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xview2.py
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xview2.py
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import os
import json
import cv2
import numpy as np
import random
from shapely import wkt
from shapely.geometry import Polygon
import torch
from torch.utils.data import Dataset, DataLoader
from utils import preprocess
import torchvision.transforms as transforms
import multiprocessing
multiprocessing.set_start_method('spawn', True)
class XView2Dataset(Dataset):
"""xView2
input: Post image
target: pixel-wise classes
"""
dmg_type = {'background': 0, 'no-damage': 1, 'minor-damage': 2, 'major-damage': 3, 'destroyed': 4,
'un-classified': 255}
diaster_type = {'earthquake': 0, 'fire': 1, 'tsunami': 2, 'volcano': 3, 'wind': 4, 'flooding': 5}
def __init__(self, root_dir, rgb_bgr='rgb', preprocessing=None, mode='train'):
assert mode in ('train', 'test')
self.mode = mode
self.root = root_dir
assert rgb_bgr in ('rgb', 'bgr')
self.rgb = bool(rgb_bgr == 'rgb')
self.preprocessing = preprocessing
self.dirs = {'train_imgs': os.path.join(self.root, 'train', 'images'),
'train_labs': os.path.join(self.root, 'train', 'labels'),
'tier3_imgs': os.path.join(self.root, 'tier3', 'images'),
'tier3_labs': os.path.join(self.root, 'tier3', 'labels'),
'test_imgs': os.path.join(self.root, 'test', 'images')}
train_imgs = [s for s in os.listdir(self.dirs['train_imgs'])]
tier3_imgs = [s for s in os.listdir(self.dirs['tier3_imgs'])]
train_labs = [s for s in os.listdir(self.dirs['train_labs'])]
tier3_labs = [s for s in os.listdir(self.dirs['tier3_labs'])]
test_imgs = [s for s in os.listdir(self.dirs['test_imgs'])]
self.sample_files = []
self.neg_sample_files = []
if self.mode == 'train':
self.add_samples_train(self.dirs['train_imgs'], self.dirs['train_labs'], train_imgs, train_labs)
self.add_samples_train(self.dirs['tier3_imgs'], self.dirs['tier3_labs'], tier3_imgs, tier3_labs)
else:
for pre in os.listdir(self.dirs['test_imgs']):
if pre[:9] != 'test_pre_':
continue
img_id = pre[9:][:-4]
post = 'test_post_' + pre[9:]
assert post in test_imgs
files = {'img_id': img_id,
'pre_img': os.path.join(self.dirs['test_imgs'], pre),
'post_img': os.path.join(self.dirs['test_imgs'], post)}
self.sample_files.append(files)
if mode == 'test':
self.data_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
def add_samples_train(self, img_dirs, lab_dirs, imgs, labs):
for pre in os.listdir(img_dirs):
if pre[-17:] != '_pre_disaster.png':
continue
chop = pre[:-4].split('_')
img_id = '_'.join(chop[:2])
post = img_id + '_post_disaster.png'
pre_json = img_id + '_pre_disaster.json'
post_json = img_id + '_post_disaster.json'
assert post in imgs
assert pre_json in labs
assert post_json in labs
assert img_id not in self.sample_files
files = {'img_id': img_id,
'pre_img': os.path.join(img_dirs, pre),
'post_img': os.path.join(img_dirs, post),
'pre_json': os.path.join(lab_dirs, pre_json),
'post_json': os.path.join(lab_dirs, post_json)}
self.sample_files.append(files)
def get_sample_info(self, idx):
files = self.sample_files[idx]
pre_img = cv2.imread(files['pre_img'])
post_img = cv2.imread(files['post_img'])
if self.rgb:
pre_img = cv2.cvtColor(pre_img, cv2.COLOR_BGR2RGB)
post_img = cv2.cvtColor(post_img, cv2.COLOR_BGR2RGB)
pre_json = json.loads(open(files['pre_json']).read())
post_json = json.loads(open(files['post_json']).read())
sample = {'pre_img': pre_img, 'post_img': post_img, 'image_id': files['img_id'],
'im_width': post_json['metadata']['width'],
'im_height': post_json['metadata']['height'],
'disaster': post_json['metadata']['disaster_type'],
'pre_meta': {m: pre_json['metadata'][m] for m in pre_json['metadata']},
'post_meta': {m: post_json['metadata'][m] for m in post_json['metadata']},
'pre_builds': dict(), 'post_builds': dict(), 'builds': dict()}
