import gradio as gr from gradio_image_prompter import ImagePrompter import os import yaml from modules.sam_inference import SamInference from modules.model_downloader import DEFAULT_MODEL_TYPE from modules.paths import (OUTPUT_DIR, SAM2_CONFIGS_DIR) from modules.utils import open_folder from modules.constants import (AUTOMATIC_MODE, BOX_PROMPT_MODE) class App: def __init__(self, args=None): self.app = gr.Blocks() self.args = args self.sam_inf = SamInference() self.image_modes = [AUTOMATIC_MODE, BOX_PROMPT_MODE] self.default_mode = AUTOMATIC_MODE default_param_config_path = os.path.join(SAM2_CONFIGS_DIR, "default_hparams.yaml") with open(default_param_config_path, 'r') as file: self.hparams = yaml.safe_load(file) @staticmethod def on_mode_change(mode: str): return [ gr.Image(visible=mode == AUTOMATIC_MODE), ImagePrompter(visible=mode == BOX_PROMPT_MODE), gr.Accordion(visible=mode == AUTOMATIC_MODE) ] def launch(self): mask_hparams = self.hparams["mask_gen_hparams"] with self.app: with gr.Row(): with gr.Column(scale=5): img_input = gr.Image(label="Input image here") img_input_prompter = ImagePrompter(label="Prompt image with Box & Point", visible=self.default_mode == BOX_PROMPT_MODE) with gr.Column(scale=5): dd_input_modes = gr.Dropdown(label="Image Input Mode", value=self.default_mode, choices=self.image_modes) dd_models = gr.Dropdown(label="Model", value=DEFAULT_MODEL_TYPE, choices=self.sam_inf.available_models) with gr.Accordion("Mask Parameters", open=False) as acc_mask_hparams: nb_points_per_side = gr.Number(label="points_per_side ", value=mask_hparams["points_per_side"], interactive=True) nb_points_per_batch = gr.Number(label="points_per_batch ", value=mask_hparams["points_per_batch"], interactive=True) sld_pred_iou_thresh = gr.Slider(label="pred_iou_thresh ", value=mask_hparams["pred_iou_thresh"], minimum=0, maximum=1, interactive=True) sld_stability_score_thresh = gr.Slider(label="stability_score_thresh ", value=mask_hparams["stability_score_thresh"], minimum=0, maximum=1, interactive=True) sld_stability_score_offset = gr.Slider(label="stability_score_offset ", value=mask_hparams["stability_score_offset"], minimum=0, maximum=1) nb_crop_n_layers = gr.Number(label="crop_n_layers ", value=mask_hparams["crop_n_layers"],) sld_box_nms_thresh = gr.Slider(label="box_nms_thresh ", value=mask_hparams["box_nms_thresh"], minimum=0, maximum=1) nb_crop_n_points_downscale_factor = gr.Number(label="crop_n_points_downscale_factor ", value=mask_hparams["crop_n_points_downscale_factor"],) nb_min_mask_region_area = gr.Number(label="min_mask_region_area ", value=mask_hparams["min_mask_region_area"],) cb_use_m2m = gr.Checkbox(label="use_m2m ", value=mask_hparams["use_m2m"]) with gr.Row(): btn_generate = gr.Button("GENERATE", variant="primary") with gr.Row(): gallery_output = gr.Gallery(label="Output images will be shown here") with gr.Column(): output_file = gr.File(label="Generated psd file", scale=9) btn_open_folder = gr.Button("📁\nOpen PSD folder", scale=1) sources = [img_input] model_params = [dd_models] auto_mask_hparams = [nb_points_per_side, nb_points_per_batch, sld_pred_iou_thresh, sld_stability_score_thresh, sld_stability_score_offset, nb_crop_n_layers, sld_box_nms_thresh, nb_crop_n_points_downscale_factor, nb_min_mask_region_area, cb_use_m2m] btn_generate.click(fn=self.sam_inf.generate_mask_app, inputs=sources + model_params + auto_mask_hparams, outputs=[gallery_output, output_file]) btn_open_folder.click(fn=lambda: open_folder(os.path.join(OUTPUT_DIR)), inputs=None, outputs=None) dd_input_modes.change(fn=self.on_mode_change, inputs=[dd_input_modes], outputs=[img_input, img_input_prompter, acc_mask_hparams]) self.app.queue().launch(inbrowser=True) if __name__ == "__main__": app = App() app.launch()