try: import detectron2 except: import os os.system('pip install git+https://github.com/facebookresearch/detectron2.git') os.system('cd /home/user/app/GLEE/glee/models/pixel_decoder/ops && python setup.py build install --user') # os.system('python -m pip install -e detectron2') import gradio as gr import numpy as np import cv2 import torch from os import path 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 scipy.optimize import linear_sum_assignment from obj365_name import categories as OBJ365_CATEGORIESV2 import copy import skvideo.io this_dir = path.dirname(path.abspath(__file__)) 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'] YTBVISOVIS_class_name = ['lizard', 'cat', 'horse', 'eagle', 'frog', 'Horse', 'monkey', 'bear', 'parrot', 'giant_panda', 'truck', 'zebra', 'rabbit', 'skateboard', 'tiger', 'shark', 'Person', 'Poultry', 'Zebra', 'Airplane', 'elephant', 'Elephant', 'Turtle', 'snake', 'train', 'Dog', 'snowboard', 'airplane', 'Lizard', 'dog', 'Cat', 'earless_seal', 'boat', 'Tiger', 'motorbike', 'duck', 'fox', 'Monkey', 'Bird', 'Bear', 'tennis_racket', 'Rabbit', 'Giraffe', 'Motorcycle', 'fish', 'Boat', 'deer', 'ape', 'Bicycle', 'Parrot', 'Cow', 'turtle', 'mouse', 'owl', 'Fish', 'surfboard', 'Giant_panda', 'Sheep', 'hand', 'Vehical', 'sedan', 'leopard', 'person', 'giraffe', 'cow'] 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_vos = get_cfg() add_deeplab_config(cfg_vos) add_glee_config(cfg_vos) conf_files_vos = 'GLEE/configs/vos_v0.yaml' cfg_vos.merge_from_file(conf_files_vos) 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 video_inference_size = 640 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,antialias=True) videoresizer = torchvision.transforms.Resize(video_inference_size,antialias=True) def segment_image(img, prompt_mode, categoryname, custom_category, expressiong, results_select, num_inst_select, threshold_select, mask_image_mix_ration, model_selection): torch.cuda.empty_cache() 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_names 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 process_frames(frame_list): clip_images = [torch.as_tensor(np.ascontiguousarray( frame[:,:,::-1].transpose(2, 0, 1))) for frame in frame_list] processed_frames = [] for ori_image in clip_images: 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 = videoresizer(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 processed_frames.append(infer_image) return torch.cat(processed_frames,dim=0), padding_size,re_size,ori_height, ori_width # [clip_lenth,3,h,w] def match_from_embds(tgt_embds, cur_embds): cur_embds = cur_embds / cur_embds.norm(dim=1)[:, None] tgt_embds = tgt_embds / tgt_embds.norm(dim=1)[:, None] cos_sim = torch.mm(cur_embds, tgt_embds.transpose(0,1)) cost_embd = 1 - cos_sim C = 1.0 * cost_embd C = C.cpu() indices = linear_sum_assignment(C.transpose(0, 1)) # target x current indices = indices[1] # permutation that makes current aligns to target return indices def segment_video(video, prompt_mode, categoryname, custom_category, expressiong, results_select, num_inst_select, threshold_select, mask_image_mix_ration, model_selection,video_frames_select, prompter): torch.cuda.empty_cache() ### model selection if model_selection == 'GLEE-Plus (SwinL)': GLEEmodel = GLEEmodel_swin print('use GLEE-Plus') clip_length = 2 #batchsize else: GLEEmodel = GLEEmodel_r50 print('use GLEE-Lite') clip_length = 4 #batchsize # read video and get sparse frames cap = cv2.VideoCapture(video) video_fps = cap.get(cv2.CAP_PROP_FPS ) print('video fps:', video_fps) frame_list = [] frac = video_fps/30 frame_count = 0 read_fps = 10 interval = int( frac *(30 /read_fps) ) #interval frames while cap.isOpened(): ret, frame = cap.read() frame_count += 1 # if frame is read correctly ret is True if not ret: print("Can't receive frame (stream end?). Exiting ...") break if frame_count % int(interval) == 0: frame_list.append(frame) cap.release() first_frame = frame_list[0] frame_list = frame_list[:video_frames_select] # max num of frames print('num frames:', len(frame_list)) video_len = len(frame_list) 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 =="YTBVIS&OVIS": batch_category_name = YTBVISOVIS_class_name elif categoryname =="OBJ365": batch_category_name = OBJ365_class_names elif categoryname =="Custom-List": batch_category_name = custom_category.split(',') else: batch_category_name = class_agnostic_name task = 'coco' prompt_list = [] topK_instance = num_inst_select prompt_mode = 'categories' results_select = ['mask', 'score', 'box', 'name'] else: topK_instance = 1 initprompt_list = {'grounding':[expressiong]} task = 'grounding' batch_category_name = [] #split long video into clips to form a batch input num_clips = math.ceil(video_len/clip_length) logits_list, boxes_list, embed_list, masks_list = [], [], [], [] for c in range(num_clips): start_idx = c*clip_length end_idx = (c+1)*clip_length clip_inputs = frame_list[start_idx:end_idx] clip_images, padding_size,re_size,ori_height, ori_width = process_frames(clip_inputs) if task=='grounding': prompt_list = {'grounding': initprompt_list['grounding']*len(clip_images)} with torch.no_grad(): (clip_output,_) = GLEEmodel(clip_images, prompt_list, task=task, batch_name_list=batch_category_name, is_train=False) logits_list.append(clip_output['pred_logits'].detach()) boxes_list.append(clip_output['pred_boxes'].detach()) embed_list.append(clip_output['pred_track_embed'].detach()) masks_list.append(clip_output['pred_masks'].detach()) #.to(self.merge_device) del clip_output torch.cuda.empty_cache() outputs = { 'pred_logits':torch.cat(logits_list,dim=0), 'pred_track_embed':torch.cat(embed_list,dim=0), 'pred_masks':torch.cat(masks_list,dim=0), 'pred_boxes': torch.cat(boxes_list,dim=0), } pred_logits = list(torch.unbind(outputs['pred_logits'])) pred_masks = list(torch.unbind(outputs['pred_masks'])) pred_embds = list(torch.unbind(outputs['pred_track_embed'])) pred_boxes = list(torch.unbind(outputs['pred_boxes'])) del outputs out_logits = [] out_masks = [] out_embds = [] out_boxes = [] out_logits.append(pred_logits[0]) out_masks.append(pred_masks[0]) out_embds.append(pred_embds[0]) out_boxes.append(pred_boxes[0]) memory_embedding = out_embds[-1] for i in range(1, len(pred_logits)): # indices = self.match_from_embds(memory_embedding, pred_embds[i]) MA_embedding = torch.stack(out_embds[-5:]).mean(0) indices = match_from_embds(MA_embedding, pred_embds[i]) out_logits.append(pred_logits[i][indices, :]) out_masks.append(pred_masks[i][indices, :, :]) out_embds.append(pred_embds[i][indices, :]) out_boxes.append(pred_boxes[i][indices, :]) score_weights = pred_logits[i][indices, :].sigmoid().max(-1)[0][:,None] memory_embedding = (memory_embedding+pred_embds[i][indices, :]*score_weights )/(1+score_weights) mask_cls = sum(out_logits)/len(out_logits) scores = mask_cls.sigmoid().max(-1)[0] scores_per_image, topk_indices = scores.topk(topK_instance, sorted=True) valid = scores_per_image>threshold_select topk_indices = topk_indices[valid] scores_per_image = scores_per_image[valid] out_logits = torch.stack(out_logits, dim=1)[topk_indices] # q numc -> q t numc mask_pred = torch.stack(out_masks, dim=1)[topk_indices] # q h w -> numinst t h w