def compare_seg_with_gt(max_n_images=10, epoch=0): data_gen_validation = SegmentationBatchGeneratorDavid( all_patients, BATCH_SIZE, validation_patients, PATCH_SIZE=INPUT_PATCH_SIZE, mode="test", ignore=[81, 10, 47], losses=None, num_batches=None, seed=10, ) data_gen_validation = seg_channel_selection_generator(data_gen_validation, [2]) data_gen_validation = center_crop_seg_generator(data_gen_validation, OUTPUT_PATCH_SIZE) data, seg, idx = data_gen_validation.next() seg = np.array(seg) seg_pred = get_segmentation(data) plt.figure(figsize=(6, 20)) n_images = np.min((seg_pred.shape[0], max_n_images)) for i in range(n_images): seg_pred[i][0, :6] = np.array([0, 1, 2, 3, 4, 5]) seg[i, 0, 0, :6] = np.array([0, 1, 2, 3, 4, 5]) plt.subplot(n_images, 2, 2 * i + 1) plt.imshow(seg[i, 0]) plt.subplot(n_images, 2, 2 * i + 2) plt.imshow(seg_pred[i]) plt.savefig(os.path.join(results_dir, "some_segmentations_ep_%d.png" % epoch))
def compare_seg_with_gt(max_n_images=10, epoch=0): data_gen_validation = SegmentationBatchGeneratorDavid( all_patients, BATCH_SIZE, validation_patients, PATCH_SIZE=OUTPUT_PATCH_SIZE, mode="test", ignore=[81], losses=None, num_batches=None, seed=10) data_gen_validation = seg_channel_selection_generator( data_gen_validation, [2]) data_gen_validation = center_crop_seg_generator(data_gen_validation, OUTPUT_PATCH_SIZE) data, seg, idx = data_gen_validation.next() seg = np.array(seg) seg_pred = get_segmentation(data) plt.figure(figsize=(6, 20)) n_images = np.min((seg_pred.shape[0], max_n_images)) for i in range(n_images): seg_pred[i][0, :6] = np.array([0, 1, 2, 3, 4, 5]) seg[i, 0, 0, :6] = np.array([0, 1, 2, 3, 4, 5]) plt.subplot(n_images, 2, 2 * i + 1) plt.imshow(seg[i, 0]) plt.subplot(n_images, 2, 2 * i + 2) plt.imshow(seg_pred[i]) plt.savefig( os.path.join(results_dir, "some_segmentations_ep_%d.png" % epoch))