Spaces:
Sleeping
Sleeping
| import cv2 | |
| import numpy as np | |
| from skimage import exposure | |
| import os | |
| from scipy.spatial import distance | |
| def ekstrakBentuk(image): | |
| # Load the image in grayscale | |
| resized_image = cv2.resize(image, (512, 512)) # Resize the image to 512x512 | |
| imageGray = cv2.cvtColor(resized_image, cv2.COLOR_BGR2GRAY) | |
| # Apply Gaussian Blur to reduce noise | |
| blurred_image = cv2.GaussianBlur(imageGray, (5, 5), 0) | |
| # Apply histogram equalization to improve contrast | |
| equalized_image = exposure.equalize_hist(blurred_image) | |
| equalized_image = (equalized_image * 255).astype(np.uint8) | |
| # Apply Sobel operator to detect edges | |
| sobel_x = cv2.Sobel(equalized_image, cv2.CV_64F, 1, 0, ksize=5) | |
| sobel_y = cv2.Sobel(equalized_image, cv2.CV_64F, 0, 1, ksize=5) | |
| sobel = np.hypot(sobel_x, sobel_y) | |
| sobel = np.uint8(sobel / np.max(sobel) * 255) | |
| # Find contours in the Sobel image | |
| contours, _ = cv2.findContours(sobel, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) | |
| # Assuming we are interested in the largest contour | |
| contour = max(contours, key=cv2.contourArea) | |
| # Calculate moments to find the centroid | |
| M = cv2.moments(contour) | |
| if M["m00"] != 0: | |
| cx = int(M["m10"] / M["m00"]) | |
| cy = int(M["m01"] / M["m00"]) | |
| else: | |
| cx, cy = 0, 0 | |
| # Calculate area | |
| area = M["m00"] | |
| # Calculate perimeter (length) | |
| perimeter = cv2.arcLength(contour, True) | |
| # Calculate the major axis length | |
| distances = [distance.euclidean((cx, cy), point[0]) for point in contour] | |
| major_axis_length = max(distances) | |
| # Calculate the minor axis length | |
| minor_axis_length = min(distances) | |
| # Calculate diameter | |
| diameter = (major_axis_length + minor_axis_length) / 2 | |
| # Calculate shape factor | |
| shape_factor = (perimeter ** 2) / (4 * np.pi * area) | |
| return major_axis_length, perimeter, diameter, area, shape_factor | |