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import os, subprocess |
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import numpy as np |
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from matplotlib import pyplot as plt |
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import torch |
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import cv2 |
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def ensure_dir(path): |
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if not os.path.exists(path): |
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os.makedirs(path) |
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def imagesc(nd_array): |
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plt.figure(figsize=(10, 10)) |
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plt.imshow(nd_array) |
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plt.colorbar() |
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plt.show() |
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def imread(img): |
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try: |
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im = cv2.imread(img) |
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except Exception as e: |
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print(e) |
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return None |
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im = im[..., ::-1] / 255. |
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return im |
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def imwrite(img, name): |
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cv2.imwrite(name, img[..., ::-1] * 255.) |
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def stereoRead(img: str): |
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im = imread(img) |
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H, W, _ = im.shape |
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assert W % 2 == 0, '%s is not a side-by-side stereo image' % img |
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left = im[:, :W // 2, ...] |
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right = im[:, W // 2:, ...] |
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return left, right |
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def stereoWright(left: np.ndarray, right: np.ndarray, name: str): |
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im = np.concatenate((left, right), axis=1) |
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imwrite(im, name) |
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def exeCmd(cmd=None): |
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print(cmd) |
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return subprocess.check_call(cmd, shell=True) |
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def img2tensor(img, cuda=True): |
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img_t = np.expand_dims(img.transpose(2, 0, 1), axis=0) |
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img_t = torch.from_numpy(img_t.astype(np.float32)) |
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if cuda: |
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img_t = img_t.cuda(non_blocking=True) |
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return img_t |
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def tensor2img(img_t): |
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if len(img_t.shape) == 4: |
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img = img_t[0].detach().cpu().numpy() |
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elif len(img_t.shape) == 3: |
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img = img_t.detach().cpu().numpy() |
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else: |
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raise NotImplementedError |
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img = img.transpose(1, 2, 0) |
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return img |
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def cal_padding(L, window_size): |
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padding_left, padding_right = 0, 0 |
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if L%window_size != 0: |
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total = window_size-L%window_size |
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padding_left = total//2 |
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padding_right = total - padding_left |
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return padding_left, padding_right |
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def requires_grad(model, flag=True): |
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for p in model.parameters(): |
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p.requires_grad = flag |
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