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Zero
Running
on
Zero
| import numpy as np | |
| import cv2 | |
| from PIL import Image | |
| from torchvision.transforms import ColorJitter | |
| class FlowAugmentor: | |
| def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=True, | |
| no_eraser_aug=True, | |
| ): | |
| # spatial augmentation params | |
| self.crop_size = crop_size | |
| self.min_scale = min_scale | |
| self.max_scale = max_scale | |
| self.spatial_aug_prob = 0.8 | |
| self.stretch_prob = 0.8 | |
| self.max_stretch = 0.2 | |
| # flip augmentation params | |
| self.do_flip = do_flip | |
| self.h_flip_prob = 0.5 | |
| self.v_flip_prob = 0.1 | |
| # photometric augmentation params | |
| self.photo_aug = ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.5 / 3.14) | |
| self.asymmetric_color_aug_prob = 0.2 | |
| if no_eraser_aug: | |
| # we disable eraser aug since no obvious improvement is observed in our experiments | |
| self.eraser_aug_prob = -1 | |
| else: | |
| self.eraser_aug_prob = 0.5 | |
| def color_transform(self, img1, img2): | |
| """ Photometric augmentation """ | |
| # asymmetric | |
| if np.random.rand() < self.asymmetric_color_aug_prob: | |
| img1 = np.array(self.photo_aug(Image.fromarray(img1)), dtype=np.uint8) | |
| img2 = np.array(self.photo_aug(Image.fromarray(img2)), dtype=np.uint8) | |
| # symmetric | |
| else: | |
| image_stack = np.concatenate([img1, img2], axis=0) | |
| image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8) | |
| img1, img2 = np.split(image_stack, 2, axis=0) | |
| return img1, img2 | |
| def eraser_transform(self, img1, img2, bounds=[50, 100]): | |
| """ Occlusion augmentation """ | |
| ht, wd = img1.shape[:2] | |
| if np.random.rand() < self.eraser_aug_prob: | |
| mean_color = np.mean(img2.reshape(-1, 3), axis=0) | |
| for _ in range(np.random.randint(1, 3)): | |
| x0 = np.random.randint(0, wd) | |
| y0 = np.random.randint(0, ht) | |
| dx = np.random.randint(bounds[0], bounds[1]) | |
| dy = np.random.randint(bounds[0], bounds[1]) | |
| img2[y0:y0 + dy, x0:x0 + dx, :] = mean_color | |
| return img1, img2 | |
| def spatial_transform(self, img1, img2, flow, occlusion=None): | |
| # randomly sample scale | |
| ht, wd = img1.shape[:2] | |
| min_scale = np.maximum( | |
| (self.crop_size[0] + 8) / float(ht), | |
| (self.crop_size[1] + 8) / float(wd)) | |
| scale = 2 ** np.random.uniform(self.min_scale, self.max_scale) | |
| scale_x = scale | |
| scale_y = scale | |
| if np.random.rand() < self.stretch_prob: | |
| scale_x *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch) | |
| scale_y *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch) | |
| scale_x = np.clip(scale_x, min_scale, None) | |
| scale_y = np.clip(scale_y, min_scale, None) | |
| if np.random.rand() < self.spatial_aug_prob: | |
| # rescale the images | |
| img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) | |
| img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) | |
| flow = cv2.resize(flow, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) | |
| flow = flow * [scale_x, scale_y] | |
| if occlusion is not None: | |
| occlusion = cv2.resize(occlusion, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) | |
| if self.do_flip: | |
| if np.random.rand() < self.h_flip_prob: # h-flip | |
| img1 = img1[:, ::-1] | |
| img2 = img2[:, ::-1] | |
| flow = flow[:, ::-1] * [-1.0, 1.0] | |
| if occlusion is not None: | |
| occlusion = occlusion[:, ::-1] | |
| if np.random.rand() < self.v_flip_prob: # v-flip | |
| img1 = img1[::-1, :] | |
| img2 = img2[::-1, :] | |
| flow = flow[::-1, :] * [1.0, -1.0] | |
| if occlusion is not None: | |
| occlusion = occlusion[::-1, :] | |
| # In case no cropping | |
| if img1.shape[0] - self.crop_size[0] > 0: | |
| y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0]) | |
| else: | |
| y0 = 0 | |
| if img1.shape[1] - self.crop_size[1] > 0: | |
| x0 = np.random.randint(0, img1.shape[1] - self.crop_size[1]) | |
| else: | |
| x0 = 0 | |
| img1 = img1[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]] | |
| img2 = img2[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]] | |
| flow = flow[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]] | |
| if occlusion is not None: | |
| occlusion = occlusion[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]] | |
| return img1, img2, flow, occlusion | |
| return img1, img2, flow | |
| def __call__(self, img1, img2, flow, occlusion=None): | |
| img1, img2 = self.color_transform(img1, img2) | |
| img1, img2 = self.eraser_transform(img1, img2) | |
| if occlusion is not None: | |
| img1, img2, flow, occlusion = self.spatial_transform( | |
| img1, img2, flow, occlusion) | |
| else: | |
| img1, img2, flow = self.spatial_transform(img1, img2, flow) | |
