DETECTION.md

Quick start: landmark detection

Install DELF library

To be able to use this code, please follow these instructions to properly install the DELF library.

Download Oxford buildings dataset

To illustrate detector usage, please download the Oxford buildings dataset, by following the instructions here. Then, create the file list_images_detector.txt as follows:

# From tensorflow/models/research/delf/delf/python/examples/
echo data/oxford5k_images/all_souls_000002.jpg >> list_images_detector.txt
echo data/oxford5k_images/all_souls_000035.jpg >> list_images_detector.txt

Download detector model

Also, you will need to download the pre-trained detector model:

# From tensorflow/models/research/delf/delf/python/examples/
mkdir parameters && cd parameters
wget http://storage.googleapis.com/delf/d2r_frcnn_20190411.tar.gz
tar -xvzf d2r_frcnn_20190411.tar.gz

Note: this is the Faster-RCNN based model. We also release a MobileNet-SSD model, see the README for download link. The instructions should work seamlessly for both models.

Detecting landmarks

Now that you have everything in place, running this command should detect boxes for the images all_souls_000002.jpg and all_souls_000035.jpg, with a threshold of 0.8, and produce visualizations.

# From tensorflow/models/research/delf/delf/python/examples/
python3 extract_boxes.py \
  --detector_path parameters/d2r_frcnn_20190411 \
  --detector_thresh 0.8 \
  --list_images_path list_images_detector.txt \
  --output_dir data/oxford5k_boxes \
  --output_viz_dir data/oxford5k_boxes_viz

Two images are generated in the data/oxford5k_boxes_viz directory, they should look similar to these ones:

Troubleshooting

matplotlib

matplotlib may complain with a message such as no display name and no $DISPLAY environment variable. To fix this, one option is add the line backend : Agg to the file .config/matplotlib/matplotlibrc. On this problem, see the discussion here.