preface
Hello! No good wine, a little long holiday, ha ha ha, today normal more learning materials ~
License plate recognition has a wide range of applications in the highway, such as our common electronic toll collection (ETC) system and traffic illegal vehicles
Testing, among other things
Like residential or underground garage access control will also be used, basically all the need for vehicle identity detection will be used.
Today xiaobian will teach you to write a simple license plate detection system, I hope you like the little attention ~ MUA!
The body of the
1) Development tools
Python3.6, Pycharm, Opencv, etc.
PIP install -i https://pypi.douban.com/simple/ + module nameCopy the code2) Principle introduction
Note that this is not license plate recognition, it is license plate detection.
Because the shape of the license plate is relatively simple, I realized it by referring to some simple traditional algorithms without using deep learning. Effect comparison
In general. It is not suitable for license plate detection in complex environment.
Direct tuning of OpenCV function interface, not from 0 to achieve, so the overall technical content is low.
3) The process is as follows
Step1:
Image pretreatment, including grayscale, Gaussian smoothing, median filtering, Sobel operator edge detection and so on.
Step2:
OpenCV is used to find the profile of the preprocessed image, and then judge whether the profile is the license plate profile according to some parameters.
Using the demonstration
Run the detect.py file in a CMD window.
Please specify the image path to be detected before using:
4) Source code display
File: Charles import cv2 import numpy as np # GaussianBlur(img, (3, 3), 0, 0, cv2.border_default) # Median filter = cv2.medianblur (Gaussian, 5) # Sobel operator # gradient direction: X sobel = cv2.sobel (median, cv2.cv_8u, 1, 0, ksize=3) # ret, binary = cv2.threshold(sobel, 170, 255, median, cv2.cv_8u, 1, 0, ksize=3) # ret, binary = cv2.threshold(sobel, 170, 255, 0) Cv2. # THRESH_BINARY) kernel function element1 = cv2. GetStructuringElement (cv2 MORPH_RECT, (9, 1) element2 = cv2. GetStructuringElement (cv2 MORPH_RECT, (9, 7)) # expansion dilation = cv2. Dilate (binary, element2. Iterations =1) # erosion = cv2. Iterations (erodes, element1, iterations=1) # erosion = cv2. iterations=3) return dilation2 def GetRegion(img): Hierarchy = cv2.findContours(img, cv2.retr_tree, cv2.CHAIN_APPROX_SIMPLE) for contour in contours: area = cv2.contourArea(contour) if (area < 2000): continue eps = 1e-3 * cv2.arcLength(contour, True) approx = cv2.approxPolyDP(contour, eps, True) rect = cv2.minAreaRect(contour) box = cv2.boxPoints(rect) box = np.int0(box) height = abs(box[0][1] - box[2][1]) Width = abs(box[0][0] -box [2][0]) ratio =float(width)/float(height) if (ratio < 5 and ratio > 1.8): regions.append(box) return regions def detect(img): # gray = cv2.cvtcolor (img, cv2.COLOR_BGR2GRAY) prc = Process(gray) regions = GetRegion(prc) print('[INFO]:Detect %d license plates' % len(regions)) for box in regions: cv2.drawContours(img, [box], 0, (0, 255, 0), 2) cv2.imshow('Result', img) cv2.imwrite('result.jpg', img) cv2.waitKey(0) cv2.destroyAllWindows()Copy the code5) Effect display
01:
Effect:
02: the original image
Effect:
conclusion
Well, that’s all for this article
License plate detection and license plate recognition is actually very mature technology. So what I do only represents the rookie level ~
Make a little progress every day, and you can stick to it with me
💖 free source code base —
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Your support is my biggest motivation!! Mua welcomes you to read the previous article
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(article summary and more your case waiting for you to learn ~ source code can find me free!)
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More content + source code in the article summary oh!! Welcome to read ~)
