import gradio as gr from gradio_image_prompter import ImagePrompter from gradio_image_prompter.image_prompter import PromptData from typing import List, Dict, Optional, Union import os import yaml from modules.sam_inference import SamInference from modules.model_downloader import DEFAULT_MODEL_TYPE from modules.paths import (OUTPUT_DIR, OUTPUT_PSD_DIR, SAM2_CONFIGS_DIR, TEMP_DIR, OUTPUT_FILTER_DIR) from modules.utils import open_folder from modules.constants import (AUTOMATIC_MODE, BOX_PROMPT_MODE, PIXELIZE_FILTER, COLOR_FILTER, DEFAULT_COLOR, DEFAULT_PIXEL_SIZE) from modules.video_utils import extract_frames, extract_sound, get_frames_from_dir, clean_temp_dir class App: def __init__(self, args=None): self.demo = gr.Blocks() self.args = args self.sam_inf = SamInference() self.image_modes = [AUTOMATIC_MODE, BOX_PROMPT_MODE] self.default_mode = BOX_PROMPT_MODE self.filter_modes = [PIXELIZE_FILTER, COLOR_FILTER] self.default_filter = PIXELIZE_FILTER self.default_color = DEFAULT_COLOR self.default_pixel_size = DEFAULT_PIXEL_SIZE 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) def mask_parameters(self, hparams: Optional[Dict] = None): if hparams is None: hparams = self.hparams["mask_hparams"] mask_components = [ gr.Number(label="points_per_side ", value=hparams["points_per_side"], interactive=True), gr.Number(label="points_per_batch ", value=hparams["points_per_batch"], interactive=True), gr.Slider(label="pred_iou_thresh ", value=hparams["pred_iou_thresh"], minimum=0, maximum=1, interactive=True), gr.Slider(label="stability_score_thresh ", value=hparams["stability_score_thresh"], minimum=0, maximum=1, interactive=True), gr.Slider(label="stability_score_offset ", value=hparams["stability_score_offset"], minimum=0, maximum=1), gr.Number(label="crop_n_layers ", value=hparams["crop_n_layers"]), gr.Slider(label="box_nms_thresh ", value=hparams["box_nms_thresh"], minimum=0, maximum=1), gr.Number(label="crop_n_points_downscale_factor ", value=hparams["crop_n_points_downscale_factor"]), gr.Number(label="min_mask_region_area ", value=hparams["min_mask_region_area"]), gr.Checkbox(label="use_m2m ", value=hparams["use_m2m"]) ] return mask_components @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), ] @staticmethod def on_filter_mode_change(mode: str): return [ gr.ColorPicker(visible=mode == COLOR_FILTER), gr.Number(visible=mode == PIXELIZE_FILTER) ] def on_video_model_change(self, model_type: str, vid_input: str): self.sam_inf.init_video_inference_state(vid_input=vid_input, model_type=model_type) frames = get_frames_from_dir(vid_dir=TEMP_DIR) initial_frame, max_frame_index = frames[0], (len(frames)-1) return [ ImagePrompter(label="Prompt image with Box & Point", value=initial_frame), gr.Slider(label="Frame Index", value=0, interactive=True, step=1, minimum=0, maximum=max_frame_index) ] @staticmethod def on_frame_change(frame_idx: int): temp_dir = TEMP_DIR frames = get_frames_from_dir(vid_dir=temp_dir) selected_frame = frames[frame_idx] return ImagePrompter(label=f"Prompt image with Box & Point", value=selected_frame) @staticmethod def on_prompt_change(prompt: Dict): image, points = prompt["image"], prompt["points"] return gr.Image(label="Preview", value=image) def launch(self): _mask_hparams = self.hparams["mask_hparams"] with self.demo: with gr.Tabs(): with gr.TabItem("Layer Divider"): with gr.Row(): with gr.Column(scale=5): img_input = gr.Image(label="Input image here", visible=self.default_mode == AUTOMATIC_MODE) img_input_prompter = ImagePrompter(label="Prompt image with Box & Point", type='pil', 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, visible=self.default_mode == AUTOMATIC_MODE) as acc_mask_hparams: mask_hparams_component = self.mask_parameters(_mask_hparams) cb_multimask_output = gr.Checkbox(label="multimask_output", value=_mask_hparams["multimask_output"]) 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, img_input_prompter, dd_input_modes] model_params = [dd_models] mask_hparams = mask_hparams_component + [cb_multimask_output] input_params = sources + model_params + mask_hparams btn_generate.click(fn=self.sam_inf.divide_layer, inputs=input_params, outputs=[gallery_output, output_file]) btn_open_folder.click(fn=lambda: open_folder(OUTPUT_PSD_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]) with gr.TabItem("Pixelize Filter"): with gr.Column(): file_vid_input = gr.File(label="Input Video here", file_types=['.mp4', '.avi', '.mov', '.wmv', '.flv', '.webm', '.mkv', '.mpeg', '.mpg', '.m4v', '.3gp', '.ts', '.vob']) with gr.Row(equal_height=True): with gr.Column(scale=9): with gr.Row(): vid_frame_prompter = ImagePrompter(label="Prompt image with Box & Point", type='pil', interactive=True, scale=5) img_preview = gr.Image(label="Preview", interactive=False, scale=5) sld_frame_selector = gr.Slider(label="Frame Index", interactive=False) with gr.Column(scale=1): dd_models = gr.Dropdown(label="Model", value=DEFAULT_MODEL_TYPE, choices=self.sam_inf.available_models) dd_filter_mode = gr.Dropdown(label="Filter Modes", interactive=True, value=self.default_filter, choices=self.filter_modes) cp_color_picker = gr.ColorPicker(label="Solid Color", interactive=True, visible=self.default_filter == COLOR_FILTER, value=self.default_color) nb_pixel_size = gr.Number(label="Pixel Size", interactive=True, minimum=1, visible=self.default_filter == PIXELIZE_FILTER, value=self.default_pixel_size) btn_generate_preview = gr.Button("GENERATE PREVIEW") with gr.Row(): btn_generate = gr.Button("GENERATE", variant="primary") with gr.Row(): vid_output = gr.Video(label="Output video") with gr.Column(): output_file = gr.File(label="Downloadable Video Output File", scale=9) btn_open_folder = gr.Button("📁\nOpen Output folder", scale=1) file_vid_input.change(fn=self.on_video_model_change, inputs=[dd_models, file_vid_input], outputs=[vid_frame_prompter, sld_frame_selector]) dd_models.change(fn=self.on_video_model_change, inputs=[dd_models, file_vid_input], outputs=[vid_frame_prompter, sld_frame_selector]) sld_frame_selector.change(fn=self.on_frame_change, inputs=[sld_frame_selector], outputs=[vid_frame_prompter],) dd_filter_mode.change(fn=self.on_filter_mode_change, inputs=[dd_filter_mode], outputs=[cp_color_picker, nb_pixel_size]) preview_params = [vid_frame_prompter, dd_filter_mode, sld_frame_selector, nb_pixel_size, cp_color_picker] btn_generate_preview.click(fn=self.sam_inf.add_filter_to_preview, inputs=preview_params, outputs=[img_preview]) btn_generate.click(fn=self.sam_inf.create_filtered_video, inputs=preview_params, outputs=[vid_output, output_file]) btn_open_folder.click(fn=lambda: open_folder(OUTPUT_FILTER_DIR), inputs=None, outputs=None) self.demo.queue().launch(inbrowser=True) if __name__ == "__main__": demo = App() demo.launch()