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| 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("pip uninstall -y gradio") | |
| # os.system("pip install gradio==4.11.0") | |
| # 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() | |
| cfg_eva02 = get_cfg() | |
| add_deeplab_config(cfg_eva02) | |
| add_glee_config(cfg_eva02) | |
| conf_files_eva02 = 'GLEE/configs/EVA02.yaml' | |
| checkpoints_eva = torch.load('GLEE_EVA02_Scaleup10m.pth') | |
| cfg_eva02.merge_from_file(conf_files_eva02) | |
| GLEEmodel_eva02 = GLEE_Model(cfg_eva02, None, device, None, True).to(device) | |
| GLEEmodel_eva02.load_state_dict(checkpoints_eva, strict=False) | |
| GLEEmodel_eva02.eval() | |
| # inference_type = 'LSJ' | |
| 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) | |
| else: | |
| resizer = torchvision.transforms.Resize(size = 1535, max_size=1536, antialias=True) | |
| videoresizer = torchvision.transforms.Resize(size = 1535, max_size=1536, 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 | |
| inference_type = 'resize_shot' | |
| print('use GLEE-Plus') | |
| elif model_selection == 'GLEE-Lite (R50)': | |
| inference_type = 'resize_shot' | |
| GLEEmodel = GLEEmodel_r50 | |
| print('use GLEE-Lite') | |
| else: | |
| GLEEmodel = GLEEmodel_eva02 | |
| print('use GLEE-Pro') | |
| inference_type = 'LSJ' | |
| 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': | |
| resize_image = resizer(ori_image) | |
| image_size = torch.as_tensor((resize_image.shape[-2],resize_image.shape[-1])) | |
| re_size = resize_image.shape[-2:] | |
| infer_image = torch.zeros(1,3,1536,1536).to(ori_image) | |
| infer_image[:,:,:image_size[0],:image_size[1]] = resize_image | |
| padding_size = (1536,1536) | |
| 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': | |
| resize_fakemask = resizer(fakemask) | |
| infer_visual_prompt = torch.zeros(1,1536,1536).to(resize_fakemask) | |
| infer_visual_prompt[:,:image_size[0],:image_size[1]] = resize_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': | |
| resize_image = resizer(ori_image) | |
| image_size = torch.as_tensor((resize_image.shape[-2],resize_image.shape[-1])) | |
| re_size = resize_image.shape[-2:] | |
| infer_image = torch.zeros(1,3,1536,1536).to(ori_image) | |
| infer_image[:,:,:image_size[0],:image_size[1]] = resize_image | |
| padding_size = (1536,1536) | |
| 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 | |
| inference_type = 'resize_shot' | |
| print('use GLEE-Plus') | |
| clip_length = 2 #batchsize | |
| elif model_selection == 'GLEE-Lite (R50)': | |
| inference_type = 'resize_shot' | |
| GLEEmodel = GLEEmodel_r50 | |
| print('use GLEE-Lite') | |
| clip_length = 4 #batchsize | |
| else: | |
| GLEEmodel = GLEEmodel_eva02 | |
| print('use GLEE-Pro') | |
| inference_type = 'LSJ' | |
| clip_length = 1 #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 score<threshold_select: | |
| continue | |
| mask = mask.reshape(mask.shape[0], mask.shape[1], 1) | |
| lar = np.concatenate((mask*COLORS[nn%12][0], mask*COLORS[nn%12][1], mask*COLORS[nn%12][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,0,1)*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<threshold_select: | |
| continue | |
| x1,y1,x2,y2 = box.long().tolist() | |
| RGB = (COLORS[nn%12][0]*255,COLORS[nn%12][1]*255,COLORS[nn%12][2]*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(score.item())[:4] | |
| else: | |
| label = expressiong | |
| if 'score' in results_select: | |
| label += str(score.item())[:4] | |
| 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), | |
| ) | |
| ourput_frames.append(ret[:,:,::-1]) | |
| # ret = ret.astype('uint8') | |
| size = (ori_width,ori_height) | |
| print('writing video...') | |
| 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(ourput_frames)): | |
| writer.writeFrame(ourput_frames[i]) | |
| writer.close() | |
| # out = cv2.VideoWriter(output_file,cv2.VideoWriter_fourcc(*'avc1'), read_fps, size) | |
| # for i in range(len(ourput_frames)): | |
| # out.write(ourput_frames[i]) | |
| # out.release() | |
| del out_logits, out_masks, out_embds, out_boxes, pred_masks | |
| torch.cuda.empty_cache() | |
| return output_file | |
| else: # visual prompt vos | |
| # image prompt segmentation | |
| topK_instance = 1 | |
| copyed_img = prompter['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 | |
| 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 | |
| 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': | |
| resize_fakemask = resizer(fakemask) | |
| infer_visual_prompt = torch.zeros(1,1536,1536).to(resize_fakemask) | |
| infer_visual_prompt[:,:image_size[0],:image_size[1]] = resize_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", | |
| "COCO-80", | |
| "", | |
| "", | |
| 30, | |
| ], | |
| [ | |
| this_dir + "/Examples/20231222.jpg", | |
| "expression", | |
| "COCO-80", | |
| "", | |
| "a purple star holding by a person ", | |
| 1, | |
| ], | |
| [ | |
| this_dir + "/Examples/000000001000.jpg", | |
| "expression", | |
| "COCO-80", | |
| "", | |
| "the left boy", | |
| 1, | |
| ], | |
| [ | |
| this_dir + "/Examples/000000001000.jpg", | |
| "expression", | |
| "COCO-80", | |
| "", | |
| "the left girl", | |
| 1, | |
| ], | |
| [ | |
| 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", | |
| "COCO-80", | |
| "", | |
| "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("<p> <img src='/file=GLEE_logo.png' aligh='center' style='float:left' width='80' > <h1 class='title is-1 publication-title'> <p style='margin-left: 20px'> GLEE: General Object Foundation Model for Images and Videos at Scale </h1> ") | |
| gr.Markdown(' [Paper](https://arxiv.org/abs/2312.09158) —— [Project Page](https://glee-vision.github.io) —— [Code](https://github.com/FoundationVision/GLEE) —— [Vdieo & Demonstration](https://youtu.be/PSVhfTPx0GQ) ') | |
| # 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)", "GLEE-Pro (EVA02L)"], 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.<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.' | |
| ) | |
| with gr.Group(visible=False,) as promptshow: | |
| 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, you can click "preview" button to preview the prompt visualization, but need redraw it again for segmentation; the segmentation mask follows the chosen prompt colors.' | |
| ) | |
| image_preview_button = gr.Button("preview & redraw") | |
| 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") | |
| image_preview_button.click(visual_prompt_preview, inputs = [img_input,prompt_mode_select] , outputs = img_showbox) | |
| # 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.<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.' | |
| ) | |
| 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:<br />\ | |
| 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.<br />\ | |
| 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=["./"]) | |