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Augmented Maps

The objective of this work is to develop a set of programs that can be used to "augment" geographic map images by associating one or more images of the point of interest closest to the center of the image and marking the location of the point of interest on the map.

For more information, visit the wiki page of this project.

Preparation interface Augment interface (Note: the images were taken in different runnings of the program, the closest interest point in the first image would be cristal)

Instructions

Preparation

To run the preparation project: at the root of the project, switch to the src directory and run python preparation.py. You can add the -d flag if you want to run in debug mode, displaying intermediate process windows or displaying logs in the program console. You can also add the -c flag to calibrate the camera.

Preparation console with debug active

Augment

To run the augment project: at the root of the project, switch to the src directory and run python augment.py. You can add the -d flag if you want to run in debug mode, displaying intermediate process windows or displaying logs in the program console.

Augment console with debug active

Flags

In both projects, it is possible to run the -h or --help flag for information on the available flags.

If you want to change the front image of the map, the path to the image can be passed as an argument of the -ib flag in both projects, or for a permanent solution, the path in the IMAGE_BASE variable (line 13) of the ./src/preparation.py file and the path in the IMAGE_BASE variable (line 17) of the ./src/augment.py file will have to be changed.

If you want to change the image to be augmented, the path in the IMAGE_TEST variable (line 18) of the ./src/augment.py file can be passed as an argument to the -it flag, or to a permanent solution.

It is still possible in the augmentation program to choose whether to draw a green circle instead of the enlarged pyramid through the -cir or --circle flag.

References

  1. S. A. K. Tareen e Z. Saleem, «A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK», em 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2018, pp. 1–10.
  2. P. F. Alcantarilla, A. Bartoli, e A. J. Davison, «KAZE Features», em Computer Vision – ECCV 2012, 2012, pp. 214–227.
  3. «Blob detection», Wikipedia. 13-Out-2018.
  4. X. Li, «Chapter 8 - Image Processing at Your Fingertips: The New Horizon of Mobile Imaging», em Academic Press Library in Signal Processing, vol. 4, J. Trussell, A. Srivastava, A. K. Roy-Chowdhury, A. Srivastava, P. A. Naylor, R. Chellappa, e S. Theodoridis, Eds. Elsevier, 2014, pp. 249–264.
  5. M. A. Fischler e R. C. Bolles, «Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography», Commun. ACM, vol. 24, n. 6, pp. 381–395, Jun. 1981.
  6. "Feature Matching", https://docs.opencv.org/3.0‐beta/doc/py_tutorials/py_feature2d/py_matcher/py_matcher.html, 2018‐12‐06
  7. "Feature Matching + Homography to find Objects", https://docs.opencv.org/3.0‐beta/doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.html, 2018‐12‐06
  8. "Pose estimation", https://docs.opencv.org/3.4/d7/d53/tutorial_py_pose.html, 2018‐12‐06
  9. "Camera calibration", https://docs.opencv.org/3.1.0/dc/dbb/tutorial_py_calibration.html, 2018‐12‐06