Python io.export_landmark_file函数代码示例

def test_export_landmark_ljson(mock_open, exists, json_dump):
    exists.return_value = False
    fake_path = '/fake/fake.ljson'
    with open(fake_path) as f:
        type(f).name = PropertyMock(return_value=fake_path)
        mio.export_landmark_file(test_lg, f, extension='ljson')
    json_dump.assert_called_once()

def test_export_landmark_pts(mock_open, exists, save_txt):
    exists.return_value = False
    fake_path = '/fake/fake.pts'
    with open(fake_path) as f:
        type(f).name = PropertyMock(return_value=fake_path)
        mio.export_landmark_file(test_lg, f, extension='pts')
    save_txt.assert_called_once()

def test_export_filepath_no_overwrite(mock_open, exists, landmark_types):
    exists.return_value = False
    mio.export_landmark_file(test_lg, fake_path)
    mock_open.assert_called_once_with("wb")
    landmark_types.__getitem__.assert_called_once_with(".fake")
    export_function = landmark_types.__getitem__.return_value
    export_function.assert_called_once()

def test_export_filepath_no_overwrite(mock_open, exists, landmark_types):
    exists.return_value = False
    mio.export_landmark_file(test_lg, fake_path)
    mock_open.assert_called_with('wb')
    landmark_types.__getitem__.assert_called_with('.fake')
    export_function = landmark_types.__getitem__.return_value
    assert export_function.call_count == 1

def test_export_filepath_overwrite_exists(mock_open, exists, landmark_types):
    exists.return_value = True
    mio.export_landmark_file(test_lg, fake_path, overwrite=True)
    mock_open.assert_called_once_with('wb')
    landmark_types.__getitem__.assert_called_once_with('.fake')
    export_function = landmark_types.__getitem__.return_value
    export_function.assert_called_once()

def test_export_filepath_explicit_ext_dot(mock_open, exists, landmark_types):
    exists.return_value = False
    mio.export_landmark_file(test_lg, fake_path, extension='.fake')
    mock_open.assert_called_once_with('wb')
    landmark_types.__getitem__.assert_called_once_with('.fake')
    export_function = landmark_types.__getitem__.return_value
    export_function.assert_called_once()

def test_export_file_handle_file_non_file_buffer(mock_open, exists, landmark_types):
    exists.return_value = False
    with open(fake_path) as f:
        del f.name  # Equivalent to raising an AttributeError side effect
        mio.export_landmark_file(test_lg, f, extension="fake")
    landmark_types.__getitem__.assert_called_once_with(".fake")
    export_function = landmark_types.__getitem__.return_value
    export_function.assert_called_once()

def test_export_file_handle_file_exists_overwrite(mock_open, exists, landmark_types):
    exists.return_value = True
    with open(fake_path) as f:
        type(f).name = PropertyMock(return_value=fake_path)
        mio.export_landmark_file(test_lg, f, overwrite=True, extension="fake")
    landmark_types.__getitem__.assert_called_once_with(".fake")
    export_function = landmark_types.__getitem__.return_value
    export_function.assert_called_once()

def test_export_filepath_explicit_ext_no_dot(mock_open, exists, landmark_types):
    exists.return_value = False
    landmark_types.__contains__.return_value = True
    mio.export_landmark_file(test_lg, fake_path, extension='fake')
    mock_open.assert_called_with('wb')
    landmark_types.__getitem__.assert_called_with('.fake')
    export_function = landmark_types.__getitem__.return_value
    assert export_function.call_count == 1

def test_export_file_handle_file_non_file_buffer(mock_open, exists,
                                                 landmark_types):
    exists.return_value = False
    landmark_types.__contains__.return_value = True
    with open(fake_path) as f:
        del f.name  # Equivalent to raising an AttributeError side effect
        mio.export_landmark_file(test_lg, f, extension='fake')
    landmark_types.__getitem__.assert_called_with('.fake')
    export_function = landmark_types.__getitem__.return_value
    assert export_function.call_count == 1

def test_export_file_handle_file_exists_overwrite(mock_open, exists,
                                                  landmark_types):
    exists.return_value = True
    landmark_types.__contains__.return_value = True
    with open(fake_path) as f:
        type(f).name = PropertyMock(return_value=fake_path)
        mio.export_landmark_file(test_lg, f, overwrite=True, extension='fake')
    landmark_types.__getitem__.assert_called_with('.fake')
    export_function = landmark_types.__getitem__.return_value
    assert export_function.call_count == 1

def test_export_landmark_ljson_nan_values(mock_open, exists):
    exists.return_value = False
    mock_writer = MagicMock()
    mock_open.return_value.__enter__.return_value = mock_writer
    fake_path = '/fake/fake.ljson'
    with open(fake_path) as f:
        type(f).name = PropertyMock(return_value=fake_path)
        mio.export_landmark_file(nan_lg, f, extension='ljson')

