首頁 > 軟體

python實現canny邊緣檢測

2020-09-14 15:01:47

canny邊緣檢測原理

canny邊緣檢測共有5部分組成,下邊我會分別來介紹。

1 高斯模糊(略)

2 計算梯度幅值和方向。

可選用的模板:soble運算元、Prewitt運算元、Roberts模板等等;

一般採用soble運算元,OpenCV也是如此,利用soble水平和垂直運算元與輸入影象折積計算dx、dy:

進一步可以得到影象梯度的幅值:

為了簡化計算,幅值也可以作如下近似:

角度為:

如下圖表示了中心點的梯度向量、方位角以及邊緣方向(任一點的邊緣與梯度向量正交) :

θ = θm = arctan(dy/dx)(邊緣方向)
α = θ + 90= arctan(dy/dx) + 90(梯度方向)

3、根據角度對幅值進行非極大值抑制

劃重點:是沿著梯度方向對幅值進行非極大值抑制,而非邊緣方向,這裡初學者容易弄混。

例如:3*3區域內,邊緣可以劃分為垂直、水平、45°、135°4個方向,同樣,梯度反向也為四個方向(與邊緣方向正交)。因此為了進行非極大值,將所有可能的方向量化為4個方向,如下圖:

即梯度方向分別為

α = 90

α = 45

α = 0

α = -45

非極大值抑制即為沿著上述4種型別的梯度方向,比較3*3鄰域內對應鄰域值的大小:

在每一點上,領域中心 x 與沿著其對應的梯度方向的兩個畫素相比,若中心畫素為最大值,則保留,否則中心置0,這樣可以抑制非極大值,保留區域性梯度最大的點,以得到細化的邊緣。

4、用雙閾值演演算法檢測和連線邊緣

1選取係數TH和TL,比率為2:1或3:1。(一般取TH=0.3或0.2,TL=0.1);

2 將小於低閾值的點拋棄,賦0;將大於高閾值的點立即標記(這些點為確定邊緣 點),賦1或255;

3將小於高閾值,大於低閾值的點使用8連通區域確定(即:只有與TH畫素連線時才會被接受,成為邊緣點,賦 1或255)

python 實現

import cv2
import numpy as np
m1 = np.array([[1, 0, -1], [2, 0, -2], [1, 0, -1]])
m2 = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])
from matplotlib import pyplot as plt
# 第一步:完成高斯平滑濾波
img = cv2.imread("B9064CF1D57871735CE11A0F368DCF27.jpg", 0)
sobel = cv2.Canny(img, 50, 100)
cv2.namedWindow('5', 0)
cv2.resizeWindow("5", 640, 480)
cv2.imshow("5", sobel) # 角度值灰度圖
img = cv2.GaussianBlur(img, (3, 3), 2)
# 第二步:完成一階有限差分計算,計算每一點的梯度幅值與方向
img1 = np.zeros(img.shape, dtype="uint8") # 與原圖大小相同
theta = np.zeros(img.shape, dtype="float") # 方向矩陣原影象大小
img = cv2.copyMakeBorder(img, 1, 1, 1, 1, borderType=cv2.BORDER_REPLICATE)
rows, cols = img.shape
for i in range(1, rows - 1):
for j in range(1, cols - 1):
Gy = [np.sum(m2 * img[i - 1:i + 2, j - 1:j + 2])]
#Gy = (np.dot(np.array([1, 1, 1]), (m2 * img[i - 1:i + 2, j - 1:j + 2]))).dot(np.array([[1], [1], [1]]))
Gx = [np.sum(m1 * img[i - 1:i + 2, j - 1:j + 2])]
#Gx = (np.dot(np.array([1, 1, 1]), (m1 * img[i - 1:i + 2, j - 1:j + 2]))).dot(np.array([[1], [1], [1]]))
if Gx[0] == 0:
theta[i - 1, j - 1] = 90
continue
else:
temp = ((np.arctan2(Gy[0], Gx[0])) * 180 / np.pi)+90
if Gx[0] * Gy[0] > 0:
if Gx[0] > 0:
# 第一象線
theta[i - 1, j - 1] = np.abs(temp)
else:
# 第三象線
theta[i - 1, j - 1] = (np.abs(temp) - 180)
if Gx[0] * Gy[0] < 0:
if Gx[0] > 0:
# 第四象線
theta[i - 1, j - 1] = (-1) * np.abs(temp)
else:
# 第二象線
theta[i - 1, j - 1] = 180 - np.abs(temp)

