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22997db | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | import logging
import shutil
from pathlib import Path
from typing import Optional
import cv2
import numpy as np
from tqdm import tqdm
class AZProcessor:
def __init__(self):
self.logger = logging.getLogger(__name__)
self.project_root = Path(__file__).parent.parent.parent.parent
self.source_path = self.project_root / "data" / "raw" / "az_handwritten"
self.temp_path = self.project_root / "data" / "temp" / "AZ"
def _normalize_documentation(self) -> None:
"""Normalize documentation to simplified format (filename label)."""
doc_path = self.temp_path / "documentation.txt"
normalized_path = self.temp_path / "normalized_documentation.txt"
with open(doc_path, 'r') as f, open(normalized_path, 'w') as out:
for line in f:
if line.startswith('#') or not line.strip():
continue
parts = line.strip().split()
if len(parts) >= 2:
filename = parts[0] # First column (filename)
label = parts[-1].upper() # Last column (label)
out.write(f"{filename} {label}\n")
normalized_path.replace(doc_path)
self.logger.info("Documentation normalized to simplified format")
def process(self) -> bool:
try:
self.logger.info("Processing A-Z Handwritten dataset...")
self.temp_path.mkdir(parents=True, exist_ok=True)
if not self.source_path.exists():
self.logger.error(f"Source dataset not found at {self.source_path}")
return False
self._copy_dataset()
self._normalize_documentation()
image_files = list((self.temp_path / "images").glob("*.png"))
total_files = len(image_files)
for idx, img_path in enumerate(tqdm(image_files, desc="Processing A-Z")):
try:
image = cv2.imread(str(img_path), cv2.IMREAD_GRAYSCALE)
if image is None:
self.logger.warning(f"Failed to read image: {img_path}")
continue
processed = self._process_single_image(image)
cv2.imwrite(str(img_path), processed)
progress = (idx + 1) / total_files * 100
self.logger.debug(f"Processed {img_path.name} ({progress:.2f}%)")
except Exception as e:
self.logger.error(f"Error processing {img_path}: {str(e)}")
continue
self.logger.info("A-Z processing completed successfully")
return True
except Exception as e:
self.logger.error(f"Error in A-Z processing: {str(e)}")
return False
def _process_single_image(self, image: np.ndarray) -> np.ndarray:
"""Process single A-Z image through enhancement pipeline."""
image = self._enhance_contrast(image)
image = self._reduce_noise(image)
image = self._center_character(image)
image = self._normalize_stroke(image)
return image
def _enhance_contrast(self, image: np.ndarray) -> np.ndarray:
"""Enhance contrast using CLAHE."""
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
return clahe.apply(image)
def _reduce_noise(self, image: np.ndarray) -> np.ndarray:
"""Remove noise preserving edges."""
denoised = cv2.bilateralFilter(image, d=5, sigmaColor=75, sigmaSpace=75)
_, binary = cv2.threshold(denoised, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
return binary
def _center_character(self, image: np.ndarray) -> np.ndarray:
"""Center character using moments."""
contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if not contours:
return image
largest_contour = max(contours, key=cv2.contourArea)
M = cv2.moments(largest_contour)
if M["m00"] == 0:
return image
cx = int(M["m10"] / M["m00"])
cy = int(M["m01"] / M["m00"])
rows, cols = image.shape
shift_x = (cols // 2) - cx
shift_y = (rows // 2) - cy
M = np.float32([[1, 0, shift_x], [0, 1, shift_y]])
return cv2.warpAffine(image, M, (cols, rows))
def _normalize_stroke(self, image: np.ndarray) -> np.ndarray:
"""Normalize stroke width."""
kernel_size = max(2, min(image.shape) // 30)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size))
return cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)
def _copy_dataset(self) -> None:
"""Copy dataset to temp directory."""
if self.source_path.exists():
shutil.copytree(self.source_path, self.temp_path, dirs_exist_ok=True)
self.logger.info(f"Copied dataset to {self.temp_path}")
else:
raise FileNotFoundError(f"Source dataset not found at {self.source_path}")
def test_processor():
"""Test AZ processor functionality."""
import tempfile
test_dir = Path(tempfile.mkdtemp())
try:
img_dir = test_dir / "data" / "raw" / "az_handwritten" / "images"
img_dir.mkdir(parents=True)
# Create test image
test_image = np.zeros((100, 100), dtype=np.uint8)
cv2.putText(test_image, "A", (25, 75), cv2.FONT_HERSHEY_SIMPLEX, 2, 255, 2)
# Add noise
noise = np.random.normal(0, 25, test_image.shape).astype(np.uint8)
noisy_image = cv2.add(test_image, noise)
cv2.imwrite(str(img_dir / "test.png"), noisy_image)
processor = AZProcessor()
processor.project_root = test_dir
processor.source_path = test_dir / "data" / "raw" / "az_handwritten"
processor.temp_path = test_dir / "data" / "temp" / "AZ"
assert processor.process() == True
processed_path = processor.temp_path / "images" / "test.png"
assert processed_path.exists()
processed_img = cv2.imread(str(processed_path), cv2.IMREAD_GRAYSCALE)
assert processed_img is not None
assert np.mean(processed_img) > 0
assert len(np.unique(processed_img)) > 1
print("All tests passed!")
finally:
shutil.rmtree(test_dir)
if __name__ == "__main__":
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
test_processor() |