| 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) |