Spaces:
Runtime error
Runtime error
| try: | |
| import detectron2 | |
| except: | |
| import os | |
| os.system('pip install git+https://github.com/facebookresearch/detectron2.git') | |
| # os.system('git clone https://github.com/facebookresearch/detectron2.git') | |
| # os.system('python -m pip install -e detectron2') | |
| import gradio as gr | |
| import numpy as np | |
| import cv2 | |
| import torch | |
| from detectron2.config import get_cfg | |
| from GLEE.glee.models.glee_model import GLEE_Model | |
| from GLEE.glee.config_deeplab import add_deeplab_config | |
| from GLEE.glee.config import add_glee_config | |
| import torch.nn.functional as F | |
| import torchvision | |
| import math | |
| from obj365_name import categories as OBJ365_CATEGORIESV2 | |
| print(f"Is CUDA available: {torch.cuda.is_available()}") | |
| # True | |
| if torch.cuda.is_available(): | |
| print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}") | |
| # Tesla T4 | |
| def box_cxcywh_to_xyxy(x): | |
| x_c, y_c, w, h = x.unbind(-1) | |
| b = [(x_c - 0.5 * w), (y_c - 0.5 * h), | |
| (x_c + 0.5 * w), (y_c + 0.5 * h)] | |
| return torch.stack(b, dim=-1) | |
| def scribble2box(img): | |
| if img.max()==0: | |
| return None, None | |
| rows = np.any(img, axis=1) | |
| cols = np.any(img, axis=0) | |
| all = np.any(img,axis=2) | |
| R,G,B,A = img[np.where(all)[0][0],np.where(all)[1][0]].tolist() # get color | |
| ymin, ymax = np.where(rows)[0][[0, -1]] | |
| xmin, xmax = np.where(cols)[0][[0, -1]] | |
| return np.array([ xmin,ymin, xmax,ymax]), (R,G,B) | |
| def LSJ_box_postprocess( out_bbox, padding_size, crop_size, img_h, img_w): | |
| # postprocess box height and width | |
| boxes = box_cxcywh_to_xyxy(out_bbox) | |
| lsj_sclae = torch.tensor([padding_size[1], padding_size[0], padding_size[1], padding_size[0]]).to(out_bbox) | |
| crop_scale = torch.tensor([crop_size[1], crop_size[0], crop_size[1], crop_size[0]]).to(out_bbox) | |
| boxes = boxes * lsj_sclae | |
| boxes = boxes / crop_scale | |
| boxes = torch.clamp(boxes,0,1) | |
| scale_fct = torch.tensor([img_w, img_h, img_w, img_h]) | |
| scale_fct = scale_fct.to(out_bbox) | |
| boxes = boxes * scale_fct | |
| return boxes | |
| COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125], | |
| [0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933], | |
| [0.494, 0.000, 0.556], [0.494, 0.000, 0.000], [0.000, 0.745, 0.000], | |
| [0.700, 0.300, 0.600],[0.000, 0.447, 0.741], [0.850, 0.325, 0.098]] | |
| coco_class_name = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'] | |
| OBJ365_class_names = [cat['name'] for cat in OBJ365_CATEGORIESV2] | |
| class_agnostic_name = ['object'] | |
| if torch.cuda.is_available(): | |
| print('use cuda') | |
| device = 'cuda' | |
| else: | |
| print('use cpu') | |
| device='cpu' | |
| cfg_r50 = get_cfg() | |
| add_deeplab_config(cfg_r50) | |
| add_glee_config(cfg_r50) | |
| conf_files_r50 = 'GLEE/configs/R50.yaml' | |
| checkpoints_r50 = torch.load('GLEE_R50_Scaleup10m.pth') | |
| cfg_r50.merge_from_file(conf_files_r50) | |
| GLEEmodel_r50 = GLEE_Model(cfg_r50, None, device, None, True).to(device) | |
| GLEEmodel_r50.load_state_dict(checkpoints_r50, strict=False) | |
| GLEEmodel_r50.eval() | |
| cfg_swin = get_cfg() | |
| add_deeplab_config(cfg_swin) | |
| add_glee_config(cfg_swin) | |
