def load_crops(dataset, nb_crops_max, nb_augmentations):
X = []
y = []
examples = get_crops_with_labels(
dataset,
nb_crops_max,
nb_augmentations,
nb_crops_per_image=NB_CROPS_PER_IMAGE,
model_image_height=MODEL_IMAGE_HEIGHT,
model_image_width=MODEL_IMAGE_WIDTH,
crop_height=CROP_HEIGHT,
crop_width=CROP_WIDTH,
drop_nonface_prob=0.5,
)
for i, (crop, face_factor) in enumerate(examples):
if i % 100 == 0:
print("Crop %d of %d" % (i + 1, nb_crops_max))
X.append(crop)
y.append(face_factor)
# all entries in X/Y same length
assert len(set([len(row) for row in X])) == 1
X = np.array(X, dtype=np.float32)
y = np.array(y, dtype=np.float32)
return X, y
def load_xy(dataset, nb_crops_max, nb_augmentations):
X = []
y = []
examples = get_crops_with_labels(dataset, nb_crops_max, nb_augmentations,
nb_crops_per_image=NB_CROPS_PER_IMAGE,
model_image_height=MODEL_IMAGE_HEIGHT,
model_image_width=MODEL_IMAGE_WIDTH,
crop_height=CROP_HEIGHT, crop_width=CROP_WIDTH)
for i, (crop, face_factor) in enumerate(examples):
if i % 100 == 0:
print("Crop %d of %d" % (i+1, nb_crops_max))
distances = [1, 2, 3, 4, 5, 7, 11]
angles = [0*np.pi, 0.25*np.pi, 0.5*np.pi, 0.75*np.pi, 1.0*np.pi, 1.25*np.pi, 1.5*np.pi, 1.75*np.pi]
glcm = greycomatrix(crop, distances, angles, 256, symmetric=True, normed=True)
dissimilarities = greycoprops(glcm, 'dissimilarity')
energies = greycoprops(glcm, 'energy')
correlations = greycoprops(glcm, 'correlation')
nb_matrices = len(distances) * len(angles)
all_values = np.zeros((3*nb_matrices))
all_values[0:nb_matrices] = dissimilarities.flatten()
all_values[nb_matrices:nb_matrices*2] = energies.flatten()
all_values[nb_matrices*2:nb_matrices*3] = correlations.flatten()
is_cat = True if face_factor >= CAT_FRACTION_THRESHOLD else False
#if is_cat or random.random() < 0.25:
X.append(all_values)
y.append(1 if is_cat else 0)
# all entries in X/Y same length
assert len(set([len(row) for row in X])) == 1
#assert len(set([len(row) for row in y])) == 1
X = np.array(X, dtype=np.float32)
y = np.array(y, dtype=np.float32)
X = scale_linear_bycolumn(X)
return X, y
