OpenCV的KCF目标跟踪、测量目标、模板匹配

import cv2

class Point:
    def __init__(self,x,y):
        self.x=x
        self.y=y
tracker = cv2.TrackerKCF_create()
vc = cv2.VideoCapture(0)
pointArr=[]
while(True):
    _, img = vc.read()

    painted = img.copy()
    (success, box)=tracker.update(img);
    if success:
        (x,y,w,h) = [int(v) for v in box]  #解构x y w h参数
        pointArr.append(Point(x + w / 2, y + h / 2))
        cv2.rectangle(painted,(x,y),(w+x,h+y),(0,0,255),3)

    if len(pointArr)>0:
        for myPoint in pointArr:
            cv2.circle(painted,(int(myPoint.x),int(myPoint.y)),1,(0,0,255),1)

    cv2.imshow("video", painted)
    key = cv2.waitKey(1) & 0xFF  # 取低八位，避免某些操作系统返回的不是ascii值
    if key == ord("s"):  # 按下s选择ROI区域
        rect = cv2.selectROI("video",img,fromCenter=False,showCrosshair=True);
        tracker.init(img.copy(),rect)

import time

import cv2


class AreaInfo:
    def __init__(self, area, contour):
        self.area = area
        self.contour = contour


def measure(srcImg):
    gray = cv2.cvtColor(srcImg, cv2.COLOR_BGR2GRAY)
    _, binary = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY);
    blur = cv2.GaussianBlur(srcImg, (11, 11), 1)
    edge = cv2.Canny(blur, 30, 200);
    _, contours ,__= cv2.findContours(edge, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    areaList = []

    for object in contours:
        areaList.append(AreaInfo(cv2.contourArea(object), object))
    maxArea = AreaInfo(0, [[0, 0]])
    for areaInfoObject in areaList:
        maxArea = areaInfoObject if areaInfoObject.area > maxArea.area else maxArea  # 迭代拿到面积最大的区域
    print(maxArea.area)
    cv2.drawContours(blur, maxArea.contour, -1, (100, 255, 40), 5)
    cv2.imshow("edge", blur)
    #cv2.waitKey(0)

apple = cv2.imread("apple.png")
measure(apple)
cv2.waitKey(0)
#cap = cv2.VideoCapture(0)
#while True:
#
#    _,src = cap.read()
#    measure(src)
#    cv2.waitKey(200)

import cv2
import numpy as np
def templateInit():
    img = cv2.imread("template.png")
    img = cv2.GaussianBlur(img,(3,3),1)
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    _,binary = cv2.threshold(gray,128,255,cv2.THRESH_BINARY_INV)
    _,contour,_ = cv2.findContours(binary,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    cv2.drawContours(binary,contour,-1,(0,0,255),2)
    #cv2.imshow("asd",binary)
    #cv2.waitKey(0)
    boundingBoxes = [cv2.boundingRect(c) for c in contour]
    contour,_ = zip(*sorted(zip(contour, boundingBoxes),key=lambda b: b[1][0], reverse=False))  # 从左到右排序
    list = []
    for (i) in contour:
        (x,y,w,h)= cv2.boundingRect(i)
        #print(x,y,w,h)
        cv2.rectangle(img,(x,y),(w+x,h+y),(0,0,255),1)
        ROI = binary[y:y + h, x:x + w]
        ROI=cv2.resize(ROI.copy(),(57,88))
        list.append(ROI)
        #cv2.imshow("感兴趣区域",ROI);
        #cv2.waitKey(0)
        #list.append()


#    cv2.imshow("asd", img)
#    cv2.waitKey(0)

    return binary,contour,list


binary,templateContours,list = templateInit()
creditCard = cv2.imread("CreditCard.png")
img = cv2.GaussianBlur(creditCard, (3, 3), 1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#_, binary = cv2.threshold(gray, 130, 255, cv2.THRESH_BINARY)
#topHat = cv2.morphologyEx(gray,cv2.MORPH_TOPHAT,np.ones((8,8),np.uint8))  # 使用礼帽提取图像中比较亮的部分
_,creditBinay = cv2.threshold(gray.copy(),128,255,cv2.THRESH_BINARY_INV)
_, creditContour, _ = cv2.findContours(creditBinay, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(creditBinay, creditContour, -1, (0, 0, 255), 2)
#cv2.imshow("asd",creditBinay)
#cv2.waitKey(0)

boundingBoxes = [cv2.boundingRect(c) for c in creditContour]
creditContour, _ = zip(*sorted(zip(creditContour, boundingBoxes), key=lambda b: b[1][0], reverse=False))  # 从左到右排序
#cv2.imshow("a",gray);
#cv2.waitKey(0)


numList=[]
filtedContour=[]
for i in creditContour:
    (x, y, w, h) = cv2.boundingRect(i)
    if (0.58 < w / h < 0.70) and (31<x<422) and (145<y<201):
        filtedContour.append(i)
cv2.drawContours(creditCard,filtedContour,-1,(0,0,255),2)
for i in filtedContour:  # 先遍历所有轮廓，找出候选的ROI区域
    (x, y, w, h) = cv2.boundingRect(i)
    cv2.rectangle(img, (x, y), (w + x, h + y), (0, 0, 255), 1)
    ROI = gray[y:y + h, x:x + w]
    _,ROI = cv2.threshold(ROI.copy(),128,255,cv2.THRESH_BINARY)
    #kernel = np.zeros((5, 5), np.uint8)  # 新建一个核
    #ROI = cv2.erode(ROI.copy(), kernel, iterations=3)
    ROI = cv2.resize(ROI.copy(), (57, 88))
    maxList = []  # 存放十种模板的匹配度
    for templateROI in list:
        result = cv2.matchTemplate(ROI,templateROI,cv2.TM_CCOEFF)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
        #cv2.rectangle(creditCard,min_loc,max_loc,(0,255,255),1)
        maxList.append(max_val)
    maxValue = 0
    Index = 0
    for i in range(10):
        if maxList[i] > maxValue:
            maxValue = maxList[i]  # 找出最大的可能性，maxList的index恰好为数字
            Index = i
    if not (maxValue == 0):
        cv2.putText(creditCard,str(Index),(x,y-10),1,1,(0,0,255))

    numList.append(Index)
    #print(Index)

cv2.imshow("asd",creditCard)
cv2.waitKey(0)
print(numList)


    # cv2.matchTemplate(creditCard)
#cv2.matchTemplate()