This repo contains the pre-compiled DepthAI Python module (compiled as an architecture-specific .so file), utilities, and a submodule that allows compiling the DepthAI Python module for other platforms.

Documentation is available at https://docs.luxonis.com.

DepthAI requites numpy and opencv-python. To get the versions of these packages you need for DepthAI, use pip: pip install -r requirements.txt

Files with .so extension are the python modules:

• depthai.cpython-36m-x86_64-linux-gnu.so built for Ubuntu 18.04 & Python 3.6
• depthai.cpython-37m-arm-linux-gnueabihf.so built for Raspbian 10 & Python 3.7

For supporting other platforms, there is an option to build the python lib from sources by grabbing the depthai-api submodule:

git submodule update --init
./depthai-api/install_dependencies.sh # Only required in first build on a given system
./depthai-api/build_py_module.sh

When updating DepthAI on these platforms it is often necessary to run ./depthai-api/build_py_module.sh --clean in order to build a new version of the depthai-api module for your chosen platform.

python3 test.py - depth & CNN inference example

OpenVINO toolkit contains components which allow conversion of existing supported trained Caffe and Tensorflow models into Intel Movidius binary format through the Intermediate Representation (IR) format.

Example of the conversion:

1. First the model_optimizer tool will convert the model to IR format:

   cd <path-to-openvino-folder>/deployment_tools/model_optimizer
   python3 mo.py --model_name ResNet50 --output_dir ResNet50_IR_FP16 --framework tf --data_type FP16 --input_model inference_graph.pb
   

   • The command will produce the following files in the ResNet50_IR_FP16 directory:
     • ResNet50.bin - weights file;
     • ResNet50.xml - execution graph for the network;
     • ResNet50.mapping - mapping between layers in original public/custom model and layers within IR.

2. The weights (.bin) and graph (.xml) files produced above (or from the Intel Model Zoo) will be required for building a blob file, with the help of the myriad_compile tool. When producing blobs, the following constraints must be applied:

   CMX-SLICES = 4 
   SHAVES = 4 
   INPUT-FORMATS = 8 
   OUTPUT-FORMATS = FP16/FP32 (host code for meta frame display should be updated accordingly)
   

   Example of command execution:

   <path-to-openvino-folder>/deployment_tools/inference_engine/lib/intel64/myriad_compile -m ./ResNet50.xml -o ResNet50.blob -ip U8 -VPU_MYRIAD_PLATFORM VPU_MYRIAD_2480 -VPU_NUMBER_OF_SHAVES 4 -VPU_NUMBER_OF_CMX_SLICES 4
   

We are actively developing the DepthAI framework, and it's crucial for us to know what kind of problems you are facing.
If you run into a problem, please follow the steps below and email [email protected]:

1. Run log_system_information.sh and share the output from (log_system_information.txt).
2. Take a photo of a device you are using (or provide us a device model)
3. Describe the expected results;
4. Describe the actual running results (what you see after started your script with DepthAI)
5. How you are using the DepthAI python API (code snippet, for example)
6. Console output