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I need to make a "mosaic" - but very simple

The best code for mosaic I've found you can see at this page:
https://github.com/codebox/mosaic
However, the code doesn't work well on my Windows computer, and also I think the code is too advanced for what it should do. Here are my requirements I've posted on reddit:
1) The main photo already has reduced number of colors (8)
2) I have already every image associated with colour needed to be replaced (e.g. number 1 is supposed to replace black pixels, number 2 replaces green pixels...)
3) I need to enlarge the photo by the small photo's size (9 x 9 small photos will produce 81 times bigger image), which should push the pixels "2n" points away from each other, but instead of producing a n x n same-coloured area around every single one of them (this is how I believe enlarging works in general, correct me if I'm wrong), it will just colour the white spaces with unrecognized colour, which is not associated with any small photo (let's call that colour C)
4) Now all it needs is to run through all non-C coloured pixels and put an image centered on that pixel, which would create the mosaic.
Since I'm pretty new to Python (esp. graphics) and need it just for one use, could someone help me with creating that code? I think that code I got inspired with is too complicated. Two things I don't need:
1) "approximation" - if the enlargement is lesser than needed for 100% quality (e.g. the pictures are 9x9, but every side of the original photo can be only 3 times larger, then the program needs to merge some pixels of different colours together, leading to quality loss)
2) association colour - picture: my palette of pictures is small and of colours as well, I can do it manually
For the ones who didn't get what I mean, here is my idea: https://ibb.co/9GNhqBx

I had a quick go using pyvips:
#!/usr/bin/python3
import sys
import os
import pyvips
if len(sys.argv) != 4:
print("usage: tile-directory input-image output-image")
sys.exit(1)
# the size of each tile ... 16x16 for us
tile_size = 16
# load all the tile images, forcing them to the tile size
print(f"loading tiles from {sys.argv[1]} ...")
for root, dirs, files in os.walk(sys.argv[1]):
tiles = [pyvips.Image.thumbnail(os.path.join(root, name), tile_size,
height=tile_size, size="force")
for name in files]
# drop any alpha
tiles = [image.flatten() if image.hasalpha() else image
for image in tiles]
# copy the tiles to memory, since we'll be using them many times
tiles = [image.copy_memory() for image in tiles]
# calculate the average rgb for an image, eg. image -> [12, 13, 128]
def avg_rgb(image):
m = image.stats()
return [m(4,i)[0] for i in range(1,4)]
# find the avg rgb for each tile
tile_colours = [avg_rgb(image) for image in tiles]
# load the main image ... we can do this in streaming mode, since we only
# make a single pass over the image
main = pyvips.Image.new_from_file(sys.argv[2], access="sequential")
# find the abs of an image, treating each pixel as a vector
def pyth(image):
return sum([band ** 2 for band in image.bandsplit()]) ** 0.5
# calculate a distance map from the main image to each tile colour
distance = [pyth(main - colour) for colour in tile_colours]
# make a distance index -- hide the tile index in the bottom 16 bits of the
# distance measure
index = [(distance[i] << 16) + i for i in range(len(distance))]
# find the minimum distance for each pixel and mask out the bottom 16 bits to
# get the tile index for each pixel
index = index[0].bandrank(index[1:], index=0) & 0xffff
# replicate each tile image to make a set of layers, and zoom the index to
# make an index matching the output size
layers = [tile.replicate(main.width, main.height) for tile in tiles]
index = index.zoom(tile_size, tile_size)
# now for each layer, select pixels matching the index
final = pyvips.Image.black(main.width * tile_size, main.height * tile_size)
for i in range(len(layers)):
final = (index == i).ifthenelse(layers[i], final)
print(f"writing {sys.argv[3]} ...")
final.write_to_file(sys.argv[3])
I hope it's easy to read. I can run it like this:
$ ./mosaic3.py smallpic/ mainpic/Use\ this.jpg x.png
loading tiles from smallpic/ ...
writing x.png ...
$
It takes about 5s on this 2015 laptop and makes this image:
I had to shrink it for upload, but here's a detail (bottom left of the first H):
Here's a google drive link to the mosaic, perhaps it'll work: https://drive.google.com/file/d/1J3ofrLUhkuvALKN1xamWqfW4sUksIKQl/view?usp=sharing
And here's this code on github: https://github.com/jcupitt/mosaic

Why does imshow display non-integer x and y values for the pixel position?

