mirror of
https://github.com/Wan-Video/Wan2.1.git
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247 lines
8.9 KiB
Python
247 lines
8.9 KiB
Python
import numpy as np
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import random
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import math
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from PIL import Image
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import cv2
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cv2.setNumThreads(0)
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cv2.ocl.setUseOpenCL(False)
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import torch
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from torchvision.transforms import ColorJitter
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import torch.nn.functional as F
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class FlowAugmentor:
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def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=True):
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# spatial augmentation params
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self.crop_size = crop_size
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self.min_scale = min_scale
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self.max_scale = max_scale
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self.spatial_aug_prob = 0.8
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self.stretch_prob = 0.8
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self.max_stretch = 0.2
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# flip augmentation params
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self.do_flip = do_flip
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self.h_flip_prob = 0.5
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self.v_flip_prob = 0.1
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# photometric augmentation params
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self.photo_aug = ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.5/3.14)
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self.asymmetric_color_aug_prob = 0.2
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self.eraser_aug_prob = 0.5
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def color_transform(self, img1, img2):
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""" Photometric augmentation """
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# asymmetric
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if np.random.rand() < self.asymmetric_color_aug_prob:
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img1 = np.array(self.photo_aug(Image.fromarray(img1)), dtype=np.uint8)
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img2 = np.array(self.photo_aug(Image.fromarray(img2)), dtype=np.uint8)
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# symmetric
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else:
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image_stack = np.concatenate([img1, img2], axis=0)
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image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
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img1, img2 = np.split(image_stack, 2, axis=0)
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return img1, img2
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def eraser_transform(self, img1, img2, bounds=[50, 100]):
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""" Occlusion augmentation """
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ht, wd = img1.shape[:2]
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if np.random.rand() < self.eraser_aug_prob:
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mean_color = np.mean(img2.reshape(-1, 3), axis=0)
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for _ in range(np.random.randint(1, 3)):
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x0 = np.random.randint(0, wd)
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y0 = np.random.randint(0, ht)
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dx = np.random.randint(bounds[0], bounds[1])
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dy = np.random.randint(bounds[0], bounds[1])
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img2[y0:y0+dy, x0:x0+dx, :] = mean_color
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return img1, img2
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def spatial_transform(self, img1, img2, flow):
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# randomly sample scale
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ht, wd = img1.shape[:2]
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min_scale = np.maximum(
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(self.crop_size[0] + 8) / float(ht),
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(self.crop_size[1] + 8) / float(wd))
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scale = 2 ** np.random.uniform(self.min_scale, self.max_scale)
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scale_x = scale
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scale_y = scale
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if np.random.rand() < self.stretch_prob:
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scale_x *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
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scale_y *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
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scale_x = np.clip(scale_x, min_scale, None)
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scale_y = np.clip(scale_y, min_scale, None)
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if np.random.rand() < self.spatial_aug_prob:
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# rescale the images
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img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
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img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
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flow = cv2.resize(flow, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
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flow = flow * [scale_x, scale_y]
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if self.do_flip:
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if np.random.rand() < self.h_flip_prob: # h-flip
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img1 = img1[:, ::-1]
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img2 = img2[:, ::-1]
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flow = flow[:, ::-1] * [-1.0, 1.0]
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if np.random.rand() < self.v_flip_prob: # v-flip
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img1 = img1[::-1, :]
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img2 = img2[::-1, :]
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flow = flow[::-1, :] * [1.0, -1.0]
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y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0])
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x0 = np.random.randint(0, img1.shape[1] - self.crop_size[1])
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img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
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img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
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flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
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return img1, img2, flow
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def __call__(self, img1, img2, flow):
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img1, img2 = self.color_transform(img1, img2)
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img1, img2 = self.eraser_transform(img1, img2)
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img1, img2, flow = self.spatial_transform(img1, img2, flow)
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img1 = np.ascontiguousarray(img1)
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img2 = np.ascontiguousarray(img2)
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flow = np.ascontiguousarray(flow)
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return img1, img2, flow
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class SparseFlowAugmentor:
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def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=False):