img1[i - 1, j - 1] = (np.sqrt(Gx[0] ** 2 + Gy[0] ** 2))
for i in range(1, rows - 2):
for j in range(1, cols - 2):
if (((theta[i, j] >= -22.5) and (theta[i, j] < 22.5)) or
((theta[i, j] <= -157.5) and (theta[i, j] >= -180)) or
((theta[i, j] >= 157.5) and (theta[i, j] < 180))):
theta[i, j] = 0.0
elif (((theta[i, j] >= 22.5) and (theta[i, j] < 67.5)) or
((theta[i, j] <= -112.5) and (theta[i, j] >= -157.5))):
theta[i, j] = -45.0
elif (((theta[i, j] >= 67.5) and (theta[i, j] < 112.5)) or
((theta[i, j] <= -67.5) and (theta[i, j] >= -112.5))):
theta[i, j] = 90.0
elif (((theta[i, j] >= 112.5) and (theta[i, j] < 157.5)) or
((theta[i, j] <= -22.5) and (theta[i, j] >= -67.5))):
theta[i, j] = 45.0
'''
for i in range(1, rows - 1):
for j in range(1, cols - 1):
Gy = [np.sum(m2 * img[i - 1:i + 2, j - 1:j + 2])]
#Gy = (np.dot(np.array([1, 1, 1]), (m2 * img[i - 1:i + 2, j - 1:j + 2]))).dot(np.array([[1], [1], [1]]))
Gx = [np.sum(m1 * img[i - 1:i + 2, j - 1:j + 2])]
#Gx = (np.dot(np.array([1, 1, 1]), (m1 * img[i - 1:i + 2, j - 1:j + 2]))).dot(np.array([[1], [1], [1]]))
if Gx[0] == 0:
theta[i - 1, j - 1] = 90
continue
else:
temp = (np.arctan2(Gy[0], Gx[0])) * 180 / np.pi)
if Gx[0] * Gy[0] > 0:
if Gx[0] > 0:
# 第一象線
theta[i - 1, j - 1] = np.abs(temp)
else:
# 第三象線
theta[i - 1, j - 1] = (np.abs(temp) - 180)
if Gx[0] * Gy[0] < 0:
if Gx[0] > 0:
# 第四象線
theta[i - 1, j - 1] = (-1) * np.abs(temp)
else:
# 第二象線
theta[i - 1, j - 1] = 180 - np.abs(temp)

img1[i - 1, j - 1] = (np.sqrt(Gx[0] ** 2 + Gy[0] ** 2))
for i in range(1, rows - 2):
for j in range(1, cols - 2):
if (((theta[i, j] >= -22.5) and (theta[i, j] < 22.5)) or
((theta[i, j] <= -157.5) and (theta[i, j] >= -180)) or
((theta[i, j] >= 157.5) and (theta[i, j] < 180))):
theta[i, j] = 90.0
elif (((theta[i, j] >= 22.5) and (theta[i, j] < 67.5)) or
((theta[i, j] <= -112.5) and (theta[i, j] >= -157.5))):
theta[i, j] = 45.0
elif (((theta[i, j] >= 67.5) and (theta[i, j] < 112.5)) or
((theta[i, j] <= -67.5) and (theta[i, j] >= -112.5))):
theta[i, j] = 0.0
elif (((theta[i, j] >= 112.5) and (theta[i, j] < 157.5)) or
((theta[i, j] <= -22.5) and (theta[i, j] >= -67.5))):
theta[i, j] = -45.0

'''
# 第三步:進行 非極大值抑制計算
img2 = np.zeros(img1.shape) # 非極大值抑制影象矩陣

for i in range(1, img2.shape[0] - 1):
for j in range(1, img2.shape[1] - 1):
# 0度j不變
if (theta[i, j] == 0.0) and (img1[i, j] == np.max([img1[i, j], img1[i + 1, j], img1[i - 1, j]])):
img2[i, j] = img1[i, j]

if (theta[i, j] == -45.0) and img1[i, j] == np.max([img1[i, j], img1[i - 1, j - 1], img1[i + 1, j + 1]]):
img2[i, j] = img1[i, j]

if (theta[i, j] == 90.0) and img1[i, j] == np.max([img1[i, j], img1[i, j + 1], img1[i, j - 1]]):
img2[i, j] = img1[i, j]

if (theta[i, j] == 45.0) and img1[i, j] == np.max([img1[i, j], img1[i - 1, j + 1], img1[i + 1, j - 1]]):
img2[i, j] = img1[i, j]

# 第四步:雙閾值檢測和邊緣連線
img3 = np.zeros(img2.shape) # 定義雙閾值影象
# TL = 0.4*np.max(img2)
# TH = 0.5*np.max(img2)
TL = 50
TH = 100
# 關鍵在這兩個閾值的選擇
for i in range(1, img3.shape[0] - 1):
for j in range(1, img3.shape[1] - 1):
if img2[i, j] < TL:
img3[i, j] = 0
elif img2[i, j] > TH:
img3[i, j] = 255
elif ((img2[i + 1, j] < TH) or (img2[i - 1, j] < TH) or (img2[i, j + 1] < TH) or
(img2[i, j - 1] < TH) or (img2[i - 1, j - 1] < TH) or (img2[i - 1, j + 1] < TH) or
(img2[i + 1, j + 1] < TH) or (img2[i + 1, j - 1] < TH)):
img3[i, j] = 255

cv2.namedWindow('1', 0)
cv2.resizeWindow("1", 640, 480)
cv2.namedWindow('2', 0)
cv2.resizeWindow("2", 640, 480)
cv2.namedWindow('3', 0)
cv2.resizeWindow("3", 640, 480)
cv2.namedWindow('4', 0)
cv2.resizeWindow("4", 640, 480)
cv2.imshow("1", img) # 原始影象
cv2.imshow("2", img1) # 梯度幅值圖
cv2.imshow("3", img2) # 非極大值抑制灰度圖
cv2.imshow("4", img3) # 最終效果圖
cv2.waitKey(0)

執行結果如下

以上就是python實現canny邊緣檢測的詳細內容,更多關於canny邊緣檢測的資料請關注it145.com其它相關文章!


IT145.com E-mail:sddin#qq.com