I tried simple image processing with Google Colaboratory.

Introduction

This article is a thin article written by a rare technical college student (transfer student) who has never even touched a PC, let alone python. There are quite a lot of points that I refer to the articles of other people. I hope you can see it with warm eyes.

Required environment

• google colaboratory
• OpenCV

Upload and view images

This time, I will upload and process any image so that it is easy to use. You can upload it by using files.upload (). When you execute the command, the upload form will be displayed, so please upload it. After that, get the file name.

Upload

from google.colab import files
uploaded_file = files.upload()
img = next(iter(uploaded_file))

Image display

Original display

The image is read using OpenCV, but since the color of the image in OpenCV is handled by BGR instead of RGB, it is converted to general RGB. Then use matplotlib to display the image.

Image display

import cv2
from matplotlib import pyplot as plt

img2 = cv2.imread(img)
#RGB conversion
src = cv2.cvtColor(img2,cv2.COLOR_BGR2RGB)
#Image display
plt.imshow(src)
#Hide value
plt.axis('off')

This will display the original image.

Color inversion

Inverting colors is easy, just put src = 255 --src. This takes advantage of the fact that the value is inverted by subtracting the original color value from white (255).

Color inversion

import cv2
from matplotlib import pyplot as plt

img2 = cv2.imread(img)
src = cv2.cvtColor(img2,cv2.COLOR_BGR2RGB)
#Inversion
src = 255 - src
plt.imshow(src)
plt.axis('off')

Execution result

Convert and display to grayscale

Conversion to grayscale can be done with cv2.cvtColor (pixels, cv2.COLOR_BGR2GR). That's the only difference from the original.

grayscale

import numpy as np
pixels = np.array(src)
import matplotlib.pyplot as plt
#Convert to grayscale
img_gray = cv2.cvtColor(pixels, cv2.COLOR_BGR2GRAY)

plt.imshow(img_gray)
plt.axis('off')

Execution result

Binarization

Finally, it is binarization. Binarization is done by first converting to grayscale and then binarizing with cv2.threshold (img_gray, 128, 255, cv2.THRESH_BINARY. The 128 part is called ** threshold **, and here Values ​​brighter than the value of are white (255) and darker values ​​are black (0). This is important for binarizing the threshold value.

Binarization

import numpy as np
pixels = np.array(src)

import matplotlib.pyplot as plt]
#Convert to grayscale
img_gray = cv2.cvtColor(pixels, cv2.COLOR_BGR2GRAY)
#Binarization
retval, img_binary = cv2.threshold(img_gray,128, 255, cv2.THRESH_BINARY)

plt.imshow(img_binary)

Execution result

Threshold = 128

Threshold = 8

Reference material

• https://takacity.blog.fc2.com/blog-entry-142.html
• https://qiita.com/croquette0212/items/36cadce5bf0fb703ed19
• https://dronebiz.net/tech/opencv/binarize