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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
# TODO: Try decoding saved videos
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()
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