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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) |