text: #! /usr/bin/env python3
  # -*- coding: utf-8 -*-
  """
  Run a YOLOv3/YOLOv2 style detection model on test images.
  """
  
  import colorsys
  import os, sys, argparse
  import cv2
  import time
  from timeit import default_timer as timer
  import tensorflow as tf
  import numpy as np
  from tensorflow.keras import backend as K
  from tensorflow.keras.models import Model, load_model
  from tensorflow.keras.layers import Input, Lambda
  from tensorflow_model_optimization.sparsity import keras as sparsity
  from PIL import Image
  
  from yolo3.model import get_yolo3_model, get_yolo3_inference_model
  from yolo3.postprocess_np import yolo3_postprocess_np
  from yolo2.model import get_yolo2_model, get_yolo2_inference_model
  from yolo2.postprocess_np import yolo2_postprocess_np
  from common.data_utils import preprocess_image
  from common.utils import get_classes, get_anchors, get_colors, draw_boxes, optimize_tf_gpu
  from tensorflow.keras.utils import multi_gpu_model
  from tensorflow.keras.utils import plot_model
  
  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
  os.environ['CUDA_VISIBLE_DEVICES'] = '0'
  
  optimize_tf_gpu(tf, K)
  
  #tf.enable_eager_execution()
  
  default_config = {
          "model_type": 'tiny_yolo3_darknet',
          "weights_path": os.path.join('weights', 'yolov3-tiny.h5'),
          "pruning_model": False,
          "anchors_path": os.path.join('configs', 'tiny_yolo3_anchors.txt'),
          "classes_path": os.path.join('configs', 'coco_classes.txt'),
          "score" : 0.1,
          "iou" : 0.4,
          "model_image_size" : (416, 416),
          "elim_grid_sense": False,
          "gpu_num" : 1,
      }
  
  
  class YOLO_np(object):
      _defaults = default_config
  
      @classmethod
      def get_defaults(cls, n):
          if n in cls._defaults:
              return cls._defaults[n]
          else:
              return "Unrecognized attribute name '" + n + "'"
  
      def __init__(self, **kwargs):
          super(YOLO_np, self).__init__()
          self.__dict__.update(self._defaults) # set up default values
          self.__dict__.update(kwargs) # and update with user overrides
          self.class_names = get_classes(self.classes_path)
          self.anchors = get_anchors(self.anchors_path)
          self.colors = get_colors(self.class_names)
          K.set_learning_phase(0)
          self.yolo_model = self._generate_model()
  
      def _generate_model(self):
          '''to generate the bounding boxes'''
          weights_path = os.path.expanduser(self.weights_path)
          assert weights_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
  
          # Load model, or construct model and load weights.
          num_anchors = len(self.anchors)
          num_classes = len(self.class_names)
          #YOLOv3 model has 9 anchors and 3 feature layers but
          #Tiny YOLOv3 model has 6 anchors and 2 feature layers,
          #so we can calculate feature layers number to get model type
          num_feature_layers = num_anchors//3
  
          try:
              if num_anchors == 5:
                  # YOLOv2 use 5 anchors
                  yolo_model, _ = get_yolo2_model(self.model_type, num_anchors, num_classes, input_shape=self.model_image_size + (3,), model_pruning=self.pruning_model)
              else:
                  yolo_model, _ = get_yolo3_model(self.model_type, num_feature_layers, num_anchors, num_classes, input_shape=self.model_image_size + (3,), model_pruning=self.pruning_model)
              yolo_model.load_weights(weights_path) # make sure model, anchors and classes match
              if self.pruning_model:
                  yolo_model = sparsity.strip_pruning(yolo_model)
              
              yolo_model.summary()
              
              dot_img_file = 'model_architecture.jpg'
              print('> plotting the model architecture and saving at ', dot_img_file)
              plot_model(yolo_model, to_file=dot_img_file, show_shapes=True)
  
          except Exception as e:
              print(repr(e))
              assert yolo_model.layers[-1].output_shape[-1] == \
                  num_anchors/len(yolo_model.output) * (num_classes + 5), \
                  'Mismatch between model and given anchor and class sizes'
          print('{} model, anchors, and classes loaded.'.format(weights_path))
          if self.gpu_num>=2:
              yolo_model = multi_gpu_model(yolo_model, gpus=self.gpu_num)
  
          return yolo_model
  
  
      def detect_image(self, image):
          if self.model_image_size != (None, None):
              assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required'
              assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required'
  
          image_data = preprocess_image(image, self.model_image_size)
          #origin image shape, in (height, width) format
          image_shape = tuple(reversed(image.size))
  
          start = time.time()
          out_boxes, out_classes, out_scores = self.predict(image_data, image_shape)
          print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
          end = time.time()
          print("Inference time: {:.8f}s".format(end - start))
  
