Month: November 2018

Deeplab v3 Test

  1. Create Python Env $ mkdir ~/tf_ros && cd ~/tf_ros $ virtualenv --system-site-packages -p python2 tensor_ros_deeplab
  2. Activate Environment source tensor_ros_deeplab/bin/activate
  3. Install tensorflow GPU pip install --upgrade tensorflow_gpu-1.12.0-cp27-none-linux_x86_64.whl
  4. Move to the Deeplab dir $ git clone https://github.com/tensorflow/models
  5. Clone Tensorflow’s Models repo containing Deeplab ~/tf_ros/models/research/deeplab
  6. Download a pretrained model: $ wget https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
  7. Use sample script for a single image: https://github.com/tensorflow/models/blob/master/research/deeplab/deeplab_demo.ipynb
  8. Change the MODEL var to the path of the previously downloaded model: MODEL = DeepLabModel("~/tf_ros/models/research/deeplab/deeplabv3_mnv2_dm05_pascal_trainaug_2018_10_01.tar.gz")
  9. To use a local image instead of a URL change the run_visualization function to:
def run_visualization(image_path):
  """Inferences DeepLab model and visualizes result."""
  try:
    original_im = Image.open(image_path)
  except IOError:
    print('Cannot read image. Please check path: ' + image_path)
    return

  print('running deeplab on image %s...' % image_path)
  resized_im, seg_map = MODEL.run(original_im)

  vis_segmentation(resized_im, seg_map)
  1. Finally save and run the script: ptyhon script_name.py