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rm unused

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encoding/datasets/cityscapes.py deleted 100644 → 0
View file @ 8c830f4c
###########################################################################
# Created by: Hang Zhang
# Email: zhang.hang@rutgers.edu
# Copyright (c) 2017
###########################################################################
import os
import sys
import numpy as np
import random
import math
from tqdm import tqdm
from PIL import Image, ImageOps, ImageFilter
import torch
import torch.utils.data as data
import torchvision.transforms as transform
class Segmentation(data.Dataset):
BASE_DIR = 'cityscapes'
def __init__(self, data_folder, mode='train', transform=None,
target_transform=None):
self.root = os.path.join(data_folder, self.BASE_DIR)
self.transform = transform
self.target_transform = target_transform
self.mode = mode
self.images, self.masks = get_city_pairs(self.root, mode)
assert (len(self.images) == len(self.masks))
print("Found {} images in subfolders of: {}\
".format(len(self.images), self.root + '/' + mode))
if len(self.images) == 0:
raise(RuntimeError("Found 0 images in subfolders of: \
" + self.root + "\n"))
def __getitem__(self, index):
img = Image.open(self.images[index]).convert('RGB')
if self.mode == 'test':
if self.transform is not None:
img = self.transform(img)
return img, os.path.basename(self.images[index])
mask = Image.open(self.masks[index])#.convert("P")
mask = np.array(mask)
mask += 1
mask[mask==256] = 0
mask = Image.fromarray(mask)
# synchrosized transform
if self.mode == 'train':
img, mask = self._sync_transform(img, mask)
elif self.mode == 'val':
img, mask = self._val_sync_transform(img, mask)
else:
raise RuntimeError('unknown mode for dataloader: {}'.format(self.mode))
# general resize, normalize and toTensor
if self.transform is not None:
#print("transform for input")
img = self.transform(img)
if self.target_transform is not None:
#print("transform for label")
mask = self.target_transform(mask)
return img, mask
def __len__(self):
return len(self.images)
def _val_sync_transform(self, img, mask):
"""
synchronized transformation
"""
outsize = 720
short = outsize
w, h = img.size
if w > h:
oh = short
ow = int(1.0 * w * oh / h)
else:
ow = short
oh = int(1.0 * h * ow / w)
img = img.resize((ow, oh), Image.BILINEAR)
mask = mask.resize((ow, oh), Image.NEAREST)
# center crop
w, h = img.size
x1 = int(round((w - outsize) / 2.))
y1 = int(round((h - outsize) / 2.))
img = img.crop((x1, y1, x1+outsize, y1+outsize))
mask = mask.crop((x1, y1, x1+outsize, y1+outsize))
return img, mask
def _sync_transform(self, img, mask):
# random mirror
if random.random() < 0.5:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
mask = mask.transpose(Image.FLIP_LEFT_RIGHT)
base_size = 2048
crop_size = 720
# random scale (short edge from 480 to 720)
long_size = random.randint(int(base_size*0.5), int(base_size*2.0))
w, h = img.size
if h > w:
oh = long_size
ow = int(1.0 * w * oh / h)
short_size = ow
else:
ow = long_size
oh = int(1.0 * h * ow / w)
short_size = oh
img = img.resize((ow, oh), Image.BILINEAR)
mask = mask.resize((ow, oh), Image.NEAREST)
# random rotate -10~10, mask using NN rotate
deg = random.uniform(-10,10)
img = img.rotate(deg, resample=Image.BILINEAR)
mask = mask.rotate(deg, resample=Image.NEAREST)
# pad crop
if short_size < crop_size:
padh = crop_size - oh if oh < crop_size else 0
padw = crop_size - ow if ow < crop_size else 0
img = ImageOps.expand(img, border=(0,0,padw,padh), fill=0)
mask = ImageOps.expand(mask, border=(0,0,padw,padh), fill=0)
# random crop 480
w, h = img.size
x1 = random.randint(0, w - crop_size)
y1 = random.randint(0, h - crop_size)
img = img.crop((x1, y1, x1+crop_size, y1+crop_size))
mask = mask.crop((x1, y1, x1+crop_size, y1+crop_size))
