Opencv行人检测实现与跟踪

# -*- coding: utf-8 -*-

import cv2
import numpy as np
import os.path as path
import argparse

parser = argparse.ArgumentParser()  # 创建ArgumentParser()对象
parser.add_argument("-a", "--algorithm", help="m (or nothing) for meanShift and c for camshift")
# 添加参数，这里意思是：在命令窗口输入时加入”-a XXX“ 可以采用不同的苏昂啊
args = vars(parser.parse_args())  # 解析添加参数
font = cv2.FONT_HERSHEY_SIMPLEX


class Pedestrian():
    def __init__(self, id, frame, track_window):
        self.id = int(id)
        x, y, w, h = track_window
        self.track_window = track_window
        self.roi = cv2.cvtColor(frame[y:y + h, x:x + w], cv2.COLOR_BGR2HSV)
        roi_hist = cv2.calcHist([self.roi], [0], None, [16], [0, 180])
        self.roi_hist = cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)

        self.kalman = cv2.KalmanFilter(4, 2)
        self.kalman.measurementMatrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], np.float32)
        self.kalman.transitionMatrix = np.array([[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32)
        self.kalman.processNoiseCov = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]],
                                               np.float32) * 0.03
        self.measurement = np.array((2, 1), np.float32)
        self.prediction = np.zeros((2, 1), np.float32)