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3bce187 | 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 | import numpy as np
from PIL import Image
from skimage.feature import hog
from skimage.color import rgb2gray
class FeatureExtractor:
"""
Extract features from images.
Current methods accept either file paths or numpy arrays representing images.
Provides HOG and color histogram features to extract a concatenated feature vector.
"""
def __init__(self, resize=None, hog_params=None, hist_bins=32):
# resize: (width, height) or None
self.resize = resize
self.hist_bins = hist_bins
self.hog_params = {} if hog_params is None else hog_params
def _load_image(self, img):
if isinstance(img, str):
im = Image.open(img).convert('RGB')
if self.resize:
im = im.resize(self.resize)
return np.asarray(im)
elif hasattr(img, 'shape'):
# numpy array must be either HxWxC or HxW
im = img
if self.resize:
im = np.asarray(Image.fromarray(im).resize(self.resize))
if im.ndim == 2:
# grayscale to RGB
im = np.stack([im, im, im], axis=-1)
return im
else:
raise ValueError('Unsupported image input type')
def extract_hog(self, img):
im = self._load_image(img)
gray = rgb2gray(im)
features = hog(gray, **self.hog_params)
return features
def color_histogram(self, img, bins=None, normalize=True):
im = self._load_image(img)
bins = self.hist_bins if bins is None else bins
chans = []
for c in range(3):
h, _ = np.histogram(im[:, :, c], bins=bins, range=(0, 255))
chans.append(h.astype(np.float32))
feat = np.concatenate(chans)
if normalize:
s = feat.sum()
if s > 0:
feat = feat / s
return feat
def extract(self, img, which=('hog', 'hist')):
pieces = []
if 'hog' in which:
pieces.append(self.extract_hog(img))
if 'hist' in which:
pieces.append(self.color_histogram(img))
if not pieces:
raise ValueError('No feature types requested')
return np.concatenate(pieces)
def extract_from_list(self, images, which=('hog', 'hist'), verbose=False):
"""images: list of file paths or numpy arrays. Returns numpy array of features."""
feats = []
for i, im in enumerate(images):
feats.append(self.extract(im, which=which))
if verbose and (i + 1) % 100 == 0:
print(f"Extracted features from {i+1} images")
return np.vstack(feats)
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