增加图像中文本行之间的间距
cof*_*win 3 python text opencv image-processing computer-vision
我有一个单行距文本段落的输入图像。我正在尝试实现类似行间距选项的功能，以增加/减少 Microsoft Word 中文本行之间的间距。当前图像是单倍行距，如何将文本转换为双倍行距？或者说.5空间？本质上，我试图动态地重组文本行之间的间距，最好使用可调整的参数。像这样的东西：
输入图像
期望的结果
我目前的尝试看起来像这样。我已经能够稍微增加间距，但文本细节似乎被侵蚀，并且行之间存在随机噪音。
关于如何改进代码或任何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()
方法#1：像素分析
获取二值图像。加载图像，转换为灰度，以及Otsu的阈值
对行像素求和。这个想法是，一行的像素和可用于确定它是否对应于文本或空白
创建新图像并添加额外的空白。我们迭代像素数组并添加额外的空白
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]
现在我们迭代每一行并对白色像素求和以生成像素数组。我们可以分析从每行中所有像素的总和生成的一列数据，以确定哪些行对应于文本。相等的数据部分0表示由空白组成的图像行。这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)
我们将数据转换为列表并迭代数据以构建最终图像。如果一行被确定为空白，那么我们将一个空白数组连接到最终图像。通过调整空数组的大小，我们可以更改添加到图像的空间量。
# 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
import cv2
import numpy as np
import matplotlib.pyplot as plt
# import pandas as pd
# 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]
# Sum white pixels in each row
# Create blank space array and and final image
pixels = np.sum(thresh, axis=1).tolist()
space = np.ones((1, w), dtype=np.uint8) * 255
result = np.zeros((0, w), dtype=np.uint8)
# 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)
# Uncomment for plot visualization
'''
x = range(len(pixels))[::-1]
df = pd.DataFrame({'y': x, 'x': pixels})
df.plot(x='x', y='y', xlim=(-2000,max(pixels) + 2000), legend=None, color='teal')
'''
cv2.imshow('result', result)
cv2.imshow('thresh', thresh)
plt.show()
cv2.waitKey()
方法#2：单独的线提取
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附加额外空格的方法。
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以将每行的单词连接成单个轮廓
提取每条线的轮廓。我们找到轮廓，使用从上到下排序
imtuils.contours.sort_contours()并提取每条线的 ROI在每行之间添加空格。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, 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]
创建水平内核并扩张
# Dilate with a horizontal kernel to connect text contours
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10,2))
dilate = cv2.dilate(thresh, kernel, iterations=2)
提取的单条线轮廓以绿色突出显示
# 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)
在每行之间添加空格。1这是像素宽空间阵列的结果
5像素宽空间阵列的结果
# 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)
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()