Frank Martin
Biomedical Engineer & Software Engineer
When I started designing my blog I wanted create a look and feel of a code editor. So instead of a regular picture I wanted to have a ascii version.
At first I used an online tool to generate the picture. While it was good enough to decide that I liked it, I felt it did not gave me enough control over the result. So I decided to write my own in Python.
Original picture that I want to convert to an ASCII version.
Algorithm
I remembered an algorithm which I have seen during a computer vision course. Which computed the average intensity of a segment in the picture and then matched it to a character with the same intensity.
I used the Python package Pillow (which is an python implementation of PIL) for most of the image processing tasks.
Preprocessing the Image
The identity box on my website is 220x220 pixels. The first step would be to crop the image to a square.
1 2 3 4 5 | from PIL import Image im = Image.open("input.jpg") # I found this a nice cutout. # (left, upper, right, lower) im.crop((500, 100, 1500, 1100)) |
We want a (single) intensity value per image segment. In a color image we have three values per pixel: red, green and blue. A first step would be to convert the image to a gray scale image. There are many ways to do this, but I decided to use the average intensity of the three color channels.
1 2 | # convert to grayscale im = im.convert("L") |
Then, I normalized the gray scale image to use the full range 0-255 to maximize the contrast between segments.
1 2 3 | from PIL import ImageOps # normalize the image im = ImageOps.autocontrast(im) |
| Original | Cropped | Grayscale | Normalized |
|---|---|---|---|
|
The original loaded image |
The square cropped image |
The gray scale image |
The normalized image |
Segmenting the Picture
Before we can compute the intensity per segment we need to split the image into segments. I decided to use a grid to use 110 segments in x and y direction. So every segment is 2x2 pixels in the final resulting ASCII-picture.
1 2 3 4 5 | n_segments = 110 (width, height) = im.size # compute the width (and height since we have a square image) of a # segment dw = width // n_segments |
The cropped image with the grid looks like this:
Visualization code
1 2 3 4 5 6 7 8 9 10 | # make a copy of the image, as we do not want to have the # segmentation gizmo in the final image im_ = im.copy() d = ImageDraw.Draw(im_) for i in range(1, n_segments): d.line((0, i*dw, img_h, i*dw), fill=255) d.line((i*dw, 0, i*dw, img_h), fill=255) display(im_processed2) |
The original image with the segmentation grid.
I used the crop function of the Image class to split the image into segments:
1 2 3 4 5 6 7 8 | im_part = im.copy() segments = [] for i in range(n_segments): for j in range(n_segments): segments.append( im_part.crop((j*dw,i*dw,(j+1)*dw,(i+1)*dw)) ) |
Draw images segments
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | def display_many(images: List[Image.Image], dim: tuple=(1, 1)) -> Image.Image: assert math.prod(dim) == len(images), 'Thats not a grid' n, m = dim img_w, _ = images[0].size new_image = Image.new('L', ((n)*img_w+n+1, (m)*img_w+m+1), 255) for i in range(1,n+1): for j in range(1,m+1): idx = (i-1)*m + (j-1) new_image.paste(images[idx], ((j-1)*img_w+(j), (i-1)*img_w+(i))) return new_image display_many(segments, (n_segments, n_segments)) |
The segmented pictures displayed in a grid.
Computing Letter Intensities
Before we can match the each segment to a character we need to compute the intensity for each character. This intensity is dependant on the font that is used. I used the Ubunto Mono font, which is the same font as this website. This code only works for mono-spaced fonts. If the font is not mono-spaced the algorithm will be much more complicated, as the final ASCII image is not a grid in that case.
The font size is not important when computing the intensities for each character, as we are computing the average pixel intensity of the character box.
compute_letter_intensity(letter: str)
1 2 3 4 5 6 7 8 9 10 11 | def compute_letter_intensity(letter: str) -> float: img_dims2 = (font_size//2, font_size) img = Image.new('L', img_dims2, color='black') d = ImageDraw.Draw(img) d.text((0,0), letter, font=fnt, fill=255, align='center') data = img.getdata() n_of_pixels = math.prod(img_dims2) avg_intensity = sum(data)/n_of_pixels return avg_intensity |
The function compute_letter_intensity returns the average pixel intensity of a character. In other words, it sums the pixel values and divides them by the number of pixels that are in the character box.
We want to match every segment in our preprocessed image to a character. So we need to compute the letter intensity for all characters we want to use in out final ASCII image.
I used a selection of letters, digits and symbols. Symbols like ", ' and / are not included, as they have a meaning within HTML. Another option would be to escape these characters, but I decided to leave them out.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # define the characters to use charactersList = list(string.ascii_letters + string.digits ) \ + [' ', '!', '?', '.', ',', ':', ';', '(', ')', '[', ']', '{', '}', '/' , '|', '-', '_', '=', '+', '*', '&', '^', '%', '$', '#', '@', '~', '`'] # load the mono-spaced font fnt = ImageFont.truetype("fonts/UbuntuMono-Regular.ttf", size=font_size) # compute the intensity of each letter intensities = {} for char in charactersList: intensities[char] = compute_letter_intensity(char) # normalize values between 0 - 1 low = min(intensities.values()) high = max(intensities.values()) - low for k, v in intensities.items(): intensities[k] = (v - low) / high |
Match letters to segments
For each segment we match the letter with the most similar intensity:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | chars = [] for objective in segment_average: chosen_char = '*' distance = 1 for char, intensity in intensities.items(): di = abs(objective - intensity) if di < distance: chosen_char = char distance = di chars.append(chosen_char) # merge chars into strings of length n_segments result_string = ''.join(chars) n = n_segments lines = [result_string[i:i+n] for i in range(0, len(result_string), n)] |
Making our final result:
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