改进文本区域检测(OpenCV,Python)

Question

我正在开展一个项目,要求我检测图像中的文本区域.这是我到目前为止使用下面的代码实现的结果.

原始图像

结果

代码如下:

import cv2
import numpy as np

# read and scale down image
img = cv2.pyrDown(cv2.imread('C:\\Users\\Work\\Desktop\\test.png', cv2.IMREAD_UNCHANGED))

# threshold image
ret, threshed_img = cv2.threshold(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),
                                  127, 255, cv2.THRESH_BINARY)
# find contours and get the external one
image, contours, hier = cv2.findContours(threshed_img, cv2.RETR_TREE,
                                         cv2.CHAIN_APPROX_SIMPLE)

# with each contour, draw boundingRect in green
# a minAreaRect in red and
# a minEnclosingCircle in blue
for c in contours:
    # get the bounding rect
    x, y, w, h = cv2.boundingRect(c)
    # draw a green rectangle to visualize the bounding rect
    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), thickness=1, lineType=8, shift=0)

    # get the min area rect
    #rect = cv2.minAreaRect(c)
    #box = cv2.boxPoints(rect)
    # convert all coordinates floating point values to int
    #box = np.int0(box)
    # draw a red 'nghien' rectangle
    #cv2.drawContours(img, [box], 0, (0, 0, 255))

    # finally, get the min enclosing circle
    #(x, y), radius = cv2.minEnclosingCircle(c)
    # convert all values to int
    #center = (int(x), int(y))
    #radius = int(radius)
    # and draw the circle in blue
    #img = cv2.circle(img, center, radius, (255, 0, 0), 2)

print(len(contours))
cv2.drawContours(img, contours, -1, (255, 255, 0), 1)

cv2.namedWindow('contours', 0)
cv2.imshow('contours', img)
while(cv2.waitKey()!=ord('q')):
    continue
cv2.destroyAllWindows()

如您所见,这可以做得比我需要的更多.如果您需要更多,请查找评论的部分.

顺便说一下,我需要的是将每个文本区域绑定在一个矩形中,而不是(靠近)脚本找到的每个字符.过滤单个数字或字母,并在一个框中舍入所有内容.

例如,框中的第一个序列,另一个中的第二个序列,依此类推.

我搜索了一下,发现了一些关于"滤镜矩形区域"的内容.我不知道它对我的目的是否有用.

看看谷歌的一些第一个结果,但大多数都不能很好地工作.我想代码需要调整一下,但我是OpenCV世界的新手.

Answer 1

使用以下代码解决.

import cv2

# Load the image
img = cv2.imread('image.png')

# convert to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# smooth the image to avoid noises
gray = cv2.medianBlur(gray,5)

# Apply adaptive threshold
thresh = cv2.adaptiveThreshold(gray,255,1,1,11,2)
thresh_color = cv2.cvtColor(thresh,cv2.COLOR_GRAY2BGR)

# apply some dilation and erosion to join the gaps - change iteration to detect more or less area's
thresh = cv2.dilate(thresh,None,iterations = 15)
thresh = cv2.erode(thresh,None,iterations = 15)

# Find the contours
image,contours,hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

# For each contour, find the bounding rectangle and draw it
for cnt in contours:
    x,y,w,h = cv2.boundingRect(cnt)
    cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
    cv2.rectangle(thresh_color,(x,y),(x+w,y+h),(0,255,0),2)

# Finally show the image
cv2.imshow('img',img)
cv2.imshow('res',thresh_color)
cv2.waitKey(0)
cv2.destroyAllWindows()

需要修改以获得下面结果的参数是迭代次数erode和dilate函数.较低的值将在(几乎)每个数字/字符周围创建更多的边界矩形.

结果