# 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 collections
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
# Lint as: python3
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.



_MODEL_LIST = [
    'mobilenet_v1_1.0_224_l2norm_quant.tflite',
    'mobilenet_v1_1.0_224_l2norm_quant_edgetpu.tflite'
]

TrainPoint = collections.namedtuple('TainPoint', ['images', 'class_id'])
TestPoint = collections.namedtuple('TainPoint', ['image', 'class_id', 'score'])


def set_input(interpreter, image):
  size = common.input_size(interpreter)

# 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_engine_test.py
import collections
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

# Lint as: python3
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.



_MODEL_LIST = [
    'mobilenet_v1_1.0_224_l2norm_quant.tflite',
    'mobilenet_v1_1.0_224_l2norm_quant_edgetpu.tflite'
]

TrainPoint = collections.namedtuple('TainPoint', ['images', 'class_id'])
TestPoint = collections.namedtuple('TainPoint', ['image', 'class_id', 'score'])


def set_input(interpreter, image):
  size = common.input_size(interpreter)

  common.set_input(interpreter, image.resize(size, Image.NEAREST))