132 lines
5.2 KiB
Python
132 lines
5.2 KiB
Python
import argparse
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import sys
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import traceback
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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from creedsolo import RSCodec
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from raptorq import Decoder
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parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument("-i", "--input", help="camera device index or input video file", default=0)
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parser.add_argument("-o", "--output", help="output file for decoded data")
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parser.add_argument("-x", "--height", help="grid height", default=100, type=int)
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parser.add_argument("-y", "--width", help="grid width", default=100, type=int)
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parser.add_argument("-f", "--fps", help="frame rate", default=30, type=int)
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parser.add_argument("-l", "--level", help="error correction level", default=0.1, type=float)
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parser.add_argument("-s", "--size", help="number of bytes to decode", default=2**16, type=int)
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parser.add_argument("-e", "--erasure", help="detect erasures", action="store_true")
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args = parser.parse_args()
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cheight = cwidth = max(args.height // 10, args.width // 10)
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frame_size = args.height * args.width - 4 * cheight * cwidth
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frame_xor = np.arange(frame_size // 2, dtype=np.uint8)
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rs_size = frame_size // 2 - (frame_size // 2 + 254) // 255 * int(args.level * 255) - 4
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rsc = RSCodec(int(args.level * 255))
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decoder = Decoder.with_defaults(args.size, rs_size)
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def find_corner(A, f):
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cx, cy = A.shape[:2]
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# Resize so smaller dim is 5
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scale = min(cx // 5, cy // 5)
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B = cv2.resize(A, (cy // scale, cx // scale), interpolation=cv2.INTER_AREA)
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guess = np.array(np.unravel_index(np.argmax(f(B)), B.shape[:2])) * scale + scale // 2
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mask = cv2.floodFill(A, np.empty(0), tuple(reversed(guess)), 1, 10, 10, cv2.FLOODFILL_MASK_ONLY)[2][
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1:-1, 1:-1
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].astype(bool)
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return np.average(np.where(mask), axis=1), np.average(A[mask], axis=0).astype(np.float64)
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if args.input.isdecimal():
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args.input = int(args.input)
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cap = cv2.VideoCapture(args.input)
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data = None
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while data is None:
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try:
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ret, raw_frame = cap.read()
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if not ret:
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print("End of stream")
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break
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cv2.imshow("", raw_frame)
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cv2.waitKey(1)
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# raw_frame is a uint8 BE CAREFUL
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raw_frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2RGB)
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X, Y = raw_frame.shape[:2]
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cx, cy = X // 4, Y // 4
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widx, wcol = find_corner(raw_frame[:cx, :cy], lambda B: (np.std(B, axis=2) < 35) * np.sum(B, axis=2))
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ridx, rcol = find_corner(raw_frame[:cx, Y - cy :], lambda B: B[:, :, 0] - B[:, :, 1] - B[:, :, 2])
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ridx[1] += Y - cy
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gidx, gcol = find_corner(raw_frame[X - cx :, :cy], lambda B: B[:, :, 1] - B[:, :, 2] - B[:, :, 0])
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gidx[0] += X - cx
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bidx, bcol = find_corner(raw_frame[X - cx :, Y - cy :], lambda B: B[:, :, 2] - B[:, :, 0] - B[:, :, 1])
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bidx[0] += X - cx
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bidx[1] += Y - cy
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# Find basis of color space
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origin = (rcol + gcol + bcol - wcol) / 2
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rcol -= origin
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gcol -= origin
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bcol -= origin
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F = 255 * np.linalg.inv(np.stack((rcol, gcol, bcol)).T)
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# Dumb perspective transform
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xv = np.linspace(
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-(cheight / 2 - 1) / (args.height - cheight + 1),
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1 + (cheight / 2 - 1) / (args.height - cheight + 1),
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args.height,
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)
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yv = np.linspace(
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-(cwidth / 2 - 1) / (args.width - cwidth + 1),
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1 + (cwidth / 2 - 1) / (args.width - cwidth + 1),
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args.width,
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)
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xp = (
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np.outer(1 - xv, 1 - yv) * widx[0]
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+ np.outer(1 - xv, yv) * ridx[0]
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+ np.outer(xv, 1 - yv) * gidx[0]
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+ np.outer(xv, yv) * bidx[0]
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)
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yp = (
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np.outer(1 - xv, 1 - yv) * widx[1]
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+ np.outer(1 - xv, yv) * ridx[1]
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+ np.outer(xv, 1 - yv) * gidx[1]
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+ np.outer(xv, yv) * bidx[1]
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)
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# plt.scatter(widx[1], widx[0])
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# plt.scatter(ridx[1], ridx[0])
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# plt.scatter(gidx[1], gidx[0])
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# plt.scatter(bidx[1], bidx[0])
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# plt.scatter(yp, xp)
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# plt.imshow(raw_frame.astype(np.uint8))
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# plt.show()
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frame = raw_frame[
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np.clip(np.round(xp).astype(np.int64), 0, X - 1), np.clip(np.round(yp).astype(np.int64), 0, Y - 1), :
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]
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frame = np.clip(np.squeeze(F @ (frame - origin)[..., np.newaxis]), 0, 255).astype(np.uint8)
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frame = (frame[:, :, 0] >> 7) + (frame[:, :, 1] >> 5 & 0b0110) + (frame[:, :, 2] >> 4 & 0b1000)
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frame = np.concatenate(
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(
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frame[:cheight, cwidth : args.width - cwidth].flatten(),
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frame[cheight : args.height - cheight].flatten(),
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frame[args.height - cheight :, cwidth : args.width - cwidth].flatten(),
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)
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)
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frame = (frame[::2] << 4) + frame[1::2]
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frame = np.pad(frame, (0, (len(frame) + 254) // 255 * 255 - len(frame)))
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frame = np.ravel(frame.reshape(255, len(frame) // 255), "F")[: frame_size // 2]
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erase_pos = list(np.where(frame == 0)[0]) if args.erasure else []
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data = decoder.decode(bytes(rsc.decode(frame ^ frame_xor, erase_pos=erase_pos)[0]))
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print("Decoded frame")
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except KeyboardInterrupt:
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sys.exit()
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except:
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traceback.print_exc()
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with open(args.output, "wb") as f:
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f.write(data)
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cap.release()
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