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version k-means qui marche!!

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mathayay
2025-11-21 14:31:37 +01:00
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__pycache__
.venv

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# Code snippets
This repo contains the code snippets I reference in [my blog](https://boonepeter.github.io/).
## Index
Code|Blog Post|
---|---|
[spotify-codes](./spotify-codes)|[How do Spotify Codes Work?](https://boonepeter.github.io/posts/2020-11-10-spotify-codes/)|
[spotify-codes-part-2](./spotify-codes-part-2)|[Spotify Codes Part 2](https://boonepeter.github.io/posts/spotify-codes-part-2/)
[snapcodes](./snapcodes)|No post yet

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# Snapcodes
Using Python 3.10.1
```bash
pip3 install -r requirements.txt
```
Running this command
```bash
python3 img_to_points.py snapcode.png
```
Gets the points from this image:
![](./src/snapcode.png)
Points:
```python
[1, 2, 3, 4, 6, 9, 14, 18, 20, 21, 22, 23, 27, 28, 29, 30, 32, 38, 39, 41, 43, 44, 45, 49, 53, 58, 59, 62, 64, 65, 66, 67, 70, 71, 73, 76, 77, 83, 87, 90, 93, 94, 96, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, 115, 118, 119, 120, 130, 131, 134, 140, 141, 144, 145, 146, 150, 153, 155, 157, 159, 161, 163, 169, 170, 171, 172, 173, 174, 175, 178, 182, 183, 184, 185, 188, 190, 192, 193, 196, 197, 202, 204, 206, 207, 209]
```
Points are numbered left to right, top to bottom, starting with 0 and ending at 211.
And this command
```bash
python3 img_to_svg.py -i snapcode.png -o output.svg
```
Outputs an SVG file:
![](./src/output.svg)
The svg is useful for checking that the point parsing was correct.

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boonepeter.github.io-code/snapcodes/requirements.txt
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imageio==2.13.5
networkx==2.6.3
numpy==1.22.1
packaging==21.3
Pillow==9.0.0
pyparsing==3.0.6
PyWavelets==1.2.0
scikit-image==0.19.1
scipy==1.7.3
tifffile==2021.11.2

