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| import gradio as gr | |
| #from google.colab import files | |
| import cv2 | |
| import numpy as np | |
| from matplotlib import pyplot as plt | |
| from scipy.signal import medfilt | |
| # upload input image | |
| #def upload_image(): | |
| # uploaded = files.upload() | |
| # file = next(iter(uploaded)) | |
| # img = cv2.imread(file) | |
| # return img | |
| # display input image | |
| #def display_image(img, title="Image"): | |
| # img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # convert from BGR to RGB for OpenCV | |
| # plt.imshow(img_rgb) | |
| # plt.title(title) | |
| # plt.axis("off") | |
| # plt.show() | |
| # convert input image to grayscale to prepare for edge detection | |
| def convert_to_grayscale_and_blur(img): | |
| img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| img_blurred = cv2.blur(img_gray, (5, 5)) # Gaussian blurring with a kernel size of (9, 3) to reduce noise | |
| return img_blurred | |
| # detect edges using Laplacian filter | |
| def calculate_gradient(img_blurred, threshold=7): | |
| laplacian = cv2.Laplacian(img_blurred, cv2.CV_8U) | |
| gradient_mask = (laplacian < threshold).astype(np.uint8) | |
| return gradient_mask | |
| # refine skyline using median filtering and morphological operation | |
| def refine_skyline(mask): | |
| kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) | |
| eroded_mask = cv2.morphologyEx(mask, cv2.MORPH_ERODE, kernel) | |
| skyline_mask = cal_skyline(eroded_mask) | |
| return skyline_mask | |
| # adjust skyline using median filtering to isolate the sky | |
| def cal_skyline(mask): | |
| h, w = mask.shape | |
| for i in range(w): | |
| column = mask[:, i] | |
| after_median = medfilt(column, kernel_size=21) | |
| try: | |
| first_white_index = np.where(after_median == 1)[0][0] | |
| first_black_index = np.where(after_median == 0)[0][0] | |
| if first_black_index > first_white_index: | |
| mask[:first_black_index, i] = 1 | |
| mask[first_black_index:, i] = 0 | |
| except IndexError: | |
| continue | |
| return mask | |
| # extract sky region by applying the mask | |
| def get_sky_region(img, mask): | |
| sky_region = cv2.bitwise_and(img, img, mask=mask) | |
| return sky_region | |
| # run in order and show the detected sky region | |
| #def sky_detector(img): | |
| # display_image(img, "Original Image") | |
| # img_blurred = convert_to_grayscale_and_blur(img) | |
| # gradient_mask = calculate_gradient(img_blurred) | |
| # skyline_mask = refine_skyline(gradient_mask) | |
| # sky_region = get_sky_region(img, skyline_mask) | |
| # display_image(sky_region, "Sky Region") | |
| # main | |
| #image = upload_image() | |
| #sky_detector(image) | |
| # run in order for Gardio | |
| def sky_detection(image): | |
| img_blurred = convert_to_grayscale_and_blur(image) | |
| gradient_mask = calculate_gradient(img_blurred) | |
| skyline_mask = refine_skyline(gradient_mask) | |
| sky_region = get_sky_region(image, skyline_mask) | |
| sky_region_rgb = cv2.cvtColor(sky_region, cv2.COLOR_BGR2RGB) # Convert to RGB for Gradio display | |
| return sky_region_rgb | |
| # set up Gardio interface | |
| interface = gr.Interface(fn=sky_detection, inputs=gr.Image(), outputs="image") | |
| interface.launch(share=True) |