deepgeo 0.0.13

Easy Deep Learning

Deep Geo

• Easy Deep Learning
• Copyright (c) 2019 InfoLab (Donggun LEE)
• PIP : https://pypi.org/project/deepgeo/
• Demo
  • deepGeo.io
  • DeepGeo.io
• How to install

  pip install deepgeo
  

  • other version

    # 0.0.1
    pip install deepgeo==0.0.1
    
    # 0.0.13
    pip install deepgeo==0.0.13
    

  • requirement
    • Python 3.6

    pip install tensorflow-gpu==1.9.0 exifread>=2.1.2 piexif>=1.1.2 pillow>=6.0.0 matplotlib>=3.1.0 scikit-image>=0.15.0 IPython>=7.5.0 keras>=2.2.4 cython>=0.29.7
    

• How to use
  • Import DeepGeo

    import deepgeo
    

  • detect

    engine = deepgeo.Engine()
    
    # add model
    engine.add_model('mscoco','maskrcnn','D:/test/config.json')
    
    # create image
    image = deepgeo.Image('uri')
    engine.detect('mscoco',image)
    print(image)
    
    ... etc ...
    

  • extends function

    import sys,os,json
    
    def init_loading_bar(max):
      max_ = str(max)
      sys.stdout.write("%s/0" % max_)
      sys.stdout.flush()
      sys.stdout.write("\b")
    
    def update_loading_bar(num):
      sys.stdout.write("%s" % str(num))
      sys.stdout.flush()
      sys.stdout.write("\b" * len(str(num)))
    
    def final_loading_bar():
      sys.stdout.write("\n")
    
    def fjson_to_imgs(engine, dataset_name, path, image_path):
      file_list = os.listdir(path)
      file_list.sort()
      init_loading_bar(len(file_list))
      count = 0
      for item in file_list:
          count+=1
          update_loading_bar(count)
          if item.find('.json') is not -1:
              data=None
              with open(path+item) as data_file:    
                  data = json.load(data_file)
              engine.add_data('mscoco',dataset_name,deepgeo.Image(data['uri'],image_path,data['annotations']))
              del data
      final_loading_bar()
      del count
      del file_list
    

  • train

    engine = deepgeo.Engine()
    engine.add_model('mscoco', 'maskrcnn', 'D:/test/config.json')
    engine.add_dataset('mscoco','train','maskrcnn')
    engine.add_dataset('mscoco','val','maskrcnn')
    fjson_to_imgs(engine, 'train','D:/test/val2017/json/','D:/test/val2017/images/')
    fjson_to_imgs(engine, 'val','D:/test/val2017/json/','D:/test/val2017/images/')
    
    ## Training...
    print(" > STEP3 : Fine tune all layers")
    engine.set_config('mscoco',{"EPOCHS":160, "LAYERS":'all',"LEARNING_RATE":engine.get_config('mscoco',"LEARNING_RATE")/10})
    engine.train('mscoco',"train","val",None)
    
    ... etc ...