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/imgs
.ipynb_checkpoints/

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import argparse
from skimage import io
import numpy as np
from skimage import io, img_as_ubyte
from skimage.filters import threshold_otsu
from skimage.color import rgb2gray, rgba2rgb
from skimage.measure import label, regionprops
## BBOX of points from all_dots_yellow.png
ALL_BBOX = [
[0.025, 0.076, 0.0583, 0.1094],
[0.025, 0.1271, 0.0583, 0.1604],
[0.025, 0.1781, 0.0583, 0.2115],
[0.025, 0.2292, 0.0583, 0.2625],
[0.025, 0.2802, 0.0583, 0.3135],
[0.025, 0.3302, 0.0583, 0.3635],
[0.025, 0.3812, 0.0583, 0.4146],
[0.025, 0.4323, 0.0583, 0.4656],
[0.025, 0.4833, 0.0583, 0.5167],
[0.025, 0.5344, 0.0583, 0.5677],
[0.025, 0.5854, 0.0583, 0.6188],
[0.025, 0.6365, 0.0583, 0.6698],
[0.025, 0.6865, 0.0583, 0.7198],
[0.025, 0.7375, 0.0583, 0.7708],
[0.025, 0.7885, 0.0583, 0.8219],
[0.025, 0.8396, 0.0583, 0.8729],
[0.025, 0.8906, 0.0583, 0.924],
[0.076, 0.025, 0.1094, 0.0583],
[0.076, 0.076, 0.1094, 0.1094],
[0.076, 0.1271, 0.1094, 0.1604],
[0.076, 0.1781, 0.1094, 0.2115],
[0.076, 0.2292, 0.1094, 0.2625],
[0.076, 0.2802, 0.1094, 0.3135],
[0.076, 0.3302, 0.1094, 0.3635],
[0.076, 0.3812, 0.1094, 0.4146],
[0.076, 0.4323, 0.1094, 0.4656],
[0.076, 0.4833, 0.1094, 0.5167],
[0.076, 0.5344, 0.1094, 0.5677],
[0.076, 0.5854, 0.1094, 0.6188],
[0.076, 0.6365, 0.1094, 0.6698],
[0.076, 0.6865, 0.1094, 0.7198],
[0.076, 0.7375, 0.1094, 0.7708],
[0.076, 0.7885, 0.1094, 0.8219],
[0.076, 0.8396, 0.1094, 0.8729],
[0.076, 0.8906, 0.1094, 0.924],
[0.076, 0.9417, 0.1094, 0.975],
[0.1271, 0.025, 0.1604, 0.0583],
[0.1271, 0.076, 0.1604, 0.1094],
[0.1271, 0.1271, 0.1604, 0.1604],
[0.1271, 0.1781, 0.1604, 0.2115],
[0.1271, 0.2292, 0.1604, 0.2625],
[0.1271, 0.2802, 0.1604, 0.3135],
[0.1271, 0.3312, 0.1604, 0.3635],
[0.1271, 0.3812, 0.1604, 0.4146],
[0.1271, 0.4323, 0.1604, 0.4656],
[0.1271, 0.4833, 0.1604, 0.5167],
[0.1271, 0.5344, 0.1604, 0.5677],
[0.1271, 0.5854, 0.1604, 0.6188],
[0.1271, 0.6365, 0.1604, 0.6698],
[0.1271, 0.6865, 0.1604, 0.7198],
[0.1271, 0.7375, 0.1604, 0.7708],
[0.1271, 0.7885, 0.1604, 0.8219],
[0.1271, 0.8396, 0.1604, 0.8729],
[0.1271, 0.8906, 0.1604, 0.924],
[0.1271, 0.9417, 0.1604, 0.975],
[0.1781, 0.025, 0.2115, 0.0583],
[0.1781, 0.076, 0.2115, 0.1094],
[0.1781, 0.1271, 0.2115, 0.1604],
[0.1781, 0.1781, 0.2115, 0.2115],
[0.1781, 0.2292, 0.2115, 0.2625],
[0.1781, 0.2802, 0.2115, 0.3135],
