How do i generate a QR code like image using python

Question

I'm not trying to make a QR code, but I am looking to make a similar type of style that is generated multiple times as an image.

This is a brief example of one of these images: it should be able to be generated randomly

Also, id also like for a chance for a how many pixels it will generate. e.g. 9 pixels = 6% chance, 18 pixels = 30% chance etc.

Thanks

Answer 1

from PIL import Image
import numpy as np
size = (50,50)
threshold = 0.5 #Chance of a pixel being white
a = np.random.uniform(0, 1, size=size)
a[a>threshold] = 0
a[a>0] = 255
im = Image.fromarray(a, mode='F')
save_path = "" #Path to save the image
im.save(rf'{save_path}'+'.tiff')

Output:

Answer 2

Basic concept:

• Create small resolution image with random 0 and 255 values.
• Resize the small image with NEAREST interpolation.

Setting the probabilities is not very clear from the question.
I tried creating a look up table that converts from from probability percent to the percentage of white pixels.
I used Cubic spline data interpolation in MATLAB, but the result is not good enough.
I leave you the job of tuning it (you may also find a formula instead).

Here is the list of stages:

• Create uniformly random distribution "small image" (size 9x9 for example).

   rand_img = np.random.rand(rows, cols)
  

• Draw a random number in range [0, 1], and convert it to percentage (multiply by 100).
  The percentage of white pixel is defined by that random number.

   p = round(random.random()*100)
  

• Pass p through look up table P and get a threshold.
  The look up tables get a probability in percentage, and return the percentage of destination white pixels.
  Divide the result by 100 to convert from percentage to range [0, 1].

   thresh = P[p] / 100  # Pass through lookup table, and convert to threshold in range [0, 1]
  

• Set all pixel below thresh to True, and above it to False.
  Convert True to 255 and False to 0.

   samll_img = rand_img < thresh
   samll_img = samll_img.astype(np.uint8)*255
  

• Resize the binary image with interpolation=cv2.INTER_NEAREST:

   img = cv2.resize(samll_img, (samll_img.shape[1]*18, samll_img.shape[0]*18), interpolation=cv2.INTER_NEAREST)
  

---

Complete code sample:

import cv2
import numpy as np
import random

# Assume each square is 18x18, and 9x9 squares and image is 162x162

cols, rows = 9, 9

# Chance for a how many pixels it will generate. e.g. 9 pixels = 6% chance, 18 pixels = 30% chance etc.
# 9 pixels is 9/(18*18) = about 3% of total pixels - change for 0.03 should be 6%
# 18 pixels is 18/(18*18) = about 6% of total pixels - change for 0.06 should be 18%

# List of probabilities (I don't know exactly how to tune it) - used as look up table.
P = [0,   1,   3,   6,  10,  14,  18,  22,  26,  29,  33,  36,  39,  41,  44,  46,  48,  50,  52,  54,  55,  57,  58,  59,  60,  60,  61,  61,  62,  62,  62,  62,  62,  62,  62,  61,  61,  61,  60,  59,  59,  58,  57,  56,  56,  55,  54,  53,  52,  51,  50,  49,  48,  47,  46,  45,  44,  44,  43,  42,  41,  41,  40,  39,  39,  39,  38,  38,  38,  38,  38,  38,  38,  39,  39,  40,  40,  41,  42,  43,  45,  46,  48,  50,  52,  54,  56,  59,  61,  64,  67,  71,  74,  78,  82,  86,  90,  94,  97,  99, 100]

rand_img = np.random.rand(rows, cols)  # Uniform random distribution 9x9 pixels.

p = round(random.random()*100)  # Get a random number in percentage.
thresh = P[p] / 100  # Pass through lookup table, and convert to threshold in range [0, 1]

# All pixel below thresh are True, and above  it are False.
samll_img = rand_img < thresh
samll_img = samll_img.astype(np.uint8)*255  # Convert True to 255 and False to 0

# Resize the image by x18 in each axis
img = cv2.resize(samll_img, (samll_img.shape[1]*18, samll_img.shape[0]*18), interpolation=cv2.INTER_NEAREST)

cv2.imwrite('img.png', img)  # Save image for testing

---

Few output samples:

---

---