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Converted multiview calibration sample to application.

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Alexander Smorkalov
2025-09-26 19:19:14 +03:00
parent 1a74264ee9
commit a6c88cde7f
4 changed files with 14 additions and 12 deletions
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doc/tutorials/calib3d/camera_multiview_calibration/multiview_calibration.markdown
+11 -11
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@@ -1,4 +1,4 @@
Multi-view Camera Calibration Tutorial {#tutorial_multiview_camera_calibration}
Multi-view Camera Calibration Tutorial {#tutorial_multiview_camera_calibration}
==========================
@tableofcontents
@@ -71,7 +71,7 @@ Assume we have `N` camera views, for each `i`-th view there are `M` images conta
Python example
--
There are two options to run the sample code in Python (`opencv/samples/python/multiview_calibration.py`) either with raw images or provided points.
There are two options to run the sample code in Python (`opencv/apps/multiview-calibration/multiview_calibration.py`) either with raw images or provided points.
The first option is to prepare `N` files where each file has the path to an image per line (images of a specific camera of the corresponding file). Leave the line empty, if there is no corresponding image for the camera in a certain frame. For example, a file for camera `i` should look like (`file_i.txt`):
```
/path/to/image_1_of_camera_i
@@ -231,25 +231,25 @@ def mutiviewCalibration (pattern_points, image_points, detection_mask):
Python sample API
----
To run the calibration procedure in Python follow the following steps (see sample code in `samples/python/multiview_calibration.py`):
To run the calibration procedure in Python follow the following steps (see sample code in `apps/multiview-calibration/multiview_calibration.py`):
-# **Prepare data**:
@snippet samples/python/multiview_calibration.py calib_init
@snippet apps/multiview-calibration/multiview_calibration.py calib_init
The detection mask matrix is later built by checking the size of image points after detection:
-# **Detect pattern points on images**:
@snippet samples/python/multiview_calibration.py detect_pattern
@snippet apps/multiview-calibration/multiview_calibration.py detect_pattern
-# **Build detection mask matrix**:
@snippet samples/python/multiview_calibration.py detection_matrix
@snippet apps/multiview-calibration/multiview_calibration.py detection_matrix
-# **Finally, the calibration function is run as follows**:
@snippet samples/python/multiview_calibration.py multiview_calib
@snippet apps/multiview-calibration/multiview_calibration.py multiview_calib
C++ sample API
@@ -289,24 +289,24 @@ Practical Debugging Techniques
-# Camera intrinsics can be better estimated when points are more scattered in the image. The following code can be used to plot out the heat map of the observed point
@snippet samples/python/multiview_calibration.py plot_detection
@snippet apps/multiview-calibration/multiview_calibration.py plot_detection
![condensely scattered](camera_multiview_calibration/images/count_example.png)
The left example is not well scattered while the right example shows a better-scattered pattern
-# Plot out the reprojection error to ensure the result is reasonable
-# If ground truth camera intrinsics are available, a visualization of the estimated error on intrinsics is provided.
@snippet samples/python/multiview_calibration.py vis_intrinsics_error
@snippet apps/multiview-calibration/multiview_calibration.py vis_intrinsics_error
resulting visualization would look similar to
![distortion error](camera_multiview_calibration/images/distort_error.jpg)
-# **Multiview calibration**
-# Use `plotCamerasPosition` in samples/python/multiview_calibration.py to plot out the graph established for multiview calibration. shows positions of cameras, checkerboard (of a random frame), and pairs of cameras connected by black lines explicitly demonstrating tuples that were used in the initial stage of stereo calibration.
-# Use `plotCamerasPosition` in apps/multiview-calibration/multiview_calibration.py to plot out the graph established for multiview calibration. shows positions of cameras, checkerboard (of a random frame), and pairs of cameras connected by black lines explicitly demonstrating tuples that were used in the initial stage of stereo calibration.
The dashed gray lines demonstrate the non-spanning tree edges that are also used in the optimization.
The width of these lines indicates the number of co-visible frames i.e. the strength of connection.
It is more desired if the edges in the graph are dense and thick. ![](camera_multiview_calibration/images/connection_tree.jpg) For the right tree, the connection for camera four is rather limited and can be strengthened
-# Visulization method for showing the reprojection error with arrows (from a given point to the back-projected one) is provided (see `plotProjection` in samples/python/multiview_calibration.py). The color of the arrows highlights the error values. Additionally, the title reports mean error on this frame and its accuracy among other frames used in calibration.
-# Visulization method for showing the reprojection error with arrows (from a given point to the back-projected one) is provided (see `plotProjection` in apps/multiview-calibration/multiview_calibration.py). The color of the arrows highlights the error values. Additionally, the title reports mean error on this frame and its accuracy among other frames used in calibration.
![](camera_multiview_calibration/images/checkerboard.jpg)