Follow Along!

Follow along: Object Detection Example

The program launching process along with parameter settings are all simplified and set up on the Jupyter Notebook Environment.
  • Open the object_detection.ipynb jupyter notebook
  • Initialize your output stream, and your path, and import in the Image library
  • Check all the available pictures with vairous objects and people
  • Pick one of the image and initialize the image/ output name.
  • Execute object detection on the chosen picture
  • Display the result
(The Jetson Board used for these examples are => Jetson Nano)

  • object_detection.ipynb

  • Running the cell code
    Ctrl + Enter

  • Initialize your output stream, and your path, and import in the Image library

from IPython.display import Image

%env DISPLAY=:0
%env PROGRAM_PATH=/home/zeta/jetson-inference/build/aarch64/bin
%env INPUT_PATH=/home/zeta/jetson-inference/build/aarch64/bin/images
%env OUTPUT_PATH=/home/zeta/jetson-inference/build/aarch64/bin/images/test

input_path='/home/zeta/jetson-inference/build/aarch64/bin/images'
output_path='/home/zeta/jetson-inference/build/aarch64/bin/images/test'

  • Check all the pictures, you may wish to pick images with cat_*.jpg, dog_*.jpg, etc.

    !ls $INPUT_PATH/cat_*
!ls $INPUT_PATH/dog_*


image_name = 'ChangeMe'
output_name = 'detect_result.jpg'
%env IMAGE_NAME = $image_name
%env OUTPUT_NAME = $output_name

Image(filename=input_path+'/'+image_name)

  • Detecting objects or people within the picture!

    %%capture
!python3 $PROGRAM_PATH/detectnet.py --network=ssd-mobilenet-v2 $INPUT_PATH/$IMAGE_NAME $OUTPUT_PATH/$OUTPUT_NAME

  • Show the resulting image

    Image(filename=output_path+'/detect_result.jpg')