Santa DetectorFather Christmas Detector
When you' re not sure what profound study is, you' re not alone.
It' s a hot subject Adrian wrote a novel about, so I barbecued him as Bluffers' Guide. With his words depth teaching is: ...a part of mechanical education which in turn is a part of AI. Whereas the AI represents a wide and varied range of technologies and architectures related to automated thinking (inference, scheduling, helicistics, etc.), the parts of mechanical education are particularly interested in patterns perception and learn from information.
Synthetic neural networks (ANNs) are a category of automatic adaptive learning algorithm that can be learned from real life information. Now we have a bird's perspective of how to study in depth, how does the detector work? He used a mathematical simulation that he had practiced on two sets of records to determine whether a picture contained Santa Claus or not.
It shoots Santa Claus in the wilderness, while the Christmas Ornament add-on delivers a winking message, accompagnied by a sounding ho, ho, ho, ho, ho from the loudspeakers. For a complete explanation of the Santa Not detector and how it works, see Adrian's Blog PyImageSearch, which provides hyperlinks to other TensorFlow and Keras based advanced training sessions on how to learn how to deal with Santa Not.
It is an ideal starting point if you want to learn more about profound study. Father Christmas could capture Adrian's smart detector and begin bypassing the cameras, and in that case we have our own Christmas detector. Using movement recognition, it informs you of its attendance (and your gifts!).
Ceras and profound study about the raspberry Pi
That I' m gonna dress up like Santa Claus! Fitting for the Christmas and holiday period, I will show you how to take a profound study style (trained with Keras) and then use it on the Raspberry Pi. Our picture classification specialist has been specially instructed to recognize if Santa Claus is in our streaming videos.
I' m not going to ruin the suprise (but it includes a 3-D Christmas tree and a funny melody). Today's blogs posting is a full tutorial on how to operate a deeper nerve center on the Raspberry Pi with Keras. I designed this to be a Not Santa detector to give you a hands-on approach to implementing it (and have some enjoyment with it).
The first part of this article discusses what a Not Santa detector is (just in case you're not familiar with HBO's Silicon Valley "Not Hotdog" detector, which has evolved a following of cultists). We will then set up our Raspberry Pi for depth training by adding TensorFlow, Keras and a number of other requirements.
As soon as our Raspberry Pi is set up for profound study, we will proceed with the development of a Python scripts that can do this: Which is an Emergency Santa Detector? Illustration 1: The Not Hotdog Detector application from HBO Silicon Valley. The A Not Santa Detector is a game in front of HBO's Silicon Valley, where players build a phone application that determines whether an entry picture is a hotshot or not:
We are now developing an emergency Christmas detector that detects whether Santa Claus is in a picture/video outline. We will learn some hands-on abilities along the way, as well as how to do it: Illustration 2: The detector set-up of Not Santa contains the Raspberry Pi 3, loudspeaker, Christmas 3-D beam and a live camera (not in the picture).
Pi implements LeNet with Keras in a Python scripts to recognize Santa Claus. When you have only one Raspberry Pi + cam module/USB cam, you are done (but you need to change the source so that it does not attempt to gain GPIO pin connections or listen to your audio through the speakers).
You should set it up as shown in picture 2 above, where I plugged in my loudspeakers, the Christmas trees and the cam (not shown because it's not in the camera). Illustration 3: My depth lesson set-up contains the Raspberry Pi and Raspberry component as well as a keypad, small HDMI screen and mice. Hopefully with this set-up we will see Santa Claus deliver presents in front of my Christmas trees.
The picture above shows my Raspberry Pi, HDMI, keyboards and Christmas critic friend who are joining me for today's workshop. On the Raspberry Pi, how do I use TensorFlow and Keras? Fig. 4: We will use Keras with the TensorFlow based TensorFlow on the Raspberry Pi to create a deeply adaptive Not Santa detector.
In the last few weeks we have learnt how to practice a Convolutional Neural Net with Keras to see if Santa Claus is in an entry picture. Today we will take the pre-trained version and use it on the Raspberry Pi. The Raspberry Pi is not designed for practicing a neuro net (outside of "toy" examples).
The Raspberry Pi can, however, be used to provide a neuro net after it has been practiced (assuming the Raspberry Pi fits into a small storage expansion, of course). My assumption is that you already have Raspberry Pi running your own version of OpenCV. First of all, if you don't have Raspberry Pi running on your computer with Raspberry Pi + OptiCV, use this demo to show you how to improve the efficiency of your Raspberry Pi + OptiCV installation (resulting in an improvement in efficiency of over 30%).
Notice: This tutorial does not work with Python 3 - you must use Python 2.7 instead. Now take the opportunity to configurate your Raspberry Pi with Python 2. Seven and open CV bonds. Make sure you set up a -p pYTON2 virtual machine in #4 of the Raspberry Pi + OpenCV install instructions.
