安装EasyOCR

使用如下命令：

pip install easyocr

注意，在本博文写的时候（2022-02-28）OpenCV版本4.5.5.62和EasyOCR不兼容。所以需要使用OpenCV4.5.4.60。

使用下面的命令进行安装：

pip install opencv-python==4.5.4.60

随后安装opencv-contrib-python：

pip install opencv-contrib-python==4.5.4.60

如果已经安装得有opencv-python和opencv-contrib-python可以使用如下命令进行反安装：


pip uninstall opencv-python
pip uninstall opencv-contrib-python

然后再重新安装：

pip install opencv-python==4.5.4.60

车牌号识别

创建一个Python文件，并添加如下代码：


from easyocr import Reader
import cv2
 
# load the image and resize it
image = cv2.imread('image1.jpg')
image = cv2.resize(image, (800, 600))

首先需要导入依赖包，然后重新设置下图片的大小。

随后初始化图片：


# convert the input image to grayscale,
# blur it, and detect the edges 
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
blur = cv2.GaussianBlur(gray, (5,5), 0) 
edged = cv2.Canny(blur, 10, 200) 
cv2.imshow('Canny', edged)
cv2.waitKey(0)
cv2.destroyAllWindows()

下面是预处理下图片


# convert the input image to grayscale,
# blur it, and detect the edges 
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
blur = cv2.GaussianBlur(gray, (5,5), 0) 
edged = cv2.Canny(blur, 10, 200) 
cv2.imshow('Canny', edged)
cv2.waitKey(0)
cv2.destroyAllWindows()

首先将图片转换为灰度图，再通过高斯滤波删除一些干扰信息，最后进行边缘检测。

输出的图片如下：

下面，通过通过边缘检测的轮廓，获取车牌的轮廓：


# find the contours, sort them, and keep only the 5 largest ones
contours, _ = cv2.findContours(edged, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key = cv2.contourArea, reverse = True)[:5]
 
# loop over the contours
for c in contours:
    # approximate each contour
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.02 * peri, True)
    # if the contour has 4 points, we can say
    # that we have found our license plate
    if len(approx) == 4:
        n_plate_cnt = approx
        break        
 
# get the bounding box of the contour and 
# extract the license plate from the image
(x, y, w, h) = cv2.boundingRect(n_plate_cnt)
license_plate = gray[y:y + h, x:x + w]

上面的代码目的是从边缘图片中找到轮廓，范围大小是从0到5。

如果找到有4个轮廓的，就说明发现车牌位置了。

最后就可以从车牌中提取号码了

现在所有初始化工作都做好了，下面使用EeayOCR进行文字识别。


# initialize the reader object
reader = Reader(['en'])
# detect the text from the license plate
detection = reader.readtext(license_plate)
 
if len(detection) == 0:
    # if the text couldn't be read, show a custom message
    text = "Impossible to read the text from the license plate"
    cv2.putText(image, text, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 3)
    cv2.imshow('Image', image)
    cv2.waitKey(0)
else:
    # draw the contour and write the detected text on the image
    cv2.drawContours(image, [n_plate_cnt], -1, (0, 255, 0), 3)
    text = f"{detection[0][1]} {detection[0][2] * 100:.2f}%"
    cv2.putText(image, text, (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 2)
    # display the license plate and the output image
    cv2.imshow('license plate', license_plate)
    cv2.imshow('Image', image)
    cv2.waitKey(0)