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5d925b4 0241c3f 5d925b4 e22760d 5d925b4 e22760d 5d925b4 e22760d 5d925b4 e22760d 5d925b4 e22760d 5d925b4 e22760d 5d925b4 e22760d 5d925b4 e22760d 5d925b4 | 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 71 72 73 74 75 76 77 78 79 80 81 82 | import numpy as np
from PIL import Image
COLOR_IMAGE_SIZE = 128
COLOR_FEATURE_DIM = 37
def extract_color_features(image, image_size=COLOR_IMAGE_SIZE):
image = image.convert("RGB").resize((image_size, image_size))
margin = int(image_size * 0.10)
image = image.crop(
(
margin,
margin,
image_size - margin,
image_size - margin,
)
)
rgb = np.asarray(image, dtype=np.float32) / 255.0
hsv = np.asarray(image.convert("HSV"), dtype=np.float32) / 255.0
rgb_flat = rgb.reshape(-1, 3)
hsv_flat = hsv.reshape(-1, 3)
saturation = hsv_flat[:, 1]
value = hsv_flat[:, 2]
foreground_mask = (saturation > 0.08) | (value < 0.92)
if foreground_mask.sum() < 256:
foreground_mask = np.ones(len(hsv_flat), dtype=bool)
selected_rgb = rgb_flat[foreground_mask]
selected_hsv = hsv_flat[foreground_mask]
hue_hist, _ = np.histogram(
selected_hsv[:, 0],
bins=12,
range=(0.0, 1.0),
)
saturation_hist, _ = np.histogram(
selected_hsv[:, 1],
bins=8,
range=(0.0, 1.0),
)
value_hist, _ = np.histogram(
selected_hsv[:, 2],
bins=8,
range=(0.0, 1.0),
)
hue_hist = hue_hist.astype(np.float32)
saturation_hist = saturation_hist.astype(np.float32)
value_hist = value_hist.astype(np.float32)
hue_hist /= max(hue_hist.sum(), 1.0)
saturation_hist /= max(saturation_hist.sum(), 1.0)
value_hist /= max(value_hist.sum(), 1.0)
rgb_mean = selected_rgb.mean(axis=0).astype(np.float32)
rgb_std = selected_rgb.std(axis=0).astype(np.float32)
rgb_median = np.median(selected_rgb, axis=0).astype(np.float32)
features = np.concatenate(
[
hue_hist,
saturation_hist,
value_hist,
rgb_mean,
rgb_std,
rgb_median,
]
).astype(np.float32)
if features.shape[0] != COLOR_FEATURE_DIM:
raise ValueError(
f"Expected {COLOR_FEATURE_DIM} color features, "
f"got {features.shape[0]}"
)
return features |