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MIVisionX Live Image Classification

This application runs know CNN image classifiers on live or pre-recorded video streams.

MIVisionX Image Classification Control

MIVisionX Image Classification

Usage

Prerequisites

Build

git clone https://github.com/ROCm/MIVisionX.git
cd MIVisionX/apps/mivisionx_openvx_classifier
mkdir build
cd build
cmake ../
make

Run

Usage: ./classifier	--label <label text> [required]
 					--video <video file> / --capture <0> [required]
 					--googlenet <googlenet weights.bin> [optional]
 					--inception <inceptionV4 weights.bin> [optional]
 					--resnet50 <resnet50 weights.bin> [optional]
 					--resnet101 <resnet101 weights.bin> [optional]
 					--resnet152 <resnet152 weights.bin> [optional]
 					--vgg16 <vgg16 weights.bin> [optional]
 					--vgg19 <vgg19 weights.bin> [optional]

Note* All the models are optional, but one of the supported model weights.bin is required

Supported Models

Generating weights.bin for different Models

  1. Download or train your caffemodel for the supported models listed above.

Here is the sample download link that contains all the prototxt:

  1. Use MIVisionX Model Compiler to extract weights.bin from the pre-trained caffe models

Note: MIVisionX installs all the model compiler scripts in /opt/rocm/libexec/mivisionx/model_compiler/python/ folder

  • Convert the pre-trained caffemodel into AMD NNIR model:
% python /opt/rocm/libexec/mivisionx/model_compiler/python/caffe_to_nnir.py <net.caffeModel> <nnirOutputFolder> --input-dims <n,c,h,w> [--verbose <0|1>]

Sample:

``` 
% python /opt/rocm/libexec/mivisionx/model_compiler/python/caffe_to_nnir.py VGG_ILSVRC_16_layers.caffemodel VGG16_NNIR --input-dims 1,3,224,224
```
  • Convert an AMD NNIR model into OpenVX C code:
% python /opt/rocm/libexec/mivisionx/model_compiler/python/nnir_to_openvx.py <nnirModelFolder> <nnirModelOutputFolder>

Sample:

``` 
% python /opt/rocm/libexec/mivisionx/model_compiler/python/nnir_to_openvx.py VGG16_NNIR VGG16_OpenVX
```

Note: The weights.bin file will be generated inside the OpenVX folder and you can use that as an input for this project.

--label

Use labels.txt or simple_labels.txt file in the data folder

--video

Run classification on pre-recorded video with this option.

--capture <0>

Run classification on the live camera feed with this option.

Note: --video and --capture options are not supported concurrently

Sample Runs

Run VGG 16 Classification on Live Video

  • Step 1: Install all the Prerequisites

Note: MIVisionX installs all the model compiler scripts in /opt/rocm/libexec/mivisionx/model_compiler/python/ folder

  • Step 2: Download pre-trained VGG 16 caffe model - VGG_ILSVRC_16_layers.caffemodel

  • Step 3: Use MIVisionX Model Compiler to extract weights.bin file from the pre-trained caffe model

  • Convert .caffemodel to NNIR
 % python /opt/rocm/libexec/mivisionx/model_compiler/python/caffe_to_nnir.py VGG_ILSVRC_16_layers.caffemodel VGG16_NNIR --input-dims 1,3,224,224
  • Convert NNIR to OpenVX
 % python /opt/rocm/libexec/mivisionx/model_compiler/python/nnir_to_openvx.py VGG16_NNIR VGG16_OpenVX

Note: Use weights.bin generated in VGG16_OpenVX folder to run the classifier on live video

./classifier 	--label PATH_TO/labels.txt 
 				      --capture 0 
 				      --vgg16 PATH_TO/VGG16_OpenVX/weights.bin

Run Multi-Model Classification on Live Video

Follow the steps above to generate weigths.bin files for the supported models and run them concurrently on a live video

./classifier  --label PATH_TO/labels.txt 
              --capture 0
              --resnet50 PATH_TO/ResNet50_OpenVX/weights.bin
              --vgg16 PATH_TO/VGG16_OpenVX/weights.bin 
              --vgg19 PATH_TO/VGG19_OpenVX/weights.bin