# gaussian blur as in PSP ?
if random.random() < 0.5:
img = img.filter(ImageFilter.GaussianBlur(
radius=random.random()))
return img, mask
def get_city_pairs(folder, mode='train'):
img_paths = []
mask_paths = []
img_folder = os.path.join(folder, 'leftImg8bit/' + mode)
mask_folder = os.path.join(folder, 'gtFine/'+ mode)
for root, directories, files in os.walk(img_folder):
for filename in files:#os.listdir(img_folder):
#basename, extension =os.path.splitext(filename)
if filename.endswith(".png"):
imgpath = os.path.join(root, filename)
foldername = os.path.basename(os.path.dirname(imgpath))
maskname = filename.replace('leftImg8bit','gtFine_trainIds')
maskpath = os.path.join(mask_folder, foldername, maskname)
if os.path.isfile(imgpath) and os.path.isfile(maskpath):
img_paths.append(imgpath)
mask_paths.append(maskpath)
else:
print('cannot find the mask or image:', imgpath, maskpath)
return img_paths, mask_paths
encoding/datasets/cityscapescoarse.py deleted 100644 → 0
View file @ 8c830f4c
###########################################################################
# Created by: Hang Zhang
# Email: zhang.hang@rutgers.edu
# Copyright (c) 2017
###########################################################################
import os
import sys
import numpy as np
import random
import math
from tqdm import tqdm
from PIL import Image, ImageOps, ImageFilter
import torch
import torch.utils.data as data
import torchvision.transforms as transform
class Segmentation(data.Dataset):
BASE_DIR = 'cityscapes'
def __init__(self, data_folder, mode='train', transform=None,
target_transform=None):
self.root = os.path.join(data_folder, self.BASE_DIR)
self.transform = transform
self.target_transform = target_transform
self.mode = mode
self.images, self.masks = get_city_pairs(self.root, mode)
assert (len(self.images) == len(self.masks))
if len(self.images) == 0:
raise(RuntimeError("Found 0 images in subfolders of: \
" + self.root + "\n"))
def __getitem__(self, index):
img = Image.open(self.images[index]).convert('RGB')
if self.mode == 'test':
if self.transform is not None:
img = self.transform(img)
return img, os.path.basename(self.images[index])
mask = Image.open(self.masks[index])#.convert("P")
mask = np.array(mask)
mask += 1
mask[mask==256] = 0
mask = Image.fromarray(mask)
# synchrosized transform
if self.mode == 'train':
img, mask = self._sync_transform(img, mask)
elif self.mode == 'val':
img, mask = self._val_sync_transform(img, mask)
else:
raise RuntimeError('unknown mode for dataloader: {}'.format(self.mode))
# general resize, normalize and toTensor
if self.transform is not None:
#print("transform for input")
img = self.transform(img)
if self.target_transform is not None:
#print("transform for label")
mask = self.target_transform(mask)
return img, mask
def __len__(self):
return len(self.images)
def _val_sync_transform(self, img, mask):
"""
synchronized transformation
"""
outsize = 720
short = outsize
w, h = img.size
if w > h:
oh = short
ow = int(1.0 * w * oh / h)
else:
ow = short
oh = int(1.0 * h * ow / w)
img = img.resize((ow, oh), Image.BILINEAR)
mask = mask.resize((ow, oh), Image.NEAREST)
# center crop
w, h = img.size
x1 = int(round((w - outsize) / 2.))
y1 = int(round((h - outsize) / 2.))
img = img.crop((x1, y1, x1+outsize, y1+outsize))
mask = mask.crop((x1, y1, x1+outsize, y1+outsize))
return img, mask
def _sync_transform(self, img, mask):
# random mirror
if random.random() < 0.5:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
mask = mask.transpose(Image.FLIP_LEFT_RIGHT)
base_size = 2048
crop_size = 720
# random scale (short edge from 480 to 720)
long_size = random.randint(int(base_size*0.5), int(base_size*2.0))
w, h = img.size
if h > w:
oh = long_size
ow = int(1.0 * w * oh / h)
short_size = ow
else:
ow = long_size
oh = int(1.0 * h * ow / w)
short_size = oh
img = img.resize((ow, oh), Image.BILINEAR)
mask = mask.resize((ow, oh), Image.NEAREST)
# random rotate -10~10, mask using NN rotate
deg = random.uniform(-10,10)
img = img.rotate(deg, resample=Image.BILINEAR)
mask = mask.rotate(deg, resample=Image.NEAREST)
# pad crop
if short_size < crop_size:
padh = crop_size - oh if oh < crop_size else 0
padw = crop_size - ow if ow < crop_size else 0
img = ImageOps.expand(img, border=(0,0,padw,padh), fill=0)
mask = ImageOps.expand(mask, border=(0,0,padw,padh), fill=0)
# random crop 480
w, h = img.size
x1 = random.randint(0, w - crop_size)
y1 = random.randint(0, h - crop_size)
img = img.crop((x1, y1, x1+crop_size, y1+crop_size))
mask = mask.crop((x1, y1, x1+crop_size, y1+crop_size))
