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from modules import scripts_postprocessing, ui_components, errors
import gradio as gr
from modules.textual_inversion import autocrop
class ScriptPostprocessingFocalCrop(scripts_postprocessing.ScriptPostprocessing):
name = "Auto focal point crop"
order = 4000
def ui(self):
with ui_components.InputAccordion(False, label="Auto focal point 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")
debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
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 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]
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