[0.1781, 0.6865, 0.2115, 0.7198],
[0.1781, 0.7375, 0.2115, 0.7708],
[0.1781, 0.7885, 0.2115, 0.8219],
[0.1781, 0.8396, 0.2115, 0.8729],
[0.1781, 0.8906, 0.2115, 0.924],
[0.1781, 0.9417, 0.2115, 0.975],
[0.2292, 0.025, 0.2625, 0.0583],
[0.2292, 0.076, 0.2625, 0.1094],
[0.2292, 0.1271, 0.2625, 0.1604],
[0.2292, 0.1781, 0.2625, 0.2115],
[0.2292, 0.2292, 0.2625, 0.2625],
[0.2292, 0.7375, 0.2625, 0.7708],
[0.2292, 0.7885, 0.2625, 0.8219],
[0.2292, 0.8396, 0.2625, 0.8729],
[0.2292, 0.8906, 0.2625, 0.924],
[0.2292, 0.9417, 0.2625, 0.975],
[0.2802, 0.025, 0.3135, 0.0583],
[0.2802, 0.076, 0.3135, 0.1094],
[0.2802, 0.1271, 0.3135, 0.1604],
[0.2802, 0.1781, 0.3135, 0.2115],
[0.2802, 0.7885, 0.3135, 0.8219],
[0.2802, 0.8396, 0.3135, 0.8729],
[0.2802, 0.8906, 0.3135, 0.924],
[0.2802, 0.9417, 0.3135, 0.975],
[0.3302, 0.025, 0.3635, 0.0583],
[0.3302, 0.076, 0.3635, 0.1094],
[0.3302, 0.1271, 0.3635, 0.1604],
[0.3302, 0.8396, 0.3635, 0.8729],
[0.3302, 0.8906, 0.3635, 0.924],
[0.3302, 0.9417, 0.3635, 0.975],
[0.3812, 0.025, 0.4146, 0.0583],
[0.3812, 0.076, 0.4146, 0.1094],
[0.3812, 0.1271, 0.4146, 0.1604],
[0.3812, 0.8396, 0.4146, 0.8729],
[0.3812, 0.8906, 0.4146, 0.924],
[0.3812, 0.9417, 0.4146, 0.975],
[0.4323, 0.025, 0.4656, 0.0583],
[0.4323, 0.076, 0.4656, 0.1094],
[0.4323, 0.1271, 0.4656, 0.1604],
[0.4323, 0.8396, 0.4656, 0.8729],
[0.4323, 0.8906, 0.4656, 0.924],
[0.4323, 0.9417, 0.4656, 0.975],
[0.4833, 0.025, 0.5167, 0.0583],
[0.4833, 0.076, 0.5167, 0.1094],
[0.4833, 0.1271, 0.5167, 0.1604],
[0.4833, 0.8396, 0.5167, 0.8729],
[0.4833, 0.8906, 0.5167, 0.924],
[0.4833, 0.9417, 0.5167, 0.975],
[0.5344, 0.025, 0.5677, 0.0583],
[0.5344, 0.076, 0.5677, 0.1094],
[0.5344, 0.1271, 0.5677, 0.1604],
[0.5344, 0.8396, 0.5677, 0.8729],
[0.5344, 0.8906, 0.5677, 0.924],
[0.5344, 0.9417, 0.5677, 0.975],
[0.5854, 0.025, 0.6188, 0.0583],
[0.5854, 0.076, 0.6188, 0.1094],
[0.5854, 0.1271, 0.6188, 0.1604],
[0.5854, 0.8396, 0.6188, 0.8729],
[0.5854, 0.8906, 0.6188, 0.924],
[0.5854, 0.9417, 0.6188, 0.975],
[0.6365, 0.025, 0.6698, 0.0583],
[0.6365, 0.076, 0.6698, 0.1094],
[0.6365, 0.1271, 0.6698, 0.1604],
[0.6365, 0.8396, 0.6698, 0.8729],
[0.6365, 0.8906, 0.6698, 0.924],
[0.6365, 0.9417, 0.6698, 0.975],
[0.6865, 0.025, 0.7198, 0.0583],
[0.6865, 0.076, 0.7198, 0.1094],
[0.6865, 0.1271, 0.7198, 0.1604],
[0.6865, 0.1781, 0.7198, 0.2115],
[0.6865, 0.7885, 0.7198, 0.8219],
[0.6865, 0.8396, 0.7198, 0.8729],
[0.6865, 0.8906, 0.7198, 0.924],
