app.py

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):
    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):
    
    ### 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 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':
                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') 
        # import pdb;pdb.set_trace()
        # 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]]
                # import pdb;pdb.set_trace()
                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()
    import pdb;pdb.set_trace()
    # get bbox from scribbles in layers
    bbox_list = [scribble2box(layer)  for layer in img['layers'] ]
    zero_mask = np.zeros_like(copyed_img) 

    for mask, (box,RGB) in zip(img['layers'], bbox_list):
        if box is None:
            continue
        
        if prompt_mode=='box':
            fakemask = np.zeros_like(copyed_img[:,:,0])
            x1 ,y1 ,x2, y2 = box
            fakemask[ y1:y2, x1:x2  ] = 1
            fakemask = fakemask>0
        elif prompt_mode=='point':
            fakemask = np.zeros_like(copyed_img[:,:,0])
            H,W = fakemask.shape
            x1 ,y1 ,x2, y2 = box
            center_x, center_y = (x1+x2)//2, (y1+y2)//2
            fakemask[ center_y-H//40:center_y+H//40, center_x-W//40:center_x+W//40  ] = 1
            fakemask = fakemask>0
        else:
            fakemask = mask[:,:,-1]
            fakemask = fakemask>0

        mask = fakemask.reshape(fakemask.shape[0], fakemask.shape[1], 1)
        lar = np.concatenate((mask*RGB[0], mask*RGB[1],mask*RGB[2]), axis = 2)
        zero_mask = zero_mask+ lar


    img_n = copyed_img + np.clip(zero_mask,0,255)
    max_p = img_n.max()
    ret = 255*img_n/max_p
    ret = ret.astype('uint8') 
    return  ret

with gr.Blocks(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='6%' >  <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) ')
    # 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=2,
                            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, 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)


    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, 200, 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)





if __name__ == '__main__':
    demo.launch(inbrowser=True, allowed_paths=["./"])