import argparse
import time

from PIL import Image
from PIL import ImageDraw

import detect
import tflite_runtime.interpreter as tflite
import platform

class TensorFlowPipeline:
    EDGETPU_SHARED_LIB = {
      'Linux': 'libedgetpu.so.1',
      'Darwin': 'libedgetpu.1.dylib',
      'Windows': 'edgetpu.dll'
    }[platform.system()]

    model = models/mobilenet_ssd_v2_coco_quant_postprocess.tflite
    label = models/coco_labels.txt

    def process(self, source):
      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=1,
                      help='Number of times to run inference')
      args = parser.parse_args()

      labels = load_labels(args.labels) if args.labels else {}
      interpreter = make_interpreter(args.model)
      interpreter.allocate_tensors()
      self.image = source
      scale = detect.set_input(interpreter, image.size,
                                lambda size: image.resize(size, Image.ANTIALIAS))

      start = time.perf_counter()
      interpreter.invoke()
      inference_time = time.perf_counter() - start
      objs = detect.get_output(interpreter, args.threshold, scale)
      print('%.2f ms' % (inference_time * 1000))

      return objs

    def load_labels(path, encoding='utf-8'):
      """Loads labels from file (with or without index numbers).
      Args:
        path: path to label file.
        encoding: label file encoding.
      Returns:
        Dictionary mapping indices to labels.
      """
      with open(path, 'r', encoding=encoding) as f:
        lines = f.readlines()
        if not lines:
          return {}

        if lines[0].split(' ', maxsplit=1)[0].isdigit():
          pairs = [line.split(' ', maxsplit=1) for line in lines]
          return {int(index): label.strip() for index, label in pairs}
        else:
          return {index: line.strip() for index, line in enumerate(lines)}


    def make_interpreter(model_file):
      model_file, *device = model_file.split('@')
      return tflite.Interpreter(
          model_path=model_file,
          experimental_delegates=[
              tflite.load_delegate(EDGETPU_SHARED_LIB,
                                   {'device': device[0]} if device else {})
          ])


    def draw_objects(draw, objs, labels):
      """Draws the bounding box and label for each object."""
      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')




import argparse
import time

from PIL import Image
from PIL import ImageDraw

import detect
import tflite_runtime.interpreter as tflite
import platform

class TensorFlowPipeline:
    EDGETPU_SHARED_LIB = {
      'Linux': 'libedgetpu.so.1',
      'Darwin': 'libedgetpu.1.dylib',
      'Windows': 'edgetpu.dll'
    }[platform.system()]

    model = models/mobilenet_ssd_v2_coco_quant_postprocess.tflite
    label = models/coco_labels.txt

    def process(self, source):
      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=1,
                      help='Number of times to run inference')
      args = parser.parse_args()

      labels = load_labels(args.labels) if args.labels else {}
      interpreter = make_interpreter(args.model)
      interpreter.allocate_tensors()
      self.image = source
      scale = detect.set_input(interpreter, image.size,
                                lambda size: image.resize(size, Image.ANTIALIAS))

      start = time.perf_counter()
      interpreter.invoke()
      inference_time = time.perf_counter() - start
      objs = detect.get_output(interpreter, args.threshold, scale)
      print('%.2f ms' % (inference_time * 1000))

      return objs

    def load_labels(path, encoding='utf-8'):
      """Loads labels from file (with or without index numbers).
      Args:
        path: path to label file.
        encoding: label file encoding.
      Returns:
        Dictionary mapping indices to labels.
      """
      with open(path, 'r', encoding=encoding) as f:
        lines = f.readlines()
        if not lines:
          return {}

        if lines[0].split(' ', maxsplit=1)[0].isdigit():
          pairs = [line.split(' ', maxsplit=1) for line in lines]
          return {int(index): label.strip() for index, label in pairs}
        else:
          return {index: line.strip() for index, line in enumerate(lines)}


    def make_interpreter(model_file):
      model_file, *device = model_file.split('@')
      return tflite.Interpreter(
          model_path=model_file,
          experimental_delegates=[
              tflite.load_delegate(EDGETPU_SHARED_LIB,
                                   {'device': device[0]} if device else {})
          ])


    def draw_objects(draw, objs, labels):
      """Draws the bounding box and label for each object."""
      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')




Loads labels from file (with or without index numbers).       Args:         path: path to label file.         encoding: label file encoding.       Returns:         Dictionary mapping indices to labels. Draws the bounding box and label for each object.