.
+-  _AprilTag
+-- _apriltags                      # home directory for all of the AprilTag software
|   +-- _example
|   +-- _scripts                    # home directory for the python script pipeline
|       +-- move_road_to_back.py
|       +-- standardize_road.py
|       +-- standardize_visualize.py    # adds a frame count to the matlab output from visualize
|       +-- rot_matrix.solve.py     # attempts to solve for calibration between road & back cams
        +-- unify_road_and_face.py  # uses data from both road & face to get COM to AprilTag & spherical coords
        +-- extract_faces_frames.py # uses output from unify... & face video to extract labelled frames
|   +-- Makefile                    # run make in apriltags/ to build the AprilTag library
|   +-- CMakeLists.txt              # CMake is called in the make process to build the C++ exe
+-- _output                         # write & defacto read folder for data in the python scripts
|   +-- _MOHAMMED DRIVING
|       +-- BACK_DATA_FRAMES.csv
|       +-- ... more unused files
|   +--_TIANJING DRIVING
|       +-- ROAD_CLEAR_2.csv
|       +-- BACK_CLEAR_2.csv
|   +-- AprilTag_Back.csv           # AprilTag (back cam) will create [ AprilTag]
|   +-- AprilTag_Road.csv           # AprilTag (road cam) will create [AprilTag]
|   +-- road_proj_to_back.csv       # Road cam data => back cam [move_road_to_back.py]
|   +-- road_normalized.csv         # road_proj data to look like visualize_2 data [std_road.py]
|   +-- visualize_frames.csv        # Visualize_2 output with frame count added [std_vis.py]
|   +-- face_data.csv               # final output from run.sh
+-- _visualize_2
|   +-- Visualize_2.py              # script to calculate center of mass from back data
|   +-- mesh_calib.npy              # calibration file required for Visualize_2.py to work properly
|   +-- rotation_functions.py       # library module for rotation calculations in Visualize_2
++- run.sh                          # master pipeline, will output labelled face images 

Running AprilTags/run.sh will do the following things.

1. Run the AprilTag software (./apriltags/build/bin/apriltags_demo) on the road facing video and back facing video in parallel -> output/road.csv, output/back.csv - expect this to take a very long time (10+ hour)
2. NOTE: Please change the sync vectors in scripts/move_road_to_back.py before running this. Run scripts/move_road_to_back.py using output/road.csv -> output/road_proj_to_back.csv
3. Run visualize_2/Visualize_2.py on back.csv -> visualize_2/meshsave_back_2.mat
4. Run scripts/standardize_visualize.py on meshsave_back_2.mat -> output/visualize_frames.csv
5. Run scripts/standarize_road.py on road.csv -> road_normalized.csv
6. Run scripts/road_to_face.py on visualize_frames.csv & road_normalized.csv and the frame offset -> face_data.csv
7. Run scripts/extract_face_frames.py -> labelled face images in imgs/*

Detect April tags (2D bar codes) in images; reports unique ID of each detection, and optionally its position and orientation relative to a calibrated camera.

See examples/apriltags_demo.cpp for a simple example that detects April tags (see tags/pdf/tag36h11.pdf) in laptop or webcam images and marks any tags in the live image.

Ubuntu dependencies: sudo apt-get install subversion cmake libopencv-dev libeigen3-dev libv4l-dev

Mac dependencies: sudo port install pkgconfig opencv eigen3

Uses the pods build system in connection with cmake, see: http://sourceforge.net/p/pods/

Michael Kaess October 2012

AprilTags were developed by Professor Edwin Olson of the University of Michigan. His Java implementation is available on this web site: http://april.eecs.umich.edu.

Olson's Java code was ported to C++ and integrated into the Tekkotsu framework by Jeffrey Boyland and David Touretzky.

See this Tekkotsu wiki article for additional links and references: http://wiki.tekkotsu.org/index.php/AprilTags

This C++ code was further modified by Michael Kaess (kaess@mit.edu) and Hordur Johannson (hordurj@mit.edu) and the code has been released under the LGPL 2.1 license.

• converted to standalone library
• added stable homography recovery using OpenCV
• robust tag code table that does not require a terminating 0 (omission results in false positives by illegal codes being accepted)
• changed example tags to agree with Ed Olson's Java version and added all his other tag families
• added principal point as parameter as in original code - essential for homography
• added some debugging code (visualization using OpenCV to show intermediate detection steps)
• added fast approximation of arctan2 from Ed's original Java code
• using interpolation instead of homography in Quad: requires less homography computations and provides a small improvement in correct detections

todo:

• significant speedup could be achieved by performing image operations using OpenCV (Gaussian filter, but also operations in TagDetector.cc)
• replacing arctan2 by precomputed lookup table
• converting matrix operations to Eigen (mostly for simplifying code, maybe some speedup)