If you increase the swapping, you can use the Raspberry Pi SD flash drive for extra storage (an important stage in compiling and installing large library files on the Raspberry Pi space limited). First, build a Python avatar called not_santa using Python 2. I' ll be explaining why Python 2. 7 once we get the TensorFlow installation command):
Note here how the -p switches to pyrthon2, which means that Python 2. So if you are new to Python environment, how they work and why we use them, please read this manual to keep up to date, as well as this great real Python virtuoso primeer.
Make sure you have installed your version of the OpenCV software with Firefox 2. When you compile 3 + 3 FreeCV binds to Python, create the symblock, and then try to get to your shell to get imported to v2, you get a puzzling tracing back saying the imported file miscarried. These next pipe instructions make sure you are in the not_santa ( or your chosen Python ) installation, otherwise install the package on the system of your Raspberry Pi.
Now we are prepared to fit TensorFlow on your Raspberry Pi. Trouble is that there is no TensorFlow officially (Google approved) distributor. There are only two kinds of precompiled TensorFlow binaries: One of the most recent versions of the Rasbian Stretch OS at the moment of this article, the Rasbian Stretch Extension, is shipped with 3K.
For a headache between 3 pythons. Four and three Pythons. 5, I chose to stay with PYTHON 2. Although I would have liked to use P3 for this manual, the setup procedure would have been more complex (I could have written several articles on TensorFlow + Keras on the raspberry pi without any problems, but since setup is not the center piece of this tutorial, I chose to keep it simpler).
Let's start installing TensorFlow for Python 2. When TensorFlow is compiled and installed (which took about an hours on my Raspberry Pi), you need to get HDF5 and h5py installed. This library will allow us to download our pre-trained models from the hard disk: Lastly, we are installing Keras and the other necessary equipment for this project:
It' very important that you have Keras 2.1 installed. TensorFlow 1.1.0 5 for TensorFlow 1.1.0 compatible. In order to test your setup, open a Python shell (in the non_santa environment) and run the following commands:'OpenCV version: 3.3. 1''Keras Version: 2.1. 5' If everything goes as expected, you should see how Keras are going to be brought in via the TensorFlow back end.
You should also verify that your CV ( v2 ) Open connections can also be import as the above shows. Fig. 5: Implementation of a depth study on the Raspberry Pi with Keras and Python. Now we are prepared to encode our emergency Santa detector with Keras, TensorFlow and the Raspberry Pi.
Again, I assume you have the same set up as me (e.g. Christmas trees and loudspeakers ), so if your set up is different, you'll have to cut the following source for it. Rows 2-12 process our import, in particular: kera is used to prepare entry boxes for grading and download our pre-trained models from the hard drive. zpiozero is used to retrieve the Christmas 3CTree. zutils is used to retrieve the streaming videos (whether Raspberry Pi cam modules or USB). za is used for non-blocking operation, especially when we want to illuminate the Christmas 3CTree or playback audio without having to block the running of the major threads. za is used to prepare entry boxes for grading and download our pre-trained models from the hard drive. za is used to retrieve the Christmas 3CTree. za is used to retrieve the streaming videos (whether Raspberry Pi cam modules or USB). za is used for non-blocking operation, especially when we want to illuminate the Christmas 3CTree or playback audio without having to block the running of the major threads. za is used to prepare entry boxes for grading and to load our pre-trained models. za is used to edit classification borders.
This is where we start to set a feature to illuminate our Christmas tree: def, Light_Tree (tree, sleep=5): For led in tree: For led in tree: With our function light_tree a construction parameter is accepted (which is accepted as Ledeboard object). First we drag over all the lights in the treevolume and accidentally shine each one to produce a "sparkling" effect (lines 17-19).
Below is an example of turned on 3-D Christmas lighting: Illustration 6: The 3-D Christmas Trees for the Raspberry Pi. The next feature deals with the playback of musical information when Santa Claus is detected: default play_christmas_music(p): Calling the IOS system is a little mini-hack, but listening to the sound via Python (with a Pygame library) is too much for this work.
Models_PATH = "santa_not_santa. model" Audiopaths = "jolly_laugh. wav" Line 38 and 39 hard code pathes to our pre-trained Keras models and our sound files. It also initializes recognition parameter used, including TOTAL_CONSEC and TOTAL_THRESH. Both of these are the number of single images with Santa Claus and the point at which we both listen to our favorite songs and turn on the trees (lines 43 and 44).
SANTA = False, a boost (line 47), is the last initialisation. We will use the SANTA tag later in the scripts as a stateflag to support our logics. Next, we download our pre-trained Keras style and initialise our Christmas tree: The Keras allows us to store patterns on the hard drive for later use.
We have stored our Not Santa models on our hard drive last weekend and this weekend we will upload them to our Raspberry Pi. Use the Keras loading_model command to upload the Keras models to line 51. You can see our trees on line 54. Like shown below, tree is a LEDBoard item from the fpiozero bundle.
We will pre-process this framework before we send it through our networking mode. Later, we will display the border on the monitor along with a text tag. We can then pre-process the picture from there and use our Keras + Dep Learn to forward it to the predictor: labels = "Not Santa" Lines 70-73 pre-process the picture and get it ready for class.
