comparison 2d_feature_extraction.py @ 7:048545339ced draft default tip

planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_feature_extraction/ commit b8e0b656d417db6e2ad0f187fc3c5afff0c3acd7

6:8e3a52b74876

    tool.parse_args()

    # Validate the input image
    try:
        label_image = tool.args.input_images['labels']
        if any(label_image.shape[label_image.axes.index(axis)] > 1 for axis in label_image.axes if axis not in 'ZYX'):
            raise ValueError(f'This tool is not applicable to images with {label_image.original_axes} axes.')

        # Extract the image features
        for section in tool.run('ZYX'):  # the validation code above guarantees that we will have only a single iteration
            df = pd.DataFrame()

            # Get the labels array and cast to `uint8` if it is `bool` (`skimage.measure.regionprops` refuses `bool` typed arrays)
            labels_section_data = section['labels'].data.squeeze()
            if np.issubdtype(labels_section_data.dtype, bool):

                elif feature_name == 'perimeter' and labels_section_data.ndim == 3:
                    df['perimeter'] = df['it'].map(
                        lambda ait: surface(labels_section_data, regions[ait].label),  # `skimage.measure.regionprops` cannot compute perimeters for 3-D data
                    )

                # Skip features that are not available when processing 3-D images
                elif feature_name in ('eccentricity', 'moments_hu', 'orientation') and labels_section_data.ndim == 3:
                    print(f'Skip feature that is not available for 3-D images: "{feature_name}"')

                # Add another feature from `regions` that was computed via `skimage.measure.regionprops`

7:048545339ced

    tool.parse_args()

    # Validate the input image
    try:
        label_image = tool.args.input_images['labels']
        if any(label_image.shape[label_image.axes.index(axis)] > 1 for axis in label_image.axes if axis not in 'XYZ'):
            raise ValueError(f'This tool is not applicable to images with {label_image.original_axes} axes.')

        # Extract the image features
        for section in tool.run('XYZ'):  # the validation code above guarantees that we will have only a single iteration
            df = pd.DataFrame()

            # Get the labels array and cast to `uint8` if it is `bool` (`skimage.measure.regionprops` refuses `bool` typed arrays)
            labels_section_data = section['labels'].data.squeeze()
            if np.issubdtype(labels_section_data.dtype, bool):

                elif feature_name == 'perimeter' and labels_section_data.ndim == 3:
                    df['perimeter'] = df['it'].map(
                        lambda ait: surface(labels_section_data, regions[ait].label),  # `skimage.measure.regionprops` cannot compute perimeters for 3-D data
                    )

                # Add the object centroid using separate columns for the different coordinates
                elif feature_name == 'centroid':
                    for axis_idx, axis in enumerate(section['labels'].axes):  # XYZ
                        if section['labels'].shape[axis_idx] > 1:
                            df[f'{feature_name}_{axis.lower()}'] = df['it'].map(
                                lambda ait: getattr(regions[ait], feature_name)[axis_idx],
                            )

                # Skip features that are not available when processing 3-D images
                elif feature_name in ('eccentricity', 'moments_hu', 'orientation') and labels_section_data.ndim == 3:
                    print(f'Skip feature that is not available for 3-D images: "{feature_name}"')

                # Add another feature from `regions` that was computed via `skimage.measure.regionprops`