pred_boxes = torch.stack(out_boxes, dim=1)[topk_indices] # q 4 -> numinst t 4 perframe_score = out_logits.sigmoid().max(-1)[0].cpu().numpy() pred_class = mask_cls[topk_indices].max(-1)[1].tolist() boxes = LSJ_box_postprocess(pred_boxes,padding_size,re_size, ori_height,ori_width) pred_masks = F.interpolate( mask_pred, 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() # [numinst,t,h,w] ourput_frames = [] for frameidx, ori_frame in enumerate(frame_list): copyed_img = ori_frame.copy() if 'mask' in results_select: zero_mask = np.zeros_like(copyed_img) for nn, (mask,score) in enumerate(zip(pred_masks[:,frameidx],perframe_score[:,frameidx])): # mask = mask.numpy() if score0 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, score) in enumerate(zip(pred_class,boxes[:,frameidx],perframe_score[:,frameidx])): if score 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 prompter['layers'] = prompter['layers'][:1] #only keep 1 prompt for VOS as model can only segment one object once infer bbox_list = [scribble2box(layer) for layer in prompter['layers'] ] visual_prompt_list = [] visual_prompt_RGB_list = [] for mask, (box,RGB) in zip(prompter['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 = videoresizer(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 ) first_frame_mask_padding = copy.deepcopy(pred_masks.detach()) 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)) output_vos_results = [] output_vos_results.append(ret) #### vos process checkpoints_VOS = torch.load('GLEE_vos_r50.pth') GLEEmodel_VOS = GLEE_Model(cfg_vos, None, device, None, True).to(device) GLEEmodel_VOS.load_state_dict(checkpoints_VOS, strict=False) GLEEmodel_VOS.eval() exist_obj_dict = {} language_dict_features_dict_init = {} language_dict_features_dict_prev = {} point_sample_extra = {} for frame_idx in range(video_len): score_dict = {} if frame_idx==0: exist_obj_dict.update({1:first_frame_mask_padding[0]>0 }) prompt_list["spatial"] = [first_frame_mask_padding[0]>0] frame_image, padding_size,re_size,ori_height, ori_width = process_frames(frame_list[frame_idx:frame_idx+1]) with torch.no_grad(): language_dict_features_dict_init[1], point_sample_extra[1] = \ GLEEmodel_VOS.vos_step1(frame_image, prompt_list, 'ytbvos', batch_name_list=['object'], is_train= False) language_dict_features_dict_prev[1] = copy.deepcopy(language_dict_features_dict_init[1]) score_dict[1] = 1.0 if frame_idx>0: cur_obj_id=1 frame_image, padding_size,re_size,ori_height, ori_width = process_frames(frame_list[frame_idx:frame_idx+1]) prompt_list["spatial"] = [exist_obj_dict[cur_obj_id]] language_dict_features_init = copy.deepcopy(language_dict_features_dict_init[cur_obj_id]) # Important language_dict_features_prev = copy.deepcopy(language_dict_features_dict_prev[cur_obj_id]) # Important language_dict_features_cur = {} language_dict_features_cur["hidden"] = torch.cat([language_dict_features_init["hidden"], language_dict_features_prev["hidden"]], dim=1) language_dict_features_cur["masks"] = torch.cat([language_dict_features_init["masks"], language_dict_features_prev["masks"]], dim=1) # concat initial prompt and last frame prompt for early fusion,but only use last frame point sampled feature for decocer self attention with torch.no_grad(): frame_output,_ = GLEEmodel_VOS.vos_step2(frame_image, task='ytbvos', language_dict_features = language_dict_features_cur, \ last_extra = point_sample_extra[cur_obj_id], batch_name_list=['object'], is_train= False) logits = frame_output['pred_scores'][0] top_k_propose = 1 topk_values, topk_indexes = torch.topk(logits.sigmoid(), top_k_propose, dim=0) mask_pred_result = frame_output['pred_masks'][0,topk_indexes] #[nk,1,H,W] # pred_embeddings = frame_output['pred_track_embed'][0,topk_indexes.squeeze()] #[nk,256] score_dict[cur_obj_id] = topk_values.item() if score_dict[cur_obj_id] > 0.3: mask_pred_result = F.interpolate( mask_pred_result, size=(padding_size[0], padding_size[1]), mode="bilinear", align_corners=False, ) exist_obj_dict[cur_obj_id] = mask_pred_result[0,0]>0 mask_pred_result = mask_pred_result[:,:,:re_size[0],:re_size[1]] mask_pred_result = F.interpolate( mask_pred_result, size=(ori_height,ori_width), mode="bilinear", align_corners=True )[0] final_mask = mask_pred_result[0]>0 final_mask = final_mask.cpu().numpy() copyed_img = frame_list[frame_idx] zero_mask = np.zeros_like(copyed_img) RGB = visual_prompt_RGB_list[0] mask = final_mask.reshape(final_mask.shape[0], final_mask.shape[1], 1) lar = np.concatenate((mask*RGB[2], mask*RGB[1],mask*RGB[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,0,255)*(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') output_vos_results.append(ret) if score_dict[cur_obj_id]>0.5: # update memory prompt_list["spatial"] = [exist_obj_dict[cur_obj_id].unsqueeze(0)] assert cur_obj_id in language_dict_features_dict_prev with torch.no_grad(): language_dict_features_dict_prev[cur_obj_id], point_sample_extra[cur_obj_id] = \ GLEEmodel_VOS.vos_step1(frame_image, prompt_list, 'ytbvos', batch_name_list=['object'], is_train= False) else: # add zero as mask copyed_img = frame_list[frame_idx] ret = copyed_img*mask_image_mix_ration ret = ret.astype('uint8') output_vos_results.append(ret[:,:,::-1]) size = (ori_width,ori_height) output_file = "test.mp4" writer = skvideo.io.FFmpegWriter(output_file, inputdict={'-r': str(read_fps)}, outputdict={'-r': str(read_fps), '-vcodec': 'libx264'}) for i in range(len(output_vos_results)): writer.writeFrame(output_vos_results[i]) writer.close() # out = cv2.VideoWriter(output_file,cv2.VideoWriter_fourcc(*'avc1'), read_fps, size) # for i in range(len(output_vos_results)): # out.write(output_vos_results[i]) # out.release() torch.cuda.empty_cache() return output_file 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 image_example_list = [ [ this_dir + "/Examples/000000480122.jpg", "categories", "OBJ365", "", "", "50", ], [ this_dir + "/Examples/20231222.jpg", "expression", "COCO-80", "", "a purple star holding by a person ", ], [ this_dir + "/Examples/000000001000.jpg", "expression", "COCO-80", "", "the left boy", ], [ this_dir + "/Examples/000000001000.jpg", "expression", "COCO-80", "", "the left girl", ], [ this_dir + "/Examples/1.png", "categories", "Custom-List", "manholecover, bollard, person, car, motobike", "", "10", ], [ this_dir + "/Examples/cat.jpg", "categories", "Custom-List", "cat_eye, cat_ear, candle", " ", "10", ], [ this_dir + "/Examples/00000.jpg", "categories", "COCO-80", "", "", "20", ], [ this_dir + "/Examples/000000340697.jpg", "categories", "COCO-80", "", "", "20", ], [ this_dir + "/Examples/sa_7842964.jpg", "categories", "COCO-80", "", "", "20", ], [ this_dir + "/Examples/1.png", "categories", "OBJ365", "", "", "50", ], [ this_dir + "/Examples/sa_7842967.jpg", "categories", "COCO-80", "", "", "20", ], [ this_dir + "/Examples/sa_7842976.jpg", "categories", "COCO-80", "", "", "20", ], [ this_dir + "/Examples/sa_7842992.jpg", "categories", "COCO-80", "", "", "20", ], [ this_dir + "/Examples/sa_7842994.jpg", "categories", "COCO-80", "", "", "20", ], ] video_example_list = [ [ this_dir + "/Examples/bike.mp4", "categories", "COCO-80", "", "", "10", ], [ this_dir + "/Examples/bike.mp4", "categories", "Custom-List", "backpack, bollard", "", "5", ], [ this_dir + "/Examples/horse.mp4", "expression", "", "", "the left horse", "10", ], [ this_dir + "/Examples/racing_car2.mp4", "categories", "COCO-80", "", "", "15", ], [ this_dir + "/Examples/racing_car3.mp4", "categories", "COCO-80", "", "", "15", ], [ this_dir + "/Examples/street.mp4", "categories", "OBJ365", "", "", "15", ], [ this_dir + "/Examples/train.mp4", "categories", "COCO-80", "", "", "15", ], ] with gr.Blocks(theme=gr.themes.Default()) as demo: # gr.Markdown('# GLEE: General Object Foundation Model for Images and Videos at Scale') gr.HTML("

GLEE: General Object Foundation Model for Images and Videos at Scale

") gr.Markdown(' [Paper](https://arxiv.org/abs/2312.09158) —— [Project Page](https://glee-vision.github.io) —— [Code](https://github.com/FoundationVision/GLEE) ') # gr.HTML(“img src=“image link” alt=“A beautiful landscape”) gr.Markdown( 'The functionality demonstration demo app of GLEE. \ Image tasks includes **arbitrary vocabulary** object detection&segmentation, \ **any form of object name**, object caption detection, \ referring expression comprehension, and interactive segmentation. \ Video tasks add object tracking 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-Plus (SwinL)" , multiselect=False, label="Model", ) with gr.Row(): with gr.Column(): prompt_mode_select = gr.Radio([ "categories", "expression", "point", "scribble", "box"], 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"], visible=True, 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, visible=False, value="dog, cat, car, person", ) input_expressiong = gr.Textbox( label="Expression", info="Input any description of an object in the image ", lines=1, visible=False, value="the red car", ) with gr.Accordion("Text based detection usage",open=False, visible=False) as textusage: gr.Markdown( 'GLEE supports three kind of object perception methods: category list, textual description, and class-agnostic.
\ 1.Select an existing category list from the "Categories" dropdown, like COCO or OBJ365, or customize your own list.
\ 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.
\ 3.For class-agnostic mode, choose "Class-Agnostic" from the "Categories" dropdown.' ) with gr.Group(visible=False,) as promptshow: with gr.Accordion("Interactive segmentation usage",open=False): gr.Markdown( 'For interactive segmentation:
\ 1.Draw points, boxes, or scribbles on the canvas for multiclass segmentation; use separate layers for different objects, adding layers with a "+" sign.
\ 2.Point mode accepts a single point only; multiple points default to the centroid, so use boxes or scribbles for larger objects.
\ 3.After drawing, click green "√" to preview the prompt visualization; the segmentation mask follows the chosen prompt colors.' ) img_showbox = gr.Image(label="visual prompt area preview") def update_component_visible(prompt,category): if prompt in ['point', 'scribble', 'box']: return { category_select:gr.Dropdown(visible=False), custom_category:gr.Textbox(visible=False), input_expressiong: gr.Textbox(visible=False), promptshow:gr.Group(visible=True), textusage:gr.Accordion(visible=False), } elif prompt == 'categories': if category == "Custom-List": return { category_select:gr.Dropdown(visible=True), custom_category:gr.Textbox(visible=True), input_expressiong: gr.Textbox(visible=False), promptshow:gr.Group(visible=False), textusage:gr.Accordion(visible=True), } return { category_select:gr.Dropdown(visible=True), custom_category:gr.Textbox(visible=False), input_expressiong: