/ObjectDetectionSampleSSD

Primary LanguageC++

Object Detection C++ Sample SSD

This topic demonstrates how to run the Object Detection sample application, which does inference using object detection networks like SSD-VGG on Intel® Processors and Intel® HD Graphics.

How It Works (main.cpp)

Upon the start-up the sample application reads command line parameters and loads a network and an image to the Inference Engine device. When inference is done, the application creates an output image and outputs data to the standard output stream.

NOTE: By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with --reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model Using General Conversion Parameters.

Running (main.cpp)

  • Edit CMakeLists.txt to switch compilation between main.cpp and main_dnn.cpp
  • Build docker using docker build -t sample:ssd
  • Run docker docker run -it sample:ssd /bin/bash
  • config.yaml should have all the paths like this
images_paths:
    - "/workspace/img.bmp"
input: "rtsp://admin:admin123@192.168.100.2:554/"
model: "/app/windows/D/workspace/CNN/imaginex/timeNattendance/models/FP32-INT1/face-detection-adas-binary-0001.bin"
config: "/app/windows/D/workspace/CNN/imaginex/timeNattendance/models/FP32-INT1/face-detection-adas-binary-0001.xml"
debug_log: false 
device: "CPU"
thr: 0.5
nms: 0.4
scale: 1.0
rgb: true
width: -1
height: -1
async: 0
backend: 2
framework: ""
target: 0

Running the application with the empty list of options yields the usage message given above and an error message.

To run the sample, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO Model Downloader or go to https://download.01.org/opencv/.