-
Notifications
You must be signed in to change notification settings - Fork 0
/
image_processing.py
70 lines (63 loc) · 2.13 KB
/
image_processing.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
"""Module for processing images and colors"""
from io import BytesIO
from functools import cache
from numpy import (
sum as numpy_sum,
absolute,
amax,
ndarray,
array,
bincount,
count_nonzero,
asarray,
uint32,
zeros
)
from PIL import Image, ImageFilter
from skimage.measure import label, regionprops
from spotify_api import public_get as client_get
@cache
def get_image_from_url(url: str) -> Image:
"""Gets an image from a URL and converts it to rgb"""
response_data = client_get(url, timeout=5)
pil_image: Image = Image.open(BytesIO(response_data.content), mode="r").convert("RGB")
return pil_image
def blob_extract(mac_image: ndarray) -> tuple[int, ndarray]:
"""Extracts blobs from a quantized image"""
blob: ndarray = label(mac_image, connectivity=2) + 1
n_blobs: int = amax(blob)
if n_blobs > 1:
count: ndarray = bincount(blob.ravel(), minlength=n_blobs + 1)[2:]
n_blobs += count_nonzero(count > 1)
return n_blobs, blob
def ccv(image_url: str, size=32, blur=2, quantized_level=16) -> tuple:
"""Calculates the Color Coherence Vector of an image"""
image: Image = get_image_from_url(image_url)
image = asarray(
image.resize(
(size, size),
Image.LANCZOS,
)
.filter(ImageFilter.GaussianBlur(radius=blur))
.convert("P", palette=Image.ADAPTIVE, colors=quantized_level),
dtype=uint32,
)
size_threshold = round(0.01 * size * size)
blob = label(image, connectivity=2) + 1
blobs = regionprops(blob)
ccv = zeros((quantized_level, 2), dtype=uint32)
for b in blobs:
size = b.area
location = b.coords[0][0]
ccv[image[location][0]][
int(size <= size_threshold)
] += size
ccv = tuple(tuple(x) for x in ccv)
return ccv
@cache
def ccv_distance(ccv_one: tuple, ccv_two: tuple) -> float:
"""Calculates the distance between two CCV vectors"""
ccv_one, ccv_two = array(ccv_one), array(ccv_two)
return numpy_sum(
[absolute(ccv_one[:, 0] - ccv_two[:, 0]) + absolute(ccv_one[:, 1] - ccv_two[:, 1])]
)