text: # Tencent is pleased to support the open source community by making ncnn available.
  #
  # Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
  #
  # Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
  # in compliance with the License. You may obtain a copy of the License at
  #
  # https://opensource.org/licenses/BSD-3-Clause
  #
  # Unless required by applicable law or agreed to in writing, software distributed
  # under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
  # CONDITIONS OF ANY KIND, either express or implied. See the License for the
  # specific language governing permissions and limitations under the License.
  
  import ncnn
  from .model_store import get_model_file
  from ..utils.objects import Detect_Object
  
  
  class Noop(ncnn.Layer):
      pass
  
  
  def Noop_layer_creator():
      return Noop()
  
  
  class MobileNetV2_SSDLite:
      def __init__(self, target_size=300, num_threads=1, use_gpu=False):
          self.target_size = target_size
          self.num_threads = num_threads
          self.use_gpu = use_gpu
  
          self.mean_vals = [127.5, 127.5, 127.5]
          self.norm_vals = [0.007843, 0.007843, 0.007843]
  
          self.net = ncnn.Net()
          self.net.opt.use_vulkan_compute = self.use_gpu
          # self.net.register_custom_layer("Silence", Noop_layer_creator)
  
          # original pretrained model from https://github.com/chuanqi305/MobileNetv2-SSDLite
          # https://github.com/chuanqi305/MobileNetv2-SSDLite/blob/master/ssdlite/voc/deploy.prototxt
          # the ncnn model https://github.com/caishanli/pyncnn-assets/tree/master/models
          self.net.load_param(get_model_file("mobilenetv2_ssdlite_voc.param"))
          self.net.load_model(get_model_file("mobilenetv2_ssdlite_voc.bin"))
  
          self.class_names = [
              "background",
              "aeroplane",
              "bicycle",
              "bird",
              "boat",
              "bottle",
              "bus",
              "car",
              "cat",
              "chair",
              "cow",
              "diningtable",
              "dog",
              "horse",
              "motorbike",
              "person",
              "pottedplant",
              "sheep",
              "sofa",
              "train",
              "tvmonitor",
          ]
  
      def __del__(self):
          self.net = None
  
      def __call__(self, img):
          img_h = img.shape[0]
          img_w = img.shape[1]
  
          mat_in = ncnn.Mat.from_pixels_resize(
              img,
              ncnn.Mat.PixelType.PIXEL_BGR,
              img_w,
              img_h,
              self.target_size,
              self.target_size,
          )
          mat_in.substract_mean_normalize(self.mean_vals, self.norm_vals)
  
          ex = self.net.create_extractor()
          ex.set_light_mode(True)
          ex.set_num_threads(self.num_threads)
  
          ex.input("data", mat_in)
  
          ret, mat_out = ex.extract("detection_out")
  
          objects = []
  
          # printf("%d %d %d\n", mat_out.w, mat_out.h, mat_out.c)
  
          # method 1, use ncnn.Mat.row to get the result, no memory copy
          for i in range(mat_out.h):
              values = mat_out.row(i)
  
              obj = Detect_Object()
              obj.label = values[0]
              obj.prob = values[1]
              obj.rect.x = values[2] * img_w
              obj.rect.y = values[3] * img_h
              obj.rect.w = values[4] * img_w - obj.rect.x
              obj.rect.h = values[5] * img_h - obj.rect.y
  
              objects.append(obj)
  
          """
          #method 2, use ncnn.Mat->numpy.array to get the result, no memory copy too
          out = np.array(mat_out)
          for i in range(len(out)):
              values = out[i]
              obj = Detect_Object()
              obj.label = values[0]
              obj.prob = values[1]
              obj.rect.x = values[2] * img_w
              obj.rect.y = values[3] * img_h
              obj.rect.w = values[4] * img_w - obj.rect.x
              obj.rect.h = values[5] * img_h - obj.rect.y
              objects.append(obj)
          """
  
          return objects
  