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#data_handler_ocr.py
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import os
from PIL import Image
import numpy as np
import torch.nn.functional as F
# Import utility functions and config
from config import CHARS, BLANK_TOKEN, IMG_HEIGHT, TRAIN_IMAGES_DIR, TEST_IMAGES_DIR
from utils_ocr import load_image_as_grayscale, binarize_image, resize_image_for_ocr, normalize_image_for_model
class CharIndexer:
"""Manages character-to-index and index-to-character mappings."""
def __init__(self, chars: str, blank_token: str):
self.char_to_idx = {char: i for i, char in enumerate(chars)}
self.idx_to_char = {i: char for i, char in enumerate(chars)}
self.blank_token_idx = len(chars) # Index for the blank token
self.idx_to_char[self.blank_token_idx] = blank_token # Add blank token to idx_to_char
self.num_classes = len(chars) + 1 # Total classes including blank
def encode(self, text: str) -> list[int]:
"""Converts a text string to a list of integer indices."""
return [self.char_to_idx[char] for char in text]
def decode(self, indices: list[int]) -> str:
"""Converts a list of integer indices back to a text string."""
# CTC decoding often produces repeated characters and blank tokens.
# This simple decoder removes blanks and duplicates.
decoded_text = []
for i, idx in enumerate(indices):
if idx == self.blank_token_idx:
continue
# Remove consecutive duplicates
if i > 0 and indices[i-1] == idx:
continue
decoded_text.append(self.idx_to_char[idx])
return "".join(decoded_text)
class OCRDataset(Dataset):
"""
Custom PyTorch Dataset for the Handwritten Name Recognition task.
Loads images and their corresponding text labels.
"""
def __init__(self, dataframe: pd.DataFrame, char_indexer: CharIndexer, image_dir: str, transform=None):
"""
Initializes the OCR Dataset.
Args:
dataframe (pd.DataFrame): A DataFrame containing 'image_path' and 'label' columns.
char_indexer (CharIndexer): An instance of CharIndexer for character encoding.
transform (callable, optional): Optional transform to be applied on an image.
"""
self.data = dataframe
self.char_indexer = char_indexer
self.image_dir = image_dir
self.transform = transform
def __len__(self) -> int:
return len(self.data)
def __getitem__(self, idx):
raw_filename_entry = self.data.iloc[idx]['FILENAME']
ground_truth_text = self.data.iloc[idx]['IDENTITY']
filename = raw_filename_entry.split(',')[0].strip() # .strip() removes any whitespace
# Construct the full image path
img_path = os.path.join(self.image_dir, filename)
# Ensure ground_truth_text is a string
ground_truth_text = str(ground_truth_text)
# Load and transform image
try:
image = Image.open(img_path).convert('L') # Convert to grayscale
except FileNotFoundError:
print(f"Error: Image file not found at {img_path}. Skipping this item.")
raise # Re-raise to let the main traceback be seen.
if self.transform:
image = self.transform(image)
image_width = image.size(2) # Assuming image is a tensor (C, H, W) after transform
text_encoded = torch.tensor(self.char_indexer.encode(ground_truth_text), dtype=torch.long)
text_length = len(text_encoded)
return image, text_encoded, image_width, text_length
def ocr_collate_fn(batch: list) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Custom collate function for the DataLoader to handle variable-width images
and variable-length text sequences for CTC loss.
"""
images, texts, image_widths, text_lengths = zip(*batch)
# Pad images to the maximum width in the current batch
max_batch_width = max(image_widths)
padded_images = [F.pad(img, (0, max_batch_width - img.shape[2]), 'constant', 0) for img in images]
images_batch = torch.stack(padded_images, 0) # Stack to (N, C, H, max_W)
# Concatenate all text sequences and get their lengths
texts_batch = torch.cat(texts, 0)
text_lengths_tensor = torch.tensor(text_lengths, dtype=torch.long)
image_widths_tensor = torch.tensor(image_widths, dtype=torch.long) # Actual widths
return images_batch, texts_batch, image_widths_tensor, text_lengths_tensor
def load_ocr_dataframes(train_csv_path: str, test_csv_path: str) -> tuple[pd.DataFrame, pd.DataFrame]:
"""
Loads training and testing dataframes.
Assumes CSVs have 'filename' and 'name' columns.
"""
train_df = pd.read_csv(train_csv_path)
test_df = pd.read_csv(test_csv_path)
return train_df, test_df
def create_ocr_dataloaders(train_df: pd.DataFrame, test_df: pd.DataFrame,
char_indexer: CharIndexer, batch_size: int) -> tuple[DataLoader, DataLoader]:
"""
Creates PyTorch DataLoader objects for OCR training and testing datasets,
using specific image directories for train/test.
