Real-Time Car Detection OpenCV Python With Source Code
The Real-Time Car Detection OpenCV Python was developed using Python OpenCV, Vehicle detection is one of the widely used features by companies and organizations these days. This technology uses computer vision to detect different types of vehicles in a video or real-time via a camera. It
A Car Detection OpenCV Python finds its applications in traffic control, car tracking, creating parking sensors, and many more. In this, we will learn how to build a car tracking system in python for both recorded and live cam streamed videos.
This Python OpenCV Project also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.
Watch the video here to see the full running source code of the Real-Time Car Detection OpenCV Python with Source Code
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To start executing Real-Time Car Detection OpenCV Python With Source Code, make sure that you have installed Python 3.9 and PyCharm on your computer.
Real-Time Car Detection OpenCV Python With Source Code: Steps on how to run the project
Time needed: 5 minutes.
These are the steps on how to run Real-Time Car Detection OpenCV Python With Source Code
- Step 1: Download the given source code below.
First, download the given source code below and unzip the source code.
- Step 2: Import the project to your PyCharm IDE.
Next, import the source code you’ve download to your PyCharm IDE.
- Step 3: Run the project.
last, run the project with the command “py main.py”
import numpy as np
Complete Source Code
import numpy as np
cascade_src = 'cars.xml'
# video = 'data/Cars_On_Highway.mp4'
video = 'data/video1.avi'
# video = 'data/video2.avi'
rectangles = 
cascade = cv2.CascadeClassifier(cascade_src)
vc = cv2.VideoCapture(filename)
rval , frame = vc.read()
rval = False
rval, frame = vc.read()
frameHeight, frameWidth, fdepth = frame.shape
frame = cv2.resize(frame, ( 600, 400 ))
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# haar detection.
cars = cascade.detectMultiScale(gray, 1.3, 3)
for (x, y, w, h) in cars:
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
# show result
if cv2.waitKey(33) == ord('q'):
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Download Source Code below
Car detection is one of the widely used features by companies and organizations these days. This technology uses computer vision to detect different types of vehicles in video or in real-time via a camera. It finds its applications in traffic control, car tracking, creating parking sensors, and many more.
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