track_attention_icd15.py

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File    :   track.py
@Time    :   2021/06/01 14:51:41
@Author  :   weijia
@Version :   1.0
@Contact :   weijiawu@zju.edu.cn
@License :   (C)Copyright 2021-2022, Zhejiang University
@Desc    :   通过在线获得的OCR结果，进行跟踪视频OCR定位
'''

# here put the import lib

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import json
import shutil
import os
import os.path as osp
import cv2
import logging
import argparse
import numpy as np
import torch
from tqdm import tqdm
from tracker.multitracker import track_online
from tracker.tools.common import logger, mkdir_if_missing, strip_points
import sys
from tracker.video_tools import visulization as vis
from tracker.config import config
from glob import glob
from PIL import Image
from infer_img_atten import PANppE2E
# from tracker.tools.online_rec import Client
import pickle
logger.setLevel(logging.INFO)
import time

# client = Client( KESS_SERVER_NAME_hori='grpc_mmu_videoOcrRecognitionV6',
#             KESS_SERVER_NAME_ver='grpc_mmu_ocrRecognitionVerticalVideo'
#             )

from xml.dom.minidom import Document
try:
    import xml.etree.cElementTree as ET  #解析xml的c语言版的模块
except ImportError:
    import xml.etree.ElementTree as ET
from tqdm import tqdm

class StorageDictionary(object):
    @staticmethod
    def dict2file(file_name, data_dict):
        try:
            import cPickle as pickle
        except ImportError:
            import pickle
        # import pickle
        output = open(file_name,'wb')
        pickle.dump(data_dict,output)
        output.close()

    @staticmethod
    def file2dict(file_name):
        try:
            import cPickle as pickle
        except ImportError:
            import pickle
        # import pickle
        pkl_file = open(file_name, 'rb')
        data_dict = pickle.load(pkl_file)
        pkl_file.close()
        return data_dict

    @staticmethod
    def dict2file_json(file_name, data_dict):
        import json, io
        with io.open(file_name, 'w', encoding='utf-8') as fp:
            # fp.write(unicode(json.dumps(data_dict, ensure_ascii=False, indent=4) ) )  #可以解决在文件里显示中文的问题，不加的话是 '\uxxxx\uxxxx'
            fp.write((json.dumps(data_dict, ensure_ascii=False, indent=4) ) )

    @staticmethod
    def file2dict_json(file_name):
        import json, io
        with io.open(file_name, 'r', encoding='utf-8') as fp:
            data_dict = json.load(fp)
        return data_dict

def Generate_Json_annotation(TL_Cluster_Video_dict, Outpu_dir,xml_dir_):
    '''   '''
    ICDAR21_DetectionTracks = {}
    text_id = 1

    doc = Document()
    video_xml = doc.createElement("Frames")

    for frame in TL_Cluster_Video_dict.keys():

        doc.appendChild(video_xml)
        aperson = doc.createElement("frame")
        aperson.setAttribute("ID", str(frame))
        video_xml.appendChild(aperson)

        ICDAR21_DetectionTracks[frame] = []
#         vis_dict[frame_id].append([track_id, bbox[:8], track_dict['text']])

        for text_list in TL_Cluster_Video_dict[frame]:
            track_id, points, text = text_list
            ICDAR21_DetectionTracks[frame].append({"points":[str(i) for i in points],"ID":str(track_id)})

            # xml
            object1 = doc.createElement("object")
            object1.setAttribute("ID", str(track_id))
            aperson.appendChild(object1)

            for i in range(4):

                name = doc.createElement("Point")
                object1.appendChild(name)
                # personname = doc.createTextNode("1")
                name.setAttribute("x", str(int(points[i*2])))
                name.setAttribute("y", str(int(points[i*2+1])))

    StorageDictionary.dict2file_json(Outpu_dir, ICDAR21_DetectionTracks)

    # xml
    f = open(xml_dir_, "w")
    f.write(doc.toprettyxml(indent="  "))
    f.close()


def demo(model, config, frame_dir, dict_cost):

#     vis_dir = osp.join(out_dir, 'frame')
#     result_file_path = os.path.join(out_dir, "result" + '.json')
#     mkdir_if_missing(out_dir)
#     mkdir_if_missing(vis_dir)
    frame_info_list = []
    # 获取单帧信息（图像OCR结果）
    # 单帧进行识别
    for img_path in tqdm(glob(osp.join(frame_dir, "*.jpg"))):
        frame_id = osp.basename(img_path).split('.')[0]
        frame_info,outputs = model.predict(img_path)

        frame_info['frame_id'] = str(int(frame_id))
        frame_info_list.append(frame_info)
        
        dict_cost["rec_head_cost"]+= outputs["rec_time"]
        dict_cost["backbone_time"]+= outputs["backbone_time"]
        dict_cost["neck_time"]+= outputs["neck_time"]
        dict_cost["det_head_time"]+= outputs["det_head_time"]
        dict_cost["desc_time"]+= outputs["desc_time"]
        dict_cost["det_post_time"] += outputs["det_post_time"]
        dict_cost["number_text"] += outputs["number_text"]
    
    start = time.time()
    # 排序
    frame_info_list = sorted(frame_info_list, key=lambda x: int(x['frame_id']))

