# 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/adapters/detect.py
# class BBox(collections.namedtuple('BBox', ['xmin', 'ymin', 'xmax', 'ymax'])):
#   def width(self):
#   def height(self):
#   def area(self):
#   def valid(self):
#   def scale(self, sx, sy):
#   def translate(self, dx, dy):
#   def map(self, f):
#   def intersect(a, b):
#   def union(a, b):
#   def iou(a, b):
# def get_objects(interpreter,
#                 score_threshold=-float('inf'),
#                 image_scale=(1.0, 1.0)):
#   def make(i):
# 
# 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: pycoral/utils/edgetpu.py
# def make_interpreter(model_path_or_content, device=None, delegate=None):
#   """Creates a new ``tf.lite.Interpreter`` instance using the given model.
# 
#   **Note:** If you have multiple Edge TPUs, you should always specify the
#   ``device`` argument.
# 
#   Args:
#      model_path_or_content (str or bytes): `str` object is interpreted as
#        model path, `bytes` object is interpreted as model content.
#      device (str): The Edge TPU device you want:
# 
#        + None      -- use any Edge TPU (this is the default)
#        + ":<N>"    -- use N-th Edge TPU (this corresponds to the enumerated
#          index position from :func:`list_edge_tpus`)
#        + "usb"     -- use any USB Edge TPU
#        + "usb:<N>" -- use N-th USB Edge TPU
#        + "pci"     -- use any PCIe Edge TPU
#        + "pci:<N>" -- use N-th PCIe Edge TPU
# 
#        If left as None, you cannot reliably predict which device you'll get.
#        So if you have multiple Edge TPUs and want to run a specific model on
#        each one, then you must specify the device.
#      delegate: A pre-loaded Edge TPU delegate object, as provided by
#        :func:`load_edgetpu_delegate`. If provided, the `device` argument
#        is ignored.
# 
#   Returns:
#      New ``tf.lite.Interpreter`` instance.
#   """
#   if delegate:
#     delegates = [delegate]
#   else:
#     delegates = [load_edgetpu_delegate({'device': device} if device else {})]
#   if isinstance(model_path_or_content, bytes):
#     return tflite.Interpreter(
#         model_content=model_path_or_content, experimental_delegates=delegates)
#   else:
#     return tflite.Interpreter(
#         model_path=model_path_or_content, experimental_delegates=delegates)


import argparse
import contextlib
import threading
import time
from PIL import Image
from pycoral.adapters import classify
from pycoral.adapters import common
from pycoral.adapters import detect
from pycoral.utils.edgetpu import list_edge_tpus
from pycoral.utils.edgetpu import make_interpreter
# 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.
r"""Demo to show running two models on one/two Edge TPU devices.

This is a dummy example that compares running two different models using one
Edge TPU vs two Edge TPUs. It requires that your system includes two Edge TPU
devices.

You give the script one classification model and one
detection model, and it runs each model the number of times specified with the

# 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/adapters/detect.py
# class BBox(collections.namedtuple('BBox', ['xmin', 'ymin', 'xmax', 'ymax'])):
#   def width(self):
#   def height(self):
#   def area(self):
#   def valid(self):
#   def scale(self, sx, sy):
#   def translate(self, dx, dy):
#   def map(self, f):
#   def intersect(a, b):
#   def union(a, b):
#   def iou(a, b):
# def get_objects(interpreter,
#                 score_threshold=-float('inf'),
#                 image_scale=(1.0, 1.0)):
#   def make(i):
# 
# 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: pycoral/utils/edgetpu.py
# def make_interpreter(model_path_or_content, device=None, delegate=None):
#   """Creates a new ``tf.lite.Interpreter`` instance using the given model.
# 
#   **Note:** If you have multiple Edge TPUs, you should always specify the
#   ``device`` argument.
# 
#   Args:
#      model_path_or_content (str or bytes): `str` object is interpreted as
#        model path, `bytes` object is interpreted as model content.
#      device (str): The Edge TPU device you want:
# 
#        + None      -- use any Edge TPU (this is the default)
#        + ":<N>"    -- use N-th Edge TPU (this corresponds to the enumerated
#          index position from :func:`list_edge_tpus`)
#        + "usb"     -- use any USB Edge TPU
#        + "usb:<N>" -- use N-th USB Edge TPU
#        + "pci"     -- use any PCIe Edge TPU
#        + "pci:<N>" -- use N-th PCIe Edge TPU
# 
#        If left as None, you cannot reliably predict which device you'll get.
#        So if you have multiple Edge TPUs and want to run a specific model on
#        each one, then you must specify the device.
#      delegate: A pre-loaded Edge TPU delegate object, as provided by
#        :func:`load_edgetpu_delegate`. If provided, the `device` argument
#        is ignored.
# 
#   Returns:
#      New ``tf.lite.Interpreter`` instance.
#   """
#   if delegate:
#     delegates = [delegate]
#   else:
#     delegates = [load_edgetpu_delegate({'device': device} if device else {})]
#   if isinstance(model_path_or_content, bytes):
#     return tflite.Interpreter(
#         model_content=model_path_or_content, experimental_delegates=delegates)
#   else:
#     return tflite.Interpreter(
#         model_path=model_path_or_content, experimental_delegates=delegates)


# Path: examples/two_models_inference.py
import argparse
import contextlib
import threading
import time
from PIL import Image
from pycoral.adapters import classify
from pycoral.adapters import common
from pycoral.adapters import detect
from pycoral.utils.edgetpu import list_edge_tpus
from pycoral.utils.edgetpu import make_interpreter

# 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.
r"""Demo to show running two models on one/two Edge TPU devices.

This is a dummy example that compares running two different models using one
Edge TPU vs two Edge TPUs. It requires that your system includes two Edge TPU
devices.

You give the script one classification model and one
detection model, and it runs each model the number of times specified with the

`num_inferences` argument, using the same image. It then reports the time