# gaussian blur as in PSP ?
if random.random() < 0.5:
img = img.filter(ImageFilter.GaussianBlur(
radius=random.random()))
return img, mask
def get_city_pairs(folder, mode='train'):
img_paths = []
mask_paths = []
if mode=='train':
img_folder = os.path.join(folder, 'leftImg8bit/train_extra')
mask_folder = os.path.join(folder, 'gtCoarse/train_extra')
else:
img_folder = os.path.join(folder, 'leftImg8bit/val')
mask_folder = os.path.join(folder, 'gtFine/val')
for root, directories, files in os.walk(img_folder):
for filename in files:
basename, extension =os.path.splitext(filename)
if filename.endswith(".png"):
imgpath = os.path.join(root, filename)
foldername = os.path.basename(os.path.dirname(imgpath))
maskname = filename.replace('leftImg8bit','gtCoarse_trainIds')
maskpath = os.path.join(mask_folder, foldername, maskname)
if os.path.isfile(imgpath) and os.path.isfile(maskpath):
img_paths.append(imgpath)
mask_paths.append(maskpath)
else:
print('cannot find the mask or image:', imgpath, maskpath)
return img_paths, mask_paths
encoding/datasets/coco.py deleted 100644 → 0
View file @ 8c830f4c
import os
from tqdm import tqdm, trange
from PIL import Image, ImageOps, ImageFilter
import torch
import torch.utils.data as data
import torchvision.transforms as transform
import random
import math
import numpy as np
from .dataset import ToLabel
"""
NUM_CHANNEL = 91
[] background
[5] airplane
[2] bicycle
[16] bird
[9] boat
[44] bottle
[6] bus
[3] car
[17] cat
[62] chair
[21] cow
[67] dining table
[18] dog
[19] horse
[4] motorcycle
[1] person
[64] potted plant
[20] sheep
[63] couch
[7] train
[72] tv
"""
catlist = [0, 5, 2, 16, 9, 44, 6, 3, 17, 62, 21, 67, 18, 19, 4,
1, 64, 20, 63, 7, 72]
class Segmentation(data.Dataset):
def __init__(self, root, mode='train', transform=None,
target_transform=None):
from pycocotools.coco import COCO
from pycocotools import mask
if mode == 'train':
print('train set')
ann_file = os.path.join(root, 'coco/annotations/instances_train2014.json')
ids_file = os.path.join(root, 'coco/annotations/train_ids.pth')
root = os.path.join(root, 'coco/train2014')
else:
print('val set')
ann_file = os.path.join(root, 'coco/annotations/instances_val2014.json')
ids_file = os.path.join(root, 'coco/annotations/val_ids.pth')
root = os.path.join(root, 'coco/val2014')
self.train = mode
self.root = root
self.coco = COCO(ann_file)
self.coco_mask = mask
if os.path.exists(ids_file):
self.ids = torch.load(ids_file)
else:
self.new_ids = []
self.ids = list(self.coco.imgs.keys())
self.preprocess(ids_file)
self.ids = self.new_ids
self.transform = transform
self.target_transform = target_transform
def preprocess(self, ids_file):
tbar = trange(len(self.ids))
for i in tbar:
img_id = self.ids[i]
cocotarget = self.coco.loadAnns(self.coco.getAnnIds(imgIds=img_id))
img_metadata = self.coco.loadImgs(img_id)[0]
mask = self._gen_seg_mask(cocotarget, img_metadata['height'],
img_metadata['width'])
# more than 1k pixels
if (mask > 0).sum() > 1000:
self.new_ids.append(img_id)
tbar.set_description('Doing: {}/{}, got {} qualified images'.\
format(i, len(self.ids), len(self.new_ids)))
print('number of qualified images: ', len(self.new_ids))
torch.save(self.new_ids, ids_file)
def __getitem__(self, index):
coco = self.coco
img_id = self.ids[index]
cocotarget = coco.loadAnns(coco.getAnnIds(imgIds=img_id))
img_metadata = coco.loadImgs(img_id)[0]
path = img_metadata['file_name']
img = Image.open(os.path.join(self.root, path)).convert('RGB')
mask = Image.fromarray(
self._gen_seg_mask(cocotarget, img_metadata['height'],
img_metadata['width'])
)
# synchrosized transform
if True:#self.train == 'train':
img, mask = self._sync_transform(img, mask)
else:
img, mask = self._val_sync_transform(img, mask)
# general resize, normalize and toTensor
if self.transform is not None:
#print("transform for input")
img = self.transform(img)
if self.target_transform is not None:
#print("transform for label")
mask = self.target_transform(mask)
return img, mask
def __len__(self):
return len(self.ids)
def _val_sync_transform(self, img, mask):
outsize = 480
short = outsize
w, h = img.size
if w > h:
oh = short
ow = int(1.0 * w * oh / h)
else:
ow = short
oh = int(1.0 * h * ow / w)
img = img.resize((ow, oh), Image.BILINEAR)
mask = mask.resize((ow, oh), Image.NEAREST)
# center crop
w, h = img.size
x1 = int(round((w - outsize) / 2.))