[0.6865, 0.9417, 0.7198, 0.975],
[0.7375, 0.025, 0.7708, 0.0583],
[0.7375, 0.076, 0.7708, 0.1094],
[0.7375, 0.1271, 0.7708, 0.1604],
[0.7375, 0.1781, 0.7708, 0.2115],
[0.7375, 0.2292, 0.7708, 0.2625],
[0.7375, 0.7375, 0.7708, 0.7708],
[0.7375, 0.7885, 0.7708, 0.8219],
[0.7375, 0.8396, 0.7708, 0.8729],
[0.7375, 0.8906, 0.7708, 0.924],
[0.7375, 0.9417, 0.7708, 0.975],
[0.7885, 0.025, 0.8219, 0.0583],
[0.7885, 0.076, 0.8219, 0.1094],
[0.7885, 0.1271, 0.8219, 0.1604],
[0.7885, 0.1781, 0.8219, 0.2115],
[0.7885, 0.2292, 0.8219, 0.2625],
[0.7885, 0.2802, 0.8219, 0.3135],
[0.7885, 0.6865, 0.8219, 0.7198],
[0.7885, 0.7375, 0.8219, 0.7708],
[0.7885, 0.7885, 0.8219, 0.8219],
[0.7885, 0.8396, 0.8219, 0.8729],
[0.7885, 0.8906, 0.8219, 0.924],
[0.7885, 0.9417, 0.8219, 0.975],
[0.8396, 0.025, 0.8729, 0.0583],
[0.8396, 0.076, 0.8729, 0.1094],
[0.8396, 0.1271, 0.8729, 0.1604],
[0.8396, 0.1781, 0.8729, 0.2115],
[0.8396, 0.2292, 0.8729, 0.2625],
[0.8396, 0.2802, 0.8729, 0.3135],
[0.8396, 0.3312, 0.8729, 0.3635],
[0.8396, 0.3812, 0.8729, 0.4146],
[0.8396, 0.4323, 0.8729, 0.4656],
[0.8396, 0.4833, 0.8729, 0.5167],
[0.8396, 0.5344, 0.8729, 0.5677],
[0.8396, 0.5854, 0.8729, 0.6188],
[0.8396, 0.6365, 0.8729, 0.6698],
[0.8396, 0.6865, 0.8729, 0.7198],
[0.8396, 0.7375, 0.8729, 0.7708],
[0.8396, 0.7885, 0.8729, 0.8219],
[0.8396, 0.8396, 0.8729, 0.8729],
[0.8396, 0.8906, 0.8729, 0.924],
[0.8396, 0.9417, 0.8729, 0.975],
[0.8906, 0.025, 0.924, 0.0583],
[0.8906, 0.076, 0.924, 0.1094],
[0.8906, 0.1271, 0.924, 0.1604],
[0.8906, 0.1781, 0.924, 0.2115],
[0.8906, 0.2292, 0.924, 0.2625],
[0.8906, 0.2802, 0.924, 0.3135],
[0.8906, 0.3302, 0.924, 0.3635],
[0.8906, 0.3812, 0.924, 0.4146],
[0.8906, 0.4323, 0.924, 0.4656],
[0.8906, 0.4833, 0.924, 0.5167],
[0.8906, 0.5344, 0.924, 0.5677],
[0.8906, 0.5854, 0.924, 0.6188],
[0.8906, 0.6365, 0.924, 0.6698],
[0.8906, 0.6865, 0.924, 0.7198],
[0.8906, 0.7375, 0.924, 0.7708],
[0.8906, 0.7885, 0.924, 0.8219],
[0.8906, 0.8396, 0.924, 0.8729],
[0.8906, 0.8906, 0.924, 0.924],
[0.8906, 0.9417, 0.924, 0.975],
[0.9417, 0.076, 0.975, 0.1094],
[0.9417, 0.1271, 0.975, 0.1604],
[0.9417, 0.1781, 0.975, 0.2115],
[0.9417, 0.2292, 0.975, 0.2625],
[0.9417, 0.2802, 0.975, 0.3135],
[0.9417, 0.3302, 0.975, 0.3635],
[0.9417, 0.3812, 0.975, 0.4146],
[0.9417, 0.4323, 0.975, 0.4656],
[0.9417, 0.4833, 0.975, 0.5167],
[0.9417, 0.5344, 0.975, 0.5677],
[0.9417, 0.5854, 0.975, 0.6188],
[0.9417, 0.6365, 0.975, 0.6698],
