aboutsummaryrefslogtreecommitdiff
path: root/decoder.py
blob: a1c0d51db3bb5ad6d9613dbe9ebacaea8172e355 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
import argparse
import time
import cv2
import numpy as np
from creedsolo import RSCodec
from raptorq import Decoder

parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-i", "--input", help="camera device index or input video file", default=0)
parser.add_argument("-o", "--output", help="output file for decoded data", default="out")
parser.add_argument("-x", "--height", help="grid height", default=100, type=int)
parser.add_argument("-y", "--width", help="grid width", default=100, type=int)
parser.add_argument("-l", "--level", help="error correction level", default=0.1, type=float)
parser.add_argument("-s", "--size", help="number of bytes to decode", type=int)
parser.add_argument("-p", "--psize", help="packet size", 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_bytes = frame_size * 3 // 8
frame_xor = np.arange(frame_bytes, dtype=np.uint8)
rs_bytes = frame_bytes - (frame_bytes + 254) // 255 * int(args.level * 255) - 4

rsc = RSCodec(int(args.level * 255))
decoder = Decoder.with_defaults(args.size, rs_bytes)


def find_corner(A, f):
    cx, cy = A.shape[:2]
    # Resize so smaller dim is 8
    scale = min(cx // 8, cy // 8)
    B = cv2.resize(A, (cy // scale, cx // scale), interpolation=cv2.INTER_AREA)
    guess = np.array(np.unravel_index(np.argmax(f(B.astype(np.float64))), B.shape[:2])) * scale + scale // 2
    mask = cv2.floodFill(
        A,
        np.empty(0),
        tuple(np.flip(guess)),
        0,
        (100, 100, 100),
        (100, 100, 100),
        cv2.FLOODFILL_MASK_ONLY + cv2.FLOODFILL_FIXED_RANGE,
    )[2][1:-1, 1:-1].astype(bool)
    return np.average(np.where(mask), axis=1), np.average(A[mask], axis=0).astype(np.float64)


if args.input.isdecimal():
    args.input = int(args.input)
cap = cv2.VideoCapture(args.input)
data = None
start_time = 0
status = 0
decoded = 0
while data is None:
    try:
        ret, raw_frame = cap.read()
        if not ret:
            print("End of stream")
            break
        if isinstance(args.input, int) and (status == 1 or (status == 0 and np.random.rand() < 0.5)):
            status = 2
            print("Skipped")
            continue
        # raw_frame is a uint8 BE CAREFUL
        if type(args.input) == int:
            # Crop image to reduce camera distortion
            X, Y = raw_frame.shape[:2]
            raw_frame = raw_frame[X // 4 : 3 * X // 4, Y // 4 : 3 * Y // 4]
        cv2.imshow("", raw_frame)
        cv2.waitKey(1)
        raw_frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2RGB)

        # Find positions and colors of corners
        X, Y = raw_frame.shape[:2]
        cx, cy = X // 3, Y // 3
        widx, wcol = find_corner(raw_frame[:cx, :cy], lambda B: np.sum(B, axis=2) - 2 * np.std(B, axis=2))
        ridx, rcol = find_corner(raw_frame[:cx, Y - cy :], lambda B: B[:, :, 0] - B[:, :, 1] - B[:, :, 2])
        ridx[1] += Y - cy
        gidx, gcol = find_corner(raw_frame[X - cx :, :cy], lambda B: B[:, :, 1] - B[:, :, 2] - B[:, :, 0])
        gidx[0] += X - cx
        bidx, bcol = find_corner(raw_frame[X - cx :, Y - cy :], lambda B: B[:, :, 2] - B[:, :, 0] - B[:, :, 1])
        bidx[0] += X - cx
        bidx[1] += Y - cy

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

        cch = cheight / 2 - 1
        ccw = cwidth / 2 - 1
        M = cv2.getPerspectiveTransform(
            np.float32([np.flip(widx), np.flip(ridx), np.flip(gidx), np.flip(bidx)]),
            np.float32(
                [
                    [ccw, cch],
                    [args.width - ccw - 1, cch],
                    [ccw, args.height - cch - 1],
                    [args.width - ccw - 1, args.height - cch - 1],
                ]
            ),
        )
        frame = cv2.warpPerspective(raw_frame, M, (args.width, args.height))
        # Convert to new color space
        frame = (np.squeeze(F @ (frame - origin)[..., np.newaxis]) >= 192).astype(np.uint8)
        frame = np.packbits(
            np.concatenate(
                (
                    frame[:cheight, cwidth : args.width - cwidth].flatten(),
                    frame[cheight : args.height - cheight].flatten(),
                    frame[args.height - cheight :, cwidth : args.width - cwidth].flatten(),
                )
            )
        )[:frame_bytes]
        reshape_len = frame_bytes // 255 * 255
        frame[:reshape_len] = np.ravel(frame[:reshape_len].reshape(255, reshape_len // 255), "F")
        data = decoder.decode(bytes(rsc.decode(bytearray(frame ^ frame_xor))[0][: args.psize]))
        decoded += 1
        status = 1
        if start_time == 0:
            start_time = time.time()
        print("Decoded frame")
    except KeyboardInterrupt:
        break
    except Exception as e:
        status = 0
        print(e)
cap.release()
print(decoded)
with open(args.output, "wb") as f:
    f.write(data)
print(8 * len(data) / (time.time() - start_time))