本文中我们将用Python3.8结合OpenCV库实现车辆检测和计数系统。最终的效果及原理实现可以参考我的上一篇文章：车辆检测(非监督学习)-1. 原理篇

在运行本文中的代码前，需要先安装如下包：

sk-video==1.1.10
opencv-python==4.5.2.52

视频转换为帧

本文中使用的算法是"帧间差分法"，所以第一步就是要把视频转换为帧的集合。

import cv2


def getFrame(sec):
    vidcap.set(cv2.CAP_PROP_POS_MSEC, sec*1000)
    hasFrames, image = vidcap.read()
    if hasFrames:
        # 会把帧保存为jpg图片
        cv2.imwrite("video/车流_frames/image"+str(count)+".jpg", image)
    return hasFrames


vidcap = cv2.VideoCapture('video/车流_1080p30fps.mp4')
sec = 0
frameRate = 0.05  # 每隔0.05s截一次图片
count = 1
success = getFrame(sec)
while success:
    count = count + 1
    sec = sec + frameRate
    sec = round(sec, 2)
    success = getFrame(sec)

处理帧

接下来就要按照如下步骤处理图片：

1. 读取图片
2. 相邻图片进行差分
3. 卡像素的阈值，把图片转换为二进制图像
4. 利用卷积合并相邻的区域
5. 限定检测区域
6. 限定区域面积，进一步过滤掉零碎区域
7. 统计车辆
8. 把处理后的图像恢复为RGB图片并进行保存
9. 把保存的图片转换为视频

具体代码实现如下

# 导入需要的库
import os
import re
import cv2  # opencv library
import numpy as np
from os.path import isfile, join
import matplotlib.pyplot as plt

# 读取图片
col_frames = os.listdir('video/车流_frames/')
col_frames.sort(key=lambda f: int(re.sub('\D', '', f)))

col_images = []
for i in col_frames:
    img = cv2.imread('video/车流_frames/'+i)
    col_images.append(img)

# 定义卷积核
kernel = np.ones((64, 64), np.uint8)

# 字体
font = cv2.FONT_HERSHEY_SIMPLEX

# 输出的图像路径
pathIn = "output/车流_frames/"

for i in range(len(col_images)-1):

    # 把RGB图像转换为Gray图像
    grayA = cv2.cvtColor(col_images[i], cv2.COLOR_BGR2GRAY)
    grayB = cv2.cvtColor(col_images[i+1], cv2.COLOR_BGR2GRAY)

# 相邻图像进行差分
    diff_image = cv2.absdiff(grayB, grayA)

    # 卡像素的阈值，把图片转换为二进制图像
    ret, thresh = cv2.threshold(diff_image, 128, 255, cv2.THRESH_BINARY)

    # 利用卷积合并相邻的区域
    dilated = cv2.dilate(thresh, kernel, iterations=1)

    # 得到轮廓
    contours, hierarchy = cv2.findContours(
        dilated.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

    # 划定检测区域，并过滤碎片化的轮廓
    valid_cntrs = []
    for cntr in contours:
        x, y, w, h = cv2.boundingRect(cntr)
        if (x <= 820) & (y >= 750) & (cv2.contourArea(cntr) >= 150):
            if (y >= 900) & (cv2.contourArea(cntr) < 200):
                break
            valid_cntrs.append(cntr)

    # 把轮廓添加到图像中
    dmy = col_images[i].copy()
    cv2.drawContours(dmy, valid_cntrs, -1, (127, 200, 0), 2)

    cv2.putText(dmy, "vehicles detected: " + str(len(valid_cntrs)),
                (55, 100), font, 1.5, (0, 180, 0), 5)

    cv2.line(dmy, (0, 750), (1919, 750),
             color=(255, 0, 0), thickness=8)

    cv2.imwrite(pathIn+str(i)+'.png', dmy)


pathOut = 'output/车流counter.mp4'  # 输出的视频路径
fps = 20.0  # 每秒播放多少帧

frame_array = []
files = [f for f in os.listdir(pathIn) if isfile(join(pathIn, f))]
files.sort(key=lambda f: int(re.sub('\D', '', f)))

for i in range(len(files)):
    filename = pathIn + files[i]

    img = cv2.imread(filename)
    height, width, layers = img.shape
    size = (width, height)

    frame_array.append(img)

# 把输出的图片转换为视频
out = cv2.VideoWriter(pathOut, cv2.VideoWriter_fourcc(*'DIVX'), fps, size)

for i in range(len(frame_array)):
    out.write(frame_array[i])

out.release()