python Canny边缘检测
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
Canny边缘检测算法(python 实现）_Master_miao的博客-CSDN博客_python canny