          #draw result on input image
          image_array = np.array(image, dtype='uint8')
          image_array = draw_boxes(image_array, out_boxes, out_classes, out_scores, self.class_names, self.colors)
          return Image.fromarray(image_array)
  
  
      def predict(self, image_data, image_shape):
          num_anchors = len(self.anchors)
          if num_anchors == 5:
              # YOLOv2 use 5 anchors
              out_boxes, out_classes, out_scores = yolo2_postprocess_np(self.yolo_model.predict(image_data), image_shape, self.anchors, len(self.class_names), self.model_image_size, max_boxes=100, elim_grid_sense=self.elim_grid_sense)
          else:
              out_boxes, out_classes, out_scores = yolo3_postprocess_np(self.yolo_model.predict(image_data), image_shape, self.anchors, len(self.class_names), self.model_image_size, max_boxes=100, elim_grid_sense=self.elim_grid_sense)
          return out_boxes, out_classes, out_scores
  
  
      def dump_model_file(self, output_model_file):
          self.yolo_model.save(output_model_file)
  
  
  
  class YOLO(object):
      _defaults = default_config
  
      @classmethod
      def get_defaults(cls, n):
          if n in cls._defaults:
              return cls._defaults[n]
          else:
              return "Unrecognized attribute name '" + n + "'"
  
      def __init__(self, **kwargs):
          super(YOLO, self).__init__()
          self.__dict__.update(self._defaults) # set up default values
          self.__dict__.update(kwargs) # and update with user overrides
          self.class_names = get_classes(self.classes_path)
          self.anchors = get_anchors(self.anchors_path)
          self.colors = get_colors(self.class_names)
          K.set_learning_phase(0)
          self.inference_model = self._generate_model()
  
      def _generate_model(self):
          '''to generate the bounding boxes'''
          weights_path = os.path.expanduser(self.weights_path)
          assert weights_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
  
          # Load model, or construct model and load weights.
          num_anchors = len(self.anchors)
          num_classes = len(self.class_names)
          #YOLOv3 model has 9 anchors and 3 feature layers but
          #Tiny YOLOv3 model has 6 anchors and 2 feature layers,
          #so we can calculate feature layers number to get model type
          num_feature_layers = num_anchors//3
  
          if num_anchors == 5:
              # YOLOv2 use 5 anchors
              inference_model = get_yolo2_inference_model(self.model_type, self.anchors, num_classes, weights_path=weights_path, input_shape=self.model_image_size + (3,), confidence=0.1, elim_grid_sense=self.elim_grid_sense)
          else:
              inference_model = get_yolo3_inference_model(self.model_type, self.anchors, num_classes, weights_path=weights_path, input_shape=self.model_image_size + (3,), confidence=0.1, elim_grid_sense=self.elim_grid_sense)
  
          inference_model.summary()
          return inference_model
  
      def predict(self, image_data, image_shape):
          out_boxes, out_scores, out_classes = self.inference_model.predict([image_data, image_shape])
  
          out_boxes = out_boxes[0]
          out_scores = out_scores[0]
          out_classes = out_classes[0]
  
          out_boxes = out_boxes.astype(np.int32)
          out_classes = out_classes.astype(np.int32)
          return out_boxes, out_classes, out_scores
  
      def detect_image(self, image):
          if self.model_image_size != (None, None):
              assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required'
              assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required'
  
          image_data = preprocess_image(image, self.model_image_size)
  
          # prepare origin image shape, (height, width) format
          image_shape = np.array([image.size[1], image.size[0]])
          image_shape = np.expand_dims(image_shape, 0)
  
          start = time.time()
          out_boxes, out_classes, out_scores = self.predict(image_data, image_shape)
          end = time.time()
          print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
          print("Inference time: {:.8f}s".format(end - start))
  
          #draw result on input image
          image_array = np.array(image, dtype='uint8')
          image_array = draw_boxes(image_array, out_boxes, out_classes, out_scores, self.class_names, self.colors)
          return Image.fromarray(image_array)
  
      def dump_model_file(self, output_model_file):
          self.inference_model.save(output_model_file)
  
      def dump_saved_model(self, saved_model_path):
          model = self.inference_model
          os.makedirs(saved_model_path, exist_ok=True)
  
          tf.keras.experimental.export_saved_model(model, saved_model_path)
          print('export inference model to %s' % str(saved_model_path))
  
  
  
  def detect_video(yolo, video_path, output_path=""):
      import cv2
      vid = cv2.VideoCapture(0 if video_path == '0' else video_path)
      if not vid.isOpened():
          raise IOError("Couldn't open webcam or video")
  
