# 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/dataset.py
# def read_label_file(file_path):
#   """Reads labels from a text file and returns it as a dictionary.
# 
#   This function supports label files with the following formats:
# 
#   + Each line contains id and description separated by colon or space.
#     Example: ``0:cat`` or ``0 cat``.
#   + Each line contains a description only. The returned label id's are based on
#     the row number.
# 
#   Args:
#     file_path (str): path to the label file.
# 
#   Returns:
#     Dict of (int, string) which maps label id to description.
#   """
#   with open(file_path, 'r', encoding='utf-8') as f:
#     lines = f.readlines()
#   ret = {}
#   for row_number, content in enumerate(lines):
#     pair = re.split(r'[:\s]+', content.strip(), maxsplit=1)
#     if len(pair) == 2 and pair[0].strip().isdigit():
#       ret[int(pair[0])] = pair[1].strip()
#     else:
#       ret[row_number] = content.strip()
#   return ret
# 
# 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 time
from PIL import Image
from PIL import ImageDraw
from pycoral.adapters import common
from pycoral.adapters import detect
from pycoral.utils.dataset import read_label_file
from pycoral.utils.edgetpu import make_interpreter
  for obj in objs:
    bbox = obj.bbox
    draw.rectangle([(bbox.xmin, bbox.ymin), (bbox.xmax, bbox.ymax)],
                   outline='red')
    draw.text((bbox.xmin + 10, bbox.ymin + 10),
              '%s\n%.2f' % (labels.get(obj.id, obj.id), obj.score),
              fill='red')


def main():
  parser = argparse.ArgumentParser(
      formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  parser.add_argument('-m', '--model', required=True,
                      help='File path of .tflite file')
  parser.add_argument('-i', '--input', required=True,
                      help='File path of image to process')
  parser.add_argument('-l', '--labels', help='File path of labels file')
  parser.add_argument('-t', '--threshold', type=float, default=0.4,
                      help='Score threshold for detected objects')
  parser.add_argument('-o', '--output',
                      help='File path for the result image with annotations')
  parser.add_argument('-c', '--count', type=int, default=5,
                      help='Number of times to run inference')
  args = parser.parse_args()

  labels = read_label_file(args.labels) if args.labels else {}
  interpreter = make_interpreter(args.model)
  interpreter.allocate_tensors()

  image = Image.open(args.input)

# 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/dataset.py
# def read_label_file(file_path):
#   """Reads labels from a text file and returns it as a dictionary.
# 
#   This function supports label files with the following formats:
# 
#   + Each line contains id and description separated by colon or space.
#     Example: ``0:cat`` or ``0 cat``.
#   + Each line contains a description only. The returned label id's are based on
#     the row number.
# 
#   Args:
#     file_path (str): path to the label file.
# 
#   Returns:
#     Dict of (int, string) which maps label id to description.
#   """
#   with open(file_path, 'r', encoding='utf-8') as f:
#     lines = f.readlines()
#   ret = {}
#   for row_number, content in enumerate(lines):
#     pair = re.split(r'[:\s]+', content.strip(), maxsplit=1)
#     if len(pair) == 2 and pair[0].strip().isdigit():
#       ret[int(pair[0])] = pair[1].strip()
#     else:
#       ret[row_number] = content.strip()
#   return ret
# 
# 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/detect_image.py
import argparse
import time
from PIL import Image
from PIL import ImageDraw
from pycoral.adapters import common
from pycoral.adapters import detect
from pycoral.utils.dataset import read_label_file
from pycoral.utils.edgetpu import make_interpreter

  for obj in objs:
    bbox = obj.bbox
    draw.rectangle([(bbox.xmin, bbox.ymin), (bbox.xmax, bbox.ymax)],
                   outline='red')
    draw.text((bbox.xmin + 10, bbox.ymin + 10),
              '%s\n%.2f' % (labels.get(obj.id, obj.id), obj.score),
              fill='red')


def main():
  parser = argparse.ArgumentParser(
      formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  parser.add_argument('-m', '--model', required=True,
                      help='File path of .tflite file')
  parser.add_argument('-i', '--input', required=True,
                      help='File path of image to process')
  parser.add_argument('-l', '--labels', help='File path of labels file')
  parser.add_argument('-t', '--threshold', type=float, default=0.4,
                      help='Score threshold for detected objects')
  parser.add_argument('-o', '--output',
                      help='File path for the result image with annotations')
  parser.add_argument('-c', '--count', type=int, default=5,
                      help='Number of times to run inference')
  args = parser.parse_args()

  labels = read_label_file(args.labels) if args.labels else {}
  interpreter = make_interpreter(args.model)
  interpreter.allocate_tensors()

  image = Image.open(args.input)

  _, scale = common.set_resized_input(