[0.9417, 0.6865, 0.975, 0.7198],
[0.9417, 0.7375, 0.975, 0.7708],
[0.9417, 0.7885, 0.975, 0.8219],
[0.9417, 0.8396, 0.975, 0.8729],
[0.9417, 0.8906, 0.975, 0.924],
]
def color_crop(im, color=[(250, 255), (245, 255), (0, 10)]):
"""Crop an image to the min and max occurances of a color range.
Accepts an image and list of colors (channels and colors must match).
"""
assert len(color) == im.shape[-1]
binary = np.ones(im.shape[:-1], dtype=bool)
for i in range(3):
binary &= (im[:,:,i] >= color[i][0]) & (im[:,:,i] <= color[i][1])
labeled = label(binary, background=0)
rp = regionprops(labeled)
bbox = max(rp, key=lambda x: x.bbox_area).bbox
return im[bbox[0]:bbox[2], bbox[1]:bbox[3], :]
def _get_points(image):
im = rgb2gray(image)
t = threshold_otsu(im)
return im > t
def get_bbox_and_centers(im, color=[(250, 255), (245, 255), (0, 10)]):
im = color_crop(im, color=color)
p = _get_points(im)
l = label(p, background=1)
rp = regionprops(l)
(maxx, maxy) = l.shape
max_size = l.shape[0] * l.shape[1] // 500
min_size = l.shape[0] * l.shape[1] // 2000
bbs = []
centers = []
for props in rp:
minr, minc, maxr, maxc = props.bbox
if props.bbox_area < max_size and props.bbox_area > min_size:
bbs.append((props.bbox[0] / maxx, props.bbox[1] / maxx, props.bbox[2] / maxx, props.bbox[3] / maxx))
centers.append(
(
(((minr / maxy) + (maxr / maxy)) / 2),
(((minc / maxx) + (maxc / maxx)) / 2)
),
)
return bbs, centers
def get_numbers(centers, bbs):
points = []
for i, b in enumerate(bbs):
for x, y in centers:
if x > b[0] and x < b[2] and y > b[1] and y < b[3]:
points.append(i)
return points
def img_to_points(filepath, check_counts=False):
im = io.imread(filepath)
color = [(250, 255), (245, 255), (0, 10)]
# convert to 3 channel
if im.shape[-1] == 4:
im = rgba2rgb(im)
# convert to uint8 (0-255)
im = img_as_ubyte(im)
try:
_, centers = get_bbox_and_centers(im, color)
except:
try:
# for some reason, some of the colors are off
# when the images are read in
color = [(250, 255), (245, 255), (75, 90)]
_, centers = get_bbox_and_centers(im, color)
except Exception as e:
raise e
if check_counts and (len(centers) < 50 or len(centers) > 120):
raise Exception("Too many or too few points")
return get_numbers(centers, ALL_BBOX)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("filepath", type=str)
parser.add_argument("--check_counts", action="store_true")
args = parser.parse_args()
print(img_to_points(args.filepath, args.check_counts))

