| import numpy as np |
| import cv2 |
| import io |
| import base64 |
| from PIL import Image |
| from app.config import config |
|
|
| def preprocess_image(image_bytes: bytes) -> np.ndarray: |
| """Preprocess uploaded image for model inference""" |
| |
| image = Image.open(io.BytesIO(image_bytes)) |
|
|
| |
| if image.mode != 'RGB': |
| image = image.convert('RGB') |
|
|
| |
| image_np = np.array(image) |
|
|
| |
| image_resized = cv2.resize( |
| image_np, |
| (config.IMG_WIDTH, config.IMG_HEIGHT), |
| interpolation=cv2.INTER_LINEAR |
| ) |
|
|
| return image_resized |
|
|
| def create_overlay_image(original: np.ndarray, mask: np.ndarray) -> np.ndarray: |
| """Create overlay visualization with red highlights""" |
| overlay = original.copy() |
| |
| overlay[mask > 127] = [255, 64, 64] |
| return overlay |
|
|
| def numpy_to_base64(image_np: np.ndarray) -> str: |
| """Convert numpy array to base64 string""" |
| if len(image_np.shape) == 2: |
| image_pil = Image.fromarray(image_np, mode='L') |
| else: |
| image_pil = Image.fromarray(image_np.astype(np.uint8)) |
|
|
| buffer = io.BytesIO() |
| image_pil.save(buffer, format='PNG') |
| img_str = base64.b64encode(buffer.getvalue()).decode() |
| return f"data:image/png;base64,{img_str}" |
|
|
| def images_to_base64_urls(original_image: np.ndarray, prob_map: np.ndarray, overlay_image: np.ndarray) -> tuple: |
| """Convert all images to base64 URLs for response when external storage not available""" |
| original_url = numpy_to_base64(original_image) |
| prediction_url = numpy_to_base64((prob_map * 255).astype(np.uint8)) |
| overlay_url = numpy_to_base64(overlay_image) |
| |
| return original_url, prediction_url, overlay_url |