I am trying to read the x and y positions of the pixels in images. This is an example of what is shown when I run:
plt.figure(1)
plt.imshow(img)
plt.title('image')
plt.show()
Why are they non-integer values? My best guess is that some scaling is occurring? I am running python on spyder as an IDE.
Edit: Here is the image:
Edit 2: Upon closer inspection, inspecting pixel by pixel, they appear to be at the .5 marks rather than 0 to 1 as well. And here is a screenshot of my axis settings... something is definitely funky here. Anybody have an idea why?

My guess is, that the float values you worry about while hovering over the shown image with your mouse is just the mouse pointer position, which does not have to be integer. Yet still lays within a pixel (squared integer area) and thus gives you information about the channels at that pixel's position.
Another way to get information about your pixels in a more controlled way is given here:
Here is my working code snippet printing the pixel colours from an image:
import os, sys
import Image
im = Image.open("image.jpg")
x = 3
y = 4
pix = im.load()
print pix[x,y]
Answer edit 2: It just makes sense like that. The pixel centers fall on the integer values .0 you expect the pixels to have. If the edges would fall on the .0 a direct mapping between pixel coordinates and pixel values would not be possible within the visualization. Also the pixel having a height and width of 1 is exactly what we would expect.

How to digitize (extract data from) a heat map image using Python?

There are several packages available to digitize the line graphs e.g. GetData Graph Digitizer. However, for digitzation of heat maps I could not find any packages or programs.
I want to digitize the heat map (images from png or jpg format) using Python. How to do it?
Do I need to write the entire code from scratch?
Or there are any packages available?

There are multiple ways to do it, many Machine Learning libraries offering custom visualization functions...easier or harder.
You need to split the problem in half.
First, using OpenCV for python or scikit-image you first have to load the images as matrices. You can set some offsets to start right at the beginning of the cells.
import cv2
# 1 - read color image (3 color channels)
image = cv2.imread('test.jpg',1)
Then, you will iterate thru the cells and read the color inside. You can normalise the result if you want. The reason we're introducing some offsets is because the heatmap doesn't start in the top left corner of the original image at (0,0). The offset_x and offset_y will be lists with 2 values each.
offset_x[0]: the offset from the left part of the image up to the beginning of the heatmap(i.e. start_of_heatmap_x)
offset_x[1]: the offset from the right part of the image up to the ending of the heatmap(i.e. image_width - end_of_heatmap_x)
offset_y[0]: the offset from the top part of the image up to the beggining of the heatmap(i.e. start_of_heatmap_y)
offset_y[1]: the offset from the bottom part of the image up to the ending of the heatmap (i.e. image_height - end_of_heatmap_y)
Also, we don't iterate up to the last column. That's because we start from the "0-th" column and we add cell_size/2 on each base local coordinates to obtain the center value of the cell.
def read_as_digital(image, cell_size, offset_x, offset_y):
# grab the image dimensions
h = image.shape[0]
w = image.shape[1]
results = []
# loop over the image, cell by cell
for y in range(offset_y[0], h-offset_y[1]-cell_size, cell_size):
row = []
for x in range(offset_x[0], w-offset_x[0]-cell_size, cell_size):
# append heatmap cell color to row
row.append(image[x+int(cell_size/2),y+int(cell_size/2)])
results.append(row)
# return the thresholded image
return results
Extracting the legend information is not hard because we can derive the values by having the limits (although this applies for linear scales).
So for example, we can derive the step on the legends (from x and y).
def generate_legend(length, offset, cell_size, legend_start, legend_end):
nr_of_cells = (length- offset[0] - offset[1])/cell_size
step_size = (legend_end - legend_start)/nr_of_cells
i=legend_start+step_size/2 # a little offset to center on the cell
values = []
while(i<legend_end):
values.append(i)
i = i+step_size
return values
Then you want to visualize them to see if everything was done right. For example, with seaborn it's very easy [1]. If you want more control, over...anything, you can use scikit learn and matplotlib [2].

