Python-OpenCV処理画像(四)(五):画像ヒストグラムと逆投影画像における境界と輪郭検出

import cv2.cv as cv

def drawGraph(ar,im, size): #Draw the histogram on the image
    minV, maxV, minloc, maxloc = cv.MinMaxLoc(ar) #Get the min and max value
    hpt = 0.9 * histsize
    for i in range(size):
        intensity = ar[i] * hpt / maxV #Calculate the intensity to make enter in the image
        cv.Line(im, (i,size), (i,int(size-intensity)),cv.Scalar(255,255,255)) #Draw the line
        i += 1

#---- Gray image
orig = cv.LoadImage("img/lena.jpg", cv.CV_8U)

histsize = 256 #Because we are working on grayscale pictures which values within 0-255

hist = cv.CreateHist([histsize], cv.CV_HIST_ARRAY, [[0,histsize]], 1)

cv.CalcHist([orig], hist) #Calculate histogram for the given grayscale picture

histImg = cv.CreateMat(histsize, histsize, cv.CV_8U) #Image that will contain the graph of the repartition of values
drawGraph(hist.bins, histImg, histsize)

cv.ShowImage("Original Image", orig)
cv.ShowImage("Original Histogram", histImg)
#---------------------

#---- Equalized image
imEq = cv.CloneImage(orig)
cv.EqualizeHist(imEq, imEq) #Equlize the original image

histEq = cv.CreateHist([histsize], cv.CV_HIST_ARRAY, [[0,histsize]], 1)
cv.CalcHist([imEq], histEq) #Calculate histogram for the given grayscale picture
eqImg = cv.CreateMat(histsize, histsize, cv.CV_8U) #Image that will contain the graph of the repartition of values
drawGraph(histEq.bins, eqImg, histsize)

cv.ShowImage("Image Equalized", imEq)
cv.ShowImage("Equalized HIstogram", eqImg)
#--------------------------------

cv.WaitKey(0)

import cv2.cv as cv

im = cv.LoadImage("img/lena.jpg", cv.CV_8U)

cv.SetImageROI(im, (1, 1,30,30))

histsize = 256 #Because we are working on grayscale pictures
hist = cv.CreateHist([histsize], cv.CV_HIST_ARRAY, [[0,histsize]], 1)
cv.CalcHist([im], hist)


cv.NormalizeHist(hist,1) # The factor rescale values by multiplying values by the factor
_,max_value,_,_ = cv.GetMinMaxHistValue(hist)

if max_value == 0:
    max_value = 1.0
cv.NormalizeHist(hist,256/max_value)

cv.ResetImageROI(im)

res = cv.CreateMat(im.height, im.width, cv.CV_8U)
cv.CalcBackProject([im], res, hist)

cv.Rectangle(im, (1,1), (30,30), (0,0,255), 2, cv.CV_FILLED)
cv.ShowImage("Original Image", im)
cv.ShowImage("BackProjected", res)
cv.WaitKey(0)
 
 
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关于边缘检测的基础来自于一个事实，即在边缘部分，像素值出现”跳跃“或者较大的变化。如果在此边缘部分求取一阶导数，就会看到极值的出现。
而在一阶导数为极值的地方，二阶导数为0，基于这个原理，就可以进行边缘检测。
关于 Laplace 算法原理，可参考
  
  
  
  

• Laplace 算子

0x01. Laplace 算法

下面的代码展示了分别对灰度化的图像和原始彩色图像中的边缘进行检测：

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)

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)

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)

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)

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)

   
   
   
   

    
    
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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)