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| """ | |
| preprocessing.py — Preprocesses a cropped line image for TrOCR input. | |
| Steps: | |
| 1. Convert to grayscale | |
| 2. Apply CLAHE (contrast enhancement) | |
| 3. Convert back to RGB (TrOCR expects RGB) | |
| 4. Resize to 384×384 | |
| 5. Return as PIL Image | |
| """ | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| TARGET_SIZE = (384, 384) | |
| def preprocess_crop(crop: np.ndarray) -> Image.Image: | |
| """ | |
| Preprocess a numpy image crop for the TrOCR model. | |
| Args: | |
| crop: numpy array (H, W, 3) in RGB format | |
| Returns: | |
| PIL Image (RGB, 384×384) | |
| """ | |
| # Step 1: Grayscale | |
| if len(crop.shape) == 3: | |
| gray = cv2.cvtColor(crop, cv2.COLOR_RGB2GRAY) | |
| else: | |
| gray = crop.copy() | |
| # Step 2: CLAHE — Contrast Limited Adaptive Histogram Equalization | |
| clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) | |
| enhanced = clahe.apply(gray) | |
| # Step 3: Back to RGB (TrOCR processor expects 3-channel) | |
| rgb = cv2.cvtColor(enhanced, cv2.COLOR_GRAY2RGB) | |
| # Step 4: Resize to target size | |
| resized = cv2.resize(rgb, TARGET_SIZE, interpolation=cv2.INTER_CUBIC) | |
| # Step 5: Convert to PIL | |
| pil_image = Image.fromarray(resized.astype(np.uint8)) | |
| return pil_image | |