コード例 #1
0
ファイル: spm_to_surf.py プロジェクト: dorianps/mmvt
def run_over_subjects(print_only=False, use_scaled_masks=False):
    for subject_fol in utils.get_subfolders(SPM_ROOT):
        subject = utils.namebase(subject_fol)
        spm_brain_file = SPM_BRAIN_TEMPLATE.format(subject=subject.upper())
        reg_file = '{}_register.lta'.format(subject)
        reg_spm_brain = '{}_reg.mgz'.format(subject)
        spm_map = os.path.basename(
            glob.glob(os.path.join(subject_fol, 'spmT_*.nii'))[0])
        spm_mask = SPM_MASK_TEMPLATE.format(subject=subject.upper())
        if use_scaled_masks:
            spm_mask_name, spm_mask_type = os.path.splitext(spm_mask)
            spm_mask = '{}_scaled{}'.format(spm_mask_name, spm_mask_type)
        spm_map_masked = '{}_masked.mgz'.format(os.path.splitext(spm_map)[0])
        fs_hemi_map = FS_HEMI_MAP_TEMPLATE.format(subject=subject,
                                                  hemi='{hemi}')
        run(subject_fol,
            spm_brain_file,
            FS_BRAIN_FILE,
            reg_file,
            reg_spm_brain,
            spm_map,
            spm_mask,
            spm_map_masked,
            fs_hemi_map,
            fs_subject=FS_SUBJECT,
            print_only=print_only)
コード例 #2
0
ファイル: spm_to_surf.py プロジェクト: pelednoam/mmvt
def run_over_subjects(print_only=False, use_scaled_masks=False):
    for subject_fol in utils.get_subfolders(SPM_ROOT):
        subject = utils.namebase(subject_fol)
        spm_brain_file = SPM_BRAIN_TEMPLATE.format(subject=subject.upper())
        reg_file = "{}_register.lta".format(subject)
        reg_spm_brain = "{}_reg.mgz".format(subject)
        spm_map = os.path.basename(glob.glob(os.path.join(subject_fol, "spmT_*.nii"))[0])
        spm_mask = SPM_MASK_TEMPLATE.format(subject=subject.upper())
        if use_scaled_masks:
            spm_mask_name, spm_mask_type = os.path.splitext(spm_mask)
            spm_mask = "{}_scaled{}".format(spm_mask_name, spm_mask_type)
        spm_map_masked = "{}_masked.mgz".format(os.path.splitext(spm_map)[0])
        fs_hemi_map = FS_HEMI_MAP_TEMPLATE.format(subject=subject, hemi="{hemi}")
        run(
            subject_fol,
            spm_brain_file,
            FS_BRAIN_FILE,
            reg_file,
            reg_spm_brain,
            spm_map,
            spm_mask,
            spm_map_masked,
            fs_hemi_map,
            fs_subject=FS_SUBJECT,
            print_only=print_only,
        )
コード例 #3
0
def scale_masks(scale = 10):
    for subject_fol in utils.get_subfolders(SPM_ROOT):
        subject = utils.namebase(subject_fol)
        spm_mask = SPM_MASK_TEMPLATE.format(subject=subject.upper())
        spm_scaled_mask = os.path.join(subject_fol, '{}_scaled{}'.format(os.path.splitext(spm_mask)[0],os.path.splitext(spm_mask)[1]))
        img = nib.load(os.path.join(subject_fol, spm_mask))
        data = img.get_data()
        affine = img.get_affine()
        scaled_data = data * scale
        new_img = nib.Nifti1Image(scaled_data, affine)
        nib.save(new_img, spm_scaled_mask)
コード例 #4
0
ファイル: spm_to_surf.py プロジェクト: dorianps/mmvt
def check_colors():
    subjects_folders = utils.get_subfolders(SPM_ROOT)
    good_subjects = ['pp002', 'pp003', 'pp004', 'pp005', 'pp006']
    subjects_folders = [os.path.join(SPM_ROOT, sub) for sub in good_subjects]
    subjects_colors = utils.get_spaced_colors(len(subjects_folders))
    # subjects_colors = utils.arr_to_colors(range(len(subjects_folders)), colors_map='Set1')
    plt.figure()
    for subject_fol, color in zip(subjects_folders, subjects_colors):
        subject = utils.namebase(subject_fol)
        plt.scatter([0], [0], label='{} {}'.format(subject, color), c=color)
    plt.legend()
    plt.show()
コード例 #5
0
ファイル: spm_to_surf.py プロジェクト: pelednoam/mmvt
def check_colors():
    subjects_folders = utils.get_subfolders(SPM_ROOT)
    good_subjects = ["pp002", "pp003", "pp004", "pp005", "pp006"]
    subjects_folders = [os.path.join(SPM_ROOT, sub) for sub in good_subjects]
    subjects_colors = utils.get_spaced_colors(len(subjects_folders))
    # subjects_colors = utils.arr_to_colors(range(len(subjects_folders)), colors_map='Set1')
    plt.figure()
    for subject_fol, color in zip(subjects_folders, subjects_colors):
        subject = utils.namebase(subject_fol)
        plt.scatter([0], [0], label="{} {}".format(subject, color), c=color)
    plt.legend()
    plt.show()
コード例 #6
0
ファイル: spm_to_surf.py プロジェクト: pelednoam/mmvt
def scale_masks(scale=10):
    for subject_fol in utils.get_subfolders(SPM_ROOT):
        subject = utils.namebase(subject_fol)
        spm_mask = SPM_MASK_TEMPLATE.format(subject=subject.upper())
        spm_scaled_mask = os.path.join(
            subject_fol, "{}_scaled{}".format(os.path.splitext(spm_mask)[0], os.path.splitext(spm_mask)[1])
        )
        img = nib.load(os.path.join(subject_fol, spm_mask))
        data = img.get_data()
        affine = img.get_affine()
        scaled_data = data * scale
        new_img = nib.Nifti1Image(scaled_data, affine)
        nib.save(new_img, spm_scaled_mask)
コード例 #7
0
ファイル: main.py プロジェクト: zhangqizky/baselinev1_2
        horizontal_flip=args.h_flip,
        vertical_flip=args.v_flip)

