print('No display found. Using non-interactive Agg backend.') matplotlib.use('Agg') import numpy as np import pandas as pd from sklearn import metrics import matplotlib.pyplot as plt sys.path += [os.path.abspath('.'), os.path.abspath('..')] # Add path to root import imsegm.utilities.experiments as tl_expt import imsegm.utilities.data_io as tl_data import imsegm.utilities.drawing as tl_visu import imsegm.labeling as seg_lbs EXPORT_VUSIALISATION = False NB_WORKERS = tl_expt.nb_workers(0.9) NAME_DIR_VISUAL_1 = 'ALL_visualisation-1' NAME_DIR_VISUAL_2 = 'ALL_visualisation-2' NAME_DIR_VISUAL_3 = 'ALL_visualisation-3' SKIP_DIRS = [ 'input', 'simple', NAME_DIR_VISUAL_1, NAME_DIR_VISUAL_2, NAME_DIR_VISUAL_3 ] NAME_CSV_STAT = 'segmented-eggs_%s.csv' PATH_IMAGES = tl_data.update_path( os.path.join('data-images', 'drosophila_ovary_slice')) PATH_RESULTS = tl_data.update_path('results', absolute=True) PATHS = { 'images': os.path.join(PATH_IMAGES, 'image', '*.jpg'), 'annots': os.path.join(PATH_IMAGES, 'annot_eggs', '*.png'), 'segments': os.path.join(PATH_IMAGES, 'segm', '*.png'),
from imsegm.labeling import histogram_regions_labels_norm from imsegm.superpixels import segment_slic_img2d, segment_slic_img3d_gray from imsegm.utilities.experiments import WrapExecuteSequence, nb_workers # from sklearn import mixture #: select basic features extracted from superpixels FTS_SET_SIMPLE = FEATURES_SET_COLOR #: select default Classifier for supervised segmentation CLASSIF_NAME = DEFAULT_CLASSIF_NAME #: select default Modeling/clustering for unsupervised segmentation CLUSTER_METHOD = DEFAULT_CLUSTERING #: define how many images will be left out during cross-validation training CROSS_VAL_LEAVE_OUT = 2 #: default number of workers NB_WORKERS = nb_workers(0.6) def pipe_color2d_slic_features_model_graphcut(image, nb_classes, dict_features, sp_size=30, sp_regul=0.2, pca_coef=None, use_scaler=True, estim_model='GMM', gc_regul=1., gc_edge_type='model', debug_visual=None): """ complete pipe-line for segmentation using superpixels, extracting features and graphCut segmentation