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Easy Deep Learning

Project description

Deep Geo

  • Easy Deep Learning
  • Copyright (c) 2019 InfoLab (Donggun LEE)
  • PIP : https://pypi.org/project/deepgeo/
  • Demo
  • 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 ...
      

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