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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',
tool='sketch',
interactive=False,
visible=False,
brush_radius=15)
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])
@property
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),
)
@torch.no_grad()
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
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