| conf_files_swin = 'GLEE/configs/SwinL.yaml' | |
| checkpoints_swin = torch.load('GLEE_SwinL_Scaleup10m.pth') | |
| cfg_swin.merge_from_file(conf_files_swin) | |
| GLEEmodel_swin = GLEE_Model(cfg_swin, None, device, None, True).to(device) | |
| GLEEmodel_swin.load_state_dict(checkpoints_swin, strict=False) | |
| GLEEmodel_swin.eval() | |
| pixel_mean = torch.Tensor( [123.675, 116.28, 103.53]).to(device).view(3, 1, 1) | |
| pixel_std = torch.Tensor([58.395, 57.12, 57.375]).to(device).view(3, 1, 1) | |
| normalizer = lambda x: (x - pixel_mean) / pixel_std | |
| inference_size = 800 | |
| inference_type = 'resize_shot' # or LSJ | |
| size_divisibility = 32 | |
| FONT_SCALE = 1.5e-3 | |
| THICKNESS_SCALE = 1e-3 | |
| TEXT_Y_OFFSET_SCALE = 1e-2 | |
| if inference_type != 'LSJ': | |
| resizer = torchvision.transforms.Resize(inference_size) | |
| def segment_image(img,prompt_mode, categoryname, custom_category, expressiong, results_select, num_inst_select, threshold_select, mask_image_mix_ration, model_selection): | |
| if model_selection == 'GLEE-Plus (SwinL)': | |
| GLEEmodel = GLEEmodel_swin | |
| print('use GLEE-Plus') | |
| else: | |
| GLEEmodel = GLEEmodel_r50 | |
| print('use GLEE-Lite') | |
| copyed_img = img['background'][:,:,:3].copy() | |
| ori_image = torch.as_tensor(np.ascontiguousarray( copyed_img.transpose(2, 0, 1))) | |
| ori_image = normalizer(ori_image.to(device))[None,] | |
| _,_, ori_height, ori_width = ori_image.shape | |
| if inference_type == 'LSJ': | |
| infer_image = torch.zeros(1,3,1024,1024).to(ori_image) | |
| infer_image[:,:,:inference_size,:inference_size] = ori_image | |
| else: | |
| resize_image = resizer(ori_image) | |
| image_size = torch.as_tensor((resize_image.shape[-2],resize_image.shape[-1])) | |
| re_size = resize_image.shape[-2:] | |
| if size_divisibility > 1: | |
| stride = size_divisibility | |
| # the last two dims are H,W, both subject to divisibility requirement | |
| padding_size = ((image_size + (stride - 1)).div(stride, rounding_mode="floor") * stride).tolist() | |
| infer_image = torch.zeros(1,3,padding_size[0],padding_size[1]).to(resize_image) | |
| infer_image[0,:,:image_size[0],:image_size[1]] = resize_image | |
| # reversed_image = infer_image*pixel_std + pixel_mean | |
| # reversed_image = torch.clip(reversed_image,min=0,max=255) | |
| # reversed_image = reversed_image[0].permute(1,2,0) | |
| # reversed_image = reversed_image.int().cpu().numpy().copy() | |
| # cv2.imwrite('test.png',reversed_image[:,:,::-1]) | |
| if prompt_mode == 'categories' or prompt_mode == 'expression': | |
| if len(results_select)==0: | |
| results_select=['box'] | |
| if prompt_mode == 'categories': | |
| if categoryname =="COCO-80": | |
| batch_category_name = coco_class_name | |
| elif categoryname =="OBJ365": | |
| batch_category_name = obj365_class_name | |
| elif categoryname =="Custom-List": | |
| batch_category_name = custom_category.split(',') | |
| else: | |
| batch_category_name = class_agnostic_name | |
| # mask_ori = torch.from_numpy(np.load('03_moto_mask.npy'))[None,] | |
| # mask_ori = (F.interpolate(mask_ori, (height, width), mode='bilinear') > 0).to(device) | |
| # prompt_list = [mask_ori[0]] | |
| prompt_list = [] | |
| with torch.no_grad(): | |
| (outputs,_) = GLEEmodel(infer_image, prompt_list, task="coco", batch_name_list=batch_category_name, is_train=False) | |
