    60,
    (CAMERA_WIDTH, CAMERA_HEIGHT),
)

GOAL_OBJECTIVES = [10, 0]

import numpy as np


def find_largest_gap_midpoint(bboxes, image_width, goal_x):
    """
    Find the x-coordinate of the midpoint of the largest gap along the x-axis where no bounding boxes overlap.

    Parameters:
    - bboxes (np.array): A numpy array where each row represents a bounding box with
                         the format [min_x, min_y, max_x, max_y, confidence, label].
    - image_width (int): The width of the image in pixels.

    Returns:
    - int: The x-coordinate of the midpoint of the largest gap where no bounding boxes overlap.
    """
    if bboxes.size == 0:
        # No bounding boxes, return the midpoint of the image as the largest gap
        return image_width // 2

    events = []
    for bbox in bboxes:
        min_x, max_x = bbox[0], bbox[2]
        events.append((min_x, "enter"))
        events.append((max_x, "exit"))

    # Include image boundaries as part of the events
    events.append(
        (0, "exit")
    )  # Start of the image, considered an 'exit' point for logic simplicity
    events.append(
        (image_width, "enter")
    )  # End of the image, considered an 'enter' point

    # Sort events, with exits before enters at the same position to ensure gap calculation correctness
    events.sort(key=lambda x: (x[0], x[1] == "enter"))

    # Sweep line algorithm to find the largest gap
    current_boxes = 1
    last_x = 0
    largest_gap = 0
    gap_start_x = None
    largest_gap_mid = None  # Midpoint of the largest gap

    for x, event_type in events:
        if current_boxes == 0 and gap_start_x is not None:
            # Calculate gap
            gap = x - gap_start_x
            if gap > largest_gap:
                largest_gap = gap
                gap_end_x = gap_start_x + x
                largest_gap_mid = (gap_start_x + x) // 2
                if goal_x < gap_end_x and goal_x > gap_start_x:
                    return goal_x
                return largest_gap_mid
                # elif goal_x > gap_end_x:
                # return max(gap_end_x - 50, largest_gap_mid)
                # elif goal_x < gap_start_x:
                # return min(gap_start_x + 50, largest_gap_mid)

        if event_type == "enter":
            current_boxes += 1
            if current_boxes == 1:
                gap_start_x = None  # No longer in a gap
        elif event_type == "exit":
            current_boxes -= 1
            if current_boxes == 0:
                gap_start_x = x  # Start of a potential gap

    return largest_gap_mid


class Operator:
    """
    Plot image and bounding box
    """

    def __init__(self):
        self.bboxs = []
        self.buffer = ""
        self.submitted = []
        self.lines = []
        self.gap_x = CAMERA_WIDTH // 2
        self.position = [0, 0, 0]

    def on_event(
        self,
        dora_event,
        send_output,
    ):
        if dora_event["type"] == "INPUT":
            id = dora_event["id"]
            value = dora_event["value"]

            if id == "position":

                value = dora_event["value"].to_numpy()
                [x, y, z] = value
                self.position = [x, y, z]

            if id == "image":

                image = (
                    value.to_numpy().reshape((CAMERA_HEIGHT, CAMERA_WIDTH, 3)).copy()
                )
                cv2.resize(image, (CAMERA_HEIGHT * 2, CAMERA_WIDTH * 2))

                cv2.putText(
                    image, self.buffer, (20, 14 + 15 * 25), FONT, 0.5, (190, 250, 0), 2
                )

                i = 0
                for text in self.submitted[::-1]:
                    color = (
                        (0, 255, 190)
                        if text["role"] == "user_message"
                        else (0, 190, 255)
                    )
                    cv2.putText(
                        image,
                        text["content"],
                        (
                            20,
                            14 + (13 - i) * 25,
                        ),
                        FONT,
                        0.5,
                        color,
                        2,
                    )
                    i += 1
                writer.write(image)
                cv2.resize(image, (CAMERA_HEIGHT * 3, CAMERA_WIDTH * 3))
                cv2.imshow("frame", image)
                if cv2.waitKey(1) & 0xFF == ord("q"):
                    return DoraStatus.STOP
            elif id == "keyboard_buffer":
                self.buffer = value[0].as_py()
            elif id == "bbox":
                self.bboxs = value.to_numpy().reshape((-1, 6))

                self.gap_x = find_largest_gap_midpoint(
                    self.bboxs, image_width=CAMERA_WIDTH, goal_x=10
                )
            elif "message" in id:
                self.submitted += [
                    {
                        "role": id,
                        "content": value[0]
                        .as_py()
                        .replace("\n", " ")
                        .replace("- ", ""),
                    }
                ]

        return DoraStatus.CONTINUE


## Angle = Arctan Proj Object y / x

## Relation linearire 0 - 60 ; 0 - CAMERA_WIDTH