for b in pre_json['features']['xy']:
buid = b['properties']['uid']
sample['pre_builds'][buid] = {p: b['properties'][p] for p in b['properties']}
poly = Polygon(wkt.loads(b['wkt']))
sample['pre_builds'][buid]['poly'] = list(poly.exterior.coords)
for b in post_json['features']['xy']:
buid = b['properties']['uid']
sample['post_builds'][buid] = {p: b['properties'][p] for p in b['properties']}
poly = Polygon(wkt.loads(b['wkt']))
sample['post_builds'][buid]['poly'] = list(poly.exterior.coords)
sample['builds'][buid] = {'poly': list(poly.exterior.coords),
'subtype': b['properties']['subtype']}
# sample['mask_img'] = self.make_mask_img(**sample)
return sample
def __getitem__(self, idx):
files = self.sample_files[idx]
pre_img = cv2.imread(files['pre_img'])
post_img = cv2.imread(files['post_img'])
if self.rgb:
pre_img = cv2.cvtColor(pre_img, cv2.COLOR_BGR2RGB)
post_img = cv2.cvtColor(post_img, cv2.COLOR_BGR2RGB)
if self.mode == 'train':
sample = self.get_sample_with_mask(files, pre_img, post_img)
sample['image_id'] = files['img_id']
if self.preprocessing is not None:
transformed = preprocess(sample['pre_img'], sample['post_img'], sample['mask_img'],
flip=self.preprocessing['flip'],
scale=self.preprocessing['scale'],
crop=self.preprocessing['crop'])
sample['pre_img'] = transformed[0]
sample['post_img'] = transformed[1]
sample['mask_img'] = transformed[2]
else:
pre_img = self.data_transforms(pre_img)
post_img = self.data_transforms(post_img)
sample = {'pre_img': pre_img, 'post_img': post_img, 'image_id': files['img_id']}
return sample
@staticmethod
def _get_building_from_json(post_json):
buildings = dict()
for b in post_json['features']['xy']:
buid = b['properties']['uid']
poly = Polygon(wkt.loads(b['wkt']))
buildings[buid] = {'poly': list(poly.exterior.coords),
'subtype': b['properties']['subtype']}
return buildings
def get_sample_with_mask(self, files, pre_img, post_img):
post_json = json.loads(open(files['post_json']).read())
sample = {'pre_img': pre_img, 'post_img': post_img, 'image_id': files['img_id'],
'disaster': self.diaster_type[post_json['metadata']['disaster_type']]}
buildings = self._get_building_from_json(post_json)
sample['mask_img'] = self.make_mask_img(**buildings)
return sample
def make_mask_img(self, **kwargs):
width = 1024
height = 1024
builings = kwargs
mask_img = np.zeros([height, width], dtype=np.uint8)
for dmg in self.dmg_type:
polys_dmg = [np.array(builings[p]['poly']).round().astype(np.int32).reshape(-1, 1, 2)
for p in builings if builings[p]['subtype'] == dmg]
cv2.fillPoly(mask_img, polys_dmg, [self.dmg_type[dmg]])
return mask_img
def show_sample(self, **kwargs):
pass
def __len__(self):
return len(self.sample_files)
if __name__ == '__main__':
root_path = "/mnt/Dataset/xView2/v2"
dataset = XView2Dataset(root_path, rgb_bgr='rgb', preprocessing={'flip': True, 'scale': None, 'crop': (513, 513)})
dataset_test = XView2Dataset(root_path, rgb_bgr='rgb',
preprocessing={'flip': False, 'scale': (0.8, 2.0), 'crop': (1024, 1024)})
n_samples = len(dataset)
n_train = int(n_samples * 0.85)
n_test = n_samples - n_train
trainset, testset = torch.utils.data.random_split(dataset, [n_train, n_test])
dataloader = DataLoader(trainset, batch_size=5, shuffle=True, num_workers=4)
for i in range(n_test):
sample = testset[i]
original_idx = testset.indices[i]
info = dataset.get_sample_info(original_idx)
info2 = dataset_test.get_sample_info(original_idx)
sample2 = dataset_test[original_idx]
print(i, original_idx, sample['disaster'], sample['image_id'], sample['post_img'].shape)
print(i, original_idx, sample2['disaster'], sample2['image_id'], sample2['post_img'].shape)
print(i, original_idx, info['disaster'], info['image_id'])
print(i, original_idx, info2['disaster'], info2['image_id'])
for i, samples in enumerate(dataloader):
print(i, samples['disaster'], samples['image_id'], samples['post_img'].shape)