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dataset.py
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dataset.py
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import os
import cv2
import glob
import numpy as np
from PIL import Image
import torch
import torchvision
from torch.utils import data
from torchvision import transforms
import torch
from torch import nn
import torchvision
import torch.nn.functional as F
def get_char_dict():
char_dict = {}
char_dict["pad"] = 0
char_dict["sos"] = 1
char_dict["eos"] = 2
for i in range(32, 127):
char_dict[chr(i)] = len(char_dict)
inverse_char_dict = {v: k for k, v in char_dict.items()}
return char_dict, inverse_char_dict
def resize_image(image, desired_size):
''' Helper function to resize an image while keeping the aspect ratio.
Parameter
---------
image: np.array
The image to be resized.
desired_size: (int, int)
The (height, width) of the resized image
Return
------
image: np.array
The image of size = desired_size
bounding box: (int, int, int, int)
(x, y, w, h) in percentages of the resized image of the original
'''
size = image.shape[:2]
if size[0] > desired_size[0] or size[1] > desired_size[1]:
ratio_w = float(desired_size[0]) / size[0]
ratio_h = float(desired_size[1]) / size[1]
ratio = min(ratio_w, ratio_h)
new_size = tuple([int(x * ratio) for x in size])
image = cv2.resize(image, (new_size[1], new_size[0]))
size = image.shape
delta_w = max(0, desired_size[1] - size[1])
delta_h = max(0, desired_size[0] - size[0])
top, bottom = delta_h // 2, delta_h - (delta_h // 2)
left, right = delta_w // 2, delta_w - (delta_w // 2)
color = image[0][0]
if color < 230:
color = 230
image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=float(color))
crop_bb = (left / image.shape[1], top / image.shape[0], (image.shape[1] - right - left) / image.shape[1],
(image.shape[0] - bottom - top) / image.shape[0])
image[image > 230] = 255
return image, crop_bb
def get_transform(phase="train"):
transfrom_PIL_list = [
transforms.RandomAffine((-2, 2), fillcolor=255),
transforms.ColorJitter(brightness=0.5),
transforms.ColorJitter(contrast=0.5),
transforms.ColorJitter(saturation=0.5),
]
transfrom_tensor_list = [
transforms.RandomErasing(p=0.5, scale=(0.02, 0.1), value=0),
]
if phase == "train":
transform = transforms.Compose([
transforms.RandomApply(transfrom_PIL_list),
transforms.ToTensor(),
# transforms.RandomApply(transfrom_tensor_list),
transforms.Normalize(
mean=[0.5],
std=[0.5]),
])
else:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
mean=[0.5],
std=[0.5]),
])
return transform
def read_img(img, inference_transform,desired_size=(128, 1024)):
img_resize, crop_cc = resize_image(img, desired_size)
img_resize = Image.fromarray(img_resize)
img_tensor = inference_transform(img_resize)
return img_tensor
class IAM_Dataset_line(data.Dataset):
"""
pytorch dataset for IAM handwritten dataset
"""
def __init__(self, dataset_path, tokenizer, phase="train", padding=128):
"""
inital dataset with path, tokenizer, when you want to train phase is "train",
for test and valid phase is "test" and "valid", padding is the max length of
your input text.
"""
self.phase = phase
self.dataset_path = dataset_path
self.padding = padding
self.tokenizer = tokenizer
self.line_imgs = self.read_train_valid_test_files()
self.label_dict = self.read_label_dict()
self.transform = get_transform(self.phase)
def __len__(self):
return len(self.line_imgs)
def read_label_dict(self):
"""
Read the line ground truth data from txt as dict
key is file name, values is texts
"""
line_txt_path = os.path.join(self.dataset_path, "ascii/lines.txt")
with open(line_txt_path, "r") as f:
lines = f.readlines()
ground_truth_dict = {}
for line in lines:
if line.startswith("#"):
continue
line = line.strip()
line_blocks = line.split(" ")
key = line_blocks[0]
texts = line_blocks[-1].replace("|", " ")
ground_truth_dict[key] = texts
return ground_truth_dict
def read_train_valid_test_files(self):
"""
Split all line imgs into train,valid,test set.
These sets are divided based on file level, which means line imgs from same file
will not be divided into different set
"""
np.random.seed(55)
folder_path = os.path.join(self.dataset_path, "lines")
folders = glob.glob(os.path.join(folder_path, "*"))
files = []
for folder in folders:
files_in_folder = glob.glob(os.path.join(folder, "*"))
files.extend(files_in_folder)
train_file_num = int(len(files) * 0.9)
valid_file_num = int(len(files) * 0.05)
files_permute = np.random.permutation(files)
train_files = files_permute[:train_file_num]
valid_files = files_permute[train_file_num:train_file_num + valid_file_num]
test_files = files_permute[train_file_num + valid_file_num:]
train_lines = []
valid_lines = []
test_lines = []
files_tuple = [(train_lines, train_files), (valid_lines, valid_files), (test_lines, test_files)]
for phase_lines, phase_files in files_tuple:
for file_folder in phase_files:
file_imgs = glob.glob(os.path.join(file_folder, "*.png"))
for img_path in file_imgs:
phase_lines.append((img_path, os.path.basename(img_path).split(".")[0]))
print("Total files: ", len(files))
print("Train files: ", len(train_files))
print("Valid files: ", len(valid_files))
print("Test files: ", len(test_files))
if self.phase == "train":
return train_lines
elif self.phase == "valid":
return valid_lines
else:
return test_lines
def read_img(self, img_path, desired_size=(128, 1024)):
img = cv2.imread(img_path, 0)
img_resize, crop_cc = resize_image(img, desired_size)
img_resize = Image.fromarray(img_resize)
img_tensor = self.transform(img_resize)
return img_tensor
def read_label(self, label_key):
text = self.label_dict[label_key]
line_label = self.tokenizer.encode(text)
input_lengths = len(line_label)
if self.padding > 0:
padded_line = np.ones(self.padding)*-1
max_len = min(self.padding, input_lengths)
padded_line[:max_len] = line_label[:max_len]
line_label = padded_line
input_lengths = max_len
label_tensor = torch.from_numpy(line_label)
input_lengths = torch.tensor(input_lengths)
return label_tensor, input_lengths
def __getitem__(self, index):
image_path, label_key = self.line_imgs[index]
X = self.read_img(image_path)
y, lengths = self.read_label(label_key)
return X.float(), y.long(), lengths.long()