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| import gc | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from isegm.inference.clicker import Click, Clicker | |
| from isegm.inference.predictors import BasePredictor | |
| from isegm.inference.transforms import ZoomIn | |
| from isegm.inference.utils import load_single_is_model | |
| from isegm.utils.vis import draw_click, draw_contour, draw_mask | |
| class InteractiveSegmentationInterface(object): | |
| def __init__(self, device: torch.device): | |
| self.device = device | |
| self._clicker = Clicker() | |
| self._pretrained_models = { | |
| 'GraCo_SimpleClick_ViT-B': {"weights": './weights/simpleclick/sbd_vit_base.pth', "lora": './weights/GraCo/sbd_vit_base_lora.pth'} | |
| } | |
| self._predictor = None | |
| self._pred_prob = None | |
| self._masked_img = None | |
| self._build_interface() | |
| self._add_functions() | |
| def _build_interface(self): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| choices = list(self._pretrained_models.keys()) | |
| self.model_name = gr.Dropdown(choices=choices, value=choices[0], label='Model') | |
| self.loaded_model = gr.Textbox(label='Loaded Model', interactive=False) | |
| self.load_button = gr.Button(value='Load Model') | |
| with gr.Row(): | |
| self.input_img = gr.Image(label='Input Image') | |
| self.click_map = gr.Image( | |
| label='Click Map', show_download_button=False, interactive=False) | |
| with gr.Row(): | |
| self.add_button = gr.Button(value='Add Click', interactive=False) | |
| self.undo_button = gr.Button(value='Undo', interactive=False) | |
| self.submit_button = gr.Button(value='Segment', interactive=False) | |
| self.drawing_board = gr.Image( | |
| label='Add Click', | |
| interactive=False, | |
| visible=False) | |
| with gr.Row(): | |
| self.pos_button = gr.Button(value='Add Positive', visible=False) | |
| self.neg_button = gr.Button(value='Add Negative', visible=False) | |
| self.cancel_button = gr.Button(value='Cancel', visible=False) | |
| with gr.Column(): | |
| self.threshold = gr.Slider( | |
| label='Threshold', | |
| minimum=0.0, | |
| maximum=1.0, | |
| value=0.5, | |
| step=0.01, | |
| interactive=False) | |
| self.granularity = gr.Slider( | |
| label='Granularity', | |
| minimum=0.0, | |
| maximum=1.0, | |
| value=1.0, | |
| step=0.01, | |
| interactive=False) | |
| self.seg_mask = gr.Image( | |
| label='Segmentation', show_download_button=False, interactive=False) | |
| def _add_functions(self): | |
| self.input_img.upload( | |
| fn=self._load_image, | |
| inputs=self.input_img, | |
| outputs=[ | |
| self.click_map, self.seg_mask, self.add_button, self.undo_button, | |
| self.submit_button, self.threshold, self.granularity, self.drawing_board, self.pos_button, | |
| self.neg_button, self.cancel_button | |
| ]) | |
| self.load_button.click( | |
| fn=self._load_model, | |
| inputs=[self.model_name, self.input_img], | |
| outputs=[self.loaded_model, self.submit_button]) | |
| self.add_button.click( | |
| fn=self._create_click, | |
| outputs=[self.drawing_board, self.pos_button, self.neg_button, self.cancel_button]) | |
| self.undo_button.click( | |
| fn=self._undo_click, | |
| outputs=[self.click_map, self.drawing_board, self.undo_button, self.submit_button]) | |
| self.pos_button.click( | |
| fn=self._add_pos_click, | |
| inputs=self.drawing_board, | |
| outputs=[ | |
| self.click_map, self.undo_button, self.submit_button, self.drawing_board, | |
| self.pos_button, self.neg_button, self.cancel_button | |
| ]) | |
| self.neg_button.click( | |
| fn=self._add_neg_click, | |
| inputs=self.drawing_board, | |
| outputs=[ | |
| self.click_map, self.undo_button, self.submit_button, self.drawing_board, | |
| self.pos_button, self.neg_button, self.cancel_button | |
