赞
踩
- import cv2
- import numpy as np
- from matplotlib import pyplot as plt
-
- img = cv2.imread('img1.png',0)
- edges = cv2.Canny(img, 100, 200)
-
- plt.subplot(121), plt.imshow(img, cmap='gray')
- plt.title('Original Image'), plt.xticks([]), plt.yticks([])
- plt.subplot(122), plt.imshow(edges, cmap='gray')
- plt.title('Edge Image'), plt.xticks([]), plt.yticks([])
- plt.show()
- import matplotlib.pyplot as plt
- import numpy as np
- import math
- import cv2
-
- img = cv2.imread('img1.png')
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
- blur = cv2.GaussianBlur(img, (5, 5), 0) # 用高斯滤波处理原图像降噪
- canny = cv2.Canny(blur, 50, 150) # 50是最小阈值,150是最大阈值
-
- sigma1 = sigma2 = 1
- sum = 0
-
- gaussian = np.zeros([5, 5])
- for i in range(5):
- for j in range(5):
- gaussian[i, j] = math.exp(-1 / 2 * (np.square(i - 3) / np.square(sigma1) # 生成二维高斯分布矩阵
- + (np.square(j - 3) / np.square(sigma2)))) / (2 * math.pi * sigma1 * sigma2)
- sum = sum + gaussian[i, j]
-
- gaussian = gaussian / sum
-
-
- # print(gaussian)
-
- def rgb2gray(rgb):
- return np.dot(rgb[..., :3], [0.299, 0.587, 0.114])
-
-
- # step1.高斯滤波
- gray = rgb2gray(img)
- W, H = gray.shape
- new_gray = np.zeros([W - 5, H - 5])
- for i in range(W - 5):
- for j in range(H - 5):
- new_gray[i, j] = np.sum(gray[i:i + 5, j:j + 5] * gaussian) # 与高斯矩阵卷积实现滤波
-
- # plt.imshow(new_gray, cmap="gray")
-
-
- # step2.增强 通过求梯度幅值
- W1, H1 = new_gray.shape
- dx = np.zeros([W1 - 1, H1 - 1])
- dy = np.zeros([W1 - 1, H1 - 1])
- d = np.zeros([W1 - 1, H1 - 1])
- for i in range(W1 - 1):
- for j in range(H1 - 1):
- dx[i, j] = new_gray[i, j + 1] - new_gray[i, j]
- dy[i, j] = new_gray[i + 1, j] - new_gray[i, j]
- d[i, j] = np.sqrt(np.square(dx[i, j]) + np.square(dy[i, j])) # 图像梯度幅值作为图像强度值
-
- # plt.imshow(d, cmap="gray")
-
-
- # setp3.非极大值抑制 NMS
- W2, H2 = d.shape
- NMS = np.copy(d)
- NMS[0, :] = NMS[W2 - 1, :] = NMS[:, 0] = NMS[:, H2 - 1] = 0
- for i in range(1, W2 - 1):
- for j in range(1, H2 - 1):
-
- if d[i, j] == 0:
- NMS[i, j] = 0
- else:
- gradX = dx[i, j]
- gradY = dy[i, j]
- gradTemp = d[i, j]
-
- # 如果Y方向幅度值较大
- if np.abs(gradY) > np.abs(gradX):
- weight = np.abs(gradX) / np.abs(gradY)
- grad2 = d[i - 1, j]
- grad4 = d[i + 1, j]
- # 如果x,y方向梯度符号相同
- if gradX * gradY > 0:
- grad1 = d[i - 1, j - 1]
- grad3 = d[i + 1, j + 1]
- # 如果x,y方向梯度符号相反
- else:
- grad1 = d[i - 1, j + 1]
- grad3 = d[i + 1, j - 1]
-
- # 如果X方向幅度值较大
- else:
- weight = np.abs(gradY) / np.abs(gradX)
- grad2 = d[i, j - 1]
- grad4 = d[i, j + 1]
- # 如果x,y方向梯度符号相同
- if gradX * gradY > 0:
- grad1 = d[i + 1, j - 1]
- grad3 = d[i - 1, j + 1]
- # 如果x,y方向梯度符号相反
- else:
- grad1 = d[i - 1, j - 1]
- grad3 = d[i + 1, j + 1]
-
- gradTemp1 = weight * grad1 + (1 - weight) * grad2
- gradTemp2 = weight * grad3 + (1 - weight) * grad4
- if gradTemp >= gradTemp1 and gradTemp >= gradTemp2:
- NMS[i, j] = gradTemp
- else:
- NMS[i, j] = 0
-
- # plt.imshow(NMS, cmap = "gray")
-
-
- # step4. 双阈值算法检测、连接边缘
- W3, H3 = NMS.shape
- DT = np.zeros([W3, H3])
- # 定义高低阈值
- TL = 0.2 * np.max(NMS)
- TH = 0.3 * np.max(NMS)
- for i in range(1, W3 - 1):
- for j in range(1, H3 - 1):
- if (NMS[i, j] < TL):
- DT[i, j] = 0
- elif (NMS[i, j] > TH):
- DT[i, j] = 1
- elif ((NMS[i - 1, j - 1:j + 1] < TH).any() or (NMS[i + 1, j - 1:j + 1]).any()
- or (NMS[i, [j - 1, j + 1]] < TH).any()):
- DT[i, j] = 1
-
- plt.figure(1)
- # 第一行第一列图形
- ax1 = plt.subplot(1, 3, 1)
- plt.sca(ax1)
- plt.imshow(img)
- plt.title("artwork")
- # 第一行第二列图形
- ax2 = plt.subplot(1, 3, 2)
- plt.sca(ax2)
- plt.imshow(canny, cmap="gray")
- plt.title("opencv Canny")
-
- ax3 = plt.subplot(1, 3, 3)
- plt.sca(ax3)
- plt.imshow(DT, cmap="gray")
- plt.title("my Canny")
- plt.show()
参考文献
Copyright © 2003-2013 www.wpsshop.cn 版权所有,并保留所有权利。