Numpy PIL Python：在空格上裁剪图像或使用直方图阈值裁剪文本

Question

How would I go about finding the bounding box or window for the region of whitespace surrounding the numbers in the image below?:

Original image:

Height: 762 pixels Width: 1014 pixels

Goal:

Something like: {x-bound:[x-upper,x-lower], y-bound:[y-upper,y-lower]} so I can crop to the text and input into tesseract or some OCR.

Attempts:

I had thought of slicing the image into hard coded chunk sizes and analysing at random, but i think it would be too slow.

Example code using pyplot adapted from ( Using python and PIL how can I grab a block of text in an image? ):

from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
im = Image.open('/home/jmunsch/Pictures/Aet62.png')
p = np.array(im)
p = p[:,:,0:3]
p = 255 - p
lx,ly,lz = p.shape

plt.plot(p.sum(axis=1))
plt.plot(p.sum(axis=0))

#I was thinking something like this 
#The image is a 3-dimensional ndarray  [[x],[y],[color?]]
#Set each value below an axes mean to 0
[item = 0 for item in p[axis=0] if item < p.mean(axis=0)]

# and then some type of enumerated groupby for each axes
#finding the mean index for each groupby(0) on axes

plt.plot(p[mean_index1:mean_index2,mean_index3:mean_index4])

Based on the graphs each of the valleys would indicate a place to bound.

• The first graph shows where lines of text would be
• The second graph shows where characters would be

Plot example plt.plot(p.sum(axis=1)) :

Plot example output plt.plot(p.sum(axis=0)) :

Related posts/docs:

• Trim whitespace using PIL
• Using python and PIL how can I grab a block of text in an image?
• Use Python / PIL or similar to shrink whitespace
• Crop the image using PIL in python
• Rectangular bounding box around blobs in a monochrome image using python
• How can I improve my paw detection?
• http://scipy-lectures.github.io/advanced/image_processing/
• http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html

update: solution by HYRY

Answer 1

I think you can use Morphology functions in scipy.ndimage , here is an example:

import pylab as pl
import numpy as np
from scipy import ndimage
img = pl.imread("Aet62.png")[:, :, 0].astype(np.uint8)
img2 = ndimage.binary_erosion(img, iterations=40)
img3 = ndimage.binary_dilation(img2, iterations=40)
labels, n = ndimage.label(img3)
counts = np.bincount(labels.ravel())
counts[0] = 0
img4 = labels==np.argmax(counts)
img5 = ndimage.binary_fill_holes(img4)
result = ~img & img5
result = ndimage.binary_erosion(result, iterations=3)
result = ndimage.binary_dilation(result, iterations=3)
pl.imshow(result, cmap="gray")

the output is: