https://github.com/crispengari/car-plates-detection-open-computer-vision
This is a simple Machine Learning open computer vision application that number plates in real time from an image using `haarcascade_russian_plate_number.xml` from opencv.
https://github.com/crispengari/car-plates-detection-open-computer-vision
ai machine-learning opencv python python3 rectangle
Last synced: 6 months ago
JSON representation
This is a simple Machine Learning open computer vision application that number plates in real time from an image using `haarcascade_russian_plate_number.xml` from opencv.
- Host: GitHub
- URL: https://github.com/crispengari/car-plates-detection-open-computer-vision
- Owner: CrispenGari
- Created: 2021-01-04T10:17:01.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2021-01-04T10:22:58.000Z (almost 5 years ago)
- Last Synced: 2025-03-27T17:02:41.300Z (6 months ago)
- Topics: ai, machine-learning, opencv, python, python3, rectangle
- Language: PowerShell
- Homepage:
- Size: 573 KB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# What is this?
This is a simple Machine Learning open computer vision
application that number plates in real time from an image
using `haarcascade_russian_plate_number.xml` from opencv.
### Demo
### App capabilities.
This app is cappable of:
* detecting car number plates in real time
* draw rectangle around each plate detected
* put text label `Plate` to show that this is a Plate detected
### First of all install `opencv-python` and `numpy`:
#### You can install `opencv-python` by running:
`python -m pip install opencv-python`
#### You can install `numpy` by running:
`python -m pip install numpy`
### Alternatively, you can install all of them by pasting the following code on your `main.py` and run it.
````buildoutcfg
try:
import cv2
import numpy as np
except ImportError as e:
from pip._internal import main as install
packages = ["numpy", "opencv-python"]
for package in packages:
install(["install", package])
finally:
pass
````
### After installation of all the packages then we are ready to go.
#### step 1:
Define a function that detects face, and accepts an image path as its argument.
#### step 2:
Load the face cascade
````buildoutcfg
plateClassifier = cv2.CascadeClassifier("cascade/haarcascade_russian_plate_number.xml")
````
#### step 3:
Load an image and Convert the image to gray-scale and detects
plates-points using the `detectMultiScale()` function.
````buildoutcfg
image = cv2.imread(image_path)
grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
plates = plateClassifier.detectMultiScale(grayImage, 1.5)
````
Now we have all the point to draw the rectangle to our original image.
#### step 4:
Detect plates and
Loop through plates array and draw a rectangle around each plate
that is going to be detected.
Draw the text as was as a rectangle that is
filled with green color that bounds the text
and show the image.
````buildoutcfg
if len(plates):
for (x, y, w, h) in plates:
cv2.rectangle(image, (x, y - 20), (x + int(w / 2), y), (0, 255, 0), -1)
cv2.putText(image,"Plate",(x + 10, y-5), cv2.FONT_HERSHEY_PLAIN,1,(255, 255, 255))
final_image = cv2.rectangle(image, (x, y), (x+w, y+h),(0, 255, 0), 1)
cv2.imshow("Number Plates Detector",final_image)
````
Now everything is ready we are only left with showing images in a loop and wait
for `q` key press to close the loop.
#### step 6:
````buildoutcfg
key = cv2.waitKey(0)
if key & 0xFF == ord('q'):
break
````
### All code in one place: `main.py`
````buildoutcfg
# Plates detector
""""
What is this?
* This is a car number plates detector using open computer vision.
* This app detects car plates from an image.
"""
try:
import cv2
import numpy as np
except ImportError as e:
from pip._internal import main as install
packages = ["numpy", "opencv-python"]
for package in packages:
install(["install", package])
finally:
pass
def plateDetector(image_path):
plateClassifier = cv2.CascadeClassifier("cascade/haarcascade_russian_plate_number.xml")
image = cv2.imread(image_path)
grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Load the cascade classifier
plates = plateClassifier.detectMultiScale(grayImage, 1.5)
if len(plates):
for (x, y, w, h) in plates:
cv2.rectangle(image, (x, y - 20), (x + int(w / 2), y), (0, 255, 0), -1)
cv2.putText(image,"Plate",(x + 10, y-5), cv2.FONT_HERSHEY_PLAIN,1,(255, 255, 255))
final_image = cv2.rectangle(image, (x, y), (x+w, y+h),(0, 255, 0), 1)
cv2.imshow("Number Plates Detector",final_image)
key = cv2.waitKey(0)
if key & 0xFF == ord('q'):
cv2.destroyAllWindows()
return
plateDetector("images/car2.jpg")
````
### Where to find the face cascade classifier?
You will find it on the opencv github [here](https://github.com/opencv/opencv/tree/master/data/haarcascades)
### Why this simple App?
This app was build for practise purposes.