Using a pretrained OpenVINO Model

_includes/openvino_step.md

@@ -0,0 +1,7 @@
+[Follow the guide for your operating system](https://docs.openvinotoolkit.org/2019_R2/index.html) on openvinotoolkit.org.
+
+DepthAI requires OpenVINO version `2019_R2`. You can specify this version on the OpenVINO download page:
+
+![openvino_version](/images/openvino_version.png)
+
+The "Full Package" download option consumes around 450 MB of disk space. We suggest this option if you plan to download pretrained models from the Open Model Zoo and have enough disk space.

_products/bw1098ffc.md

@@ -60,7 +60,7 @@ Follow the steps below to setup your DepthAI device.
 
 <h3 class="step js-toc-ignore"><span></span> Install OpenVINO.</h3>
 
-[Follow the guide for your operating system](https://docs.openvinotoolkit.org/latest/index.html) on openvinotoolkit.org.
+{% include openvino_step.md %}
 
 <h3 class="step js-toc-ignore"><span></span> Install the Python DepthAI API.</h3>
 

_products/bw1098obc.md

@@ -39,7 +39,7 @@ Follow the steps below to setup your DepthAI device.
 
 <h3 class="step js-toc-ignore"><span></span> Install OpenVINO</h3>
 
-[Follow the guide for your operating system](https://docs.openvinotoolkit.org/latest/index.html) on openvinotoolkit.org.
+{% include openvino_step.md %}
 