y1 = int(round((h - outsize) / 2.))
img = img.crop((x1, y1, x1+outsize, y1+outsize))
mask = mask.crop((x1, y1, x1+outsize, y1+outsize))
return img, mask
def _sync_transform(self, img, mask):
# random mirror
if random.random() < 0.5:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
mask = mask.transpose(Image.FLIP_LEFT_RIGHT)
base_size = 520
crop_size = 480
# random scale (short edge from 480 to 720)
long_size = random.randint(int(base_size*0.5), int(base_size*2.0))
w, h = img.size
if h > w:
oh = long_size
ow = int(1.0 * w * oh / h)
short_size = ow
else:
ow = long_size
oh = int(1.0 * h * ow / w)
short_size = oh
img = img.resize((ow, oh), Image.BILINEAR)
mask = mask.resize((ow, oh), Image.NEAREST)
# random rotate -10~10, mask using NN rotate
deg = random.uniform(-10,10)
img = img.rotate(deg, resample=Image.BILINEAR)
mask = mask.rotate(deg, resample=Image.NEAREST)
# pad crop
if short_size < crop_size:
padh = crop_size - oh if oh < crop_size else 0
padw = crop_size - ow if ow < crop_size else 0
img = ImageOps.expand(img, border=(0,0,padw,padh), fill=0)
mask = ImageOps.expand(mask, border=(0,0,padw,padh), fill=0)
# random crop 480
w, h = img.size
x1 = random.randint(0, w - crop_size)
y1 = random.randint(0, h - crop_size)
img = img.crop((x1, y1, x1+crop_size, y1+crop_size))
mask = mask.crop((x1, y1, x1+crop_size, y1+crop_size))
# gaussian blur as in PSP ?
if random.random() < 0.5:
img = img.filter(ImageFilter.GaussianBlur(
radius=random.random()))
return img, mask
def _gen_seg_mask(self, target, h, w):
mask = np.zeros((h, w), dtype=np.uint8)
coco_mask = self.coco_mask
for instance in target:
rle = coco_mask.frPyObjects(instance['segmentation'], h, w)
m = coco_mask.decode(rle)
cat = instance['category_id']
if cat in catlist:
c = catlist.index(cat)
else:
continue
if len(m.shape) < 3:
mask[:, :] += (mask == 0) * (m * c)
else:
mask[:, :] += (mask == 0) * (((np.sum(m, axis=2)) > 0) * c).astype(np.uint8)
return mask
encoding/datasets/download_datasets.sh deleted 100644 → 0
View file @ 8c830f4c
mkdir -p "$HOME"/data
cd "$HOME"/data
# augmented PASCAL VOC
wget http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz # 1.3 GB
tar -zxvf benchmark.tgz
mv benchmark_RELEASE VOCaug
<<<<<<< HEAD
# generate trainval txt
cd VOCaug/dataset/
cp train.txt trainval.txt
cat val.txt >> trainval.txt
cd -
=======
# generate trainval.txt
cd VOCaug/dataset/
cp train.txt trainval.txt
cat val.txt >> trainval.txt
cd "$HOME"/data
>>>>>>> d7e511b09ee6127be11f03497b7d83327e9f7b1b
# original PASCAL VOC 2012
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar # 2 GB
tar -xvf VOCtrainval_11-May-2012.tar
<<<<<<< HEAD
# for PASCAL VOC testset, you need to login and manually download from (http://host.robots.ox.ac.uk:8080/)
=======
cd -
>>>>>>> d7e511b09ee6127be11f03497b7d83327e9f7b1b
encoding/datasets/folder.py deleted 100644 → 0
View file @ 8c830f4c
###########################################################################
# Created by: Hang Zhang
# Email: zhang.hang@rutgers.edu
# Copyright (c) 2017
###########################################################################
import os
import sys
import numpy as np
import random
import math
import torch.utils.data as data
from PIL import Image, ImageOps
import torch.utils.data as data
import torchvision.transforms as transform
from .dataset import ToLabel
class FolderLoader(data.Dataset):
def __init__(self, root, transform=None):
self.root = root
self.transform = transform
self.images = get_folder_images(root)
if len(self.images) == 0:
raise(RuntimeError("Found 0 images in subfolders of: \
" + self.root + "\n"))
def __getitem__(self, index):
img = Image.open(self.images[index]).convert('RGB')