    # yeah this is grim, but it should work.
    assert 'null' in '{}'.format(mock_open.mock_calls)

def test_export_landmark_ljson_nan_values(mock_open, exists):
    exists.return_value = False
    fake_path = '/fake/fake.ljson'
    with open(fake_path) as f:
        type(f).name = PropertyMock(return_value=fake_path)
        mio.export_landmark_file(nan_lg, f, extension='ljson')

    # This is a bit ugly, but we parse the write calls to check that json
    # wrote null values
    first_null = mock_open.mock_calls[97][1][0][1:].strip()
    second_null = mock_open.mock_calls[98][1][0][1:].strip()
    assert first_null == 'null'
    assert second_null == 'null'

def process_frame(frame_name, clip, img_type, svm_p, loop=False):
    """
    Applies the AAM fitter (global var) in a frame. Additionally, it might apply an
    SVM to verify it's a face if required.
    :param frame_name: str: Name of the frame along with extension, e.g. '000001.png'.
    :param clip:       str: Name of the clip.
    :param img_type:   str: Suffix (extension) of the frames, e.g. '.png'.
    :param svm_p:      dict: Required params for SVM classification.
    :param loop:       bool: (optional) Declares whether this is a 2nd fit for AAM (loop).
    :return:
    """
    global fitter
    name = frame_name[:frame_name.rfind('.')]
    p0 = clip.path_read_ln[0] + name + '_0.pts'
    # find if this is 2nd fit or 1st.
    if loop:  # if 2nd fit, then if landmark is 'approved', return. Otherwise proceed.
        try:
            ln = import_landmark_file(p0)
            copy2(p0, clip.path_write_ln[0] + name + '_0.pts')
            return      # if the landmark already exists, return (for performance improvement)
        except ValueError:
            pass
        try:
            ln = import_landmark_file(clip.path_read_ln[1] + name + '_0.pts')
        except ValueError:  # either not found or no suitable importer
            return
    else:
        try:
            ln = import_landmark_file(p0)
        except ValueError:  # either not found or no suitable importer
            return
    im = im_read_greyscale(frame_name, clip.path_frames, img_type)
    if not im:
        return
    im.landmarks['PTS2'] = ln
    fr = fitter.fit_from_shape(im, im.landmarks['PTS2'].lms, crop_image=0.3)
    p_wr = clip.path_write_ln[0] + im.path.stem + '_0.pts'
    export_landmark_file(fr.fitted_image.landmarks['final'], p_wr, overwrite=True)

    # apply SVM classifier by extracting patches (is face or not).
    if not svm_p['apply']:
        return
    im.landmarks.clear()  # temp solution
    im.landmarks['ps_pbaam'] = fr.fitted_image.landmarks['final']
    im_cp = im.crop_to_landmarks_proportion(0.2, group='ps_pbaam')
    im_cp = svm_p['feat'](im_cp)
    im2 = warp_image_to_reference_shape(im_cp, svm_p['refFrame'], 'ps_pbaam')
    _p_nd = im2.extract_patches_around_landmarks(group='source', as_single_array=True,
                                                 patch_shape=svm_p['patch_s']).flatten()
    if svm_p['clf'].decision_function(_p_nd) > 0:
        copy2(p_wr, clip.path_write_ln[1] + im.path.stem + '_0.pts')

def detect_in_frame(frame_name, clip, img_type):
    # if normalise=True in im_read_greyscale: before calling dlib detector, image should be converted to uint8
    im = im_read_greyscale(frame_name, clip.path_frames, img_type, normalise=False)
    if not im:
        print(frame_name, clip.path_frames)
        return
    res_dlib = dlib_init_detector(im, group_prefix='dlib')  # call dlib detector
    im_pili = np.array(im.as_PILImage())
    for kk, g in enumerate(im.landmarks.group_labels):
        pts_end = im.path.stem + '_' + str(kk) + pts_type_out  # define the ending of each pts that will be exported
        export_landmark_file(im.landmarks[g], clip.path_write_ln[0] + pts_end, overwrite=True)
        # from bounding box to points (dlib predictor)
        init_pc = detection_to_pointgraph(predictor_dlib(im_pili, pointgraph_to_rect(im.landmarks[g].lms)))
        export_landmark_file(LandmarkGroup.init_with_all_label(init_pc), clip.path_write_ln[1] + pts_end, overwrite=True)