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# spatial augmentation params
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self.crop_size = crop_size
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self.min_scale = min_scale
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self.max_scale = max_scale
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self.spatial_aug_prob = 0.8
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self.stretch_prob = 0.8
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self.max_stretch = 0.2
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# flip augmentation params
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self.do_flip = do_flip
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self.h_flip_prob = 0.5
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self.v_flip_prob = 0.1
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# photometric augmentation params
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self.photo_aug = ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.3/3.14)
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self.asymmetric_color_aug_prob = 0.2
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self.eraser_aug_prob = 0.5
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def color_transform(self, img1, img2):
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image_stack = np.concatenate([img1, img2], axis=0)
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image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
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img1, img2 = np.split(image_stack, 2, axis=0)
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return img1, img2
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def eraser_transform(self, img1, img2):
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ht, wd = img1.shape[:2]
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if np.random.rand() < self.eraser_aug_prob:
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mean_color = np.mean(img2.reshape(-1, 3), axis=0)
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for _ in range(np.random.randint(1, 3)):
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x0 = np.random.randint(0, wd)
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y0 = np.random.randint(0, ht)
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dx = np.random.randint(50, 100)
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dy = np.random.randint(50, 100)
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img2[y0:y0+dy, x0:x0+dx, :] = mean_color
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return img1, img2
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def resize_sparse_flow_map(self, flow, valid, fx=1.0, fy=1.0):
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ht, wd = flow.shape[:2]
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coords = np.meshgrid(np.arange(wd), np.arange(ht), indexing='ij')
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coords = np.stack(coords, axis=-1)
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coords = coords.reshape(-1, 2).astype(np.float32)
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flow = flow.reshape(-1, 2).astype(np.float32)
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valid = valid.reshape(-1).astype(np.float32)
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coords0 = coords[valid>=1]
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flow0 = flow[valid>=1]
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ht1 = int(round(ht * fy))
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wd1 = int(round(wd * fx))
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coords1 = coords0 * [fx, fy]
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flow1 = flow0 * [fx, fy]
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xx = np.round(coords1[:,0]).astype(np.int32)
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yy = np.round(coords1[:,1]).astype(np.int32)
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v = (xx > 0) & (xx < wd1) & (yy > 0) & (yy < ht1)
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xx = xx[v]
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yy = yy[v]
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flow1 = flow1[v]
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flow_img = np.zeros([ht1, wd1, 2], dtype=np.float32)
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valid_img = np.zeros([ht1, wd1], dtype=np.int32)
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flow_img[yy, xx] = flow1
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valid_img[yy, xx] = 1
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return flow_img, valid_img
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def spatial_transform(self, img1, img2, flow, valid):
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# randomly sample scale
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ht, wd = img1.shape[:2]
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min_scale = np.maximum(
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(self.crop_size[0] + 1) / float(ht),
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(self.crop_size[1] + 1) / float(wd))
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scale = 2 ** np.random.uniform(self.min_scale, self.max_scale)
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scale_x = np.clip(scale, min_scale, None)
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scale_y = np.clip(scale, min_scale, None)
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if np.random.rand() < self.spatial_aug_prob:
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# rescale the images
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img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
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img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
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flow, valid = self.resize_sparse_flow_map(flow, valid, fx=scale_x, fy=scale_y)
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if self.do_flip:
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if np.random.rand() < 0.5: # h-flip
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img1 = img1[:, ::-1]
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img2 = img2[:, ::-1]
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flow = flow[:, ::-1] * [-1.0, 1.0]
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valid = valid[:, ::-1]
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margin_y = 20
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margin_x = 50
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y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0] + margin_y)
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x0 = np.random.randint(-margin_x, img1.shape[1] - self.crop_size[1] + margin_x)
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y0 = np.clip(y0, 0, img1.shape[0] - self.crop_size[0])
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x0 = np.clip(x0, 0, img1.shape[1] - self.crop_size[1])
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img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
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img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
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flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
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valid = valid[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]]
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return img1, img2, flow, valid
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def __call__(self, img1, img2, flow, valid):
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img1, img2 = self.color_transform(img1, img2)
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img1, img2 = self.eraser_transform(img1, img2)
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img1, img2, flow, valid = self.spatial_transform(img1, img2, flow, valid)
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img1 = np.ascontiguousarray(img1)
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img2 = np.ascontiguousarray(img2)
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flow = np.ascontiguousarray(flow)
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valid = np.ascontiguousarray(valid)
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return img1, img2, flow, valid
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