| topK_instance = max(num_inst_select,1) | |
| else: | |
| topK_instance = 1 | |
| prompt_list = {'grounding':[expressiong]} | |
| with torch.no_grad(): | |
| (outputs,_) = GLEEmodel(infer_image, prompt_list, task="grounding", batch_name_list=[], is_train=False) | |
| mask_pred = outputs['pred_masks'][0] | |
| mask_cls = outputs['pred_logits'][0] | |
| boxes_pred = outputs['pred_boxes'][0] | |
| scores = mask_cls.sigmoid().max(-1)[0] | |
| scores_per_image, topk_indices = scores.topk(topK_instance, sorted=True) | |
| if prompt_mode == 'categories': | |
| valid = scores_per_image>threshold_select | |
| topk_indices = topk_indices[valid] | |
| scores_per_image = scores_per_image[valid] | |
| pred_class = mask_cls[topk_indices].max(-1)[1].tolist() | |
| pred_boxes = boxes_pred[topk_indices] | |
| boxes = LSJ_box_postprocess(pred_boxes,padding_size,re_size, ori_height,ori_width) | |
| mask_pred = mask_pred[topk_indices] | |
| pred_masks = F.interpolate( mask_pred[None,], size=(padding_size[0], padding_size[1]), mode="bilinear", align_corners=False ) | |
| pred_masks = pred_masks[:,:,:re_size[0],:re_size[1]] | |
| pred_masks = F.interpolate( pred_masks, size=(ori_height,ori_width), mode="bilinear", align_corners=False ) | |
| pred_masks = (pred_masks>0).detach().cpu().numpy()[0] | |
| if 'mask' in results_select: | |
| zero_mask = np.zeros_like(copyed_img) | |
| for nn, mask in enumerate(pred_masks): | |
| # mask = mask.numpy() | |
| mask = mask.reshape(mask.shape[0], mask.shape[1], 1) | |
| lar = np.concatenate((mask*COLORS[nn%12][2], mask*COLORS[nn%12][1], mask*COLORS[nn%12][0]), axis = 2) | |
| zero_mask = zero_mask+ lar | |
| lar_valid = zero_mask>0 | |
| masked_image = lar_valid*copyed_img | |
| img_n = masked_image*mask_image_mix_ration + np.clip(zero_mask,0,1)*255*(1-mask_image_mix_ration) | |
| max_p = img_n.max() | |
| img_n = 255*img_n/max_p | |
| ret = (~lar_valid*copyed_img)*mask_image_mix_ration + img_n | |
| ret = ret.astype('uint8') | |
| else: | |
| ret = copyed_img | |
| if 'box' in results_select: | |
| line_width = max(ret.shape) /200 | |
| for nn,(classid, box) in enumerate(zip(pred_class,boxes)): | |
| x1,y1,x2,y2 = box.long().tolist() | |
| RGB = (COLORS[nn%12][2]*255,COLORS[nn%12][1]*255,COLORS[nn%12][0]*255) | |
| cv2.rectangle(ret, (x1,y1), (x2,y2), RGB, math.ceil(line_width) ) | |
| if prompt_mode == 'categories' or (prompt_mode == 'expression' and 'expression' in results_select ): | |
| if prompt_mode == 'categories': | |
| label = '' | |
| if 'name' in results_select: | |
| label += batch_category_name[classid] | |
| if 'score' in results_select: | |
| label += str(scores_per_image[nn].item())[:4] | |
| else: | |
| label = expressiong | |
| if len(label)==0: | |
| continue | |
| height, width, _ = ret.shape | |
| FONT = cv2.FONT_HERSHEY_COMPLEX | |
| label_width, label_height = cv2.getTextSize(label, FONT, min(width, height) * FONT_SCALE, math.ceil(min(width, height) * THICKNESS_SCALE))[0] | |
| cv2.rectangle(ret, (x1,y1), (x1+label_width,(y1 -label_height) - int(height * TEXT_Y_OFFSET_SCALE)), RGB, -1) | |
| cv2.putText( | |
| ret, | |
| label, | |
| (x1, y1 - int(height * TEXT_Y_OFFSET_SCALE)), | |
| fontFace=FONT, | |
| fontScale=min(width, height) * FONT_SCALE, | |
| thickness=math.ceil(min(width, height) * THICKNESS_SCALE), | |
