homography_converter

Homography Converter

This script can be used to convert a given homography for usage on different input/output resolutions and convert a given OpenCV homography for use with SpatialTransformer units.

When preprocessing the dataset and creating homography images by running ipm.py, all images are processed at their native resolution. The actual neural network training can however be performed at a decreased resolution and different aspect ratio. Additionally, the SpatialTransformer units in uNetXST work slightly differently than OpenCV's warping method. In order to configure uNetXST with correct homographies for in-network transformation, this script needs to be used.

1. Use ipm.py with -v flag to only print the computed homographies.
2. Run this script with the homographies from ipm.py as input to convert them for usage with SpatialTransformer units.
3. Create a file similar to preprocessing/homography_converter/uNetXST_homographies/1_FRLR.py and paste the converted SpatialTransformer homography there, if uNetXST is chosen as the neural network architecture. Don't forget to set the unetxst-homographies parameter in the training config file.

Note that for our datasets we already provide the correct homographies to be used within uNetXST.

Usage

usage: homography_converter.py [-h] -roi H W [-roo H W] -rni H W [-rno H W]
                               homography

Converts a given homography matrix to work on images of different resolution.
Also converts OpenCV homography matrices for use with SpatialTransformer
units.

positional arguments:
  homography  homography to convert (string representation)

optional arguments:
  -h, --help  show this help message and exit
  -roi H W    original input resolution (HxW)
  -roo H W    original output resolution (HxW)
  -rni H W    new input resolution (HxW)
  -rno H W    new output resolution (HxW)

Example

Convert homography from Inverse Perspective Mapping (ipm.py) for 604x964 front images (dataset 1_FRLR) in order to be used on 256x512 images

./ipm.py -v --drone ../camera_configs/1_FRLR/drone.yaml ../camera_configs/1_FRLR/front.yaml front ../camera_configs/1_FRLR/rear.yaml rear ../camera_configs/1_FRLR/left.yaml left ../camera_configs/1_FRLR/right.yaml right
# OpenCV homography for front:
# [[0.0, 0.8841865353311344, -253.37277367000263], [0.049056392233805146, 0.5285437237795494, -183.265385638118], [-0.0, 0.001750144780726984, -0.5285437237795492]]
# OpenCV homography for rear:
# [[6.288911300436434e-18, 0.8292344604207404, -264.08036704706365], [-0.04905639223380515, 0.5285437237795513, -135.9750235247304], [-0.0, 0.0017501447807269904, -0.5285437237795512]]
# OpenCV homography for left:
# [[0.04905639223380514, 0.7984814950483465, -264.7865925612947], [3.0038376863423275e-18, 0.4821577791689496, -159.26320930902278], [-0.0, 0.0016334684620118568, -0.49330747552758086]]
# OpenCV homography for right:
# [[-0.04905639223380516, 0.7984814950483448, -217.49623044790604], [3.0038376863423283e-18, 0.5044571718862112, -138.69450590963578], [-0.0, 0.0016334684620118542, -0.49330747552758]]

./homography_converter.py '[[0.0, 0.8841865353311344, -253.37277367000263], [0.049056392233805146, 0.5285437237795494, -183.265385638118], [-0.0, 0.001750144780726984, -0.5285437237795492]]' -roi 604 964 -rni 256 512

Convert homography from Inverse Perspective Mapping (ipm.py) for 1936x1216 front images and 1936x1216 drone images (dataset 2_F) in order to be used on 256x512 images

./ipm.py -v --drone ../camera_configs/2_F/drone.yaml ../camera_configs/2_F/front.yaml front
# OpenCV homography for front:
# [[-8.869343394420501e-18, -0.031686570310719885, 58.219888879244245], [0.017875377577360244, 0.1995620198169141, -140.13793664741047], [-0.0, 0.0004091757529793379, -0.25180641631255507]]

./homography_converter.py '[[-8.869343394420501e-18, -0.031686570310719885, 58.219888879244245], [0.017875377577360244, 0.1995620198169141, -140.13793664741047], [-0.0, 0.0004091757529793379, -0.25180641631255507]]' -roi 1216 1936 -roo 968 1936 -rni 256 512