def generate(self): self.score = 0.01 self.iou = 0.5 model_path = os.path.expanduser(self.model_path) assert model_path.endswith( '.h5'), 'Keras model or weights must be a .h5 file.' # 计算anchor数量 num_anchors = len(self.anchors) num_classes = len(self.class_names) # 载入模型,如果原来的模型里已经包括了模型结构则直接载入。 # 否则先构建模型再载入 self.yolo_model = yolo_body(Input(shape=(None, None, 3)), num_anchors // 2, num_classes) self.yolo_model.load_weights(self.model_path, by_name=True) print('{} model, anchors, and classes loaded.'.format(model_path)) # 画框设置不同的颜色 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)) # 打乱颜色 np.random.seed(10101) np.random.shuffle(self.colors) np.random.seed(None) if self.eager: self.input_image_shape = Input([ 2, ], batch_size=1) inputs = [*self.yolo_model.output, self.input_image_shape] outputs = Lambda(yolo_eval, output_shape=(1, ), name='yolo_eval', arguments={ 'anchors': self.anchors, 'num_classes': len(self.class_names), 'image_shape': self.model_image_size, 'score_threshold': self.score, 'eager': True, 'max_boxes': self.max_boxes })(inputs) self.yolo_model = Model( [self.yolo_model.input, self.input_image_shape], outputs) else: self.input_image_shape = K.placeholder(shape=(2, )) self.boxes, self.scores, self.classes = yolo_eval( self.yolo_model.output, self.anchors, num_classes, self.input_image_shape, max_boxes=self.max_boxes, score_threshold=self.score, iou_threshold=self.iou)
def generate(self): self.score = 0.01 self.iou = 0.5 model_path = os.path.expanduser(self.model_path) assert model_path.endswith( '.h5'), 'Keras model or weights must be a .h5 file.' #---------------------------------------------------# # 计算先验框的数量和种类的数量 #---------------------------------------------------# num_anchors = len(self.anchors) num_classes = len(self.class_names) #---------------------------------------------------------# # 载入模型,如果原来的模型里已经包括了模型结构则直接载入。 # 否则先构建模型再载入 #---------------------------------------------------------# try: self.yolo_model = load_model(model_path, compile=False) except: self.yolo_model = yolo_body(Input(shape=(None, None, 3)), num_anchors // 2, num_classes) self.yolo_model.load_weights(self.model_path) 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' print('{} model, anchors, and classes loaded.'.format(model_path)) # 画框设置不同的颜色 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)) # 打乱颜色 np.random.seed(10101) np.random.shuffle(self.colors) np.random.seed(None) self.input_image_shape = K.placeholder(shape=(2, )) #---------------------------------------------------------# # 在yolo_eval函数中,我们会对预测结果进行后处理 # 后处理的内容包括,解码、非极大抑制、门限筛选等 #---------------------------------------------------------# boxes, scores, classes = yolo_eval( self.yolo_model.output, self.anchors, num_classes, self.input_image_shape, max_boxes=self.max_boxes, score_threshold=self.score, iou_threshold=self.iou, letterbox_image=self.letterbox_image) return boxes, scores, classes
def generate(self): self.score = 0.01 self.iou = 0.5 model_path = os.path.expanduser(self.model_path) assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.' #---------------------------------------------------# # 计算先验框的数量和种类的数量 #---------------------------------------------------# num_anchors = len(self.anchors) num_classes = len(self.class_names) #---------------------------------------------------------# # 载入模型 #---------------------------------------------------------# self.yolo_model = yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) self.yolo_model.load_weights(self.model_path) print('{} model, anchors, and classes loaded.'.format(model_path)) # 画框设置不同的颜色 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)) # 打乱颜色 np.random.seed(10101) np.random.shuffle(self.colors) np.random.seed(None) #---------------------------------------------------------# # 在yolo_eval函数中,我们会对预测结果进行后处理 # 后处理的内容包括,解码、非极大抑制、门限筛选等 #---------------------------------------------------------# if self.eager: self.input_image_shape = Input([2,],batch_size=1) inputs = [*self.yolo_model.output, self.input_image_shape] outputs = Lambda(yolo_eval, output_shape=(1,), name='yolo_eval', arguments={'anchors': self.anchors, 'num_classes': len(self.class_names), 'image_shape': self.model_image_size, 'score_threshold': self.score, 'eager': True, 'max_boxes': self.max_boxes, 'letterbox_image': self.letterbox_image})(inputs) self.yolo_model = Model([self.yolo_model.input, self.input_image_shape], outputs) else: self.input_image_shape = K.placeholder(shape=(2, )) self.boxes, self.scores, self.classes = yolo_eval(self.yolo_model.output, self.anchors, num_classes, self.input_image_shape, max_boxes=self.max_boxes, score_threshold=self.score, iou_threshold=self.iou, letterbox_image=self.letterbox_image)