import argparse
import collections
import cv2
import matplotlib.pyplot as plt
import numpy as np
from creedsolo import RSCodec
from raptorq import Decoder

parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("file", help="output file for decoded data")
parser.add_argument("--len", help="number of bytes to decode", default=2**16, type=int)
parser.add_argument("--height", help="grid height", default=100, type=int)
parser.add_argument("--width", help="grid width", default=100, type=int)
parser.add_argument("--fps", help="framerate", default=30, type=int)
parser.add_argument("--level", help="error correction level", default=0.1, type=float)
parser.add_argument("--device", help="camera device index", default=1, type=int)
args = parser.parse_args()

cheight = cwidth = max(args.height // 10, args.width // 10)
frame_size = args.height * args.width - 4 * cheight * cwidth
frame_xor = np.arange(frame_size, dtype=np.uint8)
rs_size = frame_size - int((frame_size + 254) / 255) * int(args.level * 255) - 4

rsc = RSCodec(int(args.level * 255))
decoder = Decoder.with_defaults(args.len, rs_size)
data = None
# cap = cv2.VideoCapture(args.device)
while data is None:
    # ret, raw_frame = cap.read()
    # if not ret:
    #    continue
    raw_frame = cv2.cvtColor(
        cv2.imread("/home/a/Pictures/Camera/IMG_20240422_000849_027.jpg"),
        cv2.COLOR_BGR2RGB,
    ).astype(np.float64)

    X, Y = raw_frame.shape[:2]
    scale = min(X // 20, Y // 20)
    # Resize so smaller dim is 20
    # Use fast default interpolation for factor of 4
    # Then switch to good slow interpolation
    dframe = cv2.resize(
        cv2.resize(raw_frame, (Y // 4, X // 4)),
        (Y // scale, X // scale),  # OpenCV swaps them
        interpolation=cv2.INTER_AREA,
    )
    plt.imshow(dframe.astype(np.uint8))
    plt.show()

    def max_in_orig(x):
        return tuple(
            np.array(np.unravel_index(np.argmax(x), x.shape)) * scale + scale // 2
        )

    sumframe = np.sum(dframe, axis=2)
    # TODO: Only search in corner area
    widx = max_in_orig((np.std(dframe, axis=2) < 35) * sumframe)
    ridx = max_in_orig(2 * dframe[:, :, 0] - sumframe)
    gidx = max_in_orig(2 * dframe[:, :, 1] - sumframe)
    bidx = max_in_orig(2 * dframe[:, :, 2] - sumframe)

    # Flood fill corners
    def flood_fill(s):
        vis = np.full((X, Y), False)
        vis[s] = True
        queue = collections.deque([s])
        pos = np.array(s)
        col = np.copy(raw_frame[s])
        n = 1
        while len(queue) > 0:
            u = queue.popleft()
            for d in [(5, 0), (0, 5), (-5, 0), (0, -5)]:
                v = (u[0] + d[0], u[1] + d[1])
                if (
                    0 <= v[0] < X
                    and 0 <= v[1] < Y
                    and not vis[v]
                    and np.linalg.norm(raw_frame[v] - raw_frame[s]) < 100
                ):
                    vis[v] = True
                    pos += np.array(v)
                    col += raw_frame[v]
                    n += 1
                    queue.append(v)
        plt.imshow(raw_frame.astype(np.uint8))
        plt.scatter(*reversed(np.where(vis)))
        plt.scatter(pos[1] / n, pos[0] / n)
        plt.show()
        return pos / n, col / n

    widx, wcol = flood_fill(widx)
    ridx, rcol = flood_fill(ridx)
    gidx, gcol = flood_fill(gidx)
    bidx, bcol = flood_fill(bidx)

    # Find basis of color space
    origin = (rcol + gcol + bcol - wcol) / 2
    rcol -= origin
    gcol -= origin
    bcol -= origin
    print(origin, rcol, gcol, bcol)
    F = 255 * np.linalg.inv(np.stack((rcol, gcol, bcol)).T)

    # Dumb perspective transform
    xv = np.linspace(
        -(cheight / 2 - 1) / (args.height - cheight + 1),
        1 + (cheight / 2 - 1) / (args.height - cheight + 1),
        args.height,
    )
    yv = np.linspace(
        -(cwidth / 2 - 1) / (args.width - cwidth + 1),
        1 + (cwidth / 2 - 1) / (args.width - cwidth + 1),
        args.width,
    )
    xp = (
        np.outer(1 - xv, 1 - yv) * widx[0]
        + np.outer(1 - xv, yv) * ridx[0]
        + np.outer(xv, 1 - yv) * gidx[0]
        + np.outer(xv, yv) * bidx[0]
    )
    yp = (
        np.outer(1 - xv, 1 - yv) * widx[1]
        + np.outer(1 - xv, yv) * ridx[1]
        + np.outer(xv, 1 - yv) * gidx[1]
        + np.outer(xv, yv) * bidx[1]
    )

    plt.scatter(widx[1], widx[0])
    plt.scatter(ridx[1], ridx[0])
    plt.scatter(gidx[1], gidx[0])
    plt.scatter(bidx[1], bidx[0])
    plt.scatter(yp, xp)
    plt.imshow(raw_frame.astype(np.uint8))
    plt.show()
    print(111111111, xp)
    print(111111111, yp)

    raw_color_frame = raw_frame[xp.astype(np.int64), yp.astype(np.int64), :]
    print(raw_color_frame)
    color_frame = np.clip(
        np.squeeze(F @ (raw_color_frame - origin)[..., np.newaxis]), 0, 255
    ).astype(np.uint8)
    print(color_frame)
    frame = (
        (color_frame[:, :, 0] >> 5)
        + (color_frame[:, :, 1] >> 3 & 0b00111000)
        + (color_frame[:, :, 2] & 0b11000000)
    )
    frame_data = np.concatenate(
        (
            frame[:cheight, cwidth : args.width - cwidth].flatten(),
            frame[cheight : args.height - cheight].flatten(),
            frame[args.height - cheight :, cwidth : args.width - cwidth].flatten(),
        )
    )
    print(list(frame_data))
    tmp = rsc.decode(frame_data ^ frame_xor)
    # print(tmp, list(tmp[2]), bytes(tmp[0]))
    data = decoder.decode(bytes(tmp))
    break
with open(args.file, "wb") as f:
    f.write(data)
cap.release()