scripts/postprocessing_focal_crop.py

import gradio as gr
from modules import errors, scripts_postprocessing
from modules.textual_inversion import autocrop
from modules.ui_components import InputAccordion


class FocalCropPostprocessing(scripts_postprocessing.ScriptPostprocessing):
    name = "Auto Focal Point Crop"
    order = 4010

    def ui(self):
        with InputAccordion(False, label="Auto Focal Crop") as enable:
            face_weight = gr.Slider(
                label="Focal point face weight",
                value=0.9,
                minimum=0.0,
                maximum=1.0,
                step=0.05,
                elem_id="postprocess_focal_crop_face_weight",
            )
            entropy_weight = gr.Slider(
                label="Focal point entropy weight",
                value=0.15,
                minimum=0.0,
                maximum=1.0,
                step=0.05,
                elem_id="postprocess_focal_crop_entropy_weight",
            )
            edges_weight = gr.Slider(
                label="Focal point edges weight",
                value=0.5,
                minimum=0.0,
                maximum=1.0,
                step=0.05,
                elem_id="postprocess_focal_crop_edges_weight",
            )

            with gr.Row():
                debug = gr.Checkbox(
                    value=False,
                    label="Create debug image",
                    elem_id="train_process_focal_crop_debug",
                )
                gr.HTML("<b>Note:</b> this requires the <ins>Scale to</ins> mode")

        return {
            "enable": enable,
            "face_weight": face_weight,
            "entropy_weight": entropy_weight,
            "edges_weight": edges_weight,
            "debug": debug,
        }

    def process(
        self,
        pp: scripts_postprocessing.PostprocessedImage,
        enable,
        face_weight,
        entropy_weight,
        edges_weight,
        debug,
    ):
        if not enable:
            return

        if not pp.shared.target_width or not pp.shared.target_height:
            return

        dnn_model_path = None

        try:
            dnn_model_path = autocrop.download_and_cache_models()
        except Exception:
            errors.report(
                """Unable to load face detection model for auto crop selection.
                Falling back to the lower quality haar method.""",
                exc_info=True,
            )

        autocrop_settings = autocrop.Settings(
            crop_width=pp.shared.target_width,
            crop_height=pp.shared.target_height,
            face_points_weight=face_weight,
            entropy_points_weight=entropy_weight,
            corner_points_weight=edges_weight,
            annotate_image=debug,
            dnn_model_path=dnn_model_path,
        )

        result, *others = autocrop.crop_image(pp.image, autocrop_settings)

        pp.image = result
        pp.extra_images = [
            pp.create_copy(x, nametags=["focal-crop-debug"], disable_processing=True)
            for x in others
        ]