gr.Textbox(visible=False), promptshow:gr.Group(visible=False), textusage:gr.Accordion(visible=True), } else: return { category_select:gr.Dropdown(visible=False), custom_category:gr.Textbox(visible=False), input_expressiong: gr.Textbox(visible=True), promptshow:gr.Group(visible=False), textusage:gr.Accordion(visible=True), } def update_category_showcase(category): if category == "Custom-List": return { category_select:gr.Dropdown(visible=True), custom_category:gr.Textbox(visible=True), input_expressiong: gr.Textbox(visible=False), promptshow:gr.Group(visible=False), textusage:gr.Accordion(visible=True), } else: return { category_select:gr.Dropdown(visible=True), custom_category:gr.Textbox(visible=False), input_expressiong: gr.Textbox(visible=False), promptshow:gr.Group(visible=False), textusage:gr.Accordion(visible=True), } prompt_mode_select.input(update_component_visible, [prompt_mode_select,category_select], [category_select,custom_category,input_expressiong,promptshow,textusage]) category_select.input(update_category_showcase, [category_select], [category_select,custom_category,input_expressiong,promptshow,textusage]) # with gr.Column(): 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) gr.Examples( examples = image_example_list, inputs=[img_input, prompt_mode_select, category_select, custom_category,input_expressiong,num_inst_select], examples_per_page=20 ) with gr.Tab("Video task"): gr.Markdown( '#### Gradio only support .mp4 for HTML display. \ Due to computing resource restrictions, we sample and play the input video in 10 fps, and single video is limited (or cropped) to 10 seconds' ) with gr.Row(): with gr.Column(): # video input face video_input = gr.Video(label="Input Video", interactive=True, sources=['upload']) video_model_select = gr.Dropdown( ["GLEE-Lite (R50)", "GLEE-Plus (SwinL)"], value = "GLEE-Lite (R50)" , multiselect=False, label="Model", ) with gr.Row(): with gr.Column(): video_prompt_mode_select = gr.Radio([ "categories", "expression", "point", "scribble", "box"], label="Prompt", value= "categories" , info="What kind of prompt do you want to use?") video_category_select = gr.Dropdown( ["YTBVIS&OVIS", "COCO-80", "OBJ365", "Custom-List", "Class-Agnostic"], visible=True, value = "COCO-80" , multiselect=False, label="Categories", info="Choose an existing category list or class-agnostic" ) video_custom_category = gr.Textbox( label="Custom Category", info="Input custom category list, seperate by ',' ", lines=1, visible=False, value="dog, cat, car, person", ) video_input_expressiong = gr.Textbox( label="Expression", info="Input any description of an object in the image ", lines=2, visible=False, value="the red car", ) with gr.Accordion("Text based detection usage",open=False, visible=False) as video_textusage: gr.Markdown( 'GLEE supports three kind of object perception methods: category list, textual description, and class-agnostic.
\ 1.Select an existing category list from the "Categories" dropdown, like COCO or OBJ365, or customize your own list.
\ 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.
\ 3.For class-agnostic mode, choose "Class-Agnostic" from the "Categories" dropdown.' ) with gr.Group(visible=False,) as video_promptshow: with gr.Accordion("Interactive segmentation usage",open=False): gr.Markdown( 'For video interactive segmentation, draw a prompt on the first frame:
\ 1.Draw points, boxes, or scribbles on the canvas for multiclass segmentation; only support one object tracking in interactive mode\ 2.Point mode accepts a single point only; multiple points default to the centroid, so use boxes or scribbles for larger objects.