For more information about pre-processing for depth study, please read the starter pack of my latest volume entitled Enabling Computer Vision with Python. Prediction with our picture as arguments. Returns the picture through the neuronal net and returns a pupel with classes probability (line 77).
Let's initialise the labels with "Not Santa" (we will visit the labels again later) and the probabilities, with the value of notSanta on lines 78 and 79, using it. Let's see if Santa Claus is in the picture: if santa > notSanta: brand = "Santa" On line 83 we see if a Santa Claus is more likely than notSanta.
When this is the case, we refresh the labels and process them, followed by the increment of TOTAL_CONSEC (lines 85-90). If enough successive "Santa" Frames have happened, we must set off the Santa alarm: if not SANTA and TOTAL_CONSEC >= TOTAL_THRESH: MusicThread = Thread(target=play_christmas_music, We have two things to do if SANTA is wrong and if the TOTAL_CONSEC reaches the TOTAL_THRESH threshold:
Build and run a thread to sparkle the Christmas light (lines 98-100). As you can also see, on line 95 we put our SANTA Stat flags to tru, which means that we found Santa Claus in the entry window. If not ( SANTA is real or the TOTAL_THRESH is not fulfilled) we return TOTAL_CONSEC to zero and SANTA to False: otherwise:
Lastly, we show the border on our monitor with the text labels generated: fram = vv2. putText (frame, label, 10, 25), if given keys == ord("q"): It appends the likelihood value to the tag containing either "Santa" or "Not Santa" (line 115). Then, with the help of our openCV putText program, we can put the putText tag (in Christmas green) on the top of the border before displaying the border on the monitor (lines 116-120).
Have a look back at the 130 lineages we checked together - this framework/template can also be used for other Raspberry Pi depth reading project. Last weeks blogs posted our Not Santa Del Mobile del Mobile with pictures from the internet. I always wanted to disguise myself like good old St. Nicholas, so last weekend I ordered a low priced Santa costume:
Illustration 7: I, Adrian Rosebrock, disguised as Santa Claus. I will test our Not Santa detector developed with Keras, Python, Greenstone, Keras and OpenCV. I' m far away from Santa Claus, but the suit should do the job. Then I pointed my photo, which was fixed to the raspberry pi, at the Christmas tree in my apartment:
Illustration 8: My own Christmas trees will be used as a backdrop for our Not Santa detector depth study for the Raspberry Pi. When Santa comes by to hand out some gifts for the good guys and girl, I want to make sure that he will feel welcome by flashing the 3-D Christmas lighting and performing some Christmas carols.
Then I have launched the emergency Santa deep learn + Keras detector with the following command: In order to do this, make sure you use the "Downloads" section below to dowload the sourcecode + the pre-trained version + the sound used in this manual. When the emergency Santa detector was operational, I went into action:
Illustration 9: Successful recognition of Santa Claus in a streaming movie with depth learn, Python, Keras and a Raspberry Pi. When Santa Claus is recognized, the 3-D Christmas light comes on and the musical accompaniment begins! In order to see the complete Emergency Santa detector (with sound), watch the movie below: When Santa Claus walks into the scenery, you'll see the 3-D Christmas Ornament turn on, followed by a cheerful smile from the raspberry pi loudspeakers (audio credits for SoundBible).
Considering the small size of the networking architectures, our depth study models are amazingly precise and resilient. I' ve been good this year, so I'm sure Santa will stop by my place. I am also more self-assured than I have ever been when I saw Santa Claus bringing some presents with my Not Santa detector. I' ll probably be hacking this one before Christmas (with a call to tv2. imprint, or better yet, store the videoclip ) to make sure I put some of Santa's pictures on the hard drive as evidence.
I' m sure I will know if it's someone else putting presents under my Christmas trees. My dear Santa Claus: Today's blogs have taught you how to run a Keras Dep Learn on the Raspberry Pi. In order to achieve this, we first practiced our Keras depth training to determine whether a picture contains "Santa" or "Not Santa" on our laptop/desktop.
Subsequently, we placed TensorFlow and Keras on our raspberry pi so that we could take our skilled, deeply studying picture grader and use it on our raspberry pi. Although the Raspberry Pi is not designed for deeper nerve net development, it can be used to train deeper nerve nets - and if the net design is simple enough, we can even run our model in on-the-fly.
In order to show this, we have developed a Not Santa detector on our Raspberry Pi that can classify every single image from a streaming image. When Santa Claus is recognized, we use our GPIO pin to illuminate a 3-D Christmas pole and perform Christmas carols. Hopefully you had a lot of fun to learn how to create a Not Santa application with the Deep Learning!
When you want to keep continuing profound study and learning: then you should take a look at my new volume dealing with Python, entitled ''Deepearning for Computer Vision with Python''. In-depth, thorough tutorials to help you reproduce the latest results from your favorite depth learners list. For more information about my new work ( and to begin your trip to the Dep Depth Training Masters ), click here.
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