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| import os | |
| import numbers | |
| import torch | |
| import torch.utils.data as data | |
| import torch | |
| import torchvision.transforms as transforms | |
| import random | |
| from PIL import Image, ImageOps | |
| import numpy as np | |
| import torchvision | |
| from . import flow_transforms | |
| import pdb | |
| import cv2 | |
| from utils.flowlib import read_flow | |
| from utils.util_flow import readPFM | |
| def default_loader(path): | |
| return Image.open(path).convert('RGB') | |
| def flow_loader(path): | |
| if '.pfm' in path: | |
| data = readPFM(path)[0] | |
| data[:,:,2] = 1 | |
| return data | |
| else: | |
| return read_flow(path) | |
| def disparity_loader(path): | |
| if '.png' in path: | |
| data = Image.open(path) | |
| data = np.ascontiguousarray(data,dtype=np.float32)/256 | |
| return data | |
| else: | |
| return readPFM(path)[0] | |
| class myImageFloder(data.Dataset): | |
| def __init__(self, iml0, iml1, flowl0, loader=default_loader, dploader= flow_loader, scale=1.,shape=[320,448], order=1, noise=0.06, pca_augmentor=True, prob = 1., cover=False, black=False, scale_aug=[0.4,0.2]): | |
| self.iml0 = iml0 | |
| self.iml1 = iml1 | |
| self.flowl0 = flowl0 | |
| self.loader = loader | |
| self.dploader = dploader | |
| self.scale=scale | |
| self.shape=shape | |
| self.order=order | |
| self.noise = noise | |
| self.pca_augmentor = pca_augmentor | |
| self.prob = prob | |
| self.cover = cover | |
| self.black = black | |
| self.scale_aug = scale_aug | |
| def __getitem__(self, index): | |
| iml0 = self.iml0[index] | |
| iml1 = self.iml1[index] | |
| flowl0= self.flowl0[index] | |
| th, tw = self.shape | |
| iml0 = self.loader(iml0) | |
| iml1 = self.loader(iml1) | |
| iml1 = np.asarray(iml1)/255. | |
| iml0 = np.asarray(iml0)/255. | |
| iml0 = iml0[:,:,::-1].copy() | |
| iml1 = iml1[:,:,::-1].copy() | |
| flowl0 = self.dploader(flowl0) | |
| #flowl0[:,:,-1][flowl0[:,:,0]==np.inf]=0 # for gtav window pfm files | |
| #flowl0[:,:,0][~flowl0[:,:,2].astype(bool)]=0 | |
| #flowl0[:,:,1][~flowl0[:,:,2].astype(bool)]=0 # avoid nan in grad | |
| flowl0 = np.ascontiguousarray(flowl0,dtype=np.float32) | |
| flowl0[np.isnan(flowl0)] = 1e6 # set to max | |
| ## following data augmentation procedure in PWCNet | |
| ## https://github.com/lmb-freiburg/flownet2/blob/master/src/caffe/layers/data_augmentation_layer.cu | |
| import __main__ # a workaround for "discount_coeff" | |
| try: | |
| with open('iter_counts-%d.txt'%int(__main__.args.logname.split('-')[-1]), 'r') as f: | |
| iter_counts = int(f.readline()) | |
| except: | |
| iter_counts = 0 | |
| schedule = [0.5, 1., 50000.] # initial coeff, final_coeff, half life | |
| schedule_coeff = schedule[0] + (schedule[1] - schedule[0]) * \ | |
| (2/(1+np.exp(-1.0986*iter_counts/schedule[2])) - 1) | |
| if self.pca_augmentor: | |
| pca_augmentor = flow_transforms.pseudoPCAAug( schedule_coeff=schedule_coeff) | |
| else: | |
| pca_augmentor = flow_transforms.Scale(1., order=0) | |
| if np.random.binomial(1,self.prob): | |
| co_transform = flow_transforms.Compose([ | |
| flow_transforms.Scale(self.scale, order=self.order), | |
| #flow_transforms.SpatialAug([th,tw], trans=[0.2,0.03], order=self.order, black=self.black), | |
| flow_transforms.SpatialAug([th,tw],scale=[self.scale_aug[0],0.03,self.scale_aug[1]], | |
| rot=[0.4,0.03], | |
| trans=[0.4,0.03], | |
| squeeze=[0.3,0.], schedule_coeff=schedule_coeff, order=self.order, black=self.black), | |
| #flow_transforms.pseudoPCAAug(schedule_coeff=schedule_coeff), | |
| flow_transforms.PCAAug(schedule_coeff=schedule_coeff), | |
| flow_transforms.ChromaticAug( schedule_coeff=schedule_coeff, noise=self.noise), | |
| ]) | |
| else: | |
| co_transform = flow_transforms.Compose([ | |
| flow_transforms.Scale(self.scale, order=self.order), | |
| flow_transforms.SpatialAug([th,tw], trans=[0.4,0.03], order=self.order, black=self.black) | |
| ]) | |
| augmented,flowl0 = co_transform([iml0, iml1], flowl0) | |
| iml0 = augmented[0] | |
| iml1 = augmented[1] | |
| if self.cover: | |
| ## randomly cover a region | |
| # following sec. 3.2 of http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Hierarchical_Deep_Stereo_Matching_on_High-Resolution_Images_CVPR_2019_paper.html | |
| if np.random.binomial(1,0.5): | |
| #sx = int(np.random.uniform(25,100)) | |
| #sy = int(np.random.uniform(25,100)) | |
| sx = int(np.random.uniform(50,125)) | |
| sy = int(np.random.uniform(50,125)) | |
| #sx = int(np.random.uniform(50,150)) | |
| #sy = int(np.random.uniform(50,150)) | |
| cx = int(np.random.uniform(sx,iml1.shape[0]-sx)) | |
| cy = int(np.random.uniform(sy,iml1.shape[1]-sy)) | |
| iml1[cx-sx:cx+sx,cy-sy:cy+sy] = np.mean(np.mean(iml1,0),0)[np.newaxis,np.newaxis] | |
| iml0 = torch.Tensor(np.transpose(iml0,(2,0,1))) | |
| iml1 = torch.Tensor(np.transpose(iml1,(2,0,1))) | |
| return iml0, iml1, flowl0 | |
| def __len__(self): | |
| return len(self.iml0) | |