def resize_image(img, crop=None, scale=None):
resolution = get_resolution(img)
if crop:
img = crop_image(img, crop[0], crop[1])
if scale:
img = scale_image(img, scale[0], scale[1])
return img, resolution
def hog_features_at_scale(image, scale):
image = scale_image(image, scale)
image = image[400 // scale:, 720 // scale:, ]
hog_channel_1 = np.array(
hog_features_for_channel(image, 0, feature_vector=False))
hog_channel_2 = np.array(
hog_features_for_channel(image, 1, feature_vector=False))
hog_channel_3 = np.array(
hog_features_for_channel(image, 2, feature_vector=False))
return hog_channel_1, hog_channel_2, hog_channel_3
def test_kmedoids(pixels: np.ndarray, k: int) -> Tuple[np.ndarray, np.ndarray]:
"""
Convenience wrapper for GATech grading.
:param pixels: A numpy array of image data
:param k: Number of clusters to use
:return: A tuple of classes and centroids
"""
scaled_data = scale_image(pixels)
kmedoids = KMedoids(k=k)
kmedoids.fit(scaled_data, verbose=0)
return kmedoids.clusters, rescale_image(kmedoids.centroids)