import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt

# roi是我们需要找到的对象或区域
roi = cv.imread('rose_red.png')
hsv = cv.cvtColor(roi, cv.COLOR_BGR2HSV)

# target是我们搜索的图像
target = cv.imread('rose.png')
hsvt = cv.cvtColor(target, cv.COLOR_BGR2HSV)

# 用calcHist来找直方图，也可以用np.histogram2d
M = cv.calcHist([hsv], [0,1], None, [180,256], [0,180,0,256])
I = cv.calcHist([hsvt], [0,1], None, [180,256], [0,180,0,256])

h, s, v = cv.split(hsvt)
B = R[h.ravel(), s.ravel()]
B = np.munimum(B, 1)
B = B.reshape(hsvt.shape[:2])

disc = cv.getStructuringElement(cv.MORPH_ELLIPSE, (5,5))
cv.filter2D(B, -1, disc, B)
B = np.uint8(B)
cv.normalize(B, B, 0, 255, cv.NORM_MINMAX)

ret, thresh = cv.threshold(B, 50, 255, 0)

import numpy as np
import cv2 as cv

roi = cv.imread('rose_red.png')
hsv = cv.cvtColor(roi, cv.COLOR_BGR2HSV)

target = cv.imread('rose.png')
hsvt = cv.cvtColor(target, cv.COLOR_BGR2HSV)

# 计算对象的直方图
roihist = cv.calcHist([hsv], [0,1], None, [180,256], [0,180,0,256])

# 标准化直方图，并应用投影
cv.normalize(roihist, roihist, 0, 255, cv.NORM_MINMAX)
dst = cv.calcBackProject([hsvt], [0,1], roihist, [0,180,0,256], 1)

# 与磁盘内核进行卷积
disc = cv.getStructuringElement(cv.MORPH_ELLIPSE, (5,5))
cv.filter2D(dst, -1, disc, dst)

# 阈值、二进制按位和操作
ret, thresh = cv.threshold(dst, 50, 255, 0)
thresh = cv.merge((thresh, thresh, thresh))
res = cv.bitwise_and(target, thresh)

res = np.vstack((target, thresh, res))
cv.imwrite('res.jpg', res)

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)