使用 OpenCV 和 Tesseract 的摩洛哥车牌识别 (LPR)
Moroccan License Plate Recognition (LPR) using OpenCV and Tesseract
我正在开展一个项目,该项目旨在识别看起来像这张图片的摩洛哥车牌:
摩洛哥车牌
请问如何使用 OpenCV 切出车牌并使用 Tesseract 读取中间的数字和阿拉伯字母。
我在 Windows 10 中为 python 安装了 OpenCV 和 Tesseract。当我 运行 使用 "fra"
语言在车牌的仅文本部分安装 tesseract我得到 7714315l Bv
。如何分离数据?
编辑:
我们在摩洛哥使用的阿拉伯字母是:
أ ب ت я ح د هـ
预期结果是:77143 د 6
垂直线无关紧要,我必须用它们来分隔图像和单独读取数据。
提前致谢!
你可以使用HoughTransform因为两条垂直线是无关紧要的,裁剪图像:
import numpy as np
import cv2
image = cv2.imread("lines.jpg")
grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
dst = cv2.Canny(grayImage, 0, 150)
cv2.imwrite("canny.jpg", dst)
lines = cv2.HoughLinesP(dst, 1, np.pi / 180, 50, None, 60, 20)
lines_x = []
# Get height and width to constrain detected lines
height, width, channels = image.shape
for i in range(0, len(lines)):
l = lines[i][0]
# Check if the lines are vertical or not
angle = np.arctan2(l[3] - l[1], l[2] - l[0]) * 180.0 / np.pi
if (l[2] > width / 4) and (l[0] > width / 4) and (70 < angle < 100):
lines_x.append(l[2])
# To draw the detected lines
#cv2.line(image, (l[0], l[1]), (l[2], l[3]), (0, 0, 255), 3, cv2.LINE_AA)
#cv2.imwrite("lines_found.jpg", image)
# Sorting to get the line with the maximum x-coordinate for proper cropping
lines_x.sort(reverse=True)
crop_image = "cropped_lines"
for i in range(0, len(lines_x)):
if i == 0:
# Cropping to the end
img = image[0:height, lines_x[i]:width]
else:
# Cropping from the start
img = image[0:height, 0:lines_x[i]]
cv2.imwrite(crop_image + str(i) + ".jpg", img)
我相信你现在知道如何得到中间部分了 ;)
希望对您有所帮助!
编辑:
使用一些词法运算,还可以单独提取字符:
import numpy as np
import cv2
image = cv2.imread("lines.jpg")
grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
dst = cv2.Canny(grayImage, 50, 100)
dst = cv2.morphologyEx(dst, cv2.MORPH_RECT, np.zeros((5,5), np.uint8),
iterations=1)
cv2.imwrite("canny.jpg", dst)
im2, contours, heirarchy = cv2.findContours(dst, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
for i in range(0, len(contours)):
if cv2.contourArea(contours[i]) > 200:
x,y,w,h = cv2.boundingRect(contours[i])
# The w constrain to remove the vertical lines
if w > 10:
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 255), 1)
cv2.imwrite("contour.jpg", image)
结果:
这是我现在取得的成就...
第二张图片的检测是使用此处找到的代码进行的:License plate detection with OpenCV and Python
完整代码(从第三张图片开始工作)是这样的:
import cv2
import numpy as np
import tesserocr as tr
from PIL import Image
image = cv2.imread("cropped.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow('gray', image)
thresh = cv2.adaptiveThreshold(gray, 250, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 255, 1)
cv2.imshow('thresh', thresh)
kernel = np.ones((1, 1), np.uint8)
img_dilation = cv2.dilate(thresh, kernel, iterations=1)
im2, ctrs, hier = cv2.findContours(img_dilation.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])
clean_plate = 255 * np.ones_like(img_dilation)
for i, ctr in enumerate(sorted_ctrs):
x, y, w, h = cv2.boundingRect(ctr)
roi = img_dilation[y:y + h, x:x + w]
# these are very specific values made for this image only - it's not a factotum code
if h > 70 and w > 100:
rect = cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
clean_plate[y:y + h, x:x + w] = roi
cv2.imshow('ROI', rect)
cv2.imwrite('roi.png', roi)
img = cv2.imread("roi.png")
blur = cv2.medianBlur(img, 1)
cv2.imshow('4 - blur', blur)
pil_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
api = tr.PyTessBaseAPI()
try:
api.SetImage(pil_img)
boxes = api.GetComponentImages(tr.RIL.TEXTLINE, True)
text = api.GetUTF8Text()
finally:
api.End()
# clean the string a bit
text = str(text).strip()
plate = ""
# 77143-1916 ---> NNNNN|symbol|N
for char in text:
firstSection = text[:5]
# the arabic symbol is easy because it's nearly impossible for the OCR to misunderstood the last 2 digit
# so we have that the symbol is always the third char from the end (right to left)
symbol = text[-3]
lastChar = text[-1]
plate = firstSection + "[" + symbol + "]" + lastChar
print(plate)
cv2.waitKey(0)
对于阿拉伯语符号,您应该从 TesseractOCR 安装其他语言(并可能使用它的第 4 版)。
输出:77143[9]6
括号中的数字为阿拉伯文符号(未检测)。
希望对你有所帮助。
我正在开展一个项目,该项目旨在识别看起来像这张图片的摩洛哥车牌:
摩洛哥车牌
请问如何使用 OpenCV 切出车牌并使用 Tesseract 读取中间的数字和阿拉伯字母。
我在 Windows 10 中为 python 安装了 OpenCV 和 Tesseract。当我 运行 使用 "fra"
语言在车牌的仅文本部分安装 tesseract我得到 7714315l Bv
。如何分离数据?
