作业四

test/homework4/main.py

@@ -1,4 +1,57 @@
 # 对图片分别使用Roberts、Sobel和Prewitt三种边缘检测方式，完成边缘检测并使用matplotlib展示结果
 
 import cv2 as cv
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib import rcParams
 
+# 导入图片
+img = cv.imread('img.jpg', cv.IMREAD_GRAYSCALE)
+
+# 设置中文字体
+rcParams['font.sans-serif'] = ['SimHei']
+rcParams['axes.unicode_minus'] = False
+
+# Roberts 边缘检测
+roberts_kernel_x = np.array([[1, 0], [0, -1]], dtype=np.float32)
+roberts_kernel_y = np.array([[0, 1], [1, 0]], dtype=np.float32)
+roberts_x = cv.filter2D(img.astype(np.float32), -1, roberts_kernel_x)
+roberts_y = cv.filter2D(img.astype(np.float32), -1, roberts_kernel_y)
+roberts = cv.magnitude(roberts_x, roberts_y)
+
+# Sobel 边缘检测
+sobel_X = cv.Sobel(img, cv.CV_64F, 1, 0, ksize=3)
+Sobel_y = cv.Sobel(img, cv.CV_64F, 0, 1, ksize=3)
+sobel = cv.magnitude(sobel_X, Sobel_y)
+
+# Prewitt边缘检测
+prewitt_kernel_x = np.array([[1, 0, -1], [1, 0, -1], [1, 0, -1]], dtype=np.float32)
+prewitt_kernel_y = np.array([[1, 1, 1], [0, 0, 0], [-1, -1, -1]], dtype=np.float32)
+prewitt_x = cv.filter2D(img.astype(np.float32), -1, prewitt_kernel_x)
+prewitt_y = cv.filter2D(img.astype(np.float32), -1, prewitt_kernel_y)
+prewitt = cv.magnitude(prewitt_x, prewitt_y)
+
+# 使用 matplotlib 展示
+plt.figure(figsize=(12, 8))
+plt.subplot(2, 2, 1)
+plt.title('原图')
+plt.imshow(img, cmap='gray')
+plt.axis('off')
+
+plt.subplot(2, 2, 2)
+plt.title('Roberts 边缘检测')
+plt.imshow(roberts, cmap='gray')
+plt.axis('off')
+
+plt.subplot(2, 2, 3)
+plt.title('Sobel 边缘检测')
+plt.imshow(sobel, cmap='gray')
+plt.axis('off')
+
+plt.subplot(2, 2, 4)
+plt.title('Prewitt 边缘检测')
+plt.imshow(prewitt, cmap='gray')
+plt.axis('off')
+
+plt.tight_layout()
+plt.show()