Working with 0.1 playback spd, but only 20kbps; commented out debug code
This commit is contained in:
Kevin Zhao
parent
74dcc919d6
commit
f2e199949f
2 changed files with 86 additions and 20 deletions
43
decoder.py
43
decoder.py
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@ -94,6 +94,23 @@ if args.version == 0:
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elif args.version == 1:
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localize_corners = localize_corners_wrapper(args, input_crop_size)
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# ####
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# vid_frames = []
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# # cap = cv2.VideoCapture("/Users/kevinzhao/Downloads/IMG_0994.MOV")
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# cap = cv2.VideoCapture("vid_tiny_v1.mkv")
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# data = None
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# start_time = time.time()
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# while data is None:
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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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# vid_frames.append(raw_frame)
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# gtruth = cv2.cvtColor(vid_frames[0], cv2.COLOR_BGR2RGB)
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# ####
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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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@ -102,6 +119,7 @@ start_time = time.time()
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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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# raw_frame = cv2.resize(raw_frame, (1024, 1024), interpolation=cv2.INTER_NEAREST) # TODO: remove
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if not ret:
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print("End of stream")
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break
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@ -114,7 +132,7 @@ while data is None:
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raw_frame = raw_frame[X // 4: 3 * X // 4, Y // 4: 3 * Y // 4]
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elif args.version == 1:
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h, w, _ = raw_frame.shape
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raw_frame = raw_frame[(h - input_crop_size) // 2:-(h - input_crop_size) // 2,
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raw_frame = raw_frame[(h - input_crop_size) // 2:-(h - input_crop_size) // 2, # TODO: put back
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(w - input_crop_size) // 2:-(w - input_crop_size) // 2]
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cv2.imshow("", raw_frame)
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@ -133,11 +151,23 @@ while data is None:
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# Convert to new color space
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# calibrated_frame = (np.squeeze(F @ (frame - origin)[..., np.newaxis]) >= 192).astype(np.uint8)
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calibrated_frame = (np.squeeze(F @ (frame - origin)[..., np.newaxis]) >= 128).astype(np.uint8)
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fig, axs = plt.subplots(1, 2)
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axs[0].imshow(frame)
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axs[1].imshow(calibrated_frame * 255)
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plt.show()
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# calibrated_frame = (np.squeeze(F @ (frame - origin)[..., np.newaxis]) >= 192).astype(np.uint8) * 255
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calibrated_frame = (np.squeeze(F @ (frame - origin)[..., np.newaxis]) >= 150).astype(np.uint8) * 255
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# fig, axs = plt.subplots(1, 2)
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# axs[0].imshow(frame)
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# axs[1].imshow(calibrated_frame)
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# plt.show()
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# fig, axs = plt.subplots(1, 2)
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# correct_mask = np.logical_not((calibrated_frame != gtruth).any(axis=2))
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# calibrated_frame_copy = calibrated_frame.copy()
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# gtruth_copy = gtruth.copy()
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# calibrated_frame_copy[correct_mask] = [0, 0, 0]
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# gtruth_copy[correct_mask] = [0, 0, 0]
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# axs[0].imshow(gtruth_copy)
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# axs[1].imshow(calibrated_frame_copy)
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# plt.show()
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calibrated_frame = np.packbits(
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np.concatenate(
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(
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@ -155,6 +185,7 @@ while data is None:
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break
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except Exception as e:
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print(e)
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cap.release()
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with open(args.output, "wb") as f:
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f.write(data)
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@ -208,26 +208,61 @@ def localize_corners_wrapper(args, input_crop_size, debug=False):
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cheight = int(args.height * 0.15)
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cwidth = int(args.width * 0.15)
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cch = int(args.height * 0.15) // 4 - 1 # 0-indexed
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ccw = int(args.width * 0.15) // 4 - 1
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cch = int(args.height * 0.15) // 4
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ccw = int(args.width * 0.15) // 4
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# plt.imshow(cropped_frame)
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# plt.show()
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grid_coords = np.float32([
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[ccw, cch],
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[args.width - ccw, cch],
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[ccw, args.height - cch],
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[args.width - ccw, args.height - cch],
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])
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grid_coords -= 1/2
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#
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# grid_coords *= orig_w / args.width
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# torch_frame = transforms_f.perspective(
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# transforms.Compose([
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# # transforms.Lambda(
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# # lambda img: cv2.resize(img, (stage1_size, stage1_size), interpolation=cv2.INTER_NEAREST)),
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# transforms.ToImage(),
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# transforms.ToDtype(torch.float32, scale=True),
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# ])(cropped_frame),
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# orig_pred_pts,
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# grid_coords,
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# )
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#
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# torch_frame = cv2.resize(torch_frame.permute(1, 2, 0).numpy(), (args.width, args.height), interpolation=cv2.INTER_AREA)
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# # torch_frame = cv2.resize(torch_frame.permute(1, 2, 0).numpy(), (args.width, args.height), interpolation=cv2.INTER_NEAREST)
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#
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# # torch_frame = transforms_f.resize(torch_frame, [args.height, args.width]).permute(1, 2, 0).numpy()
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# # torch_frame = torch_frame.permute(1, 2, 0).numpy()
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# cropped_frame = (torch_frame * 255).astype(np.uint8)
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# plt.imshow(cropped_frame)
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#
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# plt.axis("off")
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# plt.show()
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# grid_coords = np.float32([
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# [ccw, cch],
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# [args.width - ccw - 1, cch],
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# [ccw, args.height - cch - 1],
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# [args.width - ccw - 1, args.height - cch - 1],
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# ])
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M = cv2.getPerspectiveTransform(
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np.float32(orig_pred_pts),
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np.float32(
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[
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[ccw, cch],
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[args.width - ccw - 1, cch],
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[ccw, args.height - cch - 1],
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[args.width - ccw - 1, args.height - cch - 1],
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]
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),
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grid_coords,
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)
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cropped_frame = cv2.warpPerspective(cropped_frame, M, (args.width, args.height))
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# 1-index
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cch += 1
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ccw += 1
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# cropped_frame = cv2.warpPerspective(cropped_frame, M, (args.width, args.height), flags=cv2.INTER_NEAREST)
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padding = math.ceil(max(args.height, args.width) / 80) # arbitrary
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