| color=(255,255,255), | |
| ) | |
| ret = ret.astype('uint8') | |
| return ret | |
| else: #visual prompt | |
| topK_instance = 1 | |
| copyed_img = img['background'][:,:,:3].copy() | |
| # get bbox from scribbles in layers | |
| bbox_list = [scribble2box(layer) for layer in img['layers'] ] | |
| visual_prompt_list = [] | |
| visual_prompt_RGB_list = [] | |
| for mask, (box,RGB) in zip(img['layers'], bbox_list): | |
| if box is None: | |
| continue | |
| if prompt_mode=='box': | |
| fakemask = np.zeros_like(copyed_img[:,:,0]) | |
| x1 ,y1 ,x2, y2 = box | |
| fakemask[ y1:y2, x1:x2 ] = 1 | |
| fakemask = fakemask>0 | |
| elif prompt_mode=='point': | |
| fakemask = np.zeros_like(copyed_img[:,:,0]) | |
| H,W = fakemask.shape | |
| x1 ,y1 ,x2, y2 = box | |
| center_x, center_y = (x1+x2)//2, (y1+y2)//2 | |
| fakemask[ center_y-H//40:center_y+H//40, center_x-W//40:center_x+W//40 ] = 1 | |
| fakemask = fakemask>0 | |
| elif prompt_mode=='scribble': | |
| fakemask = mask[:,:,-1] | |
| fakemask = fakemask>0 | |
| fakemask = torch.from_numpy(fakemask).unsqueeze(0).to(ori_image) | |
| if inference_type == 'LSJ': | |
| infer_visual_prompt = torch.zeros(1,1024,1024).to(ori_image) | |
| infer_visual_prompt[:,:inference_size,:inference_size] = fakemask | |
| else: | |
| resize_fakemask = resizer(fakemask) | |
| if size_divisibility > 1: | |
| # the last two dims are H,W, both subject to divisibility requirement | |
| infer_visual_prompt = torch.zeros(1,padding_size[0],padding_size[1]).to(resize_fakemask) | |
| infer_visual_prompt[:,:image_size[0],:image_size[1]] = resize_fakemask | |
| visual_prompt_list.append( infer_visual_prompt>0 ) | |
| visual_prompt_RGB_list.append(RGB) | |
| mask_results_list = [] | |
| for visual_prompt in visual_prompt_list: | |
| prompt_list = {'spatial':[visual_prompt]} | |
| with torch.no_grad(): | |
| (outputs,_) = GLEEmodel(infer_image, prompt_list, task="coco", batch_name_list=['object'], is_train=False, visual_prompt_type=prompt_mode ) | |
| mask_pred = outputs['pred_masks'][0] | |
| mask_cls = outputs['pred_logits'][0] | |
| boxes_pred = outputs['pred_boxes'][0] | |
| scores = mask_cls.sigmoid().max(-1)[0] | |
| scores_per_image, topk_indices = scores.topk(topK_instance, sorted=True) | |
| pred_class = mask_cls[topk_indices].max(-1)[1].tolist() | |
| pred_boxes = boxes_pred[topk_indices] | |
| boxes = LSJ_box_postprocess(pred_boxes,padding_size,re_size, ori_height,ori_width) | |
| mask_pred = mask_pred[topk_indices] | |
| pred_masks = F.interpolate( mask_pred[None,], size=(padding_size[0], padding_size[1]), mode="bilinear", align_corners=False ) | |
| pred_masks = pred_masks[:,:,:re_size[0],:re_size[1]] | |
| pred_masks = F.interpolate( pred_masks, size=(ori_height,ori_width), mode="bilinear", align_corners=False ) | |
| pred_masks = (pred_masks>0).detach().cpu().numpy()[0] | |
| mask_results_list.append(pred_masks) | |
| zero_mask = np.zeros_like(copyed_img) | |
| for mask,RGB in zip(mask_results_list,visual_prompt_RGB_list): | |
| mask = mask.reshape(mask.shape[-2], mask.shape[-1], 1) | |
| lar = np.concatenate((mask*RGB[0], mask*RGB[1],mask*RGB[2]), axis = 2) | |
| zero_mask = zero_mask+ lar | |
| lar_valid = zero_mask>0 | |
| masked_image = lar_valid*copyed_img | |
