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122 lines (83 loc) · 2.06 KB
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# image_processing.py
import base64
import io
import math
import numpy as np
from PIL import Image
from constants import (
NAMED_COLORS,
MAX_IMAGE_SIZE,
CENTER_SAMPLE_SIZE,
)
def color_distance(a, b):
return math.sqrt((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2 + (a[2] - b[2]) ** 2)
def nearest_color_name(rgb):
best_name = None
best_distance = float("inf")
for name, value in NAMED_COLORS:
d = color_distance(rgb, value)
if d < best_distance:
best_distance = d
best_name = name
return best_name
def resize_image(image: Image.Image):
image = image.convert("RGB")
w, h = image.size
scale = min(1, MAX_IMAGE_SIZE / max(w, h))
new_size = (
int(w * scale),
int(h * scale),
)
return image.resize(new_size, Image.Resampling.LANCZOS)
def center_crop(image):
image = image.convert("RGB")
w, h = image.size
size = min(w, h)
left = (w - size) // 2
top = (h - size) // 2
return image.crop(
(
left,
top,
left + size,
top + size,
)
)
def dominant_color(image):
image = center_crop(image)
image = image.resize(
(
CENTER_SAMPLE_SIZE,
CENTER_SAMPLE_SIZE,
),
Image.Resampling.LANCZOS,
)
arr = np.array(image)
r = np.median(arr[:, :, 0])
g = np.median(arr[:, :, 1])
b = np.median(arr[:, :, 2])
rgb = (
int(r),
int(g),
int(b),
)
return rgb, nearest_color_name(rgb)
def image_to_base64(image):
buffer = io.BytesIO()
image.save(
buffer,
format="JPEG",
quality=85,
)
return base64.b64encode(buffer.getvalue()).decode("utf-8")
def prepare_image(uploaded_file):
image = Image.open(uploaded_file)
resized = resize_image(image)
rgb, color_name = dominant_color(resized)
encoded = image_to_base64(resized)
return {
"image": resized,
"rgb": rgb,
"color_name": color_name,
"base64": encoded,
}