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boonepeter.github.io-code/snapcodes/src/img_to_svg.py
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import argparse
from img_to_points import img_to_points
from points_to_svg import points_to_svg
def img_to_svg(filepath, output_filepath):
"""
Converts an image to an SVG file.
"""
points = img_to_points(filepath, True)
print(points)
svg = points_to_svg(points)
with open(output_filepath, "w") as f:
f.write(svg)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Convert an image to an SVG file.")
parser.add_argument("-i", "--input", help="Input filepath", required=True)
parser.add_argument("-o", "--output", help="Output filepath", required=True)
args = parser.parse_args()
img_to_svg(args.input, args.output)

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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<svg xmlns:xlink="http://www.w3.org/1999/xlink" height="1024" version="1.1" viewBox="0 0 1024 1024" width="1024" xmlns="http://www.w3.org/2000/svg">
<path d=" M749.88,641.75 C763.31,649.46,754.85,644.24,749.88,641.75 M0,860.16 C0,950.64,73.36,1024,163.84,1024 L860.16,1024 C950.64,1024,1024,950.64,1024,860.16 L1024,163.84 C1024,73.36,950.64,0,860.16,0 L163.84,0 C73.36,0,0,73.36,0,163.84 L0,860.16" fill="#000" fill-rule="evenodd"/>
<path d="M32,163.84 C32,91.03,91.03,32,163.84,32 L860.16,32 C932.97,32,992,91.03,992,163.84 L992,860.16 C992,932.97,932.97,992,860.16,992 L163.84,992 C91.03,992,32,932.97,32,860.16 L32,163.84
M170.10500000000002,56.42
A16,16,0,0,0,170.10500000000002,88.42
A16,16,0,0,0,170.10500000000002,56.42
M218.9475,56.42
A16,16,0,0,0,218.9475,88.42
A16,16,0,0,0,218.9475,56.42
M267.79,56.42
A16,16,0,0,0,267.79,88.42
A16,16,0,0,0,267.79,56.42
M316.6325,56.42
A16,16,0,0,0,316.6325,88.42
A16,16,0,0,0,316.6325,56.42
M414.31750000000005,56.42
A16,16,0,0,0,414.31750000000005,88.42
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" fill="#FFFC00"/>
</svg>
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78
boonepeter.github.io-code/snapcodes/src/points_to_svg.py
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@@ -1,78 +0,0 @@
import argparse
positions = [
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
[0, 1, 2, 3, 4, 5, 13, 14, 15, 16, 17, 18],
[0, 1, 2, 3, 4, 14, 15, 16, 17, 18],
[0, 1, 2, 3, 15, 16, 17, 18],
[0, 1, 2, 16, 17, 18],
[0, 1, 2, 16, 17, 18],
[0, 1, 2, 16, 17, 18],
[0, 1, 2, 16, 17, 18],
[0, 1, 2, 16, 17, 18],
[0, 1, 2, 16, 17, 18],
[0, 1, 2, 16, 17, 18],
[0, 1, 2, 3, 15, 16, 17, 18],
[0, 1, 2, 3, 4, 14, 15, 16, 17, 18],
[0, 1, 2, 3, 4, 5, 13, 14, 15, 16, 17, 18],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
]
BORDER = 56.42
BORDER_C = 72.42
MULT = 48.8425
DIAMETER = 32
SVG = '''<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<svg xmlns:xlink="http://www.w3.org/1999/xlink" height="1024" version="1.1" viewBox="0 0 1024 1024" width="1024" xmlns="http://www.w3.org/2000/svg">
<path d=" M749.88,641.75 C763.31,649.46,754.85,644.24,749.88,641.75 M0,860.16 C0,950.64,73.36,1024,163.84,1024 L860.16,1024 C950.64,1024,1024,950.64,1024,860.16 L1024,163.84 C1024,73.36,950.64,0,860.16,0 L163.84,0 C73.36,0,0,73.36,0,163.84 L0,860.16" fill="#000" fill-rule="evenodd"/>
<path d="M32,163.84 C32,91.03,91.03,32,163.84,32 L860.16,32 C932.97,32,992,91.03,992,163.84 L992,860.16 C992,932.97,932.97,992,860.16,992 L163.84,992 C91.03,992,32,932.97,32,860.16 L32,163.84
{path}" fill="#FFFC00"/>
</svg>
'''
def points_to_svg(points):
counter = 0
path = ""
points.sort()
for r in range(len(positions)):
for c in range(len(positions[r])):
col = positions[r][c]
if len(points) == 0:
break
if points[0] == counter:
path += f"""M{col * MULT + BORDER_C},{r * MULT + BORDER}
A16,16,0,0,0,{col * MULT + BORDER_C},{r * MULT + BORDER + DIAMETER}
A16,16,0,0,0,{col * MULT + BORDER_C},{r * MULT + BORDER}
"""
points.pop(0)
counter += 1
return SVG.format(path=path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--points", type=str, help="Points to draw")
parser.add_argument("--output", type=str, help="Output file")
parser.add_argument("--show", action="store_true", help="Show SVG")
parser.set_defaults(points=",".join([str(i) for i in range(212)]))
args = parser.parse_args()
points = args.points.split(",")
points = [int(i) for i in points]
svg = points_to_svg(points)
if args.output:
with open(args.output, "w") as f:
f.write(svg)
if args.show:
import subprocess, os, platform
if platform.system() == 'Darwin': # macOS
subprocess.call(('open', args.output))
elif platform.system() == 'Windows': # Windows
os.startfile(args.output)
else: # linux variants
subprocess.call(('xdg-open', args.output))
if not args.show or not args.output:
print(svg)