\ 3.After drawing, click "Preview" to preview the prompt visualization; the segmentation mask follows the chosen prompt colors.' ) with gr.Row(): video_visual_prompter = gr.ImageEditor(label="visual prompter", show_label=True ,sources=['clipboard']) video_img_showbox = gr.Image(label="visual prompt area preview") video_prompt_preview = gr.Button("Preview") def update_video_component_visible(prompt,category, video): if prompt in ['point', 'scribble', 'box']: if video is None: return { video_category_select:gr.Dropdown(visible=False), video_custom_category:gr.Textbox(visible=False), video_input_expressiong: gr.Textbox(visible=False), video_promptshow:gr.Group(visible=True), video_textusage:gr.Accordion(visible=False),} else: cap = cv2.VideoCapture(video) ret, frame = cap.read() frame = frame[:,:,::-1].astype('uint8') zerolayers = np.zeros((frame.shape[0],frame.shape[1],1)).astype('uint8') alpha = 255+zerolayers newframe = np.concatenate((frame,alpha),axis=2) cap.release() return { video_category_select:gr.Dropdown(visible=False), video_custom_category:gr.Textbox(visible=False), video_input_expressiong: gr.Textbox(visible=False), video_promptshow:gr.Group(visible=True), video_textusage:gr.Accordion(visible=False), video_visual_prompter:gr.ImageEditor(value= { 'background':newframe, 'layers':[ ], 'composite':newframe }), } elif prompt == 'categories': if category == "Custom-List": return { video_category_select:gr.Dropdown(visible=True), video_custom_category:gr.Textbox(visible=True), video_input_expressiong: gr.Textbox(visible=False), video_promptshow:gr.Group(visible=False), video_textusage:gr.Accordion(visible=True), } return { video_category_select:gr.Dropdown(visible=True), video_custom_category:gr.Textbox(visible=False), video_input_expressiong: gr.Textbox(visible=False), video_promptshow:gr.Group(visible=False), video_textusage:gr.Accordion(visible=True), } else: return { video_category_select:gr.Dropdown(visible=False), video_custom_category:gr.Textbox(visible=False), video_input_expressiong: gr.Textbox(visible=True), video_promptshow:gr.Group(visible=False), video_textusage:gr.Accordion(visible=True), } def update_video_category_showcase(category): if category == "Custom-List": return { video_category_select:gr.Dropdown(visible=True), video_custom_category:gr.Textbox(visible=True), video_input_expressiong: gr.Textbox(visible=False), video_promptshow:gr.Group(visible=False), video_textusage:gr.Accordion(visible=True), } else: return { video_category_select:gr.Dropdown(visible=True), video_custom_category:gr.Textbox(visible=False), video_input_expressiong: gr.Textbox(visible=False), video_promptshow:gr.Group(visible=False), video_textusage:gr.Accordion(visible=True), } video_prompt_mode_select.input(update_video_component_visible, [video_prompt_mode_select,video_category_select,video_input], [video_category_select,video_custom_category,video_input_expressiong,video_promptshow,video_textusage,video_visual_prompter]) video_category_select.input(update_video_category_showcase, [video_category_select], [video_category_select,video_custom_category,video_input_expressiong,video_promptshow,video_textusage]) video_input.change(update_video_component_visible, [video_prompt_mode_select,video_category_select,video_input], [video_category_select,video_custom_category,video_input_expressiong,video_promptshow,video_textusage,video_visual_prompter]) with gr.Column(): video_output = gr.Video(label="Video Results") with gr.Accordion("Try More Visualization Options"): video_frames_select = gr.Slider(1, 100, value=32, step=1, label="Max frames", info="The max length for video frames, you can select fewer frames reduce the waiting time to check the effect quickly") video_results_select = gr.CheckboxGroup(["box", "mask", "name", "score", "expression"], value=["box", "mask", "name", "score", "expression"], label="Shown Results", info="The results shown on image") video_num_inst_select = gr.Slider(1, 30, value=10, step=1, label="Num of topK instances for category based detection", info="Choose between 1 and 50 for better visualization") video_threshold_select = gr.Slider(0, 1, value=0.2, label="Confidence Threshold", info="Choose threshold ") video_mask_image_mix_ration = gr.Slider(0, 1, value=0.65, label="Image Brightness Ratio", info="Brightness between image and colored masks ") video_prompt_preview.click(visual_prompt_preview, inputs = [video_visual_prompter,video_prompt_mode_select] , outputs = video_img_showbox) video_button = gr.Button("Segment&Track") video_button.click(segment_video, inputs=[video_input, video_prompt_mode_select, video_category_select, video_custom_category, video_input_expressiong, video_results_select, video_num_inst_select, video_threshold_select, video_mask_image_mix_ration, video_model_select, video_frames_select, video_visual_prompter], outputs=video_output) gr.Examples( examples = video_example_list, inputs=[video_input, video_prompt_mode_select, video_category_select, video_custom_category, video_input_expressiong,video_num_inst_select], examples_per_page=20 ) if __name__ == '__main__': demo.launch(inbrowser=True, allowed_paths=["./"])