 <h3 class="step js-toc-ignore"><span></span> Install the Python DepthAI API</h3>
 

_tutorials/openvino_model_zoo_pretrained_model.md

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+---
+layout: default
+title: DepthAI Tutorial - Use a Pre-trained OpenVINO model
+toc_title: Pre-trained OpenVINO model
+description: Stream Video from your DepthAI onto your monitor display.
+order: 3
+---
+
+# {{ page.title }}
+
+In this tutorial, you'll learn how to detect faces in realtime, even on a low-powered Raspberry Pi. I'll introduce you to the OpenVINO toolset, the Open Model Zoo (where we'll download the [face-detection-retail-0004](https://github.com/opencv/open_model_zoo/blob/2019_R2/intel_models/face-detection-retail-0004/description/face-detection-retail-0004.md) model), and show you how to generate the files needed to run model inference on your DepthAI board.
+
+![model image](/images/tutorials/pretrained_model/face-detection-retail-0001.png)
+
+Haven't heard of OpenVINO or the Open Model Zoo? I'll start with a quick introduction of why we need these tools.
+
+## What is OpenVINO?
+
+Under-the-hood, DepthAI uses the Intel MyriadX chip to perform high-speed model inference. However, you can't just dump your neural net into the chip and get high-performance for free. That's where [OpenVINO](https://docs.openvinotoolkit.org/) comes in. OpenVINO is a free toolkit that converts a deep learning model into a format that runs on Intel Hardware. Once the model is converted, it's common to see Frames Per Second (FPS) improve by 25x or more. Are a couple of small steps worth a 25x FPS increase? Often, the answer is yes!
+
+## What is the Open Model Zoo?
+
+The [Open Model Zoo](https://github.com/opencv/open_model_zoo) is a library of freely-available pre-trained models. The Zoo also contains scripts for downloading those models into a compile-ready format to run on DepthAI.
+
+DepthAI is able to run many of the object detection models in the Zoo.
+
+{: data-toc-title="DepthAI API"}
+## Install or upgrade the DepthAI API
+
+[Download](/api#install) and [upgrade](/api#upgrade) the `depthai-python-extras` GitHub repository. The DepthAI API requires Python 3.
+
+## Verify your OpenVINO install
+
+<div class="alert alert-primary" role="alert">
+<i class="material-icons">
+error
+</i>
+  Using the RPi Compute edition or a pre-flashed DepthAI µSD card? <strong>Skip this step.</strong><br/>
+  <span class="small">OpenVINO version `2019_R2` has been pre-installed.</span>
+</div>
+
+DepthAI requires OpenVINO version `2019_R2`. Let's verify that this version is installed on your host. Check your version by running the following from a terminal session:
+
+```
+cat /opt/intel/openvino/inference_engine/version.txt
+```
+
+You should see output similar to:
+
+```
+Thu Jul 18 19:41:35 MSK 2019
+f5827d4773ebbe727c9acac5f007f7d94dd4be4e
+releases_2019_R2_InferenceEngine_27579
+```
+
+Verify that you see `releases_2019_R2` in your output. If you do, move on. If you are on a different version, goto the [OpenVINO site](https://docs.openvinotoolkit.org/2019_R2/index.html) and download the `2019_R2` version for your OS:
+
+![openvino_version](/images/openvino_version.png)
+
+{: data-toc-title="Model Downloader"}
+## Check if the Model Downloader is installed
+
+When installing OpenVINO, you can choose to perform a smaller install to save disk space. This custom install may not include the model downloader script. Lets check if the downloader was installed. In a terminal session, type the following:
+
+```
+find /opt/intel/ -iname downloader.py
+find ~ -iname downloader.py
+```
+
+__Move on if you see the output below__:
+
+```
+/opt/intel//openvino_2019.2.242/deployment_tools/open_model_zoo/tools/downloader/downloader.py
+```
+
+__Didn't see any output?__ Don't fret if `downloader.py` isn't found. We'll install this below.
+
+{: data-toc-title="Install"}
+### Install Open Model Zoo Downloader
+
+If the downloader tools weren't found, we'll install the tools by cloning the [Open Model Zoo Repo](https://github.com/opencv/open_model_zoo/blob/2019_R2/tools/downloader/README.md) and installing the tool dependencies.
+
+Start a terminal session and run the following commands in your terminal:
+
+```
+cd ~
+git clone https://github.com/opencv/open_model_zoo.git
+cd open_model_zoo
+git checkout tags/2019_R2
+cd tools/downloader
+python3 -mpip install --user -r ./requirements.in
+```
+
+This clones the repo into a `~/open_model_zoo` directory, checks out the required `2019_R2` version, and installs the downloader dependencies.
+
+{: data-toc-title="Setup Downloader env var."}
+## Create an OPEN_MODEL_DOWNLOADER environment variable
+
+Typing the full path to `downloader.py` can use a lot of keystrokes. In an effort to extend your keyboard life, let's store the path to this script in an environment variable.
+
+Run the following in your terminal:
+
+```
+OPEN_MODEL_DOWNLOADER='INSERT PATH TO YOUR downloader.py SCRIPT'
+```
+
+Where `INSERT PATH TO YOUR downloader.py SCRIPT` can be found via:
+
+```
+find /opt/intel/ -iname downloader.py
+find ~ -iname downloader.py
+```
+
+For example, if you are using the RPi Compute and installed `open_model_zoo` yourself:
+
+```
+OPEN_MODEL_DOWNLOADER='/home/pi/open_model_zoo/tools/downloader/downloader.py'
+```
+
+Verify this was set correctly:
+
+```
+echo $OPEN_MODEL_DOWNLOADER
+/home/pi/open_model_zoo/tools/downloader/downloader.py
+```
+
+{: data-toc-title="Download the model"}
+## Download the face-detection-retail-0004 model
+
+We've installed everything we need to download models from the Open Model Zoo! We'll now use the [Model Downloader](https://github.com/opencv/open_model_zoo/blob/2019_R2/tools/downloader/README.md) to download the `face-detection-retail-0004` model files. Run the following in your terminal:
+
+```
+$OPEN_MODEL_DOWNLOADER --name face-detection-retail-0004 --output_dir ~/open_model_zoo_downloads/
+```
+
+This will download the model files to `~/open_model_zoo_downloads/`. Specifically, the model files we need are located at:
+
+```
+~/open_model_zoo_downloads/Retail/object_detection/face/sqnet1.0modif-ssd/0004/dldt/FP16
+```
+
+You'll see two files within the directory:
+
+```
+ls -lh ~/open_model_zoo_downloads/Retail/object_detection/face/sqnet1.0modif-ssd/0004/dldt/FP16/
+total 1.2M
