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17f1739 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 | import cv2
import numpy as np
from PIL import Image, ImageEnhance
from typing import List, Optional, Tuple
import logging
import asyncio
from pathlib import Path
from pdf2image import convert_from_path
from typing import Dict
logger = logging.getLogger(__name__)
class ImageProcessor:
"""Image processing utilities for certificate analysis"""
def __init__(self):
self.supported_formats = ['.pdf', '.jpg', '.jpeg', '.png', '.tiff', '.bmp']
self.model_version = "2024.1.0-image-processor"
logger.info(f"ImageProcessor v{self.model_version} initialized")
async def process_document(self, document_path: str) -> List[np.ndarray]:
"""Process document and extract images from all pages"""
try:
images = []
# Check file type
file_path = Path(document_path)
suffix = file_path.suffix.lower()
if suffix == '.pdf':
images = await self._extract_from_pdf(document_path)
elif suffix in ['.jpg', '.jpeg', '.png', '.tiff', '.bmp']:
images = await self._extract_from_image(document_path)
else:
raise ValueError(f"Unsupported file format: {suffix}")
if not images:
raise Exception("No images extracted from document")
# Process each image
processed_images = []
for img in images:
processed = await self._process_image(img)
if processed is not None:
processed_images.append(processed)
logger.info(f"Extracted {len(processed_images)} processed images")
return processed_images
except Exception as e:
logger.error(f"Document processing failed: {e}")
raise
async def _extract_from_pdf(self, pdf_path: str) -> List[np.ndarray]:
"""Extract images from PDF"""
try:
images = convert_from_path(pdf_path, dpi=200)
image_arrays = []
for img in images:
# Convert PIL to numpy
img_array = np.array(img)
# Convert RGB to BGR for OpenCV if needed
if len(img_array.shape) == 3 and img_array.shape[2] == 3:
img_array = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
image_arrays.append(img_array)
return image_arrays
except ImportError:
logger.error("pdf2image not installed. Install with: pip install pdf2image")
raise
except Exception as e:
logger.error(f"PDF extraction failed: {e}")
raise
async def _extract_from_image(self, image_path: str) -> List[np.ndarray]:
"""Load image from file"""
try:
img = cv2.imread(image_path)
if img is None:
raise Exception(f"Failed to load image: {image_path}")
return [img]
except Exception as e:
logger.error(f"Image loading failed: {e}")
raise
async def _process_image(self, image: np.ndarray) -> Optional[np.ndarray]:
"""Process single image for analysis"""
try:
# Convert to RGB if needed
if len(image.shape) == 3 and image.shape[2] == 4:
image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
elif len(image.shape) == 3 and image.shape[2] == 3:
pass # Already BGR
elif len(image.shape) == 2:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
else:
raise ValueError(f"Unsupported image shape: {image.shape}")
# Resize if too large
h, w = image.shape[:2]
max_dimension = 2000
if max(h, w) > max_dimension:
scale = max_dimension / max(h, w)
new_h, new_w = int(h * scale), int(w * scale)
image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_AREA)
# Enhance image for better OCR
enhanced = await self._enhance_image(image)
return enhanced
except Exception as e:
logger.error(f"Image processing failed: {e}")
return None
async def _enhance_image(self, image: np.ndarray) -> np.ndarray:
"""Enhance image for OCR"""
try:
# Convert to PIL for enhancement
pil_img = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
# Enhance contrast
enhancer = ImageEnhance.Contrast(pil_img)
pil_img = enhancer.enhance(1.2)
# Enhance sharpness
enhancer = ImageEnhance.Sharpness(pil_img)
pil_img = enhancer.enhance(1.1)
# Convert back to numpy
enhanced = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
# Additional OpenCV enhancements
# Convert to LAB color space
lab = cv2.cvtColor(enhanced, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(lab)
# Apply CLAHE to L-channel
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
cl = clahe.apply(l)
# Merge channels
limg = cv2.merge([cl, a, b])
enhanced = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
# Denoise - REMOVED for performance (too slow on CPU)
# enhanced = cv2.fastNlMeansDenoisingColored(enhanced, None, 10, 10, 7, 21)
return enhanced
except Exception as e:
logger.debug(f"Image enhancement failed, using original: {e}")
return image
def deskew_image(self, image: np.ndarray) -> np.ndarray:
"""Deskew image if tilted"""
try:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bitwise_not(gray)
# Threshold the image
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
# Find coordinates of non-zero pixels
coords = np.column_stack(np.where(thresh > 0))
# Get angle of minimum area rectangle
angle = cv2.minAreaRect(coords)[-1]
# Adjust angle
if angle < -45:
angle = 90 + angle
else:
angle = -angle
# Rotate image if angle is significant
if abs(angle) > 0.5:
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(image, M, (w, h),
flags=cv2.INTER_CUBIC,
borderMode=cv2.BORDER_REPLICATE)
return rotated
return image
except Exception as e:
logger.debug(f"Deskew failed: {e}")
return image
def detect_and_crop_edges(self, image: np.ndarray) -> np.ndarray:
"""Detect and crop edges to remove borders"""
try:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Apply edge detection
edges = cv2.Canny(gray, 50, 150)
# Find contours
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if contours:
# Find largest contour (assumed to be document)
largest_contour = max(contours, key=cv2.contourArea)
x, y, w, h = cv2.boundingRect(largest_contour)
# Add padding
padding = 20
x = max(0, x - padding)
y = max(0, y - padding)
w = min(image.shape[1] - x, w + 2 * padding)
h = min(image.shape[0] - y, h + 2 * padding)
# Crop image
cropped = image[y:y+h, x:x+w]
# Only return if crop is significantly smaller than original
if w < image.shape[1] * 0.9 or h < image.shape[0] * 0.9:
return cropped
return image
except Exception as e:
logger.debug(f"Edge cropping failed: {e}")
return image
def calculate_image_quality(self, image: np.ndarray) -> Dict[str, float]:
"""Calculate image quality metrics"""
try:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Calculate blurriness (Laplacian variance)
blur_score = cv2.Laplacian(gray, cv2.CV_64F).var()
# Calculate contrast
contrast_score = np.std(gray)
# Calculate brightness
brightness_score = np.mean(gray)
# Calculate entropy
hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
hist = hist.ravel() / hist.sum()
entropy_score = -np.sum(hist * np.log2(hist + 1e-10))
# Calculate noise
denoised = cv2.fastNlMeansDenoising(gray, None, 10, 7, 21)
noise_score = np.mean(np.abs(gray.astype(float) - denoised.astype(float)))
return {
'blur_score': float(blur_score),
'contrast_score': float(contrast_score),
'brightness_score': float(brightness_score),
'entropy_score': float(entropy_score),
'noise_score': float(noise_score),
'overall_quality': float(min(
(blur_score / 100) * 0.3 +
(contrast_score / 50) * 0.3 +
(1 - brightness_score / 255) * 0.2 +
(entropy_score / 8) * 0.2,
1.0
))
}
except Exception as e:
logger.debug(f"Quality calculation failed: {e}")
return {
'blur_score': 0.0,
'contrast_score': 0.0,
'brightness_score': 0.0,
'entropy_score': 0.0,
'noise_score': 0.0,
'overall_quality': 0.0
} |