遮罩以在Opencv中分割字符

Question

我的算法无法正常工作，应该封装车牌上的每个字母，我已经做到了：

import cv2
import numpy as np
from imutils import contours

image = cv2.imread('/content/Screenshot_3.png')
mask = np.zeros(image.shape, dtype=np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

cnts = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
(cnts, _) = contours.sort_contours(cnts, method="left-to-right")
ROI_number = 0
for c in cnts:
    area = cv2.contourArea(c)
    if area < 800 and area > 200:
        x,y,w,h = cv2.boundingRect(c)
        ROI = 255 - thresh[y:y+h, x:x+w]
        cv2.drawContours(mask, [c], -1, (255,255,255), -1)
        #cv2.imwrite('ROI_{}.png'.format(ROI_number), ROI)
        ROI_number += 1

cv2.imshow(mask)
cv2.imshow(thresh)

mask输出：

thresh输出：

原始图像是这样的：

我在做什么错了？

Answer 1

问题是由于上述连接，您的掩码尝试将W和7保留在相反的类中。如果您调整阈值，则会看到F，J，D，2或W，7。 / p>

import matplotlib.pyplot as plt
import cv2
import numpy as np
from imutils import contours

image = cv2.imread('test.png')
plt.imshow(image)
plt.show()

mask = np.zeros(image.shape, dtype=np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

kernel = np.ones((5,5), np.uint8) 
img_dilation = cv2.dilate(thresh, kernel, iterations=1) 

cnts = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
(cnts, _) = contours.sort_contours(cnts, method="left-to-right")
ROI_number = 0
for c in cnts:
    area = cv2.contourArea(c)
    if area < 10500 and area > 0:
        x,y,w,h = cv2.boundingRect(c)
        ROI = 255 - thresh[y:y+h, x:x+w]
        cv2.drawContours(mask, [c], -1, (255,255,255), -1)
        #cv2.imwrite('ROI_{}.png'.format(ROI_number), ROI)
        ROI_number += 1

plt.imshow(thresh)
plt.show()

plt.imshow(mask)
plt.show()

如您所见，一旦更改阈值，我将得到W和7，但在否定类中，所有其他字符都消失了。

我们可以通过在生成蒙版之前腐蚀阈值来解决此问题。

import matplotlib.pyplot as plt
import cv2
import numpy as np
from imutils import contours

image = cv2.imread('test.png')
plt.imshow(image)
plt.show()

mask = np.zeros(image.shape, dtype=np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

kernel = np.ones((5,5), np.uint8) 

thresh = cv2.erode(thresh, kernel, iterations=1) # eroding

cnts = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
(cnts, _) = contours.sort_contours(cnts, method="left-to-right")
ROI_number = 0
for c in cnts:
    area = cv2.contourArea(c)
    if area < 800:
        x,y,w,h = cv2.boundingRect(c)
        ROI = 255 - thresh[y:y+h, x:x+w]
        cv2.drawContours(mask, [c], -1, (255,255,255), -1)
        #cv2.imwrite('ROI_{}.png'.format(ROI_number), ROI)
        ROI_number += 1

plt.imshow(thresh)
plt.show()

plt.imshow(mask)
plt.show()

现在，您可以应用一些简单的处理以获得更清晰的蒙版。