15
boonepeter.github.io-code/snapcodes/src/process_all.py
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@@ -1,15 +0,0 @@
import os
from img_to_svg import img_to_svg
files = os.listdir("./imgs/twitter")
files = [os.path.join("./imgs/twitter", f) for f in files if f.endswith(".jpeg")]
for f in files:
output_path = f.replace(".jpeg", ".svg")
try:
img_to_svg(f, output_path)
except Exception as e:
print(f"Error processing {f}")
print(e)

BIN
boonepeter.github.io-code/snapcodes/src/snapcode.png
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1
boonepeter.github.io-code/spotify-codes-part-2/src/decode_barcode.py
View File

@@ -17,7 +17,6 @@ if __name__ == "__main__":
filename = args.filename
token = args.token
heights = get_heights(filename)
print(f"Heights: {heights}")
# drop the fist, last, and 12th bar
heights = heights[1:11] + heights[12:-1]
decoded = spotify_bar_decode(heights)

105
boonepeter.github.io-code/spotify-codes-part-2/src/get_heights.py
View File

@@ -1,78 +1,45 @@
from skimage import io
from skimage.color import rgb2gray
from skimage.filters import threshold_otsu
from skimage.measure import label, regionprops
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from skimage import io
from skimage.color import rgb2gray
from skimage.filters import threshold_otsu, gaussian
from skimage.morphology import closing, square
from skimage.measure import label, regionprops
import matplotlib.pyplot as plt
from skimage.color import rgb2gray
import matplotlib.patches as patches
def get_heights(filename: str) -> list:
"""Open an image and return a list of the bar heights.
Also saves three images: the processed binary image, the original image with drawings, and a smoothed binary image.
"""
from sklearn.cluster import KMeans
def get_heights(filename: str):
image = io.imread(filename)
im = rgb2gray(image)
binary_im = im > threshold_otsu(im)
smooth_im = gaussian(binary_im.astype(float), sigma=1)
morph_im = closing(smooth_im > 0.5, square(3))
# Apply Gaussian smoothing to binary image to make bars look rounder
smooth_im = gaussian(binary_im, sigma=2)
# Threshold again to obtain a smoothed binary
smooth_binary = smooth_im > 0.5
labeled = label(smooth_binary)
bar_dimensions = [r.bbox for r in regionprops(labeled)]
bar_dimensions.sort(key=lambda x: x[1]) # Sort left to right
# The first object (spotify logo) is the max height of the bars
logo = bar_dimensions[0]
max_height = logo[2] - logo[0]
sequence = []
# Create figure and axis for drawing
fig, ax = plt.subplots()
ax.imshow(smooth_binary, cmap='gray')
# Draw rectangle around logo
logo_rect = patches.Rectangle(
(logo[1], logo[0]),
logo[3]-logo[1],
logo[2]-logo[0],
linewidth=2,
edgecolor='yellow',
facecolor='none'
)
ax.add_patch(logo_rect)
# Add 'Logo' text near the rectangle
ax.text(logo[1], logo[0] - 10, 'Logo', color='yellow', fontsize=12, weight='bold')
# Draw bars and center markers
for bar in bar_dimensions[1:]:
height = bar[2] - bar[0]
ratio = height / max_height
ratio *= 8
ratio //= 1
val = int(ratio - 1)
sequence.append(val)
# Draw rectangle around bar
rect = patches.Rectangle(
(bar[1], bar[0]),
bar[3]-bar[1],
bar[2]-bar[0],
linewidth=1,
edgecolor='red',
facecolor='none'
)
ax.add_patch(rect)
# Draw center marker (white circle)
center_x = (bar[1] + bar[3]) / 2
center_y = (bar[0] + bar[2]) / 2
center_marker = patches.Circle((center_x, center_y), radius=5, color='black')
ax.add_patch(center_marker)
# Save processed binary image used for calculations
plt.imsave('processed_binary_image.png', binary_im, cmap='gray')
# Save smoothed binary image
plt.imsave('img_smooth.png', smooth_binary, cmap='gray')
# Save image with drawings
plt.axis('off')
plt.savefig('image_with_drawings.png', bbox_inches='tight', pad_inches=0)
plt.close()
return sequence
labeled = label(morph_im)
bar_dims = [r.bbox for r in regionprops(labeled)]
bar_dims.sort(key=lambda x: x[1]) # left to right
bars = bar_dims[1:] # skip logo
bar_heights_raw = []
for bar in bars:
top, left, bottom, right = bar
effective_height = bottom - top # use bounding box height directly
bar_heights_raw.append(effective_height)
print(len(bars))
# Cluster measured heights to 8 clusters representing discrete bar levels
bar_heights_raw_np = np.array(bar_heights_raw).reshape(-1, 1)
kmeans = KMeans(n_clusters=8, random_state=0).fit(bar_heights_raw_np)
cluster_centers = np.sort(kmeans.cluster_centers_.flatten())
# Assign each bar height to closest cluster center index (0 to 7)
predicted_levels = []
for h in bar_heights_raw:
diffs = np.abs(cluster_centers - h)
closest_cluster = np.argmin(diffs)
predicted_levels.append(closest_cluster)
print(len(predicted_levels))
return predicted_levels

