添付のコードを、Edge TPU PythonAPI 仕様から、PyCoralAPI（python)仕様に作り直したい。

Question

#■やりたいこと
Coral devboard mendel（Linux）環境でSmart Bird Feeder project.を試したいと思っています。（https://coral.ai/projects/bird-feeder/）

添付コード bird_classify.pyを実行しようとしていますが実行できず、次のようなエラーになります。

#■エラー

engine = ClassificationEngine(args.model)
NameError: name 'ClassificationEngine' is not defined

pycoral apiを使って pycoralを使うように書き換え、コードを作り直す必要があることまではわかるのですが、具体的にどのように直すと良いでしょうか。良い方法があれば教えていただければ幸いです。

#■やってみたこと
ClassificationEngineの箇所をpycoral仕様にするため、edgetpu.classification.engineを削除し、pycoralで必要になりそうなコードを追加しようと思い、次のように記述しました。

from edgetpu.classification.engine import ClassificationEngine

https://coral.ai/docs/reference/py/pycoral.utils/#module-pycoral.utils.dataset　を参照し、ClassificationEngineの代わりになりそうなもの？と思い次のようにしてみました。

from pycoral.adapters import classify
from pycoral.adapters import common
from pycoral.utils.dataset import read_label_file
from pycoral.utils.edgetpu import make_interpreter

import argparse
import time
import re
import imp
import logging
import gstreamer

from PIL import Image
from playsound import playsound

from pycoral.adapters import classify
from pycoral.adapters import common
from pycoral.utils.dataset import read_label_file
from pycoral.utils.edgetpu import make_interpreter

def save_data(image,results,path,ext='png'):
    """Saves camera frame and model inference results
    to user-defined storage directory."""
    tag = '%010d' % int(time.monotonic()*1000)
    name = '%s/img-%s.%s' %(path,tag,ext)
    image.save(name)
    print('Frame saved as: %s' %name)
    logging.info('Image: %s Results: %s', tag,results)

def load_labels(path):
    """Parses provided label file for use in model inference."""
    p = re.compile(r'\s*(\d+)(.+)')
    with open(path, 'r', encoding='utf-8') as f:
      lines = (p.match(line).groups() for line in f.readlines())
      return {int(num): text.strip() for num, text in lines}

def print_results(start_time, last_time, end_time, results):
    """Print results to terminal for debugging."""
    inference_rate = ((end_time - start_time) * 1000)
    fps = (1.0/(end_time - last_time))
    print('\nInference: %.2f ms, FPS: %.2f fps' % (inference_rate, fps))
    for label, score in results:
      print(' %s, score=%.2f' %(label, score))

def do_training(results,last_results,top_k):
    """Compares current model results to previous results and returns
    true if at least one label difference is detected. Used to collect
    images for training a custom model."""
    new_labels = [label[0] for label in results]
    old_labels = [label[0] for label in last_results]
    shared_labels  = set(new_labels).intersection(old_labels)
    if len(shared_labels) < top_k:
      print('Difference detected')
      return True

def user_selections():
    parser = argparse.ArgumentParser()
    parser.add_argument('--model', required=True,
                        help='.tflite model path')
    parser.add_argument('--labels', required=True,
                        help='label file path')
    parser.add_argument('--top_k', type=int, default=3,
                        help='number of classes with highest score to display')
    parser.add_argument('--threshold', type=float, default=0.1,
                        help='class score threshold')
    parser.add_argument('--storage', required=True,
                        help='File path to store images and results')
    parser.add_argument('--sound', required=True,
                        help='File path to deterrent sound')
    parser.add_argument('--print', default=False, required=False,
                        help='Print inference results to terminal')
    parser.add_argument('--training', default=False, required=False,
                        help='Training mode for image collection')
    args = parser.parse_args()
    return args


def main():
    
    args = user_selections()
    print("Loading %s with %s labels."%(args.model, args.labels))
    engine = ClassificationEngine(args.model)
    labels = load_labels(args.labels)
    storage_dir = args.storage

    
    logging.basicConfig(filename='%s/results.log'%storage_dir,
                        format='%(asctime)s-%(message)s',
                        level=logging.DEBUG)

    last_time = time.monotonic()
    last_results = [('label', 0)]
    def user_callback(image,svg_canvas):
        nonlocal last_time
        nonlocal last_results
        start_time = time.monotonic()
        results = engine.classify_with_image(image, threshold=args.threshold, top_k=args.top_k)
        end_time = time.monotonic()
        results = [(labels[i], score) for i, score in results]

        if args.print:
          print_results(start_time,last_time, end_time, results)

        if args.training:
          if do_training(results,last_results,args.top_k):
            save_data(image,results, storage_dir)
        else:
          
          if results[0][0] !='background':
            save_data(image, storage_dir,results)
          if 'fox squirrel, eastern fox squirrel, Sciurus niger' in results:
            playsound(args.sound)
            logging.info('Deterrent sounded')

        last_results=results
        last_time = end_time
    result = gstreamer.run_pipeline(user_callback)

if __name__ == '__main__':
    main()