Focal crop UI elements

    import modules.deepbooru as deepbooru

def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_entropy_focus=False):

def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):

    try:

        if process_caption:

            shared.interrogator.load()

            db_opts[deepbooru.OPT_INCLUDE_RANKS] = False

            deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)

        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_entropy_focus)

        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug)

    finally:

def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_entropy_focus=False):

def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):

    width = process_width

    height = process_height

    src = os.path.abspath(process_src)

            ratio = (img.height * width) / (img.width * height)

            inverse_xy = True

        processing_option_ran = False

        process_default_resize = True

        if process_split and ratio < 1.0 and ratio <= split_threshold:

            for splitted in split_pic(img, inverse_xy):

                save_pic(splitted, index, existing_caption=existing_caption)

            processing_option_ran = True

            process_default_resize = False

        if process_entropy_focus and img.height != img.width:

            autocrop_settings = autocrop.Settings(

                crop_width = width,

                crop_height = height,

                face_points_weight = 0.9,

                entropy_points_weight = 0.7,

                corner_points_weight = 0.5,

                annotate_image = False

                face_points_weight = process_focal_crop_face_weight,

                entropy_points_weight = process_focal_crop_entropy_weight,

                corner_points_weight = process_focal_crop_edges_weight,

                annotate_image = process_focal_crop_debug

            )

            focal = autocrop.crop_image(img, autocrop_settings)

            for focal in autocrop.crop_image(img, autocrop_settings):

                save_pic(focal, index, existing_caption=existing_caption)

            processing_option_ran = True

            process_default_resize = False

        if not processing_option_ran:

        if process_default_resize:

            img = images.resize_image(1, img, width, height)

            save_pic(img, index, existing_caption=existing_caption)

                    with gr.Row():

                        process_flip = gr.Checkbox(label='Create flipped copies')

                        process_split = gr.Checkbox(label='Split oversized images')

                        process_entropy_focus = gr.Checkbox(label='Create auto focal point crop')

                        process_focal_crop = gr.Checkbox(label='Auto focal point crop')

                        process_caption = gr.Checkbox(label='Use BLIP for caption')

                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)

                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)

                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)

                    with gr.Row(visible=False) as process_focal_crop_row:

                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)

                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.3, minimum=0.0, maximum=1.0, step=0.05)

                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)

                        process_focal_crop_debug = gr.Checkbox(label='Create debug image')

                    with gr.Row():

                        with gr.Column(scale=3):

                            gr.HTML(value="")

                        outputs=[process_split_extra_row],

                    )

                    process_focal_crop.change(

                        fn=lambda show: gr_show(show),

                        inputs=[process_focal_crop],

                        outputs=[process_focal_crop_row],

                    )

                with gr.Tab(label="Train"):

                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")

                    with gr.Row():

                process_caption_deepbooru,

                process_split_threshold,

                process_overlap_ratio,

                process_entropy_focus,

                process_focal_crop,

                process_focal_crop_face_weight,

                process_focal_crop_entropy_weight,

                process_focal_crop_edges_weight,

                process_focal_crop_debug,

            ],

            outputs=[

                ti_output,