Ricoh theta v live stream on mac apk#

Once this is done, the next step is to make the construction and connection with the robot. This detection will allow for future identification. The next step is to add maven repositories, as is the case with Snips and in the view of allprojectsГ repositories : maven ДЄs the 'Camera' is not just a 'Camera' :), I have added face detection capability within the assistant. Implementation 'org.tensorflow:tensorflow-android:1.7.0'ДЄndroidTestImplementation ':runner:1.0.2'ДЄndroidTestImplementation '.espresso:espresso-core:3.0.2'

Implementation ':constraint-layout:1.1.3'

Implementation fileTree(include:, dir: 'libs') Implementation 'com.theta360:pluginlibrary:2.0.0' Instructions are added to the adle in the dependencies section: implementation 'org.theta4j:theta-web-api:1.2.2' These is some permissions to AndroidManifest.xml: It is necessary to add permissions to control audio input, audio output, internet and storage. To do this, it was necessary to give some permissions to the plugin and add the Snips repositories. It was necessary for the plugin to have access to the internet, to host the attendee and to have access to the microphone. Since the camera is not a Smartphone, but has almost the same features.

Ricoh theta v live stream on mac apk#

To program the plug-in was used Android Studio (Figure 5), using this programming environment was created an apk (plug-in) that was then installed inside the camera. For this it was necessary to make some modifications to the robot and obviously program a plugin that would be able to incorporate Snips, control the robot, take pictures, etcвЂ¦Д«efore starting the description of the steps to follow to be able to use the plug-in, it is necessary that our camera is in developer mode. This second part was very interesting to do, since what I tried was to migrate all my assistant who has my robot to the camera. This is why the second part of this project is described below. However, this challenge required the programming of a plug-in in Android, as the camera has this operating system within it. If this is true, the system identifies the person or persons and sends the names to the robot in text format which is then transformed into sound. The web service is in charge of analyzing the image, this analysis consists of determining the objects that surround it (using YOLO, ) and at the same time detects if there are human faces.

The human activates the robot using the Snips activation word (hey snips), the robot using the camera captures an image, which is sent to the web service. The web service was built using Flask ( ), the process is very simple. The diagram of this first part can be seen in Figure 4. Services as, information of the camera, previous images, among others. Python 3 was used to make this access, using the вЂњrequestsвЂќ library we were able to access the different services offered by the camera. The information needed to communicate with the camera using an HTTP interface is described in the following link: The raspberry pi houses the voice assistant, communication system with IoT devices (in this case Rapid-IoT), engine control and, of course, communication with the camera, which will allow you to make the necessary requests. To do this, it is necessary to take in mind that both the camera, the robot and the web service must be connected to the same network. Using it as one more camera, so that my robot can have a global vision of its environment. ThatвЂ™s why it occurred to me to divide my idea into two parts, a first part (something simple) in which my robot uses the camera in a simple way. So, I wondered what I could do with this camera, use it to take photos, send them to a web service, analyse them and return the analysed informationвЂ¦ mmmmm for me is to itвЂ™s a waste of power.