def test_count_transitions_segment(self): img = self.img[:, :, 0] annot = self.annot.astype(int) slic = segment_slic_img2d(img, sp_size=15, relative_compact=0.2) label_hist = histogram_regions_labels_norm(slic, annot) labels = np.argmax(label_hist, axis=1) trans = count_label_transitions_connected_segments({'a': slic}, {'a': labels}) path_csv = os.path.join(PATH_OUTPUT, 'labels_transitions.csv') pd.DataFrame(trans).to_csv(path_csv) gc_regul = compute_pairwise_cost_from_transitions(trans, 10.) np.random.seed(0) features = np.tile(labels, (5, 1)).T.astype(float) features += np.random.random(features.shape) - 0.5 gmm = estim_class_model_gmm(features, 4) proba = gmm.predict_proba(features) segment_graph_cut_general(slic, proba, gc_regul)
def segment_image(imgs_idx_path, params, classif, path_out, path_visu=None, show_debug_imgs=SHOW_DEBUG_IMAGES): """ perform image segmentation on input image with given paramters and trained classifier, and save results :param (int, str) imgs_idx_path: :param {str: ...} params: segmentation parameters :param obj classif: trained classifier :param str path_out: path for output :param str path_visu: the existing patch means export also visualisation :param bool show_debug_imgs: whether show debug images :return (str, ndarray, ndarray): """ idx, path_img = parse_imgs_idx_path(imgs_idx_path) logging.debug('segmenting image: "%s"', path_img) idx_name = get_idx_name(idx, path_img) img = load_image(path_img, params['img_type']) debug_visual = dict() if show_debug_imgs else None gc_regul = params['gc_regul'] if params['gc_use_trans']: label_penalty = seg_gc.compute_pairwise_cost_from_transitions( params['label_transitions']) gc_regul = (gc_regul * label_penalty) segm_gc, segm_soft = seg_pipe.segment_color2d_slic_features_model_graphcut( img, classif, sp_size=params['slic_size'], sp_regul=params['slic_regul'], dict_features=params['features'], gc_regul=gc_regul, gc_edge_type=params['gc_edge_type'], debug_visual=debug_visual) segm_map = np.argmax(segm_soft, axis=-1) for segm, suffix in [(segm_gc, ''), (segm_map, '_MAP')]: path_img = os.path.join(path_out, idx_name + suffix + '.png') logging.debug('export segmentation: %s', path_img) if np.max(segm) <= 1: img_seg = Image.fromarray((segm * 255).astype(np.uint8)) else: img_seg = Image.fromarray(segm.astype(np.uint8)) img_seg.convert('L').save(path_img) # io.imsave(path_img, segm_gc) path_npz = os.path.join(path_out, idx_name + '.npz') np.savez_compressed(path_npz, segm_soft) # plt.imsave(os.path.join(path_out, idx_name + '_rgb.png'), seg_pipe) if params.get('visual', False) and path_visu is not None \ and os.path.isdir(path_visu): export_draw_image_segm_contour(img, segm_gc, path_visu, idx_name, '_GC') export_draw_image_segm_contour(img, segm_map, path_visu, idx_name, '_MAP') if show_debug_imgs and debug_visual is not None: path_fig = os.path.join(path_visu, str(idx_name) + '_debug.png') logging.debug('exporting (debug) visualization: %s', path_fig) fig = tl_visu.figure_segm_graphcut_debug(debug_visual) fig.savefig(path_fig, bbox_inches='tight', pad_inches=0.1) plt.close(fig) gc.collect() time.sleep(1) return idx_name, segm_map, segm_gc
def segment_image(imgs_idx_path, params, classif, path_out, path_visu=None, show_debug_imgs=SHOW_DEBUG_IMAGES): """ perform image segmentation on input image with given paramters and trained classifier, and save results :param (int, str) imgs_idx_path: :param {str: ...} params: segmentation parameters :param obj classif: trained classifier :param str path_out: path for output :param str path_visu: the existing patch means export also visualisation :return (str, ndarray, ndarray): """ idx, path_img = parse_imgs_idx_path(imgs_idx_path) logging.debug('segmenting image: "%s"', path_img) idx_name = get_idx_name(idx, path_img) img = load_image(path_img, params['img_type']) slic = seg_spx.segment_slic_img2d(img, sp_size=params['slic_size'], rltv_compact=params['slic_regul']) img = seg_pipe.convert_img_color_space(img, params.get('clr_space', 'rgb')) features, _ = seg_fts.compute_selected_features_img2d(img, slic, params['features']) labels = classif.predict(features) segm = labels[slic] path_img = os.path.join(path_out, idx_name + '.png') logging.debug('export segmentation: %s', path_img) img_seg = Image.fromarray(segm.astype(np.uint8)) img_seg.convert('L').save(path_img) # io.imsave(path_img, segm) # plt.imsave(os.path.join(path_out, idx_name + '_rgb.png'), seg_pipe) if path_visu is not None and os.path.isdir(path_visu): export_draw_image_segm_contour(img, segm, path_visu, idx_name) try: # in case some classiefier do not support predict_proba proba = classif.predict_proba(features) segm_soft = proba[slic] path_npz = os.path.join(path_out, idx_name + '.npz') np.savez_compressed(path_npz, segm_soft) except Exception: logging.warning('classif: %s not support predict_proba(.)', repr(classif)) proba = None segm_soft = None # if probabilities was not estimated of GC regul. is zero if proba is not None and params['gc_regul'] > 0: gc_regul = params['gc_regul'] if params['gc_use_trans']: label_penalty = seg_gc.compute_pairwise_cost_from_transitions( params['label_transitions']) gc_regul = (gc_regul * label_penalty) labels_gc = seg_gc.segment_graph_cut_general(slic, proba, img, features, gc_regul, edge_type=params['gc_edge_type']) # labels_gc = seg_gc.segment_graph_cut_simple(slic, proba, gc_regul) segm_gc = labels_gc[slic] # relabel according classif classes segm_gc = classif.classes_[segm_gc] path_img = os.path.join(path_out, idx_name + '_gc.png') logging.debug('export segmentation: %s', path_img) img_seg_gc = Image.fromarray(segm_gc.astype(np.uint8)) img_seg_gc.convert('L').save(path_img) # io.imsave(path_img, segm_gc) if path_visu is not None and os.path.isdir(path_visu): export_draw_image_segm_contour(img, segm_gc, path_visu, idx_name, '_gc') if show_debug_imgs: labels_map = np.argmax(proba, axis=1) plt.imsave(os.path.join(path_visu, idx_name + '_map.png'), labels_map[slic]) if not segm_soft is None: for lb in range(segm_soft.shape[2]): uc_name = idx_name + '_gc_unary-lb%i.png' % lb plt.imsave(os.path.join(path_visu, uc_name), segm_soft[:, :, lb], vmin=0., vmax=1., cmap=plt.cm.Greens) else: segm_gc = np.zeros(segm.shape) # gc.collect(), time.sleep(1) return idx_name, segm, segm_gc