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[英]Reading license plate from image using OpenCV Python and Tesseract
[英]How do I get Tesseract to read the license plate in the this Python OpenCV project?
我的 OpenCV 代码工作得很好。 它找到车牌,使用轮廓提取它的黑白版本,然后当我将它传递给 pytesseract 时,它不会读取任何字母。 我在代码的每一行都跟踪了程序,OpenCV 工作正常,但 pytesseract 不会从图像中提取文本。 没有错误,它只是不读取任何文本。 车牌是我的。
import cv2
# pip install imutils
import imutils
import pytesseract
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files (x86)\Tesseract-OCR\tesseract.exe'
# Read the image file
image = cv2.imread('LP.jpg')
# image = imutils.resize(image, width=500)
# Convert to Grayscale Image
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Removes Noise
gray_image = cv2.bilateralFilter(gray_image, 11, 17, 17)
# Canny Edge Detection
canny_edge = cv2.Canny(gray_image, 100, 200)
# Find contours based on Edges
# The code below needs an - or else you'll get a ValueError: too many values to unpack (expected 2) or a numpy error
_, contours, new = cv2.findContours(canny_edge.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=cv2.contourArea, reverse=True)[:30]
# # Initialize license Plate contour and x,y coordinates
contour_with_license_plate = None
license_plate = None
x = None
y = None
w = None
h = None
# Find the contour with 4 potential corners and create a Region of Interest around it
for contour in contours:
# Find Perimeter of contour and it should be a closed contour
perimeter = cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(contour, 0.02 * perimeter, True)
# This checks if it's a rectangle
if len(approx) == 4:
contour_with_license_plate = approx
x, y, w, h = cv2.boundingRect(contour)
license_plate = gray_image[y:y + h, x:x + w]
break
# # approximate_contours = cv2.drawContours(image, [contour_with_license_plate], -1, (0, 255, 0), 3)
# Text Recognition
text = pytesseract.image_to_string(license_plate, lang='eng')
print(text)
# Draw License Plate and write the Text
image = cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 3)
image = cv2.putText(image, text, (x-100, y-50), cv2.FONT_HERSHEY_SIMPLEX, 3, (0, 255, 0), 6, cv2.LINE_AA)
print("License Plate: ", text)
cv2.imshow("License Plate Detection", image)
cv2.waitKey(0)
这是我的部分答案,也许你可以完善它。
将adaptive-threshold
+ bitwise-not
操作应用于license_plate
变量。
结果将是:
现在,如果您阅读它:
txt = pytesseract.image_to_string(bnt, config="--psm 6")
print(txt)
结果:
277 BOY
不幸的是Q
被识别为O
。
代码:(只需将文本识别注释部分替换为以下内容)
thr = cv2.adaptiveThreshold(license_plate, 252, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY_INV, 91, 93)
bnt = cv2.bitwise_not(thr)
txt = pytesseract.image_to_string(bnt, config="--psm 6")
print(txt)
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