Python 增加图像中文本行之间的间距

我有一个输入图像的一段文字在单行间距。我正在尝试实现类似于行距选项的功能，以增加/减少Microsoft Word中文本行之间的间距。当前图像在单空间中，如何将文本转换为双空间？或者说

.5

空格？从本质上说，我试图动态地重新构造文本行之间的间距，最好是使用一个可调整的参数。大概是这样的：

输入图像

期望结果

我目前的尝试是这样的。我已经能够稍微增加间距，但文本细节似乎被侵蚀了，行间有随机噪音

关于如何改进代码或更好的方法有什么想法吗

import numpy as np 
import cv2

img = cv2.imread('text.png')
H, W = img.shape[:2]
grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
threshed = cv2.threshold(grey, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]

hist = cv2.reduce(threshed, 1, cv2.REDUCE_AVG).reshape(-1)
spacing = 2
delimeter = [y for y in range(H - 1) if hist[y] <= spacing < hist[y + 1]]
arr = []
y_prev, y_curr = 0, 0
for y in delimeter:
    y_prev = y_curr
    y_curr = y
    arr.append(threshed[y_prev:y_curr, 0:W])

arr.append(threshed[y_curr:H, 0:W])
space_array = np.zeros((10, W))
result = np.zeros((1, W))

for im in arr:
    v = np.concatenate((space_array, im), axis=0)
    result = np.concatenate((result, v), axis=0)

result = (255 - result).astype(np.uint8)
cv2.imshow('result', result)
cv2.waitKey()

将numpy导入为np
进口cv2
img=cv2.imread（'text.png'）
H、 W=图像形状[：2]
灰色=cv2.CVT颜色（img，cv2.COLOR\U BGR2GRAY）
阈值=cv2.阈值（灰色，0，255，cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU）[1]
hist=cv2.减少（脱粒，1，cv2.减少平均值）。重塑（-1）
间距=2
delimeter=[y代表范围内的y（H-1），如果hist[y]接近#1:像素分析
获取二值图像。加载图像，转换为灰度和大津阈值

对行像素求和。其思想是，行的像素和可以用来确定它是对应于文本还是空白

创建新图像并添加额外的空白。我们迭代像素阵列并添加额外的空白


二值图像

# Load image, grayscale, Otsu's threshold
image = cv2.imread('1.png')
h, w = image.shape[:2]
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]


现在，我们遍历每一行并对白色像素求和以生成像素数组。
我们可以分析由每行中所有像素之和生成的一列数据，以确定哪些行对应于文本。等于
0
的数据部分表示由空白组成的图像行。以下是数据数组的可视化：

# Sum white pixels in each row
# Create blank space array and and final image 
pixels = np.sum(thresh, axis=1).tolist()
space = np.ones((2, w), dtype=np.uint8) * 255
result = np.zeros((1, w), dtype=np.uint8)


我们将数据转换为一个列表，并对数据进行迭代以构建最终图像。如果一行被确定为空白，则我们将一个空白数组连接到最终图像。通过调整空白数组的大小，我们可以更改要添加到图像中的空间量

# Iterate through each row and add space if entire row is empty
# otherwise add original section of image to final image
for index, value in enumerate(pixels):
    if value == 0:
        result = np.concatenate((result, space), axis=0)
    row = gray[index:index+1, 0:w]
    result = np.concatenate((result, row), axis=0)

这是结果


代码

方法#2:单个行提取
对于更动态的方法，我们可以找到每条线的轮廓，然后在每个轮廓之间添加空间。我们使用与第一种方法相同的方法添加额外的空白

获取二值图像。加载图像、灰度、高斯模糊和大津阈值

连接文本轮廓。我们创建一个水平形状的内核，并进行扩展，将每行的单词连接成一个轮廓

提取每条线轮廓。我们找到轮廓，使用
imtuils.contours.sort\u contours（）从上到下排序，然后提取每条线

在每条线之间添加空白。我们创建一个空数组，并通过在每条线轮廓之间添加空白来构建新图像


二值图像

# Load image, grayscale, Otsu's threshold
image = cv2.imread('1.png')
h, w = image.shape[:2]
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]


