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| import cv2 | |
| import os | |
| from config import file_Directory | |
| import numpy as np | |
| from PIL import Image | |
| class Image_Enhance(): | |
| def __init__(self, image_path) -> None: | |
| self.image_path = image_path | |
| def brightness_Adjust(self): | |
| # Load the image | |
| image = cv2.imread(self.image_path) | |
| #Plot the original image | |
| alpha = -1.1 | |
| # control brightness by 50 | |
| beta = 70 | |
| image2 = cv2.convertScaleAbs(image, alpha=alpha, beta=beta) | |
| #Save the image | |
| # imagepth = os.path.join(os.path.dirname(self.image_path), 'Brightness & contrast.jpg') | |
| imagepth = os.path.join(file_Directory, 'Brightness & contrast.jpg') | |
| cv2.imwrite(imagepth, image2) | |
| return imagepth | |
| def remove_flash(self, imagepth): | |
| image = cv2.imread(imagepth) | |
| # cv2.cvtColor is applied over the | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| # Apply adaptive thresholding to segment the text | |
| thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 4) | |
| # Apply Gaussian blur to the grayscale image to reduce noise | |
| blurred = cv2.GaussianBlur(gray, (5, 5), 0) | |
| # Threshold the blurred image to create a binary mask for the flashlight glare | |
| _, mask = cv2.threshold(blurred, 240, 255, cv2.THRESH_BINARY_INV) | |
| # Combine the text and glare masks | |
| mask = cv2.bitwise_or(mask, thresh) | |
| # Apply morphological closing to further remove small areas of glare | |
| kernel = np.ones((5,5),np.uint8) | |
| mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) | |
| # Apply the mask to the original image to remove flashlight glare | |
| result = cv2.bitwise_and(image, image, mask=mask) | |
| cv2.imwrite(os.path.join(file_Directory, 'remove_flash.jpg'), result) | |
| def sharpen(self, imagepth): | |
| image = cv2.imread(imagepth) | |
| # Create the sharpening kernel | |
| kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]]) | |
| # Sharpen the image | |
| sharpened_image = cv2.filter2D(image, -1, kernel) | |
| #Save the image | |
| imagepath = os.path.join(file_Directory, 'sharpened_image.jpg') | |
| cv2.imwrite(imagepath, sharpened_image) | |
| return imagepath | |
| def lapacian_sharpen(self, imagepth): | |
| #Load the image | |
| image = cv2.imread(imagepth) | |
| # Sharpen the image using the Laplacian operator | |
| sharpened_image2 = cv2.Laplacian(image, cv2.CV_64F) | |
| imagepath = os.path.join(file_Directory, 'Laplacian_sharpened_image.jpg') | |
| #Save the image | |
| cv2.imwrite(imagepath, sharpened_image2) | |
| def removing_noise(self, imagepth): | |
| # Load the image | |
| image = cv2.imread(imagepth) | |
| # Remove noise using a median filter | |
| filtered_image = cv2.medianBlur(image, 1) | |
| imagepath = os.path.join(file_Directory, 'Median Blur.jpg') | |
| #Save the image | |
| cv2.imwrite(imagepath, filtered_image) | |
| return imagepath | |
| def enhance_color(self, imagepth): | |
| # Load the image | |
| image = cv2.imread(imagepth) | |
| image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV) | |
| # Adjust the hue, saturation, and value of the image | |
| # Adjusts the hue by multiplying it by 0.7 | |
| image[:, :, 0] = image[:, :, 0] * 0.7 | |
| # Adjusts the saturation by multiplying it by 1.5 | |
| image[:, :, 1] = image[:, :, 1] * 1.5 | |
| # Adjusts the value by multiplying it by 0.5 | |
| image[:, :, 2] = image[:, :, 2] * 0.5 | |
| image2 = cv2.cvtColor(image, cv2.COLOR_HSV2BGR) | |
| imagepath = os.path.join(file_Directory, 'enhanced coloured.jpg') | |
| #Save the image | |
| cv2.imwrite(imagepath, image2) | |
| obj = Image_Enhance(r"data/Catalog Digitization/ONDC Test Data _ Images/Product Images/Bru_Instant_Coffee_Powder.png") | |
| pth = obj.brightness_Adjust() | |
| sharpen = obj.sharpen(pth) | |
| lapacian_sharpen = obj.lapacian_sharpen(sharpen) | |
| noise = obj.removing_noise(sharpen) | |
| obj.enhance_color(noise) | |
| obj.remove_flash(sharpen) |