| ]) | |
| self.cancel_button.click( | |
| fn=self._cancel, | |
| outputs=[self.drawing_board, self.pos_button, self.neg_button, self.cancel_button]) | |
| self.submit_button.click( | |
| fn=self._segment, | |
| inputs=[self.input_img, self.threshold, self.granularity], | |
| outputs=[self.seg_mask, self.click_map, self.drawing_board, self.threshold, self.granularity]) | |
| self.threshold.release( | |
| fn=self._show_mask, | |
| inputs=self.threshold, | |
| outputs=[self.seg_mask, self.click_map, self.drawing_board]) | |
| def _click_map(self): | |
| if self._img is None: | |
| return None | |
| img = self._img if self._masked_img is None else self._masked_img | |
| return draw_click(img, self._clicker.get_clicks()) | |
| def _load_image(self, img): | |
| self._img = img | |
| self._img_size = img.shape[:2] | |
| self._clicker.reset_clicks() | |
| self._pred_prob = None | |
| self._masked_img = None | |
| return (self._click_map, None, gr.update(interactive=True), gr.update(interactive=False), gr.update(interactive=False), | |
| gr.update(interactive=False), gr.update(interactive=True), *self._cancel()) | |
| def _load_model(self, model_name, img): | |
| if self._predictor is not None: | |
| del self._predictor | |
| self._predictor = None | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| state_dict = torch.load(self._pretrained_models[model_name]["weights"], map_location='cpu') | |
| model = load_single_is_model(state_dict, device=self.device, lora_checkpoint=self._pretrained_models[model_name]["lora"], eval_ritm=False) | |
| zoom_in = ZoomIn(skip_clicks=-1, target_size=(448, 448)) | |
| self._predictor = BasePredictor(model, device=self.device, zoom_in=zoom_in, with_flip=True) | |
| enable_submit = img is not None and len(self._clicker) > 0 | |
| return model_name, gr.update(interactive=enable_submit) | |
| def _create_click(self): | |
| return gr.update( | |
| value=self._click_map, interactive=True, | |
| visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) | |
| def _cancel(self): | |
| return gr.update( | |
| interactive=False, visible=False), gr.update(visible=False), gr.update( | |
| visible=False), gr.update(visible=False) | |
| def _add_click(self, inp, is_positive): | |
| coords = np.nonzero(inp['mask'].sum(axis=-1)) | |
| if len(coords[0]) == 0: | |
| return (self._click_map, gr.update(interactive=False), gr.update(interactive=False), | |
| *self._cancel()) | |
| coords = (round(coords[0].mean()), round(coords[1].mean())) | |
| click = Click(is_positive=is_positive, coords=coords) | |
| self._clicker.add_click(click) | |
| return (self._click_map, gr.update(interactive=True), | |
| gr.update(interactive=self._predictor is not None), *self._cancel()) | |
| def _add_pos_click(self, inp): | |
| return self._add_click(inp, is_positive=True) | |
| def _add_neg_click(self, inp): | |
| return self._add_click(inp, is_positive=False) | |
| def _undo_click(self): | |
| self._clicker._remove_last_click() | |
| has_clicks = len(self._clicker) > 0 | |
| click_map = self._click_map | |
| return ( | |
| click_map, | |
| click_map, | |
| gr.update(interactive=has_clicks), | |
| gr.update(interactive=has_clicks), | |
| ) | |
| def _segment(self, img, threshold, granularity): | |
| self._predictor.set_input_image(img) | |
| self._pred_prob = self._predictor.get_prediction(self._clicker, gra=granularity) | |
| return (*self._show_mask(threshold), gr.update(value=0.5, interactive=True), gr.update(interactive=True)) | |
| def _show_mask(self, threshold): | |
| mask = self._pred_prob > threshold | |
| img = draw_mask(self._img, mask) | |
| img = draw_contour(img, mask) | |
| self._masked_img = img | |
| click_map = self._click_map | |
| return img, click_map, click_map | |