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| from PIL import Image | |
| import cv2 | |
| import numpy as np | |
| def pil_to_opencv(image): | |
| numpy_image = np.array(image) | |
| opencv_image = cv2.cvtColor(numpy_image, cv2.COLOR_RGB2BGR) | |
| return opencv_image | |
| def opencv_to_pil(image): | |
| # Convert OpenCV BGR image to NumPy array | |
| numpy_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| # Convert NumPy array to PIL Image | |
| pil_image = Image.fromarray(numpy_image) | |
| return pil_image | |
| def generate(image, algorithm_name): | |
| algorithm_functions = { | |
| "Sobel Edge Detection": sobel_edge_detection, | |
| "Canny Edge Detection": canny_edge_detection, | |
| "Hough Lines": hough_lines, | |
| "Laplacian Edge Detection": laplacian_edge_detection, | |
| "Contours Detection": contours_detection, | |
| "Prewitt Edge Detection": prewitt_edge_detection, | |
| "Gradient Magnitude": gradient_magnitude, | |
| "Corner Detection": corner_detection, | |
| } | |
| if algorithm_name in algorithm_functions: | |
| algorithm_function = algorithm_functions[algorithm_name] | |
| processed_image = algorithm_function(image) | |
| else: | |
| processed_image = () | |
| return processed_image | |
| def sobel_edge_detection(image): | |
| gray = pil_to_opencv(image) | |
| sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=5) | |
| sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=5) | |
| magnitude = np.sqrt(sobelx**2 + sobely**2) | |
| magnitude = np.uint8(magnitude) | |
| return magnitude | |
| def canny_edge_detection(image): | |
| gray = pil_to_opencv(image) | |
| edges = cv2.Canny(gray, 50, 150, apertureSize=3) | |
| return edges | |
| def hough_lines(image): | |
| gray = pil_to_opencv(image) | |
| edges = cv2.Canny(gray, 50, 150) | |
| lines = cv2.HoughLines(edges, 1, np.pi / 180, threshold=100) | |
| result = image.copy() | |
| for line in lines: | |
| rho, theta = line[0] | |
| a = np.cos(theta) | |
| b = np.sin(theta) | |
| x0 = a * rho | |
| y0 = b * rho | |
| x1 = int(x0 + 1000 * (-b)) | |
| y1 = int(y0 + 1000 * (a)) | |
| x2 = int(x0 - 1000 * (-b)) | |
| y2 = int(y0 - 1000 * (a)) | |
| cv2.line(result, (x1, y1), (x2, y2), (0, 0, 255), 2) | |
| print("passed") | |
| return result | |
| def laplacian_edge_detection(image): | |
| gray = pil_to_opencv(image) | |
| laplacian = cv2.Laplacian(gray, cv2.CV_64F) | |
| laplacian = np.uint8(np.absolute(laplacian)) | |
| return laplacian | |
| def contours_detection(image): | |
| gray = pil_to_opencv(image) | |
| contours, _ = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) | |
| result = np.zeros_like(image) | |
| cv2.drawContours(result, contours, -1, (0, 255, 0), 2) | |
| print("passed") | |
| return result | |
| def prewitt_edge_detection(image): | |
| gray = pil_to_opencv(image) | |
| prewittx = cv2.filter2D( | |
| gray, cv2.CV_64F, np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]) | |
| ) | |
| prewitty = cv2.filter2D( | |
| gray, cv2.CV_64F, np.array([[-1, -1, -1], [0, 0, 0], [1, 1, 1]]) | |
| ) | |
| magnitude = np.sqrt(prewittx**2 + prewitty**2) | |
| magnitude = np.uint8(magnitude) | |
| return magnitude | |
| def gradient_magnitude(image): | |
| gray = pil_to_opencv(image) | |
| sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=5) | |
| sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=5) | |
| magnitude = np.sqrt(sobelx**2 + sobely**2) | |
| magnitude = np.uint8(magnitude) | |
| print("passed") | |
| return magnitude | |
| def corner_detection(image): | |
| gray = pil_to_opencv(image) | |
| corners = cv2.goodFeaturesToTrack( | |
| gray, maxCorners=100, qualityLevel=0.01, minDistance=10 | |
| ) | |
| result = np.zeros_like(image) | |
| corners = np.int0(corners) | |
| for i in corners: | |
| x, y = i.ravel() | |
| cv2.circle(result, (x, y), 3, 255, -1) | |
| print("passed") | |
| return result | |