久久久久88色偷偷免费,午夜精品久久久久久久99,99午夜

關(guān)于邊緣檢測(cè)的基礎(chǔ)來(lái)自于一個(gè)事實(shí)，即在邊緣部分，像素值出現(xiàn)”跳躍“或者較大的變化。如果在此邊緣部分求取一階導(dǎo)數(shù)，就會(huì)看到極值的出現(xiàn)。

而在一階導(dǎo)數(shù)為極值的地方，二階導(dǎo)數(shù)為0，基于這個(gè)原理，就可以進(jìn)行邊緣檢測(cè)。

關(guān)于 Laplace 算法原理，可參考

Laplace 算子

0x01. Laplace 算法

下面的代碼展示了分別對(duì)灰度化的圖像和原始彩色圖像中的邊緣進(jìn)行檢測(cè)：


import cv2.cv as cv
 
im=cv.LoadImage('img/building.png', cv.CV_LOAD_IMAGE_COLOR)
 
# Laplace on a gray scale picture
gray = cv.CreateImage(cv.GetSize(im), 8, 1)
cv.CvtColor(im, gray, cv.CV_BGR2GRAY)
 
aperture=3
 
dst = cv.CreateImage(cv.GetSize(gray), cv.IPL_DEPTH_32F, 1)
cv.Laplace(gray, dst,aperture)
 
cv.Convert(dst,gray)
 
thresholded = cv.CloneImage(im)
cv.Threshold(im, thresholded, 50, 255, cv.CV_THRESH_BINARY_INV)
 
cv.ShowImage('Laplaced grayscale',gray)
#------------------------------------
 
# Laplace on color
planes = [cv.CreateImage(cv.GetSize(im), 8, 1) for i in range(3)]
laplace = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_16S, 1)
colorlaplace = cv.CreateImage(cv.GetSize(im), 8, 3)
 
cv.Split(im, planes[0], planes[1], planes[2], None) #Split channels to apply laplace on each
for plane in planes:
 cv.Laplace(plane, laplace, 3)
 cv.ConvertScaleAbs(laplace, plane, 1, 0)
 
cv.Merge(planes[0], planes[1], planes[2], None, colorlaplace)
 
cv.ShowImage('Laplace Color', colorlaplace)
#-------------------------------------
 
cv.WaitKey(0)

效果展示

原圖

灰度化圖片檢測(cè)

原始彩色圖片檢測(cè)

0x02. Sobel 算法

Sobel 也是很常用的一種輪廓識(shí)別的算法。

關(guān)于 Sobel 導(dǎo)數(shù)原理的介紹，可參考

Sobel 導(dǎo)數(shù)

以下是使用 Sobel 算法進(jìn)行輪廓檢測(cè)的代碼和效果


import cv2.cv as cv
 
im=cv.LoadImage('img/building.png', cv.CV_LOAD_IMAGE_GRAYSCALE)
 
sobx = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_16S, 1)
cv.Sobel(im, sobx, 1, 0, 3) #Sobel with x-order=1
 
soby = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_16S, 1)
cv.Sobel(im, soby, 0, 1, 3) #Sobel withy-oder=1
 
cv.Abs(sobx, sobx)
cv.Abs(soby, soby)
 
result = cv.CloneImage(im)
cv.Add(sobx, soby, result) #Add the two results together.
 
cv.Threshold(result, result, 100, 255, cv.CV_THRESH_BINARY_INV)
 
cv.ShowImage('Image', im)
cv.ShowImage('Result', result)
 
cv.WaitKey(0)

處理之后效果圖（感覺(jué)比Laplace效果要好些）

0x03. cv.MorphologyEx

cv.MorphologyEx 是另外一種邊緣檢測(cè)的算法


import cv2.cv as cv
 
image=cv.LoadImage('img/build.png', cv.CV_LOAD_IMAGE_GRAYSCALE)
 
#Get edges
morphed = cv.CloneImage(image)
cv.MorphologyEx(image, morphed, None, None, cv.CV_MOP_GRADIENT) # Apply a dilate - Erode
 
cv.Threshold(morphed, morphed, 30, 255, cv.CV_THRESH_BINARY_INV)
 
cv.ShowImage('Image', image)
cv.ShowImage('Morphed', morphed)
 
cv.WaitKey(0)

0x04. Canny 邊緣檢測(cè)

Canny 算法可以對(duì)直線邊界做出很好的檢測(cè)；

關(guān)于 Canny 算法原理的描述，可參考：

Canny 邊緣檢測(cè)


import cv2.cv as cv
import math
 
im=cv.LoadImage('img/road.png', cv.CV_LOAD_IMAGE_GRAYSCALE)
 
pi = math.pi #Pi value
 
dst = cv.CreateImage(cv.GetSize(im), 8, 1)
 
cv.Canny(im, dst, 200, 200)
cv.Threshold(dst, dst, 100, 255, cv.CV_THRESH_BINARY)
 
#---- Standard ----
color_dst_standard = cv.CreateImage(cv.GetSize(im), 8, 3)
cv.CvtColor(im, color_dst_standard, cv.CV_GRAY2BGR)#Create output image in RGB to put red lines
 
lines = cv.HoughLines2(dst, cv.CreateMemStorage(0), cv.CV_HOUGH_STANDARD, 1, pi / 180, 100, 0, 0)
for (rho, theta) in lines[:100]:
 a = math.cos(theta) #Calculate orientation in order to print them
 b = math.sin(theta)
 x0 = a * rho
 y0 = b * rho
 pt1 = (cv.Round(x0 1000*(-b)), cv.Round(y0 1000*(a)))
 pt2 = (cv.Round(x0 - 1000*(-b)), cv.Round(y0 - 1000*(a)))
 cv.Line(color_dst_standard, pt1, pt2, cv.CV_RGB(255, 0, 0), 2, 4) #Draw the line
 
