最佳物体检测和跟踪算法[关闭]

Question

我是Opencv的新手。我想创建一个跟踪足球运动员的物体检测算法。我想知道那个球员是谁的球衣号码。我想知道找到它的最好方法。我应该使用哪种算法。我做了一个跟踪用户颜色范围的项目，我将每个视频图像转换为hsv。但是我遇到的挑战是，在检测到球员后我怎样才能找到球衣号码。

这是我的代码 -

#Import libraries
import cv2
import os
import numpy as np

# import the necessary packages
from collections import deque
import numpy as np
import cv2
import imutils
import time


#Reading the video
vidcap = cv2.VideoCapture('football.mp4')
success,image = vidcap.read()

count = 0
success = True
idx = 0

#Read the video frame by frame
while success:
    #converting into hsv image
    hsv = cv2.cvtColor(image,cv2.COLOR_BGR2HSV)
    #green range
    lower_green = np.array([40,40, 40])
    upper_green = np.array([70, 255, 255])
    #blue range
    lower_blue = np.array([110,50,50])
    upper_blue = np.array([130,255,255])

    #Red range
    lower_red = np.array([0,31,255])
    upper_red = np.array([176,255,255])

    #white range
    lower_white = np.array([0,0,0])
    upper_white = np.array([0,0,255])

    #Define a mask ranging from lower to uppper
    mask = cv2.inRange(hsv, lower_green, upper_green)
    #Do masking
    res = cv2.bitwise_and(image, image, mask=mask)

    #convert to hsv to gray
    res_bgr = cv2.cvtColor(res,cv2.COLOR_HSV2BGR)
    res_gray = cv2.cvtColor(res,cv2.COLOR_BGR2GRAY)

    #Defining a kernel to do morphological operation in threshold image to 
    #get better output.
    kernel = np.ones((13,13),np.uint8)
    thresh = cv2.threshold(res_gray,127,255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
    thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)


    #find contours in threshold image     
    im2,contours,hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

    prev = 0
    font = cv2.FONT_HERSHEY_SIMPLEX

    for c in contours:
        x,y,w,h = cv2.boundingRect(c)
        #Detect players
        if(h>=(1.5)*w):
            if(w>15 and h>= 15):
                idx = idx+1
                player_img = image[y:y+h,x:x+w]
                player_hsv = cv2.cvtColor(player_img,cv2.COLOR_BGR2HSV)
                #If player has blue jersy
                mask1 = cv2.inRange(player_hsv, lower_blue, upper_blue)
                res1 = cv2.bitwise_and(player_img, player_img, mask=mask1)
                res1 = cv2.cvtColor(res1,cv2.COLOR_HSV2BGR)
                res1 = cv2.cvtColor(res1,cv2.COLOR_BGR2GRAY)

                nzCount = cv2.countNonZero(res1)
                #If player has red jersy
                mask2 = cv2.inRange(player_hsv, lower_red, upper_red)
                res2 = cv2.bitwise_and(player_img, player_img, mask=mask2)
                res2 = cv2.cvtColor(res2,cv2.COLOR_HSV2BGR)
                res2 = cv2.cvtColor(res2,cv2.COLOR_BGR2GRAY)
                nzCountred = cv2.countNonZero(res2)

                if(nzCount >= 20):
                    #Mark blue jersy players as france
                    cv2.putText(image, 'France', (x-2, y-2), font, 0.8, (255,0,0), 2, cv2.LINE_AA)
                    cv2.rectangle(image,(x,y),(x+w,y+h),(255,0,0),3)
                else:
                    pass
                if(nzCountred>=20):
                    #Mark red jersy players as belgium
                    cv2.putText(image, 'Belgium', (x-2, y-2), font, 0.8, (0,0,255), 2, cv2.LINE_AA)
                    cv2.rectangle(image,(x,y),(x+w,y+h),(0,0,255),3)
                else:
                    pass
        if((h>=1 and w>=1) and (h<=30 and w<=30)):
            player_img = image[y:y+h,x:x+w]

            player_hsv = cv2.cvtColor(player_img,cv2.COLOR_BGR2HSV)
            #white ball  detection
            mask1 = cv2.inRange(player_hsv, lower_white, upper_white)
            res1 = cv2.bitwise_and(player_img, player_img, mask=mask1)
            res1 = cv2.cvtColor(res1,cv2.COLOR_HSV2BGR)
            res1 = cv2.cvtColor(res1,cv2.COLOR_BGR2GRAY)
            nzCount = cv2.countNonZero(res1)


            if(nzCount >= 3):
                # detect football
                cv2.putText(image, 'football', (x-2, y-2), font, 0.8, (0,255,0), 2, cv2.LINE_AA)
                cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),3)


    cv2.imwrite("./Cropped/frame%d.jpg" % count, res)
    # print('Read a new frame: ', success)    # save frame as JPEG file 
    count += 1
    cv2.imshow('Match Detection',image)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
    success,image = vidcap.read()

vidcap.release()
cv2.destroyAllWindows()