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boonepeter.github.io-code/spotify-codes/calc_crc8.py
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import crc8
hash = crc8.crc8()
media_ref = 67775490487
ref_bytes = media_ref.to_bytes(5, byteorder="big")
print(ref_bytes)
# b'\x0f\xc7\xbb\xe9\xb7'
hash.update(ref_bytes)
check_bits = hash.digest()
print(check_bits)
# b'\x0c'

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boonepeter.github.io-code/spotify-codes/get_heights.py
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from skimage import io
from skimage.measure import label, regionprops
from skimage.filters import threshold_otsu
from skimage.color import rgb2gray
from skimage import io
from skimage.color import rgb2gray
from skimage.filters import threshold_sauvola
from skimage.exposure import equalize_adapthist
from skimage.measure import label, regionprops
from skimage.morphology import closing, square
def get_heights(filename: str) -> list:
"""Open an image and return a list of the bar heights."""
# Load image and convert to grayscale
image = io.imread(filename)
im = rgb2gray(image)
# Apply adaptive histogram equalization to enhance contrast
im_eq = equalize_adapthist(im, clip_limit=0.03)
# Adaptive thresholding using Sauvola method
window_size = 25 # Adjust window size if needed
thresh_sauvola = threshold_sauvola(im_eq, window_size=window_size)
binary_im = im_eq > thresh_sauvola
# Morphological closing to fill gaps and clean noise
binary_im = closing(binary_im, square(3))
# Label connected components
labeled = label(binary_im)
# Get bounding boxes for each detected object
bar_dimensions = [r.bbox for r in regionprops(labeled)]
# Sort bounding boxes by horizontal position (xmin)
bar_dimensions.sort(key=lambda x: x[1])
# Use the first detected object (logo) as max height reference
logo = bar_dimensions[0]
max_height = logo[2] - logo[0]
sequence = []
for bar in bar_dimensions[1:]:
height = bar[2] - bar[0]
ratio = height / max_height
ratio *= 8
ratio = int(ratio) # truncate decimal part
sequence.append(ratio - 1)
return sequence