deepgeo 0.2.1

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
  
  # 0.1.0 : Support Yolo (Requirement OpenCV, Only Detect)
  pip install deepgeo==0.1.0
  
  # 0.2.0 : Support Extendsion Library
  pip install deepgeo==0.2.0
  
  # 0.2.1 : Support Font Management
  pip install deepgeo==0.2.1
  

• requirement
  • Python 3.6

    pip install exifread piexif pillow matplotlib scikit-image
    

• How to use
  • font Management

    from deepgeo import Font
    # register
    Font.register("D:/User/Downloads/font.ttf","font")
    
    # delete
    Font.delete("font")
    

  • detect

    from deepgeo import Image, Engine
    path = "D:"
    
    engine = Engine()
    engine.add_model('maskrcnn_mscoco','maskrcnn', path+'/default_config.json')
    
    image = Image("image.jpg",path)
    image = engine.detect('maskrcnn_mscoco', image)[0]
    image.draw_annotations(image.get_annotation())
    image.save(path+"/","test","PNG")
    

  • train

    import sys,os,json
    from deepgeo import Engine, Image
    
    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,Image(data['uri'],image_path,data['annotations']))
    del data
    final_loading_bar()
    del count
    del file_list
    
    
    engine = 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 ...