import cv2
import numpy as np
import torch
from tqdm import tqdm

WIN = "window"
SPACING = 50
GREEN = (0, 255, 0)
RED = (0, 0, 255)


def area_between_curves(a, b):
    polygon = torch.cat([b, torch.flip(a, dims=[0])], dim=0)

    # Calculate the area using the shoelace formula
    x = polygon[:, 0]
    y = polygon[:, 1]
    return 0.5 * torch.abs(x @ torch.roll(y, -1) - torch.roll(x, -1) @ y)


def sample_polyline(line, t):
    deltas = seglengths(line)
    d = torch.cumsum(deltas, dim=0) / torch.sum(deltas)
    d = torch.cat([torch.zeros_like(d[0:1]), d])
    idx = torch.searchsorted(d, t)

    plus = line[idx]
    minus = line[idx - 1]
    rem = (t - d[idx - 1]) / (d[idx] - d[idx - 1] + 1e-8)
    return (1 - rem[..., None]) * minus + rem[..., None] * plus


def per_point_manhattan_distance(a, b, t):
    points_a = sample_polyline(a, t)
    points_b = sample_polyline(b, t)
    return torch.sum(torch.abs(points_a - points_b), dim=-1)


def per_point_euclidean_distance(a, b, t):
    points_a = sample_polyline(a, t)
    points_b = sample_polyline(b, t)
    return torch.norm(points_a - points_b, dim=-1)


def seglengths(line):
    return torch.norm(torch.diff(line, dim=-2), dim=-1)


def spacing_error(line):
    # Calculate the distances between consecutive points
    return torch.mean(torch.abs(SPACING - seglengths(line)))


def fit_to_line(line, *, canvas):
    total_length = np.sum(np.linalg.norm(np.diff(line, axis=0), axis=1))
    num_points = np.ceil(total_length / SPACING).astype(int)
    # Generate evenly spaced points along the line
    if len(line) < 2:
        return None

    init = np.linspace(line[0], line[-1], num_points)

    if len(line) < 3:
        return init

    fit = torch.tensor(init, dtype=torch.float32, requires_grad=True)
    line_pt = torch.tensor(line, dtype=torch.float32)
    optimizer = torch.optim.Adam([fit], lr=1)

    while True:
        optimizer.zero_grad()

        sample_points = torch.rand((num_points // 2), dtype=torch.float32)
        sample_line = sample_polyline(line_pt, sample_points)
        sample_fit = sample_polyline(fit, sample_points)

        loss = torch.mean(torch.norm(sample_line - sample_fit, dim=-1)) + spacing_error(
            fit
        )

        loss.backward()
        optimizer.step()

        img = canvas.copy()
        img = draw_line(img, line_pt.numpy(), GREEN)
        img = draw_line(img, fit.detach().numpy(), RED, with_points=True)
        # img = draw_circles(img, sample_line.numpy(), GREEN)
        # img = draw_circles(img, sample_fit.detach().numpy(), RED)
        cv2.imshow(WIN, img)
        if cv2.waitKey(1) == ord("q"):
            break

    fit = fit.detach()
    abc = area_between_curves(line_pt, fit)
    spacing_err = spacing_error(fit)
    print("Area between curves:", abc.item())
    print("Spacing error:", spacing_err.item())

    return fit.numpy()


def draw_circles(img, points, color):
    for point in points:
        cv2.circle(img, tuple(np.round(point).astype(np.int32)), 5, color, -1)
    return img


def draw_line(img, line, color, *, with_points=False):
    if len(line) > 1:
        cv2.polylines(img, [np.round(line).astype(np.int32)], False, color, 2)
    if with_points:
        img = draw_circles(img, line, color)
    return img


def main():
    line = []
    is_calculating = {}

    canvas = np.zeros((500, 500, 3), dtype=np.uint8)

    cv2.namedWindow(WIN)
    cv2.imshow(WIN, canvas)

    def on_mouse(event, x, y, flags, _):

        def lbuttondown():
            line.append((x, y))
            if len(line) == 1:
                return
            line_np = np.array(line)
            img = draw_line(canvas.copy(), line_np, GREEN)
            if (fit := fit_to_line(line_np, canvas=canvas)) is not None:
                img = draw_line(img, fit, RED)
            cv2.imshow(WIN, img)

        if is_calculating.get(event, False):
            return
        is_calculating[event] = True

        if event == cv2.EVENT_LBUTTONDOWN:
            lbuttondown()

        is_calculating[event] = False

    cv2.setMouseCallback(WIN, on_mouse)

    while True:
        if (k := cv2.waitKey(1)) == ord("c"):
            line.clear()
            cv2.imshow(WIN, canvas)
        elif k == ord("q"):
            break


if __name__ == "__main__":
    main()