车牌字符分割python opencv
License plate character segmentation python opencv
我想隔离下图中的每个字符:
它应该在每个字符周围创建一个矩形边界框。我的代码正在创建一个圆形边界框。我需要将这些孤立的字符图像提供给我训练有素的模型来预测字符。没有做过图片处理,才会问这样的问题。
这是我正在使用的代码:
# Standard imports
import cv2
import numpy as np;
from PIL import Image
params = cv2.SimpleBlobDetector_Params()
# Change thresholds
params.minThreshold = 10;
params.maxThreshold = 200;
#Filter by Color
params.filterByColor=False
params.blobColor=255
# Filter by Area.
params.filterByArea = False
params.minArea = 50
# Filter by Circularity
params.filterByCircularity = False
params.minCircularity = 0.0785
#
# # Filter by Convexity
params.filterByConvexity = False
params.minConvexity = 0.87
#
# # Filter by Inertia
params.filterByInertia = False
params.minInertiaRatio = 0.01
# Read image
im = cv2.imread("C:\xx\testimages\bw_plate.jpg", cv2.IMREAD_GRAYSCALE)
cv2.threshold(im,200,255,cv2.THRESH_BINARY_INV,im)
# Set up the detector with default parameters.
detector = cv2.SimpleBlobDetector_create(params)
# Detect blobs.
keypoints = detector.detect(im)
# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
im_with_keypoints = cv2.drawKeypoints(im, keypoints, np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# Show keypoints
cv2.imshow("Keypoints", im_with_keypoints)
cv2.waitKey(0)
我使用以下代码的输出是:
为什么没有正确检测到 0 和 2?另外,如何为每个孤立的字符创建单独的 jpeg 文件?
我的项目的 C++ 实现使用 CblobResult class 进行分段。 python 中是否有任何等效的库?
分割后每个字符的最终输出必须如下所示:
去除背景噪音后,您可以像这样输入图像:
然后你可以使用下面的代码得到你想要的:
import cv2
img = cv2.imread('test4.jpg', 0)
cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU,img)
image, contours, hier = cv2.findContours(img, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
contours = sorted(contours, key=lambda ctr: cv2.boundingRect(ctr)[0])
cv2.imshow("contours", img)
cv2.waitKey(0)
d=0
for ctr in contours:
# Get bounding box
x, y, w, h = cv2.boundingRect(ctr)
# Getting ROI
roi = image[y:y+h, x:x+w]
cv2.imshow('character: %d'%d,roi)
cv2.imwrite('character_%d.png'%d, roi)
cv2.waitKey(0)
cv2.destroyAllWindows()
d+=1
我想隔离下图中的每个字符:
它应该在每个字符周围创建一个矩形边界框。我的代码正在创建一个圆形边界框。我需要将这些孤立的字符图像提供给我训练有素的模型来预测字符。没有做过图片处理,才会问这样的问题。
这是我正在使用的代码:
# Standard imports
import cv2
import numpy as np;
from PIL import Image
params = cv2.SimpleBlobDetector_Params()
# Change thresholds
params.minThreshold = 10;
params.maxThreshold = 200;
#Filter by Color
params.filterByColor=False
params.blobColor=255
# Filter by Area.
params.filterByArea = False
params.minArea = 50
# Filter by Circularity
params.filterByCircularity = False
params.minCircularity = 0.0785
#
# # Filter by Convexity
params.filterByConvexity = False
params.minConvexity = 0.87
#
# # Filter by Inertia
params.filterByInertia = False
params.minInertiaRatio = 0.01
# Read image
im = cv2.imread("C:\xx\testimages\bw_plate.jpg", cv2.IMREAD_GRAYSCALE)
cv2.threshold(im,200,255,cv2.THRESH_BINARY_INV,im)
# Set up the detector with default parameters.
detector = cv2.SimpleBlobDetector_create(params)
# Detect blobs.
keypoints = detector.detect(im)
# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
im_with_keypoints = cv2.drawKeypoints(im, keypoints, np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# Show keypoints
cv2.imshow("Keypoints", im_with_keypoints)
cv2.waitKey(0)
我使用以下代码的输出是:
为什么没有正确检测到 0 和 2?另外,如何为每个孤立的字符创建单独的 jpeg 文件?
我的项目的 C++ 实现使用 CblobResult class 进行分段。 python 中是否有任何等效的库?
分割后每个字符的最终输出必须如下所示:
去除背景噪音后,您可以像这样输入图像:
然后你可以使用下面的代码得到你想要的:
import cv2
img = cv2.imread('test4.jpg', 0)
cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU,img)
image, contours, hier = cv2.findContours(img, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
contours = sorted(contours, key=lambda ctr: cv2.boundingRect(ctr)[0])
cv2.imshow("contours", img)
cv2.waitKey(0)
d=0
for ctr in contours:
# Get bounding box
x, y, w, h = cv2.boundingRect(ctr)
# Getting ROI
roi = image[y:y+h, x:x+w]
cv2.imshow('character: %d'%d,roi)
cv2.imwrite('character_%d.png'%d, roi)
cv2.waitKey(0)
cv2.destroyAllWindows()
d+=1