How to get border pixels of an image in python?

I have an image, using steganography I want to save the data in border pixels only.
In other words, I want to save data only in the least significant bits(LSB) of border pixels of an image.
Is there any way to get border pixels to store data( max 15 characters text) in the border pixels?
Plz, help me out...

OBTAINING BORDER PIXELS:
Masking operations are one of many ways to obtain the border pixels of an image. The code would be as follows:
a= cv2.imread('cal1.jpg')
bw = 20 //width of border required
mask = np.ones(a.shape[:2], dtype = "uint8")
cv2.rectangle(mask, (bw,bw),(a.shape[1]-bw,a.shape[0]-bw), 0, -1)
output = cv2.bitwise_and(a, a, mask = mask)
cv2.imshow('out', output)
cv2.waitKey(5000)
After I get an array of ones with the same dimension as the input image, I use cv2.rectangle function to draw a rectangle of zeros. The first argument is the image you want to draw on, second argument is start (x,y) point and the third argument is the end (x,y) point. Fourth argument is the color and '-1' represents the thickness of rectangle drawn (-1 fills the rectangle). You can find the documentation for the function here.
Now that we have our mask, you can use 'cv2.bitwise_and' (documentation) function to perform AND operation on the pixels. Basically what happens is, the pixels that are AND with '1' pixels in the mask, retain their pixel values. Pixels that are AND with '0' pixels in the mask are made 0. This way you will have the output as follows:
.
The input image was :
You have the border pixels now!
Using LSB planes to store your info is not a good idea. It makes sense when you think about it. A simple lossy compression would affect most of your hidden data. Saving your image as JPEG would result in loss of info or severe affected info. If you want to still try LSB, look into bit-plane slicing. Through bit-plane slicing, you basically obtain bit planes (from MSB to LSB) of the image. (image from researchgate.net)
I have done it in Matlab and not quite sure about doing it in python. In Matlab,
the function, 'bitget(image, 1)', returns the LSB of the image. I found a question on bit-plane slicing using python here. Though unanswered, you might want to look into the posted code.

To access border pixel and enter data into it.
A shape of an image is accessed by t= img.shape. It returns a tuple of the number of rows, columns, and channels.A component is RGB which 1,2,3 respectively.int(r[0]) is variable in which a value is stored.
import cv2
img = cv2.imread('xyz.png')
t = img.shape
print(t)
component = 2
img.itemset((0,0,component),int(r[0]))
img.itemset((0,t[1]-1,component),int(r[1]))
img.itemset((t[0]-1,0,component),int(r[2]))
img.itemset((t[0]-1,t[1]-1,component),int(r[3]))
print(img.item(0,0,component))
print(img.item(0,t[1]-1,component))
print(img.item(t[0]-1,0,component))
print(img.item(t[0]-1,t[1]-1,component))
cv2.imwrite('output.png',img)

Distortion effect using OpenCv-python

I want to create distortion effect like Spiral, stretch, fisheye, Wedge and other effect like underwater and snow like this website using cv2 library in python.

I figured out fisheye distortion.
In OpenCV version 3.0 and above it is possible to perform it using cv2.fisheye.undistortImage(). I have the code in python if you need.
This is what I got for the following input image:
Input Image:
Distorted image:
The function accepts a matrix, which upon modification yield different distortions of the image.
UPDATE
In order to add a snowfall effect you can add some noise like Poisson noise.

