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| import os | |
| import gradio as gr | |
| import numpy as np | |
| import cv2 | |
| from PIL import Image | |
| import torch | |
| from inference import SegmentPredictor | |
| from utils import generate_PCL, PCL3 | |
| sam = SegmentPredictor() | |
| red = (255,0,0) | |
| blue = (0,0,255) | |
| annos = [] | |
| block = gr.Blocks() | |
| with block: | |
| # States | |
| def point_coords_empty(): | |
| return [] | |
| def point_labels_empty(): | |
| return [] | |
| raw_image = gr.Image(type='pil', visible=False) | |
| point_coords = gr.State(point_coords_empty) | |
| point_labels = gr.State(point_labels_empty) | |
| masks = gr.State([]) | |
| cutout_idx = gr.State(set()) | |
| # UI | |
| with gr.Column(): | |
| with gr.Row(): | |
| input_image = gr.Image(label='Input', height=512, type='pil') | |
| masks_annotated_image = gr.AnnotatedImage(label='Segments', height=512) | |
| pcl_figure = gr.Plot(label='3D Reconstruction') | |
| #cutout_galary = gr.Gallery(label='Cutouts', object_fit='contain', height=512) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| with gr.Row(): | |
| point_label_radio = gr.Radio(label='Point Label', choices=[1,0], value=1) | |
| text = gr.Textbox(label='Mask Name') | |
| reset_btn = gr.Button('New Mask') | |
| sam_sgmt_everything_btn = gr.Button('Segment Everything!', variant = 'primary') | |
| sam_decode_btn = gr.Button('Predict using points!', variant = 'primary') | |
| depth_reconstruction_btn = gr.Button('Depth Reconstruction', variant = 'primary') | |
| # components | |
| components = {point_coords, point_labels, raw_image, masks, cutout_idx, input_image, | |
| point_label_radio, text, reset_btn, sam_sgmt_everything_btn, | |
| sam_decode_btn, depth_reconstruction_btn, masks_annotated_image} | |
| def on_depth_reconstruction_btn_click(inputs): | |
| print("depth reconstruction") | |
| image = inputs[raw_image] | |
| # depth reconstruction | |
| fig = PCL3(image) | |
| return {pcl_figure: fig} | |
| depth_reconstruction_btn.click(on_depth_reconstruction_btn_click, components, [pcl_figure], queue=False) | |
| # event - init coords | |
| def on_reset_btn_click(raw_image): | |
| return raw_image, point_coords_empty(), point_labels_empty(), None, [] | |
| reset_btn.click(on_reset_btn_click, [raw_image], [input_image, point_coords, point_labels], queue=False) | |
| def on_input_image_upload(input_image): | |
| print("encoding") | |
| # encode image on upload | |
| sam.encode(input_image) | |
| print("encoding done") | |
| return input_image, point_coords_empty(), point_labels_empty(), None | |
| input_image.upload(on_input_image_upload, [input_image], [raw_image, point_coords, point_labels], queue=False) | |
| # event - set coords | |
| def on_input_image_select(input_image, point_coords, point_labels, point_label_radio, evt: gr.SelectData): | |
| x, y = evt.index | |
| color = red if point_label_radio == 0 else blue | |
| img = np.array(input_image) | |
| cv2.circle(img, (x, y), 5, color, -1) | |
| img = Image.fromarray(img) | |
| point_coords.append([x,y]) | |
| point_labels.append(point_label_radio) | |
| return img, point_coords, point_labels | |
| input_image.select(on_input_image_select, [input_image, point_coords, point_labels, point_label_radio], [input_image, point_coords, point_labels], queue=False) | |
| def on_click_sam_dencode_btn(inputs): | |
| print("inferencing") | |
| image = inputs[raw_image] | |
| generated_mask, _, _ = sam.cond_pred(pts=np.array(inputs[point_coords]), lbls=np.array(inputs[point_labels])) | |
| inputs[masks].append((generated_mask, inputs[text])) | |
| return {masks_annotated_image: (image, inputs[masks])} | |
| sam_decode_btn.click(on_click_sam_dencode_btn, components, [masks_annotated_image, masks, cutout_idx], queue=True) | |
| if __name__ == '__main__': | |
| block.queue() | |
| block.launch() |