blob_doh skimage.feature.blob_doh(image, min_sigma=1, max_sigma=30, num_sigma=10, threshold=0.01, overlap=0.5, log_scale=False)

inverse skimage.filters.inverse(data, impulse_response=None, filter_params={}, max_gain=2, predefined_filter=None)

Module: novice

blob_dog skimage.feature.blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0, overlap=0.5)

RAG class skimage.future.graph.RAG(label_image=None, connectivity=1, data=None, **attr)

cut_normalized skimage.future.graph.cut_normalized(labels, rag, thresh=0.001, num_cuts=10, in_place=True, max_edge=1.0)

ORB class skimage.feature.ORB(downscale=1.2, n_scales=8, n_keypoints=500, fast_n=9, fast_threshold=0.08, harris_k=0.04)

label skimage.measure.label(input, neighbors=None, background=None, return_num=False, connectivity=None)

How to parallelize loops In image processing, we frequently apply the same algorithm on a large batch of images. In this paragraph, we propose to use

resize skimage.transform.resize(image, output_shape, order=1, mode='constant', cval=0, clip=True, preserve_range=False)