Python Opencv-contrib Camshift kalman卡尔曼滤波 KCF算法 CSRT算法 目标跟踪实现_卡尔曼滤波目标跟踪 论文代码 python

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本次课题实现目标跟踪一共用到了三个算法，分别是Camshift、Kalman、CSRT，基于Python语言的Tkinter模块实现GUI与接口设计，项目一共包含三个文件：

main.py：

# coding:utf-8
# 主模块


import Tkinter
import tkFileDialog
import cv2
import time
from PIL import ImageTk
# 导入自定义模块
import track
import utils


# 设置窗口800*480
root = Tkinter.Tk()
root.title("基于视频的实时行人追踪")
root.geometry("800x480")

# 设置背景
canvas = Tkinter.Canvas(root, width=800, height=480, highlightthickness=0, borderwidth=0)
background_image = ImageTk.PhotoImage(file="background.jpg")  # 项目本地路径（背景图片）
canvas.create_image(0, 0, anchor="nw", image=background_image)
canvas.pack()

# 显示提示
label_a = Tkinter.Label(root, text="基于视频的实时行人追踪", font=("KaiTi", 20), height=2)
label_a.pack()
canvas.create_window(400, 100, height=25, window=label_a)

# 显示路径
show_path = Tkinter.StringVar()
show_path.set("请选择一个文件夹")

# 显示路径标签
label_b = Tkinter.Label(root, textvariable=show_path, font=("Times New Roman", 15), height=2)
label_b.pack()
canvas.create_window(400, 150, window=label_b)

# 坐标库
ROI = utils.ROI()
# 路径库
path = utils.Path()


# 选择序列
def hit_button_a():
    path.init(tkFileDialog.askdirectory(title="Select Folder"))
    # 显示路径
    if path.img_path != "":
        show_path.set("文件路径：" + str(path.img_path)[:-1] + "\n序列总数：" + str(path.sum))
    else:
        show_path.set("路径错误！")


button_a = Tkinter.Button(root, text="选择序列", font=("KaiTi", 15), height=2, command=hit_button_a)
button_a.pack()
canvas.create_window(400, 200, height=20, window=button_a)


# ROI
def hit_button_b():
    # 读取首帧图像
    first_image = cv2.imread(path.pics_list[0])
    # ROI
    ROI.init_window(cv2.selectROI(windowName="ROI", img=first_image, showCrosshair=True, fromCenter=False))
    cv2.destroyAllWindows()


button_b = Tkinter.Button(root, text="标记目标", font=("KaiTi", 15), heigh=2, command=hit_button_b)
button_b.pack()
canvas.create_window(400, 250, height=20, window=button_b)


# 目标追踪

def hit_button_c():
    global camshift, kcf, csrt
    index = utils.index(path.groundtruth_path)  # 读取真值
    firstframe = True
    kalman_xy = track.KalmanFilter()
    kalman_size = track.KalmanFilter()
    bbox = [0, 0, 0, 0]

    for i in range(0, path.sum):
        start = time.time()  # 开始计时
        frame = cv2.imread(path.pics_list[i])  # 读取
        if firstframe:
            camshift = track.Camshift(frame, ROI.window)
            kcf = track.KCFtracker(frame, ROI.window)
            firstframe = False
            continue
        # camshift.update(frame)
        ok = kcf.update(frame)
        if not ok:
            mes = (bbox[0], bbox[1], bbox
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