| img1 = np.ascontiguousarray(img1) | |
| img2 = np.ascontiguousarray(img2) | |
| flow = np.ascontiguousarray(flow) | |
| if occlusion is not None: | |
| occlusion = np.ascontiguousarray(occlusion) | |
| return img1, img2, flow, occlusion | |
| return img1, img2, flow | |
| class SparseFlowAugmentor: | |
| def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=False, | |
| no_eraser_aug=True, | |
| ): | |
| # spatial augmentation params | |
| self.crop_size = crop_size | |
| self.min_scale = min_scale | |
| self.max_scale = max_scale | |
| self.spatial_aug_prob = 0.8 | |
| self.stretch_prob = 0.8 | |
| self.max_stretch = 0.2 | |
| # flip augmentation params | |
| self.do_flip = do_flip | |
| self.h_flip_prob = 0.5 | |
| self.v_flip_prob = 0.1 | |
| # photometric augmentation params | |
| self.photo_aug = ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.3 / 3.14) | |
| self.asymmetric_color_aug_prob = 0.2 | |
| if no_eraser_aug: | |
| # we disable eraser aug since no obvious improvement is observed in our experiments | |
| self.eraser_aug_prob = -1 | |
| else: | |
| self.eraser_aug_prob = 0.5 | |
| def color_transform(self, img1, img2): | |
| image_stack = np.concatenate([img1, img2], axis=0) | |
| image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8) | |
| img1, img2 = np.split(image_stack, 2, axis=0) | |
| return img1, img2 | |
| def eraser_transform(self, img1, img2): | |
| ht, wd = img1.shape[:2] | |
| if np.random.rand() < self.eraser_aug_prob: | |
| mean_color = np.mean(img2.reshape(-1, 3), axis=0) | |
| for _ in range(np.random.randint(1, 3)): | |
| x0 = np.random.randint(0, wd) | |
| y0 = np.random.randint(0, ht) | |
| dx = np.random.randint(50, 100) | |
| dy = np.random.randint(50, 100) | |
| img2[y0:y0 + dy, x0:x0 + dx, :] = mean_color | |
| return img1, img2 | |
| def resize_sparse_flow_map(self, flow, valid, fx=1.0, fy=1.0): | |
| ht, wd = flow.shape[:2] | |
| coords = np.meshgrid(np.arange(wd), np.arange(ht)) | |
| coords = np.stack(coords, axis=-1) | |
| coords = coords.reshape(-1, 2).astype(np.float32) | |
| flow = flow.reshape(-1, 2).astype(np.float32) | |
| valid = valid.reshape(-1).astype(np.float32) | |
| coords0 = coords[valid >= 1] | |
| flow0 = flow[valid >= 1] | |
| ht1 = int(round(ht * fy)) | |
| wd1 = int(round(wd * fx)) | |
| coords1 = coords0 * [fx, fy] | |
| flow1 = flow0 * [fx, fy] | |
| xx = np.round(coords1[:, 0]).astype(np.int32) | |
| yy = np.round(coords1[:, 1]).astype(np.int32) | |
| v = (xx > 0) & (xx < wd1) & (yy > 0) & (yy < ht1) | |
| xx = xx[v] | |
| yy = yy[v] | |
| flow1 = flow1[v] | |
| flow_img = np.zeros([ht1, wd1, 2], dtype=np.float32) | |
| valid_img = np.zeros([ht1, wd1], dtype=np.int32) | |
| flow_img[yy, xx] = flow1 | |
| valid_img[yy, xx] = 1 | |
| return flow_img, valid_img | |
| def spatial_transform(self, img1, img2, flow, valid): | |
| # randomly sample scale | |
| ht, wd = img1.shape[:2] | |
| min_scale = np.maximum( | |
| (self.crop_size[0] + 1) / float(ht), | |
| (self.crop_size[1] + 1) / float(wd)) | |
| scale = 2 ** np.random.uniform(self.min_scale, self.max_scale) | |
| scale_x = np.clip(scale, min_scale, None) | |
| scale_y = np.clip(scale, min_scale, None) | |
| if np.random.rand() < self.spatial_aug_prob: | |
| # rescale the images | |
| img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) | |
| img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) | |
| flow, valid = self.resize_sparse_flow_map(flow, valid, fx=scale_x, fy=scale_y) | |
| if self.do_flip: | |
| if np.random.rand() < 0.5: # h-flip | |
| img1 = img1[:, ::-1] | |
| img2 = img2[:, ::-1] | |
| flow = flow[:, ::-1] * [-1.0, 1.0] | |
| valid = valid[:, ::-1] | |
| margin_y = 20 | |
| margin_x = 50 | |
| y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0] + margin_y) | |
| x0 = np.random.randint(-margin_x, img1.shape[1] - self.crop_size[1] + margin_x) | |
| y0 = np.clip(y0, 0, img1.shape[0] - self.crop_size[0]) | |
| x0 = np.clip(x0, 0, img1.shape[1] - self.crop_size[1]) | |
| img1 = img1[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]] | |
| img2 = img2[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]] | |
| flow = flow[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]] | |
| valid = valid[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]] | |
| return img1, img2, flow, valid | |
| def __call__(self, img1, img2, flow, valid): | |
| img1, img2 = self.color_transform(img1, img2) | |
| img1, img2 = self.eraser_transform(img1, img2) | |
| img1, img2, flow, valid = self.spatial_transform(img1, img2, flow, valid) | |
| img1 = np.ascontiguousarray(img1) | |
| img2 = np.ascontiguousarray(img2) | |
| flow = np.ascontiguousarray(flow) | |
| valid = np.ascontiguousarray(valid) | |
| return img1, img2, flow, valid | |