创建水平内核并扩展


提取的单独线条轮廓以绿色高亮显示


在每行之间添加空白。下面是使用
1
像素宽的空格数组的结果


结果为
5
像素宽的空间阵列


完整代码

import cv2
import numpy as np 
from imutils import contours

# Load image, grayscale, blur, Otsu's threshold
image = cv2.imread('1.png')
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
invert = 255 - thresh  
height, width = image.shape[:2]

# Dilate with a horizontal kernel to connect text contours
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10,2))
dilate = cv2.dilate(thresh, kernel, iterations=2)

# Extract each line contour
lines = []
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
(cnts, _) = contours.sort_contours(cnts, method="top-to-bottom")
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    cv2.rectangle(image, (0, y), (width, y+h), (36,255,12), 2)
    line = original[y:y+h, 0:width]
    line = cv2.cvtColor(line, cv2.COLOR_BGR2GRAY)
    lines.append(line)

# Append white space in between each line
space = np.ones((1, width), dtype=np.uint8) * 255
result = np.zeros((0, width), dtype=np.uint8)
result = np.concatenate((result, space), axis=0)
for line in lines:
    result = np.concatenate((result, line), axis=0)
    result = np.concatenate((result, space), axis=0)

cv2.imshow('result', result)
cv2.imshow('image', image)
cv2.imshow('dilate', dilate)
cv2.waitKey()

你可以水平模糊或使用水平核的形态学。阈值。然后得到每个区域的轮廓，这些轮廓应该与文本行相对应。然后提取它们，并在新的干净背景图像上以更大的间距写入它们。一个微不足道的误称——你所谓的直方图实际上不是直方图。它是一个c由每行中所有像素的总和生成的数据列。（总和类似于将所有列平均为一列，即块大小调整）。直方图是一种概率分布；它显示计数与可能值。您正在计算计数与列中的位置。但是，解决方案非常好！谢谢，您说得对，它不是直方图。它是像素行数据的总和。我已更新了postGreat解释。！！
# Dilate with a horizontal kernel to connect text contours
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10,2))
dilate = cv2.dilate(thresh, kernel, iterations=2)

# Extract each line contour
lines = []
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
(cnts, _) = contours.sort_contours(cnts, method="top-to-bottom")
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    cv2.rectangle(image, (0, y), (width, y+h), (36,255,12), 2)
    line = original[y:y+h, 0:width]
    line = cv2.cvtColor(line, cv2.COLOR_BGR2GRAY)
    lines.append(line)

# Append white space in between each line
space = np.ones((1, width), dtype=np.uint8) * 255
result = np.zeros((0, width), dtype=np.uint8)
result = np.concatenate((result, space), axis=0)
for line in lines:
    result = np.concatenate((result, line), axis=0)
    result = np.concatenate((result, space), axis=0)

import cv2
import numpy as np 
from imutils import contours

# Load image, grayscale, blur, Otsu's threshold
image = cv2.imread('1.png')
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
invert = 255 - thresh  
height, width = image.shape[:2]

# Dilate with a horizontal kernel to connect text contours
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10,2))
dilate = cv2.dilate(thresh, kernel, iterations=2)

# Extract each line contour
lines = []
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
(cnts, _) = contours.sort_contours(cnts, method="top-to-bottom")
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    cv2.rectangle(image, (0, y), (width, y+h), (36,255,12), 2)
    line = original[y:y+h, 0:width]
    line = cv2.cvtColor(line, cv2.COLOR_BGR2GRAY)
    lines.append(line)

# Append white space in between each line
space = np.ones((1, width), dtype=np.uint8) * 255
result = np.zeros((0, width), dtype=np.uint8)
result = np.concatenate((result, space), axis=0)
for line in lines:
    result = np.concatenate((result, line), axis=0)
    result = np.concatenate((result, space), axis=0)

cv2.imshow('result', result)
cv2.imshow('image', image)
cv2.imshow('dilate', dilate)
cv2.waitKey()