我有单行间距的一段文字的输入图像。我正在尝试实现行间距选项,以增加/减少Microsoft Word中文本行之间的间距。当前图像位于单个空格中,如何将文本转换为两个空格?还是说.5
空间?本质上,我试图动态调整文本行之间的间距,最好使用可调参数。像这样:
输入图像
所需结果
我目前的尝试是这样的。我已经能够略微增加间距,但文字细节似乎受到侵蚀,并且行与行之间存在随机噪声。
关于如何改进代码或任何更好方法的任何想法?
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()
参考方案
方法#1:像素分析
二进制图像
# 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阵列。
我们可以分析从每一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。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,然后遍历数据以构建最终图像。如果确定一行是空白,则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,我们可以更改要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
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来添加额外的空白。
imtuils.contours.sort_contours()
从上到下进行排序,并提取每行ROI 之间添加空白来构建新图像
二进制图像
# 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。这是1
像素宽空间数组的结果
结果为5
像素宽空间数组
# 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()
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