    val_datagen = ImageDataGenerator(
        preprocessing_function=preprocessing_function)

    train_generator = train_datagen.flow_from_directory(TRAIN_DIR,
                                                        target_size=(HEIGHT,
                                                                     WIDTH),
                                                        batch_size=BATCH_SIZE)

    validation_generator = val_datagen.flow_from_directory(
        VAL_DIR, target_size=(HEIGHT, WIDTH), batch_size=BATCH_SIZE)

    # Save the list of classes for prediction mode later
    class_list = utils.get_subfolders(TRAIN_DIR)
    utils.save_class_list(class_list, model_name=args.model, dataset_name="")

    finetune_model = utils.build_finetune_model(base_model,
                                                dropout=args.dropout,
                                                fc_layers=FC_LAYERS,
                                                num_classes=len(class_list))

    if args.continue_training:
        finetune_model.load_weights("./checkpoints/" + args.model +
                                    "_model_weights.h5")
        print("load success!")

    adam = Adam(lr=0.00001)
    finetune_model.compile(adam,
                           loss='categorical_crossentropy',
コード例 #8
0
    val_datagen = ImageDataGenerator(
        preprocessing_function=preprocessing_function)

    train_generator = train_datagen.flow_from_directory(
        BASE_IMG_DIR + TRAIN_DIR,
        target_size=(HEIGHT, WIDTH),
        batch_size=BATCH_SIZE)

    validation_generator = val_datagen.flow_from_directory(
        BASE_IMG_DIR + VAL_DIR,
        target_size=(HEIGHT, WIDTH),
        batch_size=BATCH_SIZE)

    # Save the list of classes for prediction mode later
    class_list = utils.get_subfolders(BASE_IMG_DIR + TRAIN_DIR)
    utils.save_class_list(class_list, model_name=args.model)

    finetune_model = utils.build_finetune_model(base_model,
                                                dropout=args.dropout,
                                                fc_layers=FC_LAYERS,
                                                num_classes=len(class_list))

    if args.continue_training:
        finetune_model.load_weights("./checkpoints/" + args.model +
                                    "_model_weights.h5")

    adam = Adam(lr=0.00001)
    finetune_model.compile(adam,
                           loss='categorical_crossentropy',
                           metrics=['accuracy'])