"""
train_dataset = OCRDataset(train_df, TRAIN_IMAGES_DIR, char_indexer)
test_dataset = OCRDataset(test_df, TEST_IMAGES_DIR, char_indexer)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True,
num_workers=0, collate_fn=ocr_collate_fn)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False,
num_workers=0, collate_fn=ocr_collate_fn)
=======
#data_handler_ocr.py
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import os
from PIL import Image
import numpy as np
import torch.nn.functional as F
# Import utility functions and config
from config import CHARS, BLANK_TOKEN, IMG_HEIGHT, TRAIN_IMAGES_DIR, TEST_IMAGES_DIR
from utils_ocr import load_image_as_grayscale, binarize_image, resize_image_for_ocr, normalize_image_for_model
class CharIndexer:
"""Manages character-to-index and index-to-character mappings."""
def __init__(self, chars: str, blank_token: str):
self.char_to_idx = {char: i for i, char in enumerate(chars)}
self.idx_to_char = {i: char for i, char in enumerate(chars)}
self.blank_token_idx = len(chars) # Index for the blank token
self.idx_to_char[self.blank_token_idx] = blank_token # Add blank token to idx_to_char
self.num_classes = len(chars) + 1 # Total classes including blank
def encode(self, text: str) -> list[int]:
"""Converts a text string to a list of integer indices."""
return [self.char_to_idx[char] for char in text]
def decode(self, indices: list[int]) -> str:
"""Converts a list of integer indices back to a text string."""
# CTC decoding often produces repeated characters and blank tokens.
# This simple decoder removes blanks and duplicates.
decoded_text = []
for i, idx in enumerate(indices):
if idx == self.blank_token_idx:
continue
# Remove consecutive duplicates
if i > 0 and indices[i-1] == idx:
continue
decoded_text.append(self.idx_to_char[idx])
return "".join(decoded_text)
class OCRDataset(Dataset):
"""
Custom PyTorch Dataset for the Handwritten Name Recognition task.
Loads images and their corresponding text labels.
"""
def __init__(self, dataframe: pd.DataFrame, char_indexer: CharIndexer, image_dir: str, transform=None):
"""
Initializes the OCR Dataset.
Args:
dataframe (pd.DataFrame): A DataFrame containing 'image_path' and 'label' columns.
char_indexer (CharIndexer): An instance of CharIndexer for character encoding.
transform (callable, optional): Optional transform to be applied on an image.
"""
self.data = dataframe
self.char_indexer = char_indexer
self.image_dir = image_dir
self.transform = transform
def __len__(self) -> int:
return len(self.data)
def __getitem__(self, idx):
raw_filename_entry = self.data.iloc[idx]['FILENAME']
ground_truth_text = self.data.iloc[idx]['IDENTITY']
filename = raw_filename_entry.split(',')[0].strip() # .strip() removes any whitespace
# Construct the full image path
img_path = os.path.join(self.image_dir, filename)
# Ensure ground_truth_text is a string
ground_truth_text = str(ground_truth_text)
# Load and transform image
try:
image = Image.open(img_path).convert('L') # Convert to grayscale
except FileNotFoundError:
print(f"Error: Image file not found at {img_path}. Skipping this item.")
raise # Re-raise to let the main traceback be seen.
if self.transform:
image = self.transform(image)
image_width = image.size(2) # Assuming image is a tensor (C, H, W) after transform
text_encoded = torch.tensor(self.char_indexer.encode(ground_truth_text), dtype=torch.long)
text_length = len(text_encoded)
return image, text_encoded, image_width, text_length
def ocr_collate_fn(batch: list) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Custom collate function for the DataLoader to handle variable-width images
and variable-length text sequences for CTC loss.
"""
images, texts, image_widths, text_lengths = zip(*batch)
# Pad images to the maximum width in the current batch
max_batch_width = max(image_widths)
padded_images = [F.pad(img, (0, max_batch_width - img.shape[2]), 'constant', 0) for img in images]
images_batch = torch.stack(padded_images, 0) # Stack to (N, C, H, max_W)
# Concatenate all text sequences and get their lengths
texts_batch = torch.cat(texts, 0)
text_lengths_tensor = torch.tensor(text_lengths, dtype=torch.long)
image_widths_tensor = torch.tensor(image_widths, dtype=torch.long) # Actual widths
return images_batch, texts_batch, image_widths_tensor, text_lengths_tensor
def load_ocr_dataframes(train_csv_path: str, test_csv_path: str) -> tuple[pd.DataFrame, pd.DataFrame]:
"""
Loads training and testing dataframes.
Assumes CSVs have 'filename' and 'name' columns.
"""
train_df = pd.read_csv(train_csv_path)
test_df = pd.read_csv(test_csv_path)
return train_df, test_df
def create_ocr_dataloaders(train_df: pd.DataFrame, test_df: pd.DataFrame,
char_indexer: CharIndexer, batch_size: int) -> tuple[DataLoader, DataLoader]:
"""
Creates PyTorch DataLoader objects for OCR training and testing datasets,
using specific image directories for train/test.
"""
train_dataset = OCRDataset(train_df, TRAIN_IMAGES_DIR, char_indexer)
test_dataset = OCRDataset(test_df, TEST_IMAGES_DIR, char_indexer)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True,
num_workers=0, collate_fn=ocr_collate_fn)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False,
num_workers=0, collate_fn=ocr_collate_fn)
>>>>>>> ee59e5b21399d8b323cff452a961ea2fd6c65308
return train_loader, test_loader |