c8b28432a637a780eed96547260722ff3dede57e,niftynet/engine/sampler_selective.py,,candidate_indices,#Any#Any#Any#,203

Before Change

        spatial_win_sizes = win_sizes[:N_SPATIAL]
        // spatial_win_sizes = [win_size[:N_SPATIAL]
        //                      for win_size in win_sizes.values()]
        spatial_win_sizes = np.asarray(spatial_win_sizes, dtype=np.int32)
        max_spatial_win = spatial_win_sizes[0]
        // Create segmentation for this label
        list_counts = []
        shape_ones = np.asarray(data.shape)
        // print(shape_ones, max_spatial_win)
        half_max_size = np.floor(max_spatial_win / 2)
        padding = []
        for i in range(0, len(win_sizes)):
            if i < N_SPATIAL:
                shape_ones[i] -= 2 * half_max_size
                padding = padding + [[half_max_size, half_max_size], ]
            else:
                padding = padding + [[0, 0], ]
        // print(shape_ones, padding)
        final = np.pad(np.ones(shape_ones), np.asarray(padding,
                                                       dtype=np.int32),
                       "constant")
        new_win_size = np.copy(win_sizes)
        // new_win_size[:N_SPATIAL] = win_sizes[0]/8
        window_mean = np.ones(new_win_size, dtype=np.int32)
        mean_counts_size = []
        // print(unique)
        for value in unique:
            // print(np.sum(data), "sum in data", np.prod(data.shape),
            //       " elements in data")
            seg_label = np.copy(data)
            seg_label = np.asarray(seg_label, dtype=np.int32)
            // print(np.sum(seg_label))
            seg_label = np.where(seg_label == value, np.ones_like(data),
                           np.zeros_like(
                data))
            // print(np.sum(seg_label), " num values in seg_label ", value)
            label_size = create_label_size_map(seg_label, 1)
            // print(value, np.sum(seg_label), seg_label.shape,
            //       window_mean.shape, num_min)
            // print("Begin fft convolve")
            counts_window = fftconvolve(seg_label, window_mean, "same")
            // print("finished fft convolve")
            valid_places = np.where(counts_window > np.max([num_min, 1]),
                                    np.ones_like(data), np.zeros_like(data))
            counts_size = fftconvolve(label_size * valid_places, window_mean,
                                      "same")
            mean_counts_size_temp = np.nan_to_num(
                counts_size * 1.0 / counts_window)
            mean_counts_size_temp = np.where(counts_window == 0, np.zeros_like(
                data), mean_counts_size_temp)
            // print(np.max(counts_size), " max size")
            // print(np.sum(valid_places), value)
            if value in list_labels:
                // print(value, "in list_labels")
                mean_counts_size.append(mean_counts_size_temp)
                final = valid_places * final
                print("final calculated for value in list_labels")
            else:
                list_counts.append(valid_places)
        // print(len(list_counts))
        print("final characterisation")
        for i in range(0, len(list_counts)):
            // print(final.shape, list_counts[i].shape, np.max(final), np.max(
            //     list_counts[i]))
            final += list_counts[i]
        print("initialising candidates", num_labels_add)
        candidates = np.zeros_like(data, dtype=np.int32)
        candidates[final >= num_labels_add+1] = 1
        print(np.sum(candidates), "number of candidates")

After Change

        //     // print(final.shape, list_counts[i].shape, np.max(final), np.max(
        //     //     list_counts[i]))
        //     final += list_counts[i]
        final = np.sum(list_counts)
        print("initialising candidates", num_labels_add)
        candidates = np.zeros_like(data, dtype=np.int32)
        candidates[final >= num_labels_add + 1] = 1
        print(np.sum(candidates), "number of candidates")

Instances