#---- Probabilistic ----
color_dst_proba = cv.CreateImage(cv.GetSize(im), 8, 3)
cv.CvtColor(im, color_dst_proba, cv.CV_GRAY2BGR) # idem
 
rho=1
theta=pi/180
thresh = 50
minLength= 120 # Values can be changed approximately to fit your image edges
maxGap= 20
 
lines = cv.HoughLines2(dst, cv.CreateMemStorage(0), cv.CV_HOUGH_PROBABILISTIC, rho, theta, thresh, minLength, maxGap)
for line in lines:
 cv.Line(color_dst_proba, line[0], line[1], cv.CV_RGB(255, 0, 0), 2, 8)
 
cv.ShowImage('Image',im)
cv.ShowImage('Cannied', dst)
cv.ShowImage('Hough Standard', color_dst_standard)
cv.ShowImage('Hough Probabilistic', color_dst_proba)
cv.WaitKey(0)

原圖

使用 Canny 算法處理之后

標(biāo)記出標(biāo)準(zhǔn)的直線

標(biāo)記出所有可能的直線

0x05. 輪廓檢測(cè)

OpenCV 提供一個(gè) FindContours 函數(shù)可以用來(lái)檢測(cè)出圖像中對(duì)象的輪廓：


import cv2.cv as cv
 
orig = cv.LoadImage('img/build.png', cv.CV_LOAD_IMAGE_COLOR)
im = cv.CreateImage(cv.GetSize(orig), 8, 1)
cv.CvtColor(orig, im, cv.CV_BGR2GRAY)
#Keep the original in colour to draw contours in the end
 
cv.Threshold(im, im, 128, 255, cv.CV_THRESH_BINARY)
cv.ShowImage('Threshold 1', im)
 
element = cv.CreateStructuringElementEx(5*2 1, 5*2 1, 5, 5, cv.CV_SHAPE_RECT)
 
cv.MorphologyEx(im, im, None, element, cv.CV_MOP_OPEN) #Open and close to make appear contours
cv.MorphologyEx(im, im, None, element, cv.CV_MOP_CLOSE)
cv.Threshold(im, im, 128, 255, cv.CV_THRESH_BINARY_INV)
cv.ShowImage('After MorphologyEx', im)
# --------------------------------
 
vals = cv.CloneImage(im) #Make a clone because FindContours can modify the image
contours=cv.FindContours(vals, cv.CreateMemStorage(0), cv.CV_RETR_LIST, cv.CV_CHAIN_APPROX_SIMPLE, (0,0))
 
_red = (0, 0, 255); #Red for external contours
_green = (0, 255, 0);# Gren internal contours
levels=2 #1 contours drawn, 2 internal contours as well, 3 ...
cv.DrawContours (orig, contours, _red, _green, levels, 2, cv.CV_FILLED) #Draw contours on the colour image
 
cv.ShowImage('Image', orig)
cv.WaitKey(0)

效果圖：

原圖

識(shí)別結(jié)果

0x06. 邊界檢測(cè)


import cv2.cv as cv
 
im = cv.LoadImage('img/build.png', cv.CV_LOAD_IMAGE_GRAYSCALE)
 
dst_32f = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_32F, 1)
 
neighbourhood = 3
aperture = 3
k = 0.01
maxStrength = 0.0
threshold = 0.01
nonMaxSize = 3
 
cv.CornerHarris(im, dst_32f, neighbourhood, aperture, k)
 
minv, maxv, minl, maxl = cv.MinMaxLoc(dst_32f)
 
dilated = cv.CloneImage(dst_32f)
cv.Dilate(dst_32f, dilated) # By this way we are sure that pixel with local max value will not be changed, and all the others will
 
localMax = cv.CreateMat(dst_32f.height, dst_32f.width, cv.CV_8U)
cv.Cmp(dst_32f, dilated, localMax, cv.CV_CMP_EQ) #compare allow to keep only non modified pixel which are local maximum values which are corners.
 
threshold = 0.01 * maxv
cv.Threshold(dst_32f, dst_32f, threshold, 255, cv.CV_THRESH_BINARY)
 
cornerMap = cv.CreateMat(dst_32f.height, dst_32f.width, cv.CV_8U)
cv.Convert(dst_32f, cornerMap) #Convert to make the and
cv.And(cornerMap, localMax, cornerMap) #Delete all modified pixels
 
radius = 3
thickness = 2
 
l = []
for x in range(cornerMap.height): #Create the list of point take all pixel that are not 0 (so not black)
 for y in range(cornerMap.width):
 if cornerMap[x,y]:
 l.append((y,x))
 
for center in l:
 cv.Circle(im, center, radius, (255,255,255), thickness)
 
 
cv.ShowImage('Image', im)
cv.ShowImage('CornerHarris Result', dst_32f)
cv.ShowImage('Unique Points after Dilatation/CMP/And', cornerMap)
 
cv.WaitKey(0)

本站僅提供存儲(chǔ)服務(wù)，所有內(nèi)容均由用戶發(fā)布，如發(fā)現(xiàn)有害或侵權(quán)內(nèi)容，請(qǐng)點(diǎn)擊舉報(bào)。