Introduction

Xiaobian hasn't updated these days! Because I don't have time to practice car test simulation ~ it's been a long time since I started learning to practice car today.

Last night, I was still nervous and fell asleep slowly. I ran to the examination site early in the morning and waited for several hours. long long ago......

The moment he shouted to me, he walked in confidently:

The result hung on the last subject, 2333333~ little tears streaming down his face.

Xiaobian has survived in despair. He hasn't updated it for a long time. Because SO is persistent in the examination and vehicles, what he learns and teaches today is also about the vehicle detection system based on opencv!!!

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Think about it. If you can integrate the vehicle detection system into the traffic light camera, you can easily track many useful things at the same time:

Many people learn Python and don't know where to start.

Many people study python，After mastering the basic grammar, I don't know where to find cases.

Many people who have been able to learn cases do not know how to learn more advanced knowledge.

So for these three types of people, I will provide you with a good learning platform, free access to video tutorials, e-books, and the source code of the course!

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Welcome to join us, discuss and study together

 

• How many cars are there at the intersection during the day?
• When is the traffic jam?
• What kind of vehicles (heavy vehicles, cars, etc.) are passing through the intersection?
• Is there any way to optimize traffic and distribute it through different streets?

There are many examples that will not be listed one by one. Applications are endless~

First, install the environment:

Let's import the required libraries and modules - opencv installation:

pip install opencv-python

import os
import re
import cv2 # opencv library
import numpy as np
from os.path import isfile, join
import matplotlib.pyplot as plt

Save frames in a folder in the working directory and import frames and save:

# get file names of the frames
col_frames = os.listdir('frames/')
# sort file names
col_frames.sort(key=lambda f: int(re.sub('\D', '', f)))
# empty list to store the frames
col_images=[]
for i in col_frames:
    # read the frames
    img = cv2.imread('frames/'+i)
    # append the frames to the list
    col_images.append(img)

Let's show two consecutive frames:

# plot 13th frame
i = 13
for frame in [i, i+1]:
    plt.imshow(cv2.cvtColor(col_images[frame], cv2.COLOR_BGR2RGB))
    plt.title("frame: "+str(frame))
    plt.show()

Obtaining the difference between the pixel values of two consecutive frames will help us observe the moving target. So let's use this technique on the above two frames:

# convert the frames to grayscale
grayA = cv2.cvtColor(col_images[i], cv2.COLOR_BGR2GRAY)
grayB = cv2.cvtColor(col_images[i+1], cv2.COLOR_BGR2GRAY)
# plot the image after frame differencing
plt.imshow(cv2.absdiff(grayB, grayA), cmap = 'gray')
plt.show()

 

 

Now we can clearly see the moving target in frames 13 and 14. Everything else that hasn't moved has been subtracted.

Image preprocessing - adds contours to all moving vehicles in all frames:

# specify video name
pathOut = 'vehicle_detection_v3.mp4'
# specify frames per second
fps = 14.0

Next, read the last frame in the list:

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]
    #read frames
    img = cv2.imread(filename)
    height, width, layers = img.shape
    size = (width,height)
    #inserting the frames into an image array
    frame_array.append(img)

Finally, the following code is used to make the target detection video:

 

out = cv2.VideoWriter(pathOut,cv2.VideoWriter_fourcc(*'DIVX'), fps, size)
for i in range(len(frame_array)):
    # writing to a image array
    out.write(frame_array[i])
out.release()

 

All right! Have you learned?