| img_n = masked_image*mask_image_mix_ration + np.clip(zero_mask,0,255)*(1-mask_image_mix_ration) | |
| max_p = img_n.max() | |
| img_n = 255*img_n/max_p | |
| ret = (~lar_valid*copyed_img)*mask_image_mix_ration + img_n | |
| ret = ret.astype('uint8') | |
| # cv2.imwrite('00020_inst.jpg', cv2.cvtColor(ret, cv2.COLOR_BGR2RGB)) | |
| return ret | |
| # def get_select_coordinates(img): | |
| # # img{'background': (H,W,3) | |
| # # 'layers': list[ (H,W,4(RGBA)) ], draw map | |
| # # 'composite': (H,W,4(RGBA))} ori_img concat drow | |
| # ori_img = img['background'][:,:,:3].copy() | |
| # # get bbox from scribbles in layers | |
| # bbox_list = [scribble2box(layer) for layer in img['layers'] ] | |
| # for mask, (box,RGB) in zip(img['layers'], bbox_list): | |
| # if box is None: | |
| # continue | |
| # cv2.rectangle(ori_img, (box[0],box[1]), (box[2],box[3]),RGB, 3) | |
| # return ori_img | |
| def visual_prompt_preview(img, prompt_mode): | |
| copyed_img = img['background'][:,:,:3].copy() | |
| # get bbox from scribbles in layers | |
| bbox_list = [scribble2box(layer) for layer in img['layers'] ] | |
| zero_mask = np.zeros_like(copyed_img) | |
| for mask, (box,RGB) in zip(img['layers'], bbox_list): | |
| if box is None: | |
| continue | |
| if prompt_mode=='box': | |
| fakemask = np.zeros_like(copyed_img[:,:,0]) | |
| x1 ,y1 ,x2, y2 = box | |
| fakemask[ y1:y2, x1:x2 ] = 1 | |
| fakemask = fakemask>0 | |
| elif prompt_mode=='point': | |
| fakemask = np.zeros_like(copyed_img[:,:,0]) | |
| H,W = fakemask.shape | |
| x1 ,y1 ,x2, y2 = box | |
| center_x, center_y = (x1+x2)//2, (y1+y2)//2 | |
| fakemask[ center_y-H//40:center_y+H//40, center_x-W//40:center_x+W//40 ] = 1 | |
| fakemask = fakemask>0 | |
| else: | |
| fakemask = mask[:,:,-1] | |
| fakemask = fakemask>0 | |
| mask = fakemask.reshape(fakemask.shape[0], fakemask.shape[1], 1) | |
| lar = np.concatenate((mask*RGB[0], mask*RGB[1],mask*RGB[2]), axis = 2) | |
| zero_mask = zero_mask+ lar | |
| img_n = copyed_img + np.clip(zero_mask,0,255) | |
| max_p = img_n.max() | |
| ret = 255*img_n/max_p | |
| ret = ret.astype('uint8') | |
| return ret | |
| with gr.Blocks() as demo: | |
| gr.Markdown('# GLEE: General Object Foundation Model for Images and Videos at Scale') | |
| gr.Markdown('## [Paper](ArXiv) - [Project Page](https://glee-vision.github.io) - [Code](https://github.com/FoundationVision/GLEE) ') | |
| gr.Markdown( | |
| '**The functionality demonstration demo app of GLEE. Select a Tab for image or video tasks. Image tasks includes arbitrary vocabulary object detection&segmentation, any form of object name or object caption detection, referring expression comprehension, and interactive segmentation. Video tasks add object tracking functionality based on image tasks.**' | |
| ) | |
| with gr.Tab("Image task"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| img_input = gr.ImageEditor() | |
| model_select = gr.Dropdown( | |
| ["GLEE-Lite (R50)", "GLEE-Plus (SwinL)"], value = "GLEE-Lite (R50)" , multiselect=False, label="Model", | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt_mode_select = gr.Radio(["point", "scribble", "box", "categories", "expression"], label="Prompt", value= "categories" , info="What kind of prompt do you want to use?") | |
| category_select = gr.Dropdown( | |
