55 lines
2.3 KiB
Python
55 lines
2.3 KiB
Python
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from modules import scripts_postprocessing, ui_components, errors
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import gradio as gr
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from modules.textual_inversion import autocrop
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class ScriptPostprocessingFocalCrop(scripts_postprocessing.ScriptPostprocessing):
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name = "Auto focal point crop"
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order = 4010
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def ui(self):
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with ui_components.InputAccordion(False, label="Auto focal point crop") as enable:
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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")
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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")
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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")
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debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
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return {
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"enable": enable,
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"face_weight": face_weight,
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"entropy_weight": entropy_weight,
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"edges_weight": edges_weight,
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"debug": debug,
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}
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def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, face_weight, entropy_weight, edges_weight, debug):
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if not enable:
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return
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if not pp.shared.target_width or not pp.shared.target_height:
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return
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dnn_model_path = None
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try:
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dnn_model_path = autocrop.download_and_cache_models()
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except Exception:
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errors.report("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", exc_info=True)
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autocrop_settings = autocrop.Settings(
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crop_width=pp.shared.target_width,
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crop_height=pp.shared.target_height,
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face_points_weight=face_weight,
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entropy_points_weight=entropy_weight,
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corner_points_weight=edges_weight,
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annotate_image=debug,
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dnn_model_path=dnn_model_path,
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)
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result, *others = autocrop.crop_image(pp.image, autocrop_settings)
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pp.image = result
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pp.extra_images = [pp.create_copy(x, nametags=["focal-crop-debug"], disable_processing=True) for x in others]
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