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import os
import numpy as np
import pickle
from imutils import paths
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import logging
import sys
from PIL import Image
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
img_height, img_width = 28, 28
def get_data(imagePaths, csv_path, verbose=245):
# Load the CSV mapping
try:
df = pd.read_csv(csv_path, encoding='utf-8')
char_to_class = dict(zip(df['char'], df['class']))
logger.info(f"Loaded CSV with {len(char_to_class)} character mappings")
except Exception as e:
logger.error(f"Error loading CSV: {e}")
return None, None
# initialize the list of features and labels
data = []
labels = []
failed_images = 0
successful_images = 0
# loop over the input images
for (i, imagePath) in enumerate(imagePaths):
try:
# Check if file exists and is readable
if not os.path.exists(imagePath):
failed_images += 1
continue
# Extract the folder name (character) from the path
path_parts = imagePath.split(os.path.sep)
if len(path_parts) < 2:
failed_images += 1
continue
folder_name = path_parts[-2] # Second to last part is folder name
# Check if this character is in our mapping
if folder_name not in char_to_class:
failed_images += 1
if failed_images <= 5:
logger.warning(f"Unmapped character: '{folder_name}' in path: {imagePath}")
continue
# Load and preprocess the image using PIL
try:
with Image.open(imagePath) as img:
# Convert to grayscale
if img.mode != 'L':
img = img.convert('L')
# Resize to target dimensions
img = img.resize((img_width, img_height), Image.Resampling.LANCZOS)
# Convert to numpy array and normalize
image_array = np.array(img, dtype=np.float32) / 255.0
except Exception as e:
logger.debug(f"PIL failed to read {imagePath}: {e}")
failed_images += 1
continue
# Get the class label from mapping
label = char_to_class[folder_name]
# Add to our dataset
data.append(image_array)
labels.append(label)
successful_images += 1
# show an update every 'verbose' images
if verbose > 0 and successful_images > 0 and (successful_images) % verbose == 0:
logger.info(f"Processed {successful_images} images successfully")
except Exception as e:
failed_images += 1
if failed_images <= 5: # Log first few errors
logger.error(f"Error processing image {imagePath}: {e}")
continue
logger.info(f"Successfully processed {successful_images} images")
logger.info(f"Failed to process {failed_images} images")
if successful_images == 0:
logger.error("No images were successfully processed!")
return None, None
# Convert to numpy arrays
data = np.array(data)
labels = np.array(labels)
# Reshape data to add channel dimension (grayscale)
data = data.reshape((data.shape[0], img_height, img_width, 1))
# show some information on memory consumption of the images
logger.info(f"Features matrix: {data.nbytes / (1024 * 1000.0):.1f}MB")
logger.info(f"Number of classes: {len(np.unique(labels))}")
logger.info(f"Total samples: {len(data)}")
return data, labels
if __name__ == "__main__":
# Get image paths
dataset_path = './dataset_ka_kha'
if not os.path.exists(dataset_path):
logger.error(f"Dataset path {dataset_path} does not exist!")
sys.exit(1)
imagePaths = list(paths.list_images(dataset_path))
logger.info(f"Found {len(imagePaths)} images in dataset")
if len(imagePaths) == 0:
logger.error("No images found in the dataset directory!")
sys.exit(1)
# Process the data using PIL instead of OpenCV
data, labels = get_data(imagePaths, 'sample.csv', 20000) # Reduced verbose for more frequent updates
if data is None or labels is None:
logger.error("Failed to prepare data!")
sys.exit(1)
# Save both data and labels
os.makedirs('dataset_pickles', exist_ok=True)
with open('dataset_pickles/tigrigna_dataset.pickle', 'wb') as f:
pickle.dump((data, labels), f)
logger.info("Data preparation complete. Saved to dataset_pickles/tigrigna_dataset.pickle") |