Dubhe/dubhe_data_process/program/exec/track/track.py

# !/usr/bin/env python
# -*- coding:utf-8 -*-

"""
Copyright 2020 Tianshu AI Platform. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

     http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
=============================================================
"""
import sched

from abc import ABC
from program.abstract.algorithm import Algorithm
from program.exec.track.track_only.hog_track import *

schedule = sched.scheduler(time.time, time.sleep)

delayId = ""


class Track(Algorithm, ABC):

    def __init__(self):
        pass

    def execute(task):
        return Track.trackProcess(task)

    def trackProcess(task):
        """Track task method.
                Args:
                  task: dataset id.
                  key: video file path.
                Returns:
                  True: track success
                  False: track failed
        """
        global delayId
        image_list = []
        label_list = []
        images_data = task['images']
        path = task['path']
        dataset_id = task['id']
        result = True

        for file in images_data:
            filePath = path + "/origin/" + file
            annotationPath = path + "/annotation/" + file.split('.')[0]
            if not os.path.exists(filePath):
                continue
            if not os.path.exists(annotationPath):
                continue
            image_list.append(filePath)
            label_list.append(annotationPath)
        image_num = len(label_list)
        track_det = Detector(
            'xxx.avi',
            min_confidence=0.35,
            max_cosine_distance=0.2,
            max_iou_distance=0.7,
            max_age=30,
            out_dir='results/')
        track_det.write_img = False
        RET = track_det.run_track(image_list, label_list)
        finished_json = {'id': dataset_id}
        if RET == 'OK':
            return finished_json, result
        else:
            return finished_json, result