在Covid-19期间测量社交距离

Today, unfortunately, everyone is familiar with the term “social distance”. It’s something we will have to live with for a while until everything returns to normal. At Immune Technology Institute we have tried to develop an application using the TensorFlow Object Detection API for identifying and measuring the social distance between pedestrians.

不幸的是，今天，每个人都对“社会距离”一词很熟悉。我们将不得不忍受一段时间，直到一切恢复正常。在免疫技术学院，我们尝试使用TensorFlow对象检测API开发应用程序，以识别和测量行人之间的社交距离。

等待…什么是TensorFlow对象检测API？ (Wait… What is the TensorFlow Object Detection API?)

The TensorFlow Object Detection API is the framework for creating a deep learning network that solves object detection problems. It contains some pre-trained models trained on different datasets which can be used for inference.

TensorFlow对象检测API是用于创建解决对象检测问题的深度学习网络的框架。它包含一些在不同数据集上训练的预训练模型，可用于推理。

Additionally, we can use this framework for applying transfer learning in pre-trained models that were previously trained on large datasets which enables us to customize these models for a certain task. For instance, we can apply transfer learning to train a model for identifying if a person is wearing a mask or not.

此外，我们可以使用此框架在预先训练的模型中应用迁移学习 ，这些模型以前是在大型数据集上进行训练的，这使我们能够针对特定任务自定义这些模型。例如，我们可以应用转移学习来训练一个模型，以识别一个人是否戴着口罩。

The intuition behind transfer learning for image classification is that if a model is trained on a large and general enough dataset, this model will effectively serve as a generic model of the visual world. You can then take advantage of these learned feature maps without having to start from scratch by training a large model on a large dataset.

转移学习进行图像分类的直觉是，如果在足够大且足够通用的数据集上训练模型，则该模型将有效地充当视觉世界的通用模型。然后，您可以利用这些学习的功能图，而不必通过在大型数据集上训练大型模型而从头开始。

In this case, we did not need to apply transfer learning since we want to identify pedestrians, and there are already some models trained to inference this. We have used the model ssd_mobilenet_v2_coco_2018_03_29 which has been trained on these objects:

在这种情况下，我们不需要应用转移学习，因为我们想识别行人，并且已经有一些模型经过训练可以推断出这一点。我们使用了模型ssd_mobilenet_v2_coco_2018_03_29 ，该模型已针对以下对象进行了训练：

Categories for model ssd_mobilenet_v2_coco_2018_03_29

型号ssd_mobilenet_v2_coco_2018_03_29的类别

For this use case, we only need to identify and display pedestrians, therefore we will create a function for filtering predictions by label and we are only going to display the person (id=1) labels.

对于此用例，我们只需要标识并显示行人，因此我们将创建一个用于按标签过滤预测的函数，并且仅显示person (id=1)标签。

我们去吧…一些代码！ (Let’s go… Some Code!)

When I developed this code the TensorFlow Object Detection API had not full support for TensorFlow 2 but on July 10th Google released a new version, developing support for some new functionalities. In this case, I have used TensorFlow 1 with the release r1.13.0 of TF Object Detection API and all the capacity of Google Colab for this experiment.

当我开发此代码时， TensorFlow对象检测API并不完全支持TensorFlow 2，但Google在7月10日发布了新版本，开发了对某些新功能的支持。在这种情况下，我已将TensorFlow 1与TF Object Detection API的 r1.13.0版本以及Google Colab的所有功能结合使用。

You can find the code in my GitHub.

您可以在我的GitHub中找到代码。

First of all, we need to create our working directory, called Projects/Pedestrian_Detection in our Drive, where we will clone the TensorFlow Object Detection API repository. Then, we will able to start a notebook with Google Colab.

首先，我们需要在云端硬盘中创建名为Projects/Pedestrian_Detection工作目录，在该目录中，我们将克隆TensorFlow对象检测API存储库。然后，我们将能够使用Google Colab启动笔记本。

Creating a Google Colab inside of Projects/Pedestrian_Detection

First of all, we need to mount our drive inside our notebook, we just need to follow the instructions.

首先，我们需要将驱动器安装在笔记本电脑中，我们只需要遵循说明即可。

Mount Drive Image in Google Colab

在Google Colab中挂载驱动器映像

Then, we’ll be able to change thepath to our root folder Projects/Pedestrian_Detection.

然后，我们将能够更改到根文件夹Projects/Pedestrian_Detection的path 。

Changing path to our root folder

更改我们的根文件夹的路径

Now, we can clone the repository of TF Object Detection, in this case the release r1.13.0.

现在，我们可以克隆TF Object Detection的存储库，在本例中为r1.13.0版本。

Why did I use this release?