forked from tkuanlun350/Tensorflow-SegNet
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datasets.py
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datasets.py
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import random
import numpy as np
import cv2
from glob import glob
import gzip
import pickle
import torch.utils.data as data
import os
from os.path import join, basename, dirname, exists
from utils import maybe_download
#from args import *
home = os.environ['HOME']
class ArtPrint(data.Dataset):
'''artifact printings dataset'''
def __init__(self, root='/root/datasets', transform=None):
self.transform=transform
self.root = join(root,'artifact_images','artifact_images') #zip problem, sorry
# download and put dataset in correct directory
maybe_download('https://www.dropbox.com/s/gyod1hqau4a9lnj/artifact_images.zip?dl=0',
'artifact_images', root, 'folder')
#if exists(join(self.root,'words.tgz')):
# if not exists(join(self.root, 'words')):
# os.makedirs(join(self.root, 'words'))
# os.system('tar xvzf '+join(self.root, 'words.tgz')+' --directory '+join(self.root, 'words'))
# os.system('rm '+join(self.root,'words.tgz'))
# begin collecting all words in IAM dataset frm the words.txt summary file at the root of IAM directiory
labelsFile = open(join(self.root,'databook.txt'))
#chars = set()
self.samples = []
#ct=0
for line in labelsFile:
#ct+=1
# ignore comment line
if not line or line[0] == '#':
continue
lineSplit = line.strip().split(' ')
assert len(lineSplit) ==3
#fileNameSplit = lineSplit[0].split('-')
imgPath = lineSplit[0].replace('/root/datasets/artifact_images',self.root)
# GT text are columns starting at 9
labelPath = lineSplit[1].replace('/root/datasets/artifact_images',self.root)
gt_text=lineSplit[2]
# put sample into list
# qyk exclude empty images
# if '---' not in label: # qyk: data clean
# img_test=cv2.imread(fileName, cv2.IMREAD_GRAYSCALE) #qyk: data clean
# if not (img_test is None or np.min(img_test.shape) <= 1): #qyk: data clean
# self.samples.append( (fileName, label) ) #qyk
self.samples.append((imgPath,labelPath,gt_text))
# makes list of characters
# chars = chars.union(set(list(label)))
#self.charList = sorted(list(chars))
#if ct>=1000:
# break
def __str__(self):
return 'Artifact word image dataset. Data location: '+self.root+', Length: '+str(len(self.samples))
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
label = cv2.imread(self.samples[idx][1],cv2.IMREAD_GRAYSCALE)
# img = preprocess(cv2.imread(self.samples[i][0], cv2.IMREAD_GRAYSCALE),
# args.imgsize, self.args, False, is_testing)
img = cv2.imread(self.samples[idx][0], cv2.IMREAD_GRAYSCALE)
#gt=self.samples[idx][2]
if self.transform:
img = self.transform(img)
label=self.transform(label)
return img, label
class ArtPrintNoIntsect(data.Dataset):
'''artifact printings dataset - no intersect label'''
def __init__(self, root='/root/datasets', transform=None):
self.transform=transform
self.root = join(root,'artifact_images_no_intersect','artifact_images_no_intersect') #zip problem, sorry
# download and put dataset in correct directory
maybe_download('https://www.dropbox.com/s/rogd4d5ilfm4g5e/artifact_images_no_intersect.zip?dl=0',
'artifact_images_no_intersect', root, 'folder')
#if exists(join(self.root,'words.tgz')):
# if not exists(join(self.root, 'words')):
# os.makedirs(join(self.root, 'words'))
# os.system('tar xvzf '+join(self.root, 'words.tgz')+' --directory '+join(self.root, 'words'))
# os.system('rm '+join(self.root,'words.tgz'))
# begin collecting all words in IAM dataset frm the words.txt summary file at the root of IAM directiory
labelsFile = open(join(self.root,'databook.txt'))
#chars = set()
self.samples = []
#ct=0
for line in labelsFile:
#ct+=1
# ignore comment line
if not line or line[0] == '#':
continue
lineSplit = line.strip().split(' ')
assert len(lineSplit) ==3
#fileNameSplit = lineSplit[0].split('-')
imgPath = lineSplit[0].replace('/root/datasets/artifact_images_no_intersect',self.root)
# GT text are columns starting at 9
labelPath = lineSplit[1].replace('/root/datasets/artifact_images_no_intersect',self.root)
gt_text=lineSplit[2]
# put sample into list
# qyk exclude empty images
# if '---' not in label: # qyk: data clean
# img_test=cv2.imread(fileName, cv2.IMREAD_GRAYSCALE) #qyk: data clean
# if not (img_test is None or np.min(img_test.shape) <= 1): #qyk: data clean
# self.samples.append( (fileName, label) ) #qyk
self.samples.append((imgPath,labelPath,gt_text))
# makes list of characters
# chars = chars.union(set(list(label)))
#self.charList = sorted(list(chars))
#if ct>=10000:
#break
def __str__(self):
return 'Artifact word image dataset - no intersect label. Data location: '+self.root+', Length: '+str(len(self.samples))
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
label = cv2.imread(self.samples[idx][1],cv2.IMREAD_GRAYSCALE)
# img = preprocess(cv2.imread(self.samples[i][0], cv2.IMREAD_GRAYSCALE),
# args.imgsize, self.args, False, is_testing)
img = cv2.imread(self.samples[idx][0], cv2.IMREAD_GRAYSCALE)
#gt=self.samples[idx][2]
if self.transform:
img = self.transform(img)
label=self.transform(label)
return img, label
if __name__=='__main__':
artp=ArtPrint()
leng=artp.__len__()
print(leng)
for idx in range(leng):
img,label=artp.__getitem__(idx)
if img.shape!=(32,128) or label.shape!=(32,128):
print('-----')
print(img.shape)
print(label.shape)