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import argparse
import datetime
import json
import random
import time
import multiprocessing
from pathlib import Path
import os
import cv2
import numpy as np
import torch
from torch.utils.data import DataLoader, DistributedSampler
import hotr.data.datasets as datasets
import hotr.util.misc as utils
from hotr.engine.arg_parser import get_args_parser
from hotr.data.datasets import build_dataset, get_coco_api_from_dataset
from hotr.data.datasets.vcoco import make_hoi_transforms
from PIL import Image
from hotr.util.logger import print_params, print_args

import copy
from hotr.data.datasets import builtin_meta
from PIL import Image
import requests
# import mmcv
from matplotlib import pyplot as plt
import imageio

from tools.vis_tool import *
from hotr.models.detr import build

def change_format(results,valid_ids):
    
    boxes,labels,pair_score =\
                    list(map(lambda x: x.cpu().numpy(), [results['boxes'], results['labels'], results['pair_score']]))
    output_i={}
    output_i['predictions']=[]
    output_i['hoi_prediction']=[]

    h_idx=np.where(labels==1)[0]
    for box,label in zip(boxes,labels):
        
        output_i['predictions'].append({'bbox':box.tolist(),'category_id':label})
    
    for i,verb in enumerate(pair_score):
        if i in [1,4,10,23,26,5,18]:
            continue
        for j,hum in enumerate(h_idx):
            for k in range(len(boxes)):
                if verb[j][k]>0:
                    output_i['hoi_prediction'].append({'subject_id':hum,'object_id':k,'category_id':i+2,'score':verb[j][k]})
            
    return output_i
def vis(args,input_img=None,id=294,return_img=False):

    if args.frozen_weights is not None:
        print("Freeze weights for detector")

    device = torch.device(args.device)

    # fix the seed for reproducibility
    seed = args.seed + utils.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)

    # Data Setup
    dataset_train = build_dataset(image_set='train', args=args)
    args.num_classes = dataset_train.num_category()
    args.num_actions = dataset_train.num_action()
    args.action_names = dataset_train.get_actions()
    if args.share_enc: args.hoi_enc_layers = args.enc_layers
    if args.pretrained_dec: args.hoi_dec_layers = args.dec_layers
    if args.dataset_file == 'vcoco':
        # Save V-COCO dataset statistics
        args.valid_ids = np.array(dataset_train.get_object_label_idx()).nonzero()[0]
        args.invalid_ids = np.argwhere(np.array(dataset_train.get_object_label_idx()) == 0).squeeze(1)
        args.human_actions = dataset_train.get_human_action()
        args.object_actions = dataset_train.get_object_action()
        args.num_human_act = dataset_train.num_human_act()
    elif args.dataset_file == 'hico-det':
        args.valid_obj_ids = dataset_train.get_valid_obj_ids()
    print_args(args)

    args.HOIDet=True
    args.eval=True
    args.pretrained_dec=True
    args.share_enc=True
    args.share_dec_param = True
    if args.dataset_file=='hico-det':
        args.valid_ids=args.valid_obj_ids
 
    # Model Setup
    model, criterion, postprocessors = build(args)
    model.to(device)

    model_without_ddp = model

    n_parameters = print_params(model)

    param_dicts = [
        {"params": [p for n, p in model_without_ddp.named_parameters() if "backbone" not in n and p.requires_grad]},
        {
            "params": [p for n, p in model_without_ddp.named_parameters() if "backbone" in n and p.requires_grad],
            "lr": args.lr_backbone,
        },
    ]

    output_dir = Path(args.output_dir)
    
    checkpoint = torch.load(args.resume, map_location='cpu')
    #수정
    module_name=list(checkpoint['model'].keys())
    model_without_ddp.load_state_dict(checkpoint['model'], strict=False)
    
    # if not args.video_vis:
    # url='http://images.cocodataset.org/val2014/COCO_val2014_{}.jpg'.format(str(id).zfill(12))
    # req = requests.get(url, stream=True, timeout=1, verify=False).raw
    
    if input_img is None:
        req = args.image_dir
        img = Image.open(req).convert('RGB')
    else:
        # import pdb;pdb.set_trace()
        img = input_img

    w,h=img.size
    orig_size = torch.as_tensor([int(h), int(w)]).unsqueeze(0).to(device)

    transform=make_hoi_transforms('val')
    sample=img.copy()
    sample,_=transform(sample,None)
    sample = sample.unsqueeze(0).to(device)
    with torch.no_grad():
        model.eval()
        out=model(sample)
        results = postprocessors['hoi'](out, orig_size,dataset=args.dataset_file,args=args)
        output_i=change_format(results[0],args.valid_ids)

    out_dir = './vis'
    image = np.asarray(img, dtype=np.uint8)[:,:,::-1]
    # image = cv2.imdecode(image_nparray, cv2.IMREAD_COLOR)

    vis_img=draw_img_vcoco(image,output_i,top_k=args.topk,threshold=args.threshold,color=builtin_meta.COCO_CATEGORIES)        
    plt.imshow(cv2.cvtColor(vis_img,cv2.COLOR_BGR2RGB))
    # import pdb;pdb.set_trace()
    if return_img:
        return Image.fromarray(vis_img)
    else:
        cv2.imwrite('./vis_res/vis1.jpg',vis_img)
    
