Giter Site home page Giter Site logo

optical_illusion's Introduction

Coding an Optical Illusion in Python

png

I recently found a cool optical illusion online that makes a black and white image seem colored. It does so by imposing a colored grid on top of the black and white image, tricking your eyes into filling in the rest of the colors. You can try it out on Google Colab without downloading any code as well.

Getting Started

Let's replicate this in python. We'll use the open CV library to convert the color image into black and white, and then draw some grid lines over them.

import cv2
import matplotlib.pyplot as plt
import numpy as np

%matplotlib inline

Choosing an image

Since we're on google colab, you can either upload an image to the colab filesystem by clicking the folder button on the left sidebar, or run the curl command to download an image.

!curl -o input.jpg https://unicun.azureedge.net/wp-content/uploads/2019/03/Lego-Blocks-For-Adults.jpg
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 93698  100 93698    0     0  60921      0  0:00:01  0:00:01 --:--:-- 60882
img_path = "input.jpg"
img = plt.imread(img_path)
plt.figure(figsize=(5,5))
plt.imshow(img)
<matplotlib.image.AxesImage at 0x7fc03e6a7c50>

png

Converting to black and white

This image is an RGB format array, so the dimensions are (H, W, C). We'll convert this to black and white using cv2. CV2 actually reads colors as BGR, so we'll have to reverse the channels.

Also, since the black and white images is 1 channel, but we want to draw colored lines onto the black and white image, we'll have to convert it back to 3 channel.

bw_img = cv2.cvtColor(img[:,:,::-1], cv2.COLOR_BGR2GRAY) # reverse RGB to BGR before giving to CV2
bw_img = cv2.cvtColor(bw_img, cv2.COLOR_GRAY2BGR) # go back to 3 channel
plt.figure(figsize=(5,5))
plt.imshow(bw_img, cmap='Greys_r')
<matplotlib.image.AxesImage at 0x7fc03ec67f28>

png

Drawing the grid lines

Finally, here is where the magic happens. We'll draw grid lines over the image. You can play with the number of lines in the x and y dimension of the grid with the grid_x_dim and grid_y_dim parameters. Increasing the number of lines in the grid will generally make the image look more colorful.

You can also control the thickness of the line.

grid_img = bw_img.copy()
H, W, C = grid_img.shape
grid_x_dim = 100
grid_y_dim = 100
thickness = 1


line_width = W / grid_x_dim
line_height = H / grid_y_dim

# draw vertical lines
for i in range(grid_x_dim):
    for j in range(grid_y_dim):
        start_x, start_y = int(i * line_width), int(j * line_height)
        end_x, end_y = int(i * line_width), int((j+1) * line_height)
        # determine the color
        mid_x = int(start_x + 0.5*(end_x - start_x))
        mid_y = int(start_y + 0.5*(end_y - start_y))
        mid_color = img[mid_y, mid_x, :].tolist() # row by column, so y, x
        color = mid_color
        cv2.line(grid_img,  (start_x, start_y), (end_x, end_y), color, thickness, 1)

# draw horizontal lines
for i in range(grid_y_dim):
    for j in range(grid_x_dim):
        start_x, start_y =  int(j * line_width), int(i * line_height)
        end_x, end_y =  int((j+1) * line_width), int(i * line_height)
        # determine the color
        mid_x = int(start_x + 0.5*(end_x - start_x))
        mid_y = int(start_y + 0.5*(end_y - start_y))
        mid_color = img[mid_y, mid_x, :].tolist() # row by column, so y, x
        color = mid_color
        cv2.line(grid_img,  (start_x, start_y), (end_x, end_y), color, thickness, 1)
plt.figure(figsize=(5,5))
plt.imshow(grid_img)
<matplotlib.image.AxesImage at 0x7fc0470689e8>

png

Let's compare the images side by side. As you can see, the illusion isn't as vibrant as the original image, but it sure doesn't look black and white!

all_img = np.concatenate([img, grid_img, bw_img], axis=1)
plt.figure(figsize=(15,5))
plt.imshow(all_img)
<matplotlib.image.AxesImage at 0x7fc03eca7048>

png

Code for Optical Illusion

Finally, here is some self contained code for doing everything above.

import cv2
import matplotlib.pyplot as plt
import numpy as np

def optical_illusion(img_path, grid_dim=(75, 75), line_thickness=3, show_all=False):
    """
    img_path: path of the input image
    grid_dim: dimensions of the grid
    line_thickness: thickness of each line
    show_all: render input image, optical illusion, and bw image side by side.

    Converts RGB image into BW image, and then draws colored grid lines over them.
    You can tune the grid dimensions, as well as the thickness of each line.
    """
    grid_x_dim, grid_y_dim = grid_dim
    thickness = line_thickness
    
    img = plt.imread(img_path)
    bw_img = cv2.cvtColor(img[:,:,::-1], cv2.COLOR_BGR2GRAY)
    bw_img = cv2.cvtColor(bw_img, cv2.COLOR_GRAY2BGR)
    grid_img = bw_img.copy()
    W,H,C = grid_img.shape
  

    line_width = H / grid_x_dim
    line_height = W / grid_y_dim

    # draw vertical lines
    for i in range(grid_x_dim-1):
        for j in range(grid_y_dim-1):
            start_x, start_y = int(i * line_width), int(j * line_height)
            end_x, end_y = int(i * line_width), int((j+1) * line_height)
            # determine the color
            mid_x = int(start_x + 0.5*(end_x - start_x))
            mid_y = int(start_y + 0.5*(end_y - start_y))
            mid_color = img[mid_y, mid_x, :].tolist() # row by column, so y, x
            color = mid_color
            cv2.line(grid_img,  (start_x, start_y), (end_x, end_y), color, thickness, 1)

    # draw horizontal lines
    for i in range(grid_y_dim-1):
        for j in range(grid_x_dim-1):
            start_x, start_y =  int(j * line_width), int(i * line_height)
            end_x, end_y =  int((j+1) * line_width), int(i * line_height)
            # determine the color
            mid_x = int(start_x + 0.5*(end_x - start_x))
            mid_y = int(start_y + 0.5*(end_y - start_y))
            mid_color = img[mid_y, mid_x, :].tolist() # row by column, so y, x
            color = mid_color
            cv2.line(grid_img,  (start_x, start_y), (end_x, end_y), color, thickness, 1)
    
    save_img = grid_img
    if show_all:
        save_img = np.concatenate([img, grid_img, bw_img], axis=1)
    return save_img
# let's explore some more images!
!curl -o minecraft.jpg  https://www.xboxone-hq.com/images/games/screenshots/35-minecraft-xbox-one-edition-screenshot-1421916887.jpg
!curl -o astroworld.jpg  https://upload.wikimedia.org/wikipedia/en/0/0b/Astroworld_by_Travis_Scott.jpg
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  146k  100  146k    0     0   113k      0  0:00:01  0:00:01 --:--:--  113k
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  127k  100  127k    0     0   227k      0 --:--:-- --:--:-- --:--:--  226k
img = optical_illusion("minecraft.jpg", grid_dim=(100, 150), line_thickness=1, show_all=False)
plt.figure(figsize=(20,10))
plt.imshow(img)
<matplotlib.image.AxesImage at 0x7fc03e50e400>

png

img = optical_illusion("astroworld.jpg", grid_dim=(100, 100), line_thickness=1, show_all=True)
plt.figure(figsize=(30,10))
plt.imshow(img)
<matplotlib.image.AxesImage at 0x7fc03e449ac8>

png

optical_illusion's People

Contributors

edwhu avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar

Forkers

vibster

optical_illusion's Issues

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.