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FlowRefine.py
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import torch
import os
import random
import cv2
import cvbase as cvb
import numpy as np
import torch.utils.data as data
import utils.image as im
class FlowSeq(data.Dataset):
def __init__(self, config, isTest=False):
super(FlowSeq, self).__init__()
self.config = config
self.data_items = []
self.isTest = isTest
self.size = self.config.IMAGE_SHAPE
self.res_size = self.config.RES_SHAPE
self.isTest = isTest
self.data_list = config.EVAL_LIST if isTest else config.TRAIN_LIST
with open(self.data_list, 'r') as f:
for line in f:
line = line.strip()
line = line.strip(' ')
line_split = line.split(' ')
flow_dir = line_split[0:22]
if self.config.DATA_ROOT is not None:
initial_flow_dir = [os.path.join(self.config.DATA_ROOT, x) for x in flow_dir]
if self.config.GT_FLOW_ROOT is not None:
gt_flow_dir = [os.path.join(self.config.GT_FLOW_ROOT, x) for x in flow_dir]
else:
gt_flow_dir = initial_flow_dir
if self.config.get_mask:
mask_dir = line_split[22:44]
if not self.config.FIX_MASK:
mask_dir = [os.path.join(self.config.MASK_ROOT, x) for x in mask_dir]
else:
mask_dir = [os.path.join(self.config.MASK_ROOT) for x in mask_dir]
video_class_no = int(line_split[-1])
if isTest:
output_dirs = line_split[-2]
if self.config.get_mask:
self.data_items.append((initial_flow_dir, video_class_no, gt_flow_dir, mask_dir, output_dirs))
else:
self.data_items.append((initial_flow_dir, video_class_no, gt_flow_dir, output_dirs))
else:
self.data_items.append((initial_flow_dir, video_class_no, gt_flow_dir))
def __len__(self):
return len(self.data_items)
def __getitem__(self, idx):
flow_dir = self.data_items[idx][0]
video_class_no = self.data_items[idx][1]
gt_dir = self.data_items[idx][2]
if self.config.get_mask:
mask_dirs = self.data_items[idx][3]
if self.isTest:
output_dirs = self.data_items[idx][-1]
mask_set = []
flow_mask_cat_set = []
flow_masked_set = []
gt_flow_set = []
gt_dir_set = [gt_dir[5], gt_dir[16]]
for p in gt_dir_set:
tmp_flow = cvb.read_flow(p)
tmp_flow = self._flow_tf(tmp_flow)
gt_flow_set.append(tmp_flow)
if self.config.MASK_MODE == 'bbox':
tmp_bbox = im.random_bbox(self.config)
tmp_mask = im.bbox2mask(self.config, tmp_bbox)
tmp_mask = tmp_mask[0, 0, :, :]
fix_mask = np.expand_dims(tmp_mask, axis=2)
elif self.config.MASK_MODE == 'mid-bbox':
tmp_mask = im.mid_bbox_mask(self.config)
tmp_mask = tmp_mask[0, 0, :, :]
fix_mask = np.expand_dims(tmp_mask, axis=2)
f_flow_dir = flow_dir[:11]
r_flow_dir = flow_dir[11:]
for i in range(11):
tmp_flow = cvb.read_flow(f_flow_dir[i])
if self.config.get_mask:
tmp_mask = cv2.imread(mask_dirs[i],
cv2.IMREAD_UNCHANGED)
tmp_mask = self._mask_tf(tmp_mask)
else:
if self.config.FIX_MASK:
tmp_mask = fix_mask.copy()
else:
tmp_bbox = im.random_bbox(self.config)
tmp_mask = im.bbox2mask(self.config, tmp_bbox)
tmp_mask = tmp_mask[0, 0, :, :]
tmp_mask = np.expand_dims(tmp_mask, axis=2)
tmp_flow = self._flow_tf(tmp_flow)
tmp_flow_masked = tmp_flow
flow_masked_set.append(tmp_flow_masked)
mask_set.append(tmp_mask)
mask_set.append(tmp_mask)
tmp_flow_mask_cat = np.concatenate((tmp_flow_masked, tmp_mask), axis=2)
flow_mask_cat_set.append(tmp_flow_mask_cat)
for i in range(11):
tmp_flow = cvb.read_flow(r_flow_dir[i])
tmp_flow = self._flow_tf(tmp_flow)
if self.config.get_mask:
tmp_mask = cv2.imread(mask_dirs[i+11],
cv2.IMREAD_UNCHANGED)
tmp_mask = self._mask_tf(tmp_mask)
else:
if self.config.FIX_MASK:
tmp_mask = fix_mask.copy()
else:
tmp_bbox = im.random_bbox(self.config)
tmp_mask = im.bbox2mask(self.config, tmp_bbox)
tmp_mask = tmp_mask[0, 0, :, :]
tmp_mask = np.expand_dims(tmp_mask, axis=2)
tmp_flow_masked = tmp_flow
flow_masked_set.append(tmp_flow_masked)
mask_set.append(tmp_mask)
mask_set.append(tmp_mask)
tmp_flow_mask_cat = np.concatenate((tmp_flow_masked, tmp_mask), axis=2)
flow_mask_cat_set.append(tmp_flow_mask_cat)
flow_mask_cat = np.concatenate(flow_mask_cat_set, axis=2)
flow_masked = np.concatenate(flow_masked_set, axis=2)
gt_flow = np.concatenate(gt_flow_set, axis=2)
mask = np.concatenate(mask_set, axis=2)
flow_mask_cat = torch.from_numpy(flow_mask_cat).permute(2, 0, 1).contiguous().float()
flow_masked = torch.from_numpy(flow_masked).permute(2, 0, 1).contiguous().float()
gt_flow = torch.from_numpy(gt_flow).permute(2, 0, 1).contiguous().float()
mask = torch.from_numpy(mask).permute(2, 0, 1).contiguous().float()
if self.isTest:
return flow_mask_cat, flow_masked, gt_flow, mask, output_dirs
else:
return flow_mask_cat, flow_masked, gt_flow, mask
def _img_tf(self, img):
img = cv2.resize(img, (self.size[1], self.size[0]))
img = img / 127.5 - 1
return img
def _mask_tf(self, mask):
mask = cv2.resize(mask, (self.size[1], self.size[0]),
interpolation=cv2.INTER_NEAREST)
mask = mask[:, :, 0]
mask = np.expand_dims(mask, axis=2)
mask = mask / 255
return mask
def _flow_tf(self, flow):
origin_shape = flow.shape
flow = cv2.resize(flow, (self.res_size[1], self.res_size[0]))
flow[:, :, 0] = flow[:, :, 0].clip(-1. * origin_shape[1], origin_shape[1]) / origin_shape[1] * self.res_size[1]
flow[:, :, 1] = flow[:, :, 1].clip(-1. * origin_shape[0], origin_shape[0]) / origin_shape[0] * self.res_size[0]
return flow