| ["COCO-80", "OBJ365", "Custom-List", "Class-Agnostic"], value = "COCO-80" , multiselect=False, label="Categories", info="Choose an existing category list or class-agnostic" | |
| ) | |
| custom_category = gr.Textbox( | |
| label="Custom Category", | |
| info="Input custom category list, seperate by ',' ", | |
| lines=1, | |
| value="dog, cat, car, person", | |
| ) | |
| input_expressiong = gr.Textbox( | |
| label="Expression", | |
| info="Input any description of an object in the image ", | |
| lines=2, | |
| value="the red car", | |
| ) | |
| # with gr.Column(): | |
| with gr.Group(): | |
| with gr.Accordion("Interactive segmentation usage",open=False): | |
| gr.Markdown( | |
| 'For interactive segmentation:<br />\ | |
| 1.Draw points, boxes, or scribbles on the canvas for multiclass segmentation; use separate layers for different objects, adding layers with a "+" sign.<br />\ | |
| 2.Point mode accepts a single point only; multiple points default to the centroid, so use boxes or scribbles for larger objects.<br />\ | |
| 3.After drawing, click green "√" to preview the prompt visualization; the segmentation mask follows the chosen prompt colors.' | |
| ) | |
| with gr.Accordion("Text based detection usage",open=False): | |
| gr.Markdown( | |
| 'GLEE supports three kind of object perception methods: category list, textual description, and class-agnostic.<br />\ | |
| 1.Select an existing category list from the "Categories" dropdown, like COCO or OBJ365, or customize your own list.<br />\ | |
| 2.Enter arbitrary object name in "Custom Category", or choose the expression model and describe the object in "Expression Textbox" for single object detection only.<br />\ | |
| 3.For class-agnostic mode, choose "Class-Agnostic" from the "Categories" dropdown.' | |
| ) | |
| img_showbox = gr.Image(label="visual prompt area preview") | |
| with gr.Column(): | |
| image_segment = gr.Image(label="detection and segmentation results") | |
| with gr.Accordion("Try More Visualization Options"): | |
| results_select = gr.CheckboxGroup(["box", "mask", "name", "score", "expression"], value=["box", "mask", "name", "score"], label="Shown Results", info="The results shown on image") | |
| num_inst_select = gr.Slider(1, 50, value=15, step=1, label="Num of topK instances for category based detection", info="Choose between 1 and 50 for better visualization") | |
| threshold_select = gr.Slider(0, 1, value=0.2, label="Confidence Threshold", info="Choose threshold ") | |
| mask_image_mix_ration = gr.Slider(0, 1, value=0.65, label="Image Brightness Ratio", info="Brightness between image and colored masks ") | |
| image_button = gr.Button("Detect & Segment") | |
| img_input.change(visual_prompt_preview, inputs = [img_input,prompt_mode_select] , outputs = img_showbox) | |
| image_button.click(segment_image, inputs=[img_input, prompt_mode_select, category_select, custom_category,input_expressiong, results_select, num_inst_select, threshold_select, mask_image_mix_ration,model_select], outputs=image_segment) | |
| with gr.Tab("Video task"): | |
| with gr.Row(): | |
| gr.Markdown( | |
| '# Due to computational resource limitations, support for video tasks is being processed and is expected to be available within a week.' | |
| ) | |
| video_input = gr.Image() | |
| video_button = gr.Button("Segment&Track") | |
| if __name__ == '__main__': | |
| demo.launch(inbrowser=True,) | |