# Path: pycoral/adapters/classify.py
# def num_classes(interpreter):
# def get_scores(interpreter):
# def get_classes_from_scores(scores,
#                             top_k=float('inf'),
#                             score_threshold=-float('inf')):
# def get_classes(interpreter, top_k=float('inf'), score_threshold=-float('inf')):
# 
# Path: pycoral/adapters/common.py
# def output_tensor(interpreter, i):
# def input_details(interpreter, key):
# def input_size(interpreter):
# def input_tensor(interpreter):
# def set_input(interpreter, data):
# def set_resized_input(interpreter, size, resize):
# 
# Path: pycoral/learn/imprinting/engine.py
# class ImprintingEngine:
#   def __init__(self, model_path, keep_classes=False):
#   def embedding_dim(self):
#   def num_classes(self):
#   def serialize_extractor_model(self):
#   def serialize_model(self):
#   def train(self, embedding, class_id):
# 
# Path: pycoral/utils/edgetpu.py
# _EDGETPU_SHARED_LIB = {
#   'Linux': 'libedgetpu.so.1',
#   'Darwin': 'libedgetpu.1.dylib',
#   'Windows': 'edgetpu.dll'
# }[platform.system()]
# def load_edgetpu_delegate(options=None):
# def make_interpreter(model_path_or_content, device=None, delegate=None):
# def _is_valid_ctypes_input(input_data):
# def _gst_buffer_map(buffer):
# def _check_input_size(input_size, expected_input_size):
# def run_inference(interpreter, input_data):
# class _GstMapInfo(ctypes.Structure):
# 
# Path: tests/test_utils.py
# _TEST_DATA_DIR = ''
#   _TEST_DATA_DIR = args.test_data_dir
# def get_test_tmpdir():
# def test_data_path(path, *paths):
# def test_image(path, *paths):
# def temporary_file(suffix=None):
# def generate_random_input(seed, n):
# def prepare_images(image_list, directory, shape):
# def area(box):
# def iou(box_a, box_b):
# def prepare_data_set_from_directory(path, test_ratio, fixed_order):
# def coral_test_main():


import contextlib
import os
import unittest
from PIL import Image
from pycoral.adapters import classify
from pycoral.adapters import common
from pycoral.learn.imprinting import engine
from pycoral.utils import edgetpu
from tests import test_utils
    for class_id, images in enumerate(train_input):
      for image in images:
        with test_image(image) as img:
          common.set_input(extractor, img.resize(size, Image.NEAREST))
          extractor.invoke()
          imprinting_engine.train(
              classify.get_scores(extractor), class_id=num_classes + class_id)

    print('----------------     Training finished   -----------------')
    with test_utils.temporary_file(suffix='.tflite') as output_model_path:
      output_model_path.write(imprinting_engine.serialize_model())

      # Evaluate
      print('----------------     Start evaluating    -----------------')
      classifier = edgetpu.make_interpreter(
          output_model_path.name, delegate=self.delegate)
      classifier.allocate_tensors()

      # top[i] represents number of top (i+1) correct inference.
      top_k_correct_count = [0] * top_k_range
      image_num = 0
      for category, image_list in test_set.items():
        n = len(image_list)
        print('Evaluating {} ({} images)'.format(category, n))
        for image_name in image_list:
          with test_image(os.path.join(dataset_path, category,
                                       image_name)) as img:
            # Set threshold as a negative number to ensure we get top k
            # candidates even if its score is 0.
            size = common.input_size(classifier)

# Path: pycoral/adapters/classify.py
# def num_classes(interpreter):
# def get_scores(interpreter):
# def get_classes_from_scores(scores,
#                             top_k=float('inf'),
#                             score_threshold=-float('inf')):
# def get_classes(interpreter, top_k=float('inf'), score_threshold=-float('inf')):
# 
# Path: pycoral/adapters/common.py
# def output_tensor(interpreter, i):
# def input_details(interpreter, key):
# def input_size(interpreter):
# def input_tensor(interpreter):
# def set_input(interpreter, data):
# def set_resized_input(interpreter, size, resize):
# 
# Path: pycoral/learn/imprinting/engine.py
# class ImprintingEngine:
#   def __init__(self, model_path, keep_classes=False):
#   def embedding_dim(self):
#   def num_classes(self):
#   def serialize_extractor_model(self):
#   def serialize_model(self):
#   def train(self, embedding, class_id):
# 
# Path: pycoral/utils/edgetpu.py
# _EDGETPU_SHARED_LIB = {
#   'Linux': 'libedgetpu.so.1',
#   'Darwin': 'libedgetpu.1.dylib',
#   'Windows': 'edgetpu.dll'
# }[platform.system()]
# def load_edgetpu_delegate(options=None):
# def make_interpreter(model_path_or_content, device=None, delegate=None):
# def _is_valid_ctypes_input(input_data):
# def _gst_buffer_map(buffer):
# def _check_input_size(input_size, expected_input_size):
# def run_inference(interpreter, input_data):
# class _GstMapInfo(ctypes.Structure):
# 
# Path: tests/test_utils.py
# _TEST_DATA_DIR = ''
#   _TEST_DATA_DIR = args.test_data_dir
# def get_test_tmpdir():
# def test_data_path(path, *paths):
# def test_image(path, *paths):
# def temporary_file(suffix=None):
# def generate_random_input(seed, n):
# def prepare_images(image_list, directory, shape):
# def area(box):
# def iou(box_a, box_b):
# def prepare_data_set_from_directory(path, test_ratio, fixed_order):
# def coral_test_main():


# Path: tests/imprinting_evaluation_test.py
import contextlib
import os
import unittest
from PIL import Image
from pycoral.adapters import classify
from pycoral.adapters import common
from pycoral.learn.imprinting import engine
from pycoral.utils import edgetpu
from tests import test_utils

    for class_id, images in enumerate(train_input):
      for image in images:
        with test_image(image) as img:
          common.set_input(extractor, img.resize(size, Image.NEAREST))
          extractor.invoke()
          imprinting_engine.train(
              classify.get_scores(extractor), class_id=num_classes + class_id)

    print('----------------     Training finished   -----------------')
    with test_utils.temporary_file(suffix='.tflite') as output_model_path:
      output_model_path.write(imprinting_engine.serialize_model())

      # Evaluate
      print('----------------     Start evaluating    -----------------')
      classifier = edgetpu.make_interpreter(
          output_model_path.name, delegate=self.delegate)
      classifier.allocate_tensors()

      # top[i] represents number of top (i+1) correct inference.
      top_k_correct_count = [0] * top_k_range
      image_num = 0
      for category, image_list in test_set.items():
        n = len(image_list)
        print('Evaluating {} ({} images)'.format(category, n))
        for image_name in image_list:
          with test_image(os.path.join(dataset_path, category,
                                       image_name)) as img:
            # Set threshold as a negative number to ensure we get top k
            # candidates even if its score is 0.
            size = common.input_size(classifier)

            common.set_input(classifier, img.resize(size, Image.NEAREST))