    # else:
    #     frames=[]
    #     video_file=id
    
    #     video_reader = mmcv.VideoReader('./vid/'+video_file+'.mp4')
    #     fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    #     video_writer = cv2.VideoWriter(
    #             './vid/'+video_file+'_vis.mp4', fourcc, video_reader.fps,
    #             (video_reader.width, video_reader.height))

    #     orig_size = torch.as_tensor([int(video_reader.height), int(video_reader.width)]).unsqueeze(0).to(device)
    #     transform=make_hoi_transforms('val')

    #     for frame in mmcv.track_iter_progress(video_reader):

    #         frame=mmcv.imread(frame)
    #         frame=frame.copy()
  
    #         frame=Image.fromarray(frame,'RGB')

    #         sample,_=transform(frame,None)
    #         sample=sample.unsqueeze(0).to(device)

    #         with torch.no_grad():
    #             model.eval()
    #             out=model(sample)
    #             results = postprocessors['hoi'](out, orig_size,dataset='vcoco',args=args)
    #             output_i=change_format(results[0],args.valid_ids)

    #         vis_img=draw_img_vcoco(np.array(frame),output_i,top_k=args.topk,threshold=args.threshold,color=builtin_meta.COCO_CATEGORIES)
    #         frames.append(vis_img)
    #         video_writer.write(vis_img)

    #     with imageio.get_writer("smiling.gif", mode="I") as writer:
    #         for idx, frame in enumerate(frames):
    #             # print("Adding frame to GIF file: ", idx + 1)
    #             writer.append_data(frame)
    #     if video_writer:
    #         video_writer.release()
    #     cv2.destroyAllWindows()
 

# def visualization(id, video_vis=False, dataset_file='vcoco', path_id = 0 ,data_path='v-coco', threshold=0.4, topk=10,aug_path = '[]'):

#     parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])
#     checkpoint_dir= './checkpoints/vcoco/checkpoint.pth' if dataset_file=='vcoco' else './checkpoints/hico-det/hico_ft_q16.pth'
#     with open('./v-coco/data/vcoco_test.ids') as file:
#       test_idxs = [line.rstrip('\n') for line in file]
#     if not video_vis:
#       id = test_idxs[id]
#     args = parser.parse_args(args=['--dataset_file',dataset_file,'--data_path',data_path,'--resume',checkpoint_dir,'--num_hoi_queries' ,'16','--temperature' ,'0.05', '--augpath_name',aug_path ,'--path_id','{}'.format(path_id)])
#     args.video_vis=video_vis
#     args.threshold=threshold
#     args.topk=topk
    
#     if args.output_dir:
#         Path(args.output_dir).mkdir(parents=True, exist_ok=True)
#     vis(args,id)

# 230727 for huggingface
def visualization(input_img,threshold,topk):

    parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])
    args = parser.parse_args(args=[])
    args.threshold = threshold
    args.topk = int(topk)
    
    # checkpoint_dir= './checkpoints/vcoco/checkpoint.pth' if dataset_file=='vcoco' else './checkpoints/hico-det/hico_ft_q16.pth'
    args.resume= './checkpoints/vcoco/checkpoint.pth'
    # with open('./v-coco/data/splits/vcoco_test.ids') as file:
    #   test_idxs = [line.rstrip('\n') for line in file]
    # # if not video_vis:
    # id = test_idxs[309]
    # args = parser.parse_args()
    args.dataset_file = 'vcoco'
    args.data_path = 'v-coco'
    # args.resume = checkpoint_dir
    args.num_hoi_queries = 16
    args.temperature = 0.05
    args.augpath_name = ['p2','p3','p4']
    # args.path_id = 1
    # args.threshold = threshold
    # args.topk = topk
    if args.output_dir:
        Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    vis(args,input_img=input_img,return_img=True)

if __name__ == '__main__':
    parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])
    parser.add_argument('--threshold',help='score threshold for visualization', default=0.4, type=float)
    # parser.add_argument('--path_id',help='index of inference path', default=1, type=int)
    parser.add_argument('--topk',help='topk prediction', default=5, type=int)
    parser.add_argument('--video_vis', action='store_true')
    parser.add_argument('--image_dir', default='', type=str)
    args = parser.parse_args()
    # checkpoint_dir= './checkpoints/vcoco/checkpoint.pth' if dataset_file=='vcoco' else './checkpoints/hico-det/hico_ft_q16.pth'
    args.resume= './checkpoints/vcoco/checkpoint.pth'
    with open('./v-coco/data/splits/vcoco_test.ids') as file:
      test_idxs = [line.rstrip('\n') for line in file]
    # if not video_vis:
    id = test_idxs[309]
    # args = parser.parse_args()
    # args.dataset_file = 'vcoco'
    # args.data_path = 'v-coco'
    # args.resume = checkpoint_dir
    # args.num_hoi_queries = 16
    # args.temperature = 0.05
    args.augpath_name = ['p2','p3','p4']
    # args.path_id = 1
    
    if args.output_dir:
        Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    vis(args,id)