text: # -*- coding: utf-8 -*-
  """
  Class definition of YOLO_v3 style detection model on image and video
  """
  from tensorflow.compat.v1 import ConfigProto
  from tensorflow.compat.v1 import InteractiveSession
  
  config = ConfigProto()
  config.gpu_options.allow_growth = True
  session = InteractiveSession(config=config)
  
  import colorsys
  import os
  from timeit import default_timer as timer
  
  import numpy as np
  from keras import backend as K
  from keras.models import load_model
  from keras.layers import Input
  from PIL import Image, ImageFont, ImageDraw
  
  from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
  from yolo3.utils import letterbox_image
  import os
  from keras.utils import multi_gpu_model
  
  class YOLO(object):
      _defaults = {
          "model_path": 'model_data/yolo.h5',
          "anchors_path": 'model_data/yolo_anchors.txt',
          "classes_path": 'model_data/coco_classes.txt',
          "score" : 0.3,
          "iou" : 0.45,
          "model_image_size" : (416, 416),
          "gpu_num" : 1,
      }
  
      @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):
          self.__dict__.update(self._defaults) # set up default values
          self.__dict__.update(kwargs) # and update with user overrides
          self.class_names = self._get_class()
          self.anchors = self._get_anchors()
          self.sess = K.get_session()
          #print(self.__dict__)
          print(self.model_path)
          print(self.classes_path)
          self.boxes, self.scores, self.classes = self.generate()
  
      def _get_class(self):
          classes_path = os.path.expanduser(self.classes_path)
          with open(classes_path) as f:
              class_names = f.readlines()
          class_names = [c.strip() for c in class_names]
          return class_names
  
      def _get_anchors(self):
          anchors_path = os.path.expanduser(self.anchors_path)
          with open(anchors_path) as f:
              anchors = f.readline()
          anchors = [float(x) for x in anchors.split(',')]
          return np.array(anchors).reshape(-1, 2)
  
      def generate(self):
          model_path = os.path.expanduser(self.model_path)
          assert model_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)
          is_tiny_version = num_anchors==6 # default setting
          try:
              self.yolo_model = load_model(model_path, compile=False)
          except:
              print('yolo load_model fail')
              self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \
                  if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
              self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match
          else:
              print('yolo load_model ok')
              assert self.yolo_model.layers[-1].output_shape[-1] == \
                  num_anchors/len(self.yolo_model.output) * (num_classes + 5), \
                  'Mismatch between model and given anchor and class sizes'
          #print('yolo model: ', self.yolo_model.summary() )
          print('yolo model num of layers: ', len(self.yolo_model.layers))
          print('{} model, anchors, and classes loaded.'.format(model_path))
          #exit()
          # Generate colors for drawing bounding boxes.
          hsv_tuples = [(x / len(self.class_names), 1., 1.)
                        for x in range(len(self.class_names))]
          self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
          self.colors = list(
              map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
                  self.colors))
          np.random.seed(10101)  # Fixed seed for consistent colors across runs.
          np.random.shuffle(self.colors)  # Shuffle colors to decorrelate adjacent classes.
          np.random.seed(None)  # Reset seed to default.
  
          # Generate output tensor targets for filtered bounding boxes.
          self.input_image_shape = K.placeholder(shape=(2, ))
          if self.gpu_num>=2:
              self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num)
          boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
                  len(self.class_names), self.input_image_shape,
                  score_threshold=self.score, iou_threshold=self.iou)
          return boxes, scores, classes
  
      def detect_image(self, image):
          start = timer()
  
          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'
              boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))
          else:
              new_image_size = (image.width - (image.width % 32),
                                image.height - (image.height % 32))
              boxed_image = letterbox_image(image, new_image_size)
          image_data = np.array(boxed_image, dtype='float32')
  
          print(image_data.shape)
          image_data /= 255.
          image_data = np.expand_dims(image_data, 0)  # Add batch dimension.
  
          out_boxes, out_scores, out_classes = self.sess.run(
              [self.boxes, self.scores, self.classes],
              feed_dict={
                  self.yolo_model.input: image_data,
                  self.input_image_shape: [image.size[1], image.size[0]],
                  K.learning_phase(): 0
              })
  
          print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
  
          font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
                      size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
          thickness = (image.size[0] + image.size[1]) // 300
  
          for i, c in reversed(list(enumerate(out_classes))):
              predicted_class = self.class_names[c]
              box = out_boxes[i]
              score = out_scores[i]
  
              label = '{} {:.2f}'.format(predicted_class, score)
              draw = ImageDraw.Draw(image)
              label_size = draw.textsize(label, font)
  
              top, left, bottom, right = box
              top = max(0, np.floor(top + 0.5).astype('int32'))
              left = max(0, np.floor(left + 0.5).astype('int32'))
              bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
              right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
              print(label, (left, top), (right, bottom))
  
              if top - label_size[1] >= 0:
                  text_origin = np.array([left, top - label_size[1]])
              else:
                  text_origin = np.array([left, top + 1])
  
              # My kingdom for a good redistributable image drawing library.
              for i in range(thickness):
                  draw.rectangle(
                      [left + i, top + i, right - i, bottom - i],
                      outline=self.colors[c])
              draw.rectangle(
                  [tuple(text_origin), tuple(text_origin + label_size)],
                  fill=self.colors[c])
              draw.text(text_origin, label, fill=(0, 0, 0), font=font)
              del draw
  
          end = timer()
          print(end - start)
          return image
  
      def close_session(self):
          self.sess.close()
  
  def detect_video(yolo, video_path, output_path=""):
      import cv2
      vid = cv2.VideoCapture(video_path)
      if not vid.isOpened():
          raise IOError("Couldn't open webcam or video")
      video_FourCC    = int(vid.get(cv2.CAP_PROP_FOURCC))
      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, 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
      yolo.close_session()
  
  