I heard that shooting people illegally earn 100,000 a month? Then I use python to write an automatic detection of whether the vehicle is illegal or not, is it not a fortune?

Introduction​

SO is obsessed with the test and the vehicle, so the study and the teaching today are also about the opencv-based vehicle detection system! ! !

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Think about it, if you could integrate a vehicle detection system in a traffic light camera, you could easily track a lot of useful things at the same time:

• How many vehicles 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 a way to optimize traffic and distribute it through different streets?

There are many examples that are not listed one by one. Apps are endless~

First environment installation:

We first 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 the frame in a folder in the working directory and import the frame 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 display 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()

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Getting the difference of the pixel values ​​of two consecutive frames will help us to observe moving objects. So, let's use the technique on the two frames above:

# 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()

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Now we can clearly see the moving target in frames 13 and 14. Everything else that didn't move is subtracted.

Image preprocessing - added 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 use the following code to make an object 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()

Okay! Did you learn it?

Summarize

I pray that I will pass the next two exams! Must pass! Must pass! !

If you see this, it means that you like this article, remember Sanlian ~ I love you.

In the end, the editor will share with you some python gift packages [Jiajun Yang: 605018913] to help you learn python better!