    # 执行跟踪
    re_results = track_online(config['tracker'], frame_info_list)
    dict_cost["track_pos_cost"] += time.time() - start
    
    result_dict = {}
    for frame_id in range(len(frame_info_list)):
        frame_id= frame_id+1
        
        if str(frame_id) not in re_results:
            result_dict[str(frame_id)] = []
            pass
        else:
            lines = re_results[str(frame_id)]
            result_dict[str(frame_id)] = lines

    return result_dict,dict_cost





def track(model, data_root, config, save_images=False, save_videos=False):
    dataset_result = {}
    seqs = os.listdir(data_root)
    
    import time
    start = time.time()   
    image_len = 0
    
    dict_cost = {
    "rec_head_cost" : 0,
    "backbone_time" : 0,
    "neck_time" : 0,
    "det_head_time" : 0,
    "desc_time" : 0,
    "track_pos_cost" : 0,
    "det_post_time" : 0,
     "number_text": 0 
    }
    
    for seq in tqdm(seqs):
        print("跟踪{}中".format(seq))
        frame_dir = osp.join(data_root, seq)
        if not os.path.isdir(frame_dir):
            continue
#         output_dir = osp.join(out_dir, seq)
#         mkdir_if_missing(output_dir)
        image_len += len(os.listdir(frame_dir))
        
        seq_results,dict_cost = demo(model, config,
                            frame_dir,dict_cost)
        dataset_result[seq] = seq_results
        
    for video_name in dataset_result:
        annotation_one = dataset_result[video_name]
        
        xml_name = video_name.split("_")
        xml_name = xml_name[0] + "_" + xml_name[1]
#         xml_name = video_name.replace("/","_")
        
        predict_path = os.path.join("./outputs/pan_pp_r18_ICDAR15/xml","res_{}.xml".format(xml_name.replace("V","v")))
        json_path = os.path.join("./outputs/pan_pp_r18_ICDAR15/json","{}.json".format(video_name))
        
#         predict_path = os.path.join("./outputs/pan_pp_r18_minetto_desc/xml","res_{}.xml".format(xml_name.replace("V","v")))
#         json_path = os.path.join("./outputs/pan_pp_r18_minetto_desc/json","{}.json".format(video_name))

#         predict_path = os.path.join("./outputs/pan_pp_r18_YVT_desc/xml","res_{}.xml".format(xml_name.replace("V","v")))
#         json_path = os.path.join("./outputs/pan_pp_r18_YVT_desc/json","{}.json".format(video_name))

#         predict_path = os.path.join("./outputs/pan_pp_r18_BOVText_desc/xml","res_{}.xml".format(xml_name.replace("V","v")))
#         json_path = os.path.join("./outputs/pan_pp_r18_BOVText_desc/json","{}.json".format(xml_name))
        
        Generate_Json_annotation(annotation_one,json_path,predict_path)
    print("time cost:",time.time() - start)
    print(image_len)
    
    print(dict_cost.keys())
    print("backbone_time cost:",dict_cost["backbone_time"])
    print("neck_time cost:",dict_cost["neck_time"])
    print("det_head_time cost:",dict_cost["det_head_time"])
    print("rec_head_cost cost:",dict_cost["rec_head_cost"])
    print("desc_time cost:",dict_cost["desc_time"])
    print("track_pos_cost:",dict_cost["track_pos_cost"])
    print("det_post_time:",dict_cost["det_post_time"])
    print("number_text:",dict_cost["number_text"])
    
def mkdir_if_missing(d):
    if not osp.exists(d):
        os.makedirs(d)

if __name__ == '__main__':
    from tracker.video_tools import evaluation

    ids = 'online_config_601_5fps'
    config_path = './config/pan_pp/pan_pp_r18_atten_ic15_desc.py'
    checkpoint_path = './outputs/pan_pp_r18_atten_ic15_desc/50_0_0_0_0_checkpoint.pth.tar'# 3_162_0_0_0_checkpoint.pth.tar' 
    data_root=  '/share/wuweijia/Data/ICDAR2013_video/test/frames'
    
#     data_root = "/home/wangjue_Cloud/wuweijia/Data/VideoText/minetto/minetto_test"
#     data_root = "/home/wangjue_Cloud/wuweijia/Data/VideoText/YVT/YVT_test"
#     data_root = "/share/wuweijia/MyBenchMark/MMVText/BOVTextV2/Test/Frames"
    

    pANppE2E = PANppE2E(checkpoint_path, config_path, ctc=False)
    track(pANppE2E, data_root, config,
                    save_images=False,
                    save_videos=False)