Here is a replacement block to map out a fisheye in the middle of the image. Please look elsewhere for details on the math. Use this in place of the 2 for loops in the previous code.
As stated in the first half of my answer (see previous answer), the purpose of this block is to create 2 maps that work together to remap the source image into the destination image.
To create the two maps, this block sweeps through 2 for loops with the dimensions of the image. Values are calculated for the X and y maps (flex_x and flex_y). It starts with assigning each to simply x and y for a 1-to-1 replacement map. Then, if the radius (r) is between 0 and 1, the map for the tangential slide for the fisheye is applied and new flex_x and flex_y values are mapped.
Please see my other answer for more details.
# create simple maps with a modified assignment
# outside the bulge is normal, inside is modified
# this is where the magic is assembled
for y in range(h):
ny = ((2*y-250)/(h-250))-1 #play with the 250's to move the y
ny2 = ny*ny
for x in range(w):
nx = ((2*x-50)/(w-50))-1 #play with the 50's to move the x
nx2 = nx*nx
r = math.sqrt(nx2+ny2)
flex_x[y,x] = x
flex_y[y,x] = y
if r>0 and r<1:
nr1 = 1 - r**2
nr2 = math.sqrt(nr1)
nr = (r + (1.0-nr2)) / 2.0
theta = math.atan2(ny,nx)
nxn = nr*math.cos(theta)
nyn = nr*math.sin(theta)
flex_x[y,x] = (((nxn+1)*w)/2.0)
flex_y[y,x] = (((nyn+1)*h)/2.0)

Here is half of an answer. The cv2.remap function uses maps to choose a pixel from the source for each pixel in the destination. alkasm's answer to this: How do I use OpenCV's remap function?
does a great job of defining the process, but glosses over the usefulness of those maps. If you can get creative in the maps, you can make any effect you want. Here is what I came up with.
The program starts by loading the image and resizing it. This is a convenience for a smaller screen. Then the empty maps are created.
The maps need to be the same dimensions as the image that is being processed, but with a depth of 1. If the resized original is 633 x 400 x 3, the maps both need to be 633 x 400.
When the remapping is done, cv2.remap will used the value at each coordinate in the maps to determine which pixel in the original to use in the destination. For each x,y in the destination, dest[x,y] = src[map1[x,y],map2[x,y]].
The simplest mapping would be if for every (x,y), map1(x,y)=x and map2(x,y)=y. This creates a 1-to-1 map, and the destination would match the source. In this example, a small offset is added to each value. The cosine function in the offset creates both positive and negative shifts, creating waves in the final image.
Note that creating the maps is slow, but the cv2.remap is fast. Once you have created the map, the cv2.remap is fast enough to be applied to frames of video.
import numpy as np #create waves
import cv2
import math
# read in image and resize down to width of 400
# load your image file here
image = cv2.imread("20191114_154534.jpg")
r = 400.0 / image.shape[1]
dim = (400, int(image.shape[0] * r))
# Perform the resizing of the image
resized = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)
# Grab the dimensions of the image and calculate the center
# of the image (center not needed at this time)
(h, w, c) = resized.shape
center = (w // 2, h // 2)
# set up the x and y maps as float32
flex_x = np.zeros((h,w),np.float32)
flex_y = np.zeros((h,w),np.float32)
# create simple maps with a modified assignment
# the math modifier creates ripples. increase the divisor for less waves,
# increase the multiplier for greater movement
# this is where the magic is assembled
for y in range(h):
for x in range(w):
flex_x[y,x] = x + math.cos(x/15) * 15
flex_y[y,x] = y + math.cos(y/30) * 25
# do the remap this is where the magic happens
dst = cv2.remap(resized,flex_x,flex_y,cv2.INTER_LINEAR)
#show the results and wait for a key
cv2.imshow("Resized",resized)
cv2.imshow("Flexed",dst)
cv2.waitKey(0)
cv2.destroyAllWindows()