text: # -*- coding: utf-8 -*-
  """
  Class definition of YOLO_v3 style detection model on image and video
  """
  
  import colorsys
  import os
  from timeit import default_timer as timer
  import operator
  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
  import cv2
  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": "Data/Model_Weights/trained_weights_final.h5",
          "anchors_path": "Data/Model_Weights/yolo_anchors.txt",
          "classes_path": "Data/Model_Weights/data_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()
          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.
          start = timer()
          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:
              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:
              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"
  
          end = timer()
          print(
              "{} model, anchors, and classes loaded in {:.2f}sec.".format(
                  model_path, end - start
              )
          )
  
          # Generate colors for drawing bounding boxes.
          if len(self.class_names) == 1:
              self.colors = ["GreenYellow"]
          else:
              hsv_tuples = [
                  (x / len(self.class_names), 1.0, 1.0)
                  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
      # Adding vehicleId
      def detect_image(self, image, show_stats=True):
          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")
          if show_stats:
              print(image_data.shape)
          image_data /= 255.0
          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,
              },
          )
          if show_stats:
              print("Found {} boxes for {}".format(len(out_boxes), "img"))
          out_prediction = []
  
          font_path = os.path.join(os.path.dirname(__file__), "font/FiraMono-Medium.otf")
          font = ImageFont.truetype(
              font=font_path, size=np.floor(3e-2 * image.size[1] + 0.5).astype("int32")
          )
          thickness = (image.size[0] + image.size[1]) // 300
          for a,c in reversed(list(enumerate(out_classes))):
              predicted_class = self.class_names[c]
              score = out_scores[a]
              if predicted_class == 'plate':
                  
                  if score>0.1:
                      box = out_boxes[a]
                      
              
                      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"))
              
                          # image was expanded to model_image_size: make sure it did not pick
                          # up any box outside of original image (run into this bug when
                          # lowering confidence threshold to 0.01)
                      #if top > image.size[1] or right > image.size[0]:
                          # continue
                      if show_stats:
                          print(label, (left, top), (right, bottom))
              
                          # output as xmin, ymin, xmax, ymax, class_index, confidence
                      out_prediction.append([left, top, right, bottom, predicted_class, score])
              
                      if top - label_size[1] >= 0:
                          text_origin = np.array([left, top - label_size[1]])
                      else:
                          text_origin = np.array([left, bottom])
              
                          # 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=(255,255,0)
                          )
                      draw.rectangle(
                          [tuple(text_origin), tuple(text_origin + label_size)],
                              fill=(255,255,0),
                      )
              
                      draw.text(text_origin, label, fill=(0, 0, 0), font=font)
                      del draw
                  else:
                      continue
  
          end = timer()
          if show_stats:
              print("Time spent: {:.3f}sec".format(end - start))
          return out_prediction, image
  
      def close_session(self):
          self.sess.close()
  
  
  
  def detect_video(yolo, video_path, output_path=""):
  
      frame_no = 0
      vid = cv2.VideoCapture(video_path)
      if not vid.isOpened():
          raise IOError("Couldn't open webcam or video")
      video_FourCC = cv2.VideoWriter_fourcc(*"mp4v")  # 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(
              "Processing {} with frame size {} at {:.1f} FPS".format(
                  os.path.basename(video_path), video_size, video_fps
              )
          )
          # 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 vid.isOpened():
          return_value, frame = vid.read()
          global c
          c=[]
          
          
              
          def line_definer(event,x,y,flags,param):
                   
                      if event == cv2.EVENT_LBUTTONDOWN:
                          c.append(x)
                          c.append(y)
                      
                      
          cv2.setMouseCallback('result',line_definer)
              
  
          
          cv2.line(frame,(123,322),(739,269),(0,0,255),2)
          frame_no = frame_no+1
          if not return_value:
              break
          # opencv images are BGR, translate to RGB
          frame = frame[:, :, ::-1]
          image = Image.fromarray(frame)
          out_pred, image = yolo.detect_image(image, show_stats=False)
          # print(len(out_pred))
          if len(out_pred) != 0:
              for i in range(len(out_pred)):
                  if int((out_pred[i][1]+out_pred[i][3])/2)>=int((322+269)/2):
                      
              
              
              
                      
                      result = np.asarray(image)
                      cv2.line(result,(123,322),(739,269),(0,255,0),2)
                      
                     
                      if out_pred[i][5] >0.3:
                              roi = frame[out_pred[i][1]:out_pred[i][3],out_pred[i][0]:out_pred[i][2]]
                              cv2.imwrite('C:/Users/Tushar Goel/Desktop/objects/{} {}.jpg'.format(frame_no,i),roi)
                             
                      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,
                      )
                      
                  else:
                      result = np.asarray(image)
          else:
                  result = np.asarray(image)
  
          cv2.namedWindow("result", cv2.WINDOW_NORMAL)
          cv2.resizeWindow("result",600,600)
          cv2.imshow("result", result)
          if isOutput:
                  out.write(result[:, :, ::-1])
          if cv2.waitKey(1) & 0xFF == ord('q'):
                  break
      vid.release()
      out.release()
      # yolo.close_session()
  