+-rw-r--r-- 1 pi pi 1.2M Feb 28 10:04 face-detection-retail-0004.bin
+-rw-r--r-- 1 pi pi  47K Feb 28 10:04 face-detection-retail-0004.xml
+```
+
+The model is in the OpenVINO Intermediate Representation (IR) format:
+
+* face-detection-retail-0004.xml - Describes the network topology
+* face-detection-retail-0004.bin - Contains the weights and biases binary data.
+
+This means we are ready to compile the model for the MyriadX!
+
+## Compile the model
+
+The MyriadX chip used on our DepthAI board does not use the IR format files directly. Instead, we need to generate two files:
+
+* `face-detection-retail-0004.blob` - We'll create this file with the `myriad_compile` command.
+* `face-detection-retail-0004.json` - A `blob_file_config` file in JSON format. This describes the format of the output tensors.
+
+We'll start by creating the `blob` file.
+
+### Locate myriad_compile
+
+Let's find where `myriad_compile` is located. In your terminal, run:
+
+```
+find /opt/intel/ -iname myriad_compile
+```
+
+On an RPi compute, I see the following:
+
+```
+find /opt/intel/ -iname myriad_compile
+/opt/intel/openvino/deployment_tools/inference_engine/lib/armv7l/myriad_compile
+```
+
+On an Intel-based architecture, `armv7l` will be `intel64`.
+
+Since it's such a long path, let's store the `myriad_compile` executable in an environment variable (just like `OPEN_MODEL_DOWNLOADER`). On an RPi:
+
+```
+MYRIAD_COMPILE='/opt/intel/openvino/deployment_tools/inference_engine/lib/armv7l/myriad_compile'
+```
+
+### Run myriad_compile
+
+```
+$MYRIAD_COMPILE -m ~/open_model_zoo_downloads/Retail/object_detection/face/sqnet1.0modif-ssd/0004/dldt/FP16/face-detection-retail-0004.xml -ip U8 -VPU_MYRIAD_PLATFORM VPU_MYRIAD_2480 -VPU_NUMBER_OF_SHAVES 4 -VPU_NUMBER_OF_CMX_SLICES 4
+```
+
+You should see:
+
+```
+Inference Engine:
+	API version ............ 2.0
+	Build .................. custom_releases/2019/R2_f5827d4773ebbe727c9acac5f007f7d94dd4be4e
+	Description ....... API
+Done
+```
+
+Where's the blob file? It's located in the same folder as `face-detection-retail-0004.xml`:
+
+```
+ls -lh ~/open_model_zoo_downloads/Retail/object_detection/face/sqnet1.0modif-ssd/0004/dldt/FP16
+total 2.5M
+-rw-r--r-- 1 pi pi 1.2M Feb 28 10:04 face-detection-retail-0004.bin
+-rw-r--r-- 1 pi pi 1.3M Feb 28 10:37 face-detection-retail-0004.blob
+-rw-r--r-- 1 pi pi  47K Feb 28 10:04 face-detection-retail-0004.xml
+```
+
+{: #blob_file_config}
+## Create the blob config file
+
+The MyriadX needs both a `blob` file (which we just created) and a `blob_file_config` in JSON format. We'll create this config file manually. In your terminal:
+
+```
+cd ~/open_model_zoo_downloads/Retail/object_detection/face/sqnet1.0modif-ssd/0004/dldt/FP16
+touch face-detection-retail-0004.json
+```
+
+Copy and paste the following into `face-detection-retail-0004.json`:
+
+```json
+{
+    "tensors":
+    [
+        {
+            "output_tensor_name": "out",
+            "output_dimensions": [1, 1, 100, 7],
+            "output_entry_iteration_index": 2,
+            "output_properties_dimensions": [3],
+            "property_key_mapping":
+            [
+                [],
+                [],
+                [],
+                ["image_id", "label", "conf", "x_min", "y_min", "x_max", "y_max"]
+            ],
+            "output_properties_type": "f16"
+        }
+    ]
+}
+```
+
+What do these values mean?
+
+* `output_dimensions` - The neural net has an [output format](https://docs.openvinotoolkit.org/latest/_models_intel_face_detection_retail_0004_description_face_detection_retail_0004.html#outputs) of `[1, 1, N, 7]` where `N` is the number of detected bounding boxes. Each detection has the format: `[image_id, label, conf, x_min, y_min, x_max, y_max]`. We specify `100` instead of `N` to indicate we'll handle up to 100 bounding boxes, which should be more than enough possible faces to detect.
+* `output_entry_iteration_index` - Indicates the array position we'll use to iterate over detections. `N` is in the 2nd position (index start = 0).
+* `property_key_mapping` - Maps the properties to friendly names we can access when running model inference in our too-be-created Python script.
+* `output_properties_type` - We're using a model with `f16` precision.
+
+{: data-toc-title="Run the model"}
+## Run and display the model output
+
+With both `blob` and a `json` blob config file, we're ready to roll! We'll create a Python script to display the results of our face detections in realtime.
+
+If you haven't created a `depthai-tutorials` folder yet, lets do that:
+
+```
+cd ~
+mkdir -p depthai-tutorials/2-face-detection-retail/
+cd depthai-tutorials/2-face-detection-retail/
+```
+
+Download [this ready-to-go script](https://github.com/luxonis/depthai-tutorials/blob/master/2-face-detection-retail/face-detection-retail-0004.py) for running the `face-detection-retail-0004` model:
+
+```
+wget https://raw.githubusercontent.com/luxonis/depthai-tutorials/master/2-face-detection-retail/face-detection-retail-0004.py
+```
+
+The script assumes the `blob` and `json` files are located in the directory we used in this tutorial:
+
+```
+~/open_model_zoo_downloads/Retail/object_detection/face/sqnet1.0modif-ssd/0004/dldt/FP16`
+```
+
+Execute the script to see an annotated video stream of face detections:
+
+```
+python3 face-detection-retail-0004.py
+```
+
+You should see output annotated output similar to:
+
+![model image](/images/tutorials/pretrained_model/previewout.png)
+
+Substitute your face for mine, of course.
+
+## Reviewing the flow
+
+The flow we walked through works for other pre-trained object detection models in the Open Model Zoo:
+
+1. Download the model:
+    ```
+    $OPEN_MODEL_DOWNLOADER --name [INSERT MODEL NAME] --output_dir ~/open_model_zoo_downloads/
+    ```
+2. Create the MyriadX blob file:
+    ```
+    $MYRIAD_COMPILE -m [INSERT PATH TO MODEL XML FILE] -ip U8 -VPU_MYRIAD_PLATFORM VPU_MYRIAD_2480 -VPU_NUMBER_OF_SHAVES 4 -VPU_NUMBER_OF_CMX_SLICES 4
+    ```
+3. Create a [JSON config file](#blob_file_config) based on the model output.
+4. Write a script (similar [to this](https://github.com/luxonis/depthai-tutorials/blob/master/2-face-detection-retail/face-detection-retail-0004.py)) utilizing the DepthAI API, specifying the path to the blob file and its config via the `blob_file` and `blob_file_config` Pipeline config settings.