      # here we encode the video to MPEG-4 for better compatibility, you can use ffmpeg later
      # to convert it to x264 to reduce file size:
      # ffmpeg -i test.mp4 -vcodec libx264 -f mp4 test_264.mp4
      #
      #video_FourCC    = cv2.VideoWriter_fourcc(*'XVID') if video_path == '0' else int(vid.get(cv2.CAP_PROP_FOURCC))
      video_FourCC    = cv2.VideoWriter_fourcc(*'XVID') if video_path == '0' else cv2.VideoWriter_fourcc(*"mp4v")
      video_fps       = vid.get(cv2.CAP_PROP_FPS)
      video_size      = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),
                          int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)))
      isOutput = True if output_path != "" else False
      if isOutput:
          print("!!! TYPE:", type(output_path), type(video_FourCC), type(video_fps), type(video_size))
          out = cv2.VideoWriter(output_path, video_FourCC, (5. if video_path == '0' else video_fps), video_size)
      accum_time = 0
      curr_fps = 0
      fps = "FPS: ??"
      prev_time = timer()
      while True:
          return_value, frame = vid.read()
          image = Image.fromarray(frame)
          image = yolo.detect_image(image)
          result = np.asarray(image)
          curr_time = timer()
          exec_time = curr_time - prev_time
          prev_time = curr_time
          accum_time = accum_time + exec_time
          curr_fps = curr_fps + 1
          if accum_time > 1:
              accum_time = accum_time - 1
              fps = "FPS: " + str(curr_fps)
              curr_fps = 0
          cv2.putText(result, text=fps, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
                      fontScale=0.50, color=(255, 0, 0), thickness=2)
          cv2.namedWindow("result", cv2.WINDOW_NORMAL)
          cv2.imshow("result", result)
          if isOutput:
              out.write(result)
          if cv2.waitKey(1) & 0xFF == ord('q'):
              break
      # Release everything if job is finished
      vid.release()
      if isOutput:
          out.release()
      cv2.destroyAllWindows()
  
  
  def detect_img(yolo):
      while True:
          img = input('Input image filename:')
          try:
              image = Image.open(img)
          except:
              print('Open Error! Try again!')
              continue
          else:
              r_image = yolo.detect_image(image)
              r_image.save("TEST.jpg")
              r_image.show()
  
  
  if __name__ == '__main__':
      # class YOLO defines the default value, so suppress any default here
      parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description='demo or dump out YOLO h5 model')
      '''
      Command line options
      '''
      parser.add_argument(
          '--model_type', type=str,
          help='YOLO model type: yolo3_mobilenet_lite/tiny_yolo3_mobilenet/yolo3_darknet/..., default ' + YOLO.get_defaults("model_type")
      )
  
      parser.add_argument(
          '--weights_path', type=str,
          help='path to model weight file, default ' + YOLO.get_defaults("weights_path")
      )
  
      parser.add_argument(
          '--pruning_model', default=False, action="store_true",
          help='Whether to be a pruning model/weights file, default ' + str(YOLO.get_defaults("pruning_model"))
      )
  
      parser.add_argument(
          '--anchors_path', type=str,
          help='path to anchor definitions, default ' + YOLO.get_defaults("anchors_path")
      )
  
      parser.add_argument(
          '--classes_path', type=str,
          help='path to class definitions, default ' + YOLO.get_defaults("classes_path")
      )
  
      parser.add_argument(
          '--model_image_size', type=str,
          help='model image input size as <height>x<width>, default ' +
          str(YOLO.get_defaults("model_image_size")[0])+'x'+str(YOLO.get_defaults("model_image_size")[1]),
          default=str(YOLO.get_defaults("model_image_size")[0])+'x'+str(YOLO.get_defaults("model_image_size")[1])
      )
  
      parser.add_argument(
          '--elim_grid_sense', default=False, action="store_true",
          help = "Eliminate grid sensitivity, default " + str(YOLO.get_defaults("elim_grid_sense"))
      )
  
      parser.add_argument(
          '--gpu_num', type=int,
          help='Number of GPU to use, default ' + str(YOLO.get_defaults("gpu_num"))
      )
      parser.add_argument(
          '--image', default=False, action="store_true",
          help='Image detection mode, will ignore all positional arguments'
      )
      '''
      Command line positional arguments -- for video detection mode
      '''
      parser.add_argument(
          "--input", nargs='?', type=str,required=False,default='./path2your_video',
          help = "Video input path"
      )
  
      parser.add_argument(
          "--output", nargs='?', type=str, default="",
          help = "[Optional] Video output path"
      )
      '''
      Command line positional arguments -- for model dump
      '''
      parser.add_argument(
          '--dump_model', default=False, action="store_true",
          help='Dump out training model to inference model'
      )
  
      parser.add_argument(
          '--output_model_file', type=str,
          help='output inference model file'
      )
  
      args = parser.parse_args()
      # param parse
      if args.model_image_size:
          height, width = args.model_image_size.split('x')
          args.model_image_size = (int(height), int(width))
          assert (args.model_image_size[0]%32 == 0 and args.model_image_size[1]%32 == 0), 'model_image_size should be multiples of 32'
  
      # get wrapped inference object
      yolo = YOLO_np(**vars(args))
  
      if args.dump_model:
          """
          Dump out training model to inference model
          """
          if not args.output_model_file:
              raise ValueError('output model file is not specified')
  
          print('Dumping out training model to inference model')
          yolo.dump_model_file(args.output_model_file)
          sys.exit()
  
      if args.image:
          """
          Image detection mode, disregard any remaining command line arguments
          """
          print("Image detection mode")
          if "input" in args:
              print(" Ignoring remaining command line arguments: " + args.input + "," + args.output)
          detect_img(yolo)
      elif "input" in args:
          detect_video(yolo, args.input, args.output)
      else:
          print("Must specify at least video_input_path.  See usage with --help.")
  