| import cv2 |
| import matplotlib.pyplot as plt |
| from super_image import EdsrModel, ImageLoader |
| from PIL import Image |
| def preprocess_image(image_path): |
| img = cv2.imread(image_path) |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
| return img |
| def show_image(img): |
| plt.imshow(img, cmap='gray') |
| plt.axis('off') |
| plt.show() |
| def save_processed_image(img): |
| output_path = "processed_images/processed_image.jpg" |
| cv2.imwrite(output_path, img) |
| return output_path |
| '''def createBoundingBox(img): |
| ocr_data = pytesseract.image_to_data(img, output_type=pytesseract.Output.DICT) |
| n_boxes = len(ocr_data['level']) |
| for i in range(n_boxes): |
| if ocr_data['level'][i] == 3: |
| (x, y, w, h) = (ocr_data['left'][i], ocr_data['top'][i], ocr_data['width'][i], ocr_data['height'][i]) |
| cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 5) |
| plt.imshow(img, cmap='gray') |
| plt.axis('off') |
| plt.show() |
| ''' |
|
|
| def super_resolution(img): |
| model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2) |
| pil_img = Image.fromarray(img) |
| inputs = ImageLoader.load_image(pil_img) |
| preds = model(inputs) |
|
|
| ImageLoader.save_image(preds, 'processed_images/processed_image.jpg') |
| def process_image(image_path): |
| img = preprocess_image(image_path) |
| super_resolution(img) |
| |
| if __name__ == "__main__": |
| image_path = "Projects/HandwritingOCR/captured_images/captured_image.jpg" |
| process_image(image_path) |
