# 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
    """
    imprinting_engine = engine.ImprintingEngine(model_path, keep_classes)

    extractor = edgetpu.make_interpreter(
        imprinting_engine.serialize_extractor_model(), delegate=self.delegate)
    extractor.allocate_tensors()

    num_classes = imprinting_engine.num_classes

    print('---------------      Parsing dataset      ----------------')
    print('Dataset path:', dataset_path)

    # train in fixed order to ensure the same evaluation result.
    train_set, test_set = test_utils.prepare_data_set_from_directory(
        dataset_path, test_ratio, True)

    print('Image list successfully parsed! Number of Categories = ',
          len(train_set))
    print('---------------  Processing training data ----------------')
    print('This process may take more than 30 seconds.')
    train_input = []
    labels_map = {}
    for class_id, (category, image_list) in enumerate(train_set.items()):
      print('Processing {} ({} images)'.format(category, len(image_list)))
      train_input.append(
          [os.path.join(dataset_path, category, image) for image in image_list])
      labels_map[num_classes + class_id] = category

    # train
    print('----------------      Start training     -----------------')

# 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

    """
    imprinting_engine = engine.ImprintingEngine(model_path, keep_classes)

    extractor = edgetpu.make_interpreter(
        imprinting_engine.serialize_extractor_model(), delegate=self.delegate)
    extractor.allocate_tensors()

    num_classes = imprinting_engine.num_classes

    print('---------------      Parsing dataset      ----------------')
    print('Dataset path:', dataset_path)

    # train in fixed order to ensure the same evaluation result.
    train_set, test_set = test_utils.prepare_data_set_from_directory(
        dataset_path, test_ratio, True)

    print('Image list successfully parsed! Number of Categories = ',
          len(train_set))
    print('---------------  Processing training data ----------------')
    print('This process may take more than 30 seconds.')
    train_input = []
    labels_map = {}
    for class_id, (category, image_list) in enumerate(train_set.items()):
      print('Processing {} ({} images)'.format(category, len(image_list)))
      train_input.append(
          [os.path.join(dataset_path, category, image) for image in image_list])
      labels_map[num_classes + class_id] = category

    # train
    print('----------------      Start training     -----------------')

    size = common.input_size(extractor)