if self.transform is not None:
img = self.transform(img)
return img, os.path.basename(self.images[index])
def __len__(self):
return len(self.images)
def get_folder_images(img_folder):
img_paths = []
for filename in os.listdir(img_folder):
if filename.endswith(".jpg"):
imgpath = os.path.join(img_folder, filename)
img_paths.append(imgpath)
return img_paths
class Dataloder():
def __init__(self, args):
# the data augmentation is implemented as part of the dataloader
assert(args.test)
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize(args.mean, args.std)])
args.test_batch_size = 1
assert(args.test_folder is not None)
print('loading the data from: {}'.format(args.test_folder))
testset = FolderLoader(args.test_folder, input_transform)
kwargs = {'num_workers': args.workers, 'pin_memory': True} \
if args.cuda else {}
self.trainloader = None
self.testloader = data.DataLoader(testset,
batch_size=args.test_batch_size,
shuffle=False, **kwargs)
def getloader(self):
return self.trainloader, self.testloader
encoding/datasets/hpw18.py deleted 100644 → 0
View file @ 8c830f4c
# created by: Sean Liu
# Amazon Lab 126
from __future__ import print_function
import errno
import hashlib
import os
import sys
import tarfile
import numpy as np
import random
import math
import torch.utils.data as data
import PIL
from PIL import Image, ImageOps
from six.moves import urllib
class Segmentation_HPW18(data.Dataset):
CLASSES = [
'background', 'hat', 'hair', 'sunglasses', 'upper-clothes',
'skirt', 'pants', 'dress', 'belt', 'left-shoe', 'right-shoe',
'face', 'left-leg', 'right-leg', 'left-arm', 'right-arm', 'bag',
'scarf'
]
URL = "/cvdata1/lliuqian/humanParsingDataset"
FILE = "hpw18.tar.gz"
MD5 = ''
BASE_DIR = ''
def __init__(self,
root,
train=True,
transform=None,
target_transform=None,
download=False):
self.root = root
_hpw18_root = os.path.join(self.root, self.BASE_DIR)
_mask_dir = os.path.join(_hpw18_root, 'SegmentationClassAug_256x384')
_image_dir = os.path.join(_hpw18_root, 'JPEGImages_256x384')
self.transform = transform
self.target_transform = target_transform
self.train = train
if download:
self._download()
# train/val/test splits are pre-cut
_splits_dir = _hpw18_root
_split_f = os.path.join(_splits_dir, 'humanparsingImageMask_256x384_absPath_train.txt')
if not self.train:
_split_f = os.path.join(_splits_dir, 'humanparsingImageMask_256x384_absPath_val.txt')
print("reading from ", _split_f)
self.images = []
self.masks = []
with open(os.path.join(_split_f), "r") as lines:
for line in lines:
s = line.split()
_image = s[0] # image absolution path
_mask = s[1] # mask absolution path
assert os.path.isfile(_image)
assert os.path.isfile(_mask)
self.images.append(_image)
self.masks.append(_mask)
assert (len(self.images) == len(self.masks))
def __getitem__(self, index):
_img = Image.open(self.images[index]).convert('RGB')
_timg = Image.open(self.masks[index])
_target = np.array(_timg, dtype=np.uint8)
_target = Image.fromarray(_target)
# synchrosized transform
if self.train:
_img, _target = self._sync_transform( _img, _target)
# general resize, normalize and toTensor
if self.transform is not None:
_img = self.transform(_img)
if self.target_transform is not None:
_target = self.target_transform(_target)
return _img, _target
def __len__(self):
return len(self.images)
def _sync_transform(self, img, mask):
# random rotate -10~10
deg = random.uniform(-10,10)
img = img.rotate(deg)
mask = mask.rotate(deg, PIL.Image.NEAREST)
return img, mask
if __name__ == '__main__':
hpw18 = Segmentation_HPW18('/cvdata1/lliuqian/', train=True)
print(hpw18[0])
print (len(hpw18))
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