问题
I want to remove anything other than text from a license plate with a binary filter.
I have the projections on each axis but I don't know how to apply it. My idea is to erase the white outlines.
This is the image I'm working for now:
This is the projection in Axis X:
from matplotlib import pyplot as plt
import pylab
(rows,cols)=img.shape
h_projection = np.array([ x/255/rows for x in img.sum(axis=0)])
plt.plot(range(cols), h_projection.T)
And this is the result:
As you can see in the graph, at the end the line is shot by the white contour.
How can I erase everything that is at a certain threshold of the photo? Every help is appreciated
回答1:
So, you want to extract the black areas within the white characters. For example, you can select the columns (or rows) in your histograms where the value is less than a certain threshold.
from matplotlib import pyplot as plt
import pylab
import numpy as np
img = plt.imread('binary_image/iXWgw.png')
(rows,cols)=img.shape
h_projection = np.array([ x/rows for x in img.sum(axis=0)])
threshold = (np.max(h_projection) - np.min(h_projection)) / 4
print("we will use threshold {} for horizontal".format(threshold))
# select the black areas
black_areas = np.where(h_projection < threshold)
fig = plt.figure(figsize=(16,8))
fig.add_subplot(121)
for j in black_areas:
img[:, j] = 0
plt.plot((j, j), (0, 1), 'g-')
plt.plot(range(cols), h_projection.T)
v_projection = np.array([ x/cols for x in img.sum(axis=1)])
threshold = (np.max(v_projection) - np.min(v_projection)) / 4
print("we will use threshold {} for vertical".format(threshold))
black_areas = np.where(v_projection < threshold)
fig.add_subplot(122)
for j in black_areas:
img[j, :] = 0
plt.plot((0,1), (j,j), 'g-')
plt.plot(v_projection, range(rows))
plt.show()
# obscurate areas on the image
plt.figure(figsize=(16,12))
plt.subplot(211)
plt.title("Image with the projection mask")
plt.imshow(img)
# erode the features
import scipy
plt.subplot(212)
plt.title("Image after erosion (suggestion)")
eroded_img = scipy.ndimage.morphology.binary_erosion(img, structure=np.ones((5,5))).astype(img.dtype)
plt.imshow(eroded_img)
plt.show()
So now you have the horizontal and vertical projections, that look like this
And after that you can apply the mask: there are several ways of doing this, in the code is already applied within the for loop, where we set img[:,j] = 0 for the columns, and img[j,:] = 0 for the rows. It was easy and I think intuitive, but you can look for other methods.
As a suggestion, I would say you can look into the morphological operator of erosion that can help to separate the white parts.
So the output would look like this.
Unfortunately, the upper and lower part still show white regions. You can manually set the rows to white img[:10,:] = 0, img[100:,:] = 0, but that probably would not work on all the images you have (if you are trying to train a neural network I assume you have lots of them, so you need to have a code that works on all of them.
So, since now you ask for segmentation also, this opens another topic. Segmentation is a complex task, and it is not as straightforward as a binary mask. I would strongly suggest you read some material on that before you just apply something without understanding. For example here a guide on image processing with scipy, but you may look for more.
As a suggestion and a small snippet to make it work, you can use the labeling from scipy.ndimage.
Here a small part of code (from the guide)
label_im, nb_labels = scipy.ndimage.label(eroded_img)
plt.figure(figsize=(16,12))
plt.subplot(211)
plt.title("Segmentation")
plt.imshow(label_im)
plt.subplot(212)
plt.title("One Object as an example")
plt.imshow(label_im == 6) # change number for the others!
Which will output:
As an example I showed the S letter. if you change label_im == 6 you will get the next letter. As you will see yourself, it is not always correct and other little pieces of the image are also considered as objects. So you will have to work a little bit more on that.
来源：https://stackoverflow.com/questions/62127537/how-to-clean-binary-image-using-horizontal-projection