改良後的的opencv 學生證跟蹤

http://blog.csdn.net/dgut_guangdian/article/details/78535646

我上一篇寫的跟蹤很容易誤判有很大隨機性，而且不太準確現在做了一個改良版本：

import numpy as np
import math
import cv2
import time

def nothing(x):
    pass


cap = cv2.VideoCapture(0)   #打開攝像頭0


var = cv2.CAP_PVAPI
LowerRed = np.array([24, 75, 37])


# 創建改變顏色的滾動條
cv2.namedWindow("fps")  #新建一個窗口


cv2.createTrackbar('R','fps',89,255,nothing)    #想要追蹤顏色的R值
cv2.createTrackbar('G','fps',254,255,nothing)   #想要追蹤顏色的G值
cv2.createTrackbar('B','fps',254,255,nothing)   #想要追蹤顏色的B值
cv2.createTrackbar('threshold','fps',19,255,nothing)    #二值化閾值

while(1):
    #滑動條賦值
    r = cv2.getTrackbarPos('R', 'fps')
    g = cv2.getTrackbarPos('G', 'fps')
    b = cv2.getTrackbarPos('B', 'fps')
    c = cv2.getTrackbarPos('threshold', 'fps')
    UpperRed = np.array([r, g, b])
    ret, frame = cap.read()#得到每一幀
    ret, frame1 = cap.read()  # 得到每一幀

    #hsv
    HSV= cv2.cvtColor(frame, cv2.COLOR_RGB2HSV)
    cv2.imshow("hsv",HSV)
    #遮罩
    mask = cv2.inRange(HSV, LowerRed, UpperRed)
    kerne4=(11,11)
    mask = cv2.morphologyEx(mask,cv2.MORPH_OPEN,kerne4)#閉運算讓噪點更少
    cv2.imshow("mask",mask)
    RED_Things = cv2.bitwise_and(frame, frame, mask=mask)
    cv2.imshow("red", RED_Things)
    img_gray = cv2.cvtColor(RED_Things, cv2.COLOR_BGR2GRAY)  # 灰度化
    ret, img_threshold = cv2.threshold(img_gray, c, 255, cv2.THRESH_BINARY)  # 二值化
    # 膨脹+腐蝕等形態學變化
    kerne1 = np.ones((3, 3), np.uint8)
    img_erosin = cv2.erode(img_threshold, kerne1, iterations=1)
    #cv2.imshow("dil",img_erosin)
    kerne2 = np.ones((45, 45), np.uint8)
    img_dilation = cv2.dilate(img_erosin, kerne2, iterations=1)
    kerne3 = np.ones((11, 11), np.uint8)
    img_dilation1 = cv2.dilate(img_dilation,kerne3,iterations=1)
    #cv2.imshow("ers",img_dilation1)
    kerne3 = np.ones((51, 51), np.uint8)
    img_erosin1 = cv2.erode(img_dilation1, kerne3, iterations=1)
    #圖像相與
    img_bit = cv2.bitwise_and(frame, frame, mask=img_erosin1)
    cv2.imshow("bit",img_bit)





    # 邊緣caany
    img_gray1 = cv2.cvtColor(img_bit, cv2.COLOR_BGR2GRAY)  # 灰度化
    ret, img_threshold1 = cv2.threshold(img_gray1, c, 255, cv2.THRESH_BINARY)  # 二值化
    canny = cv2.Canny(img_threshold1, 10, 200)  # apertureSize默認爲3
    #cv2.imshow("img1",img_threshold1)


    coutours = cv2.findContours(img_threshold1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2]#得到輪廓
    #由於可能遇到畫面中沒有綠色的場景避免程序意外中斷所以把計算寫入了try裏面


    if len(coutours) > 0:

        c = max(coutours, key=cv2.contourArea)


        M = cv2.moments(c) #計算中心




    cx = int(M["m10"] / M["m00"])
    cy = int(M["m01"] / M["m00"])
    #得到中心然後在畫面上顯示
    cv2.circle(frame1, (cx, cy), 9, (255, 0, 255), -1)
    cv2.line(frame1,(cx,0),(cx,700),(255,0,0),3)
    cv2.line(frame1,(0,cy),(700,cy),(255,0,0),3)
    

    cv2.imshow("fps", frame1)




    if cv2.waitKey(1) & 0xFF == ord('q'):   #按q保存一張圖片
        cv2.imwrite("E:\cpy\pictures\\pic.jpg", frame1)
        break
cap.release()
cv2.destroyAllWindows()

喜歡的點個贊把~