编辑:
我们在摩洛哥使用的阿拉伯字母是:
أ ب ت я ح د هـ
预期结果是:77143 د 6
垂直线无关紧要,我必须用它们来分隔图像和单独读取数据。
提前致谢!
你可以使用HoughTransform因为两条垂直线是无关紧要的,裁剪图像:
import numpy as np
import cv2
image = cv2.imread("lines.jpg")
grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
dst = cv2.Canny(grayImage, 0, 150)
cv2.imwrite("canny.jpg", dst)
lines = cv2.HoughLinesP(dst, 1, np.pi / 180, 50, None, 60, 20)
lines_x = []
# Get height and width to constrain detected lines
height, width, channels = image.shape
for i in range(0, len(lines)):
l = lines[i][0]
# Check if the lines are vertical or not
angle = np.arctan2(l[3] - l[1], l[2] - l[0]) * 180.0 / np.pi
if (l[2] > width / 4) and (l[0] > width / 4) and (70 < angle < 100):
lines_x.append(l[2])
# To draw the detected lines
#cv2.line(image, (l[0], l[1]), (l[2], l[3]), (0, 0, 255), 3, cv2.LINE_AA)
#cv2.imwrite("lines_found.jpg", image)
# Sorting to get the line with the maximum x-coordinate for proper cropping
lines_x.sort(reverse=True)
crop_image = "cropped_lines"
for i in range(0, len(lines_x)):
if i == 0:
# Cropping to the end
img = image[0:height, lines_x[i]:width]
else:
# Cropping from the start
img = image[0:height, 0:lines_x[i]]
cv2.imwrite(crop_image + str(i) + ".jpg", img)
我相信你现在知道如何得到中间部分了 ;) 希望对您有所帮助!
编辑:
使用一些词法运算,还可以单独提取字符:
import numpy as np
import cv2
image = cv2.imread("lines.jpg")
grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
dst = cv2.Canny(grayImage, 50, 100)
dst = cv2.morphologyEx(dst, cv2.MORPH_RECT, np.zeros((5,5), np.uint8),
iterations=1)
cv2.imwrite("canny.jpg", dst)
im2, contours, heirarchy = cv2.findContours(dst, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
for i in range(0, len(contours)):
if cv2.contourArea(contours[i]) > 200:
x,y,w,h = cv2.boundingRect(contours[i])
# The w constrain to remove the vertical lines
if w > 10:
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 255), 1)
cv2.imwrite("contour.jpg", image)
结果:
这是我现在取得的成就...
第二张图片的检测是使用此处找到的代码进行的:License plate detection with OpenCV and Python
完整代码(从第三张图片开始工作)是这样的:
import cv2
import numpy as np
import tesserocr as tr
from PIL import Image
image = cv2.imread("cropped.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow('gray', image)
thresh = cv2.adaptiveThreshold(gray, 250, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 255, 1)
cv2.imshow('thresh', thresh)
kernel = np.ones((1, 1), np.uint8)
img_dilation = cv2.dilate(thresh, kernel, iterations=1)
im2, ctrs, hier = cv2.findContours(img_dilation.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])
clean_plate = 255 * np.ones_like(img_dilation)
for i, ctr in enumerate(sorted_ctrs):
x, y, w, h = cv2.boundingRect(ctr)
roi = img_dilation[y:y + h, x:x + w]
# these are very specific values made for this image only - it's not a factotum code
if h > 70 and w > 100:
rect = cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
clean_plate[y:y + h, x:x + w] = roi
cv2.imshow('ROI', rect)
cv2.imwrite('roi.png', roi)
img = cv2.imread("roi.png")
blur = cv2.medianBlur(img, 1)
cv2.imshow('4 - blur', blur)
pil_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
api = tr.PyTessBaseAPI()
try:
api.SetImage(pil_img)
boxes = api.GetComponentImages(tr.RIL.TEXTLINE, True)
text = api.GetUTF8Text()
finally:
api.End()
# clean the string a bit
text = str(text).strip()
plate = ""
# 77143-1916 ---> NNNNN|symbol|N
for char in text:
firstSection = text[:5]
# the arabic symbol is easy because it's nearly impossible for the OCR to misunderstood the last 2 digit
# so we have that the symbol is always the third char from the end (right to left)
symbol = text[-3]
lastChar = text[-1]
plate = firstSection + "[" + symbol + "]" + lastChar
print(plate)
cv2.waitKey(0)
对于阿拉伯语符号,您应该从 TesseractOCR 安装其他语言(并可能使用它的第 4 版)。
输出:77143[9]6
括号中的数字为阿拉伯文符号(未检测)。
希望对你有所帮助。