VPI - Vision Programming Interface

1.2 Release



Remap applies a generic geometrical transformation to an image. Typical uses include:

  • Lens distortion correction.
  • User-defined border extension.
  • Conversion between different projections.

The algorithm can work on dense or sparse warp maps. A dense warp map stores the remapped position of all pixels in the output image, whereas a sparse map stores a subset of that. It's more efficient to process the latter than the former, but quality might decrease depending on how much distortion is applied by the mapping.

The example below shows the famous "Little Planet" (stereographic) projection applied to a equirectangular panorama image, where \(R\) is the planet radius.

Input Mapping Result

Kyu Shiba Rikyu Garden: on the bridge by heiwa4126 is licensed under CC BY 2.0

\begin{align*} \theta(x,y) &= \pi + \arctan\left(\frac{y}{x}\right) \\ \phi(x,y) &= \frac{\pi}{2} - 2\arctan\left(\frac{r}{2R}\right) \\ r &= \sqrt{x^2+y^2} \\ \end{align*}

This image is a derivative of Kyu Shiba Rikyu Garden: on the bridge by heiwa4126, used under CC BY 2.0


The algorithm uses a user-provided VPIWarpMap that maps every pixel in the output image to the corresponding pixel in the input image. It supports dense and sparse mappings.

Mapping operation

The mapping operation is composed of two steps:

  1. A dense map is generated by up-sampling the input sparse map using bi-quadratic interpolation.
  2. The pixel in the input image corresponding to the control point in the output image is sampled using the user-provided interpolator.

Remap supports the following pixel interpolators:

When the corresponding source pixel falls outside image border, Remap will use the border extension to decide what pixel value to pick.

The following border extensions are available:

Warp grid definition

Warp grids can be of 3 types:

  • dense: provides maximum quality in exchange for some performance hit.
  • uniformly sparse: provides a balance between quality and speed. Useful when the distribution of details in the resulting image isn't known a priori.
  • non-uniformly sparse: Useful when distribution of details in the resulting image is known. This allows increased quality and performance by sampling less in areas with low detail, and sampling more densely in areas with more detail.

When defining a sparse map, the output image can be split into at most 16 regions (4 horizontally and 4 vertically), with different control point interval (density) in each row or column of regions. The interval spacing must be expressed in power-of-two number of pixels in between control points. There are some other restrictions in the grid layout. Please consult VPIWarpGrid for more information.

Non-uniform grid

To define a dense map, simply set up the warp grid to have just one region, and both horizontal and vertical spacing to 1.


  1. Import VPI module
    import vpi
  2. Create a identity warp map, i.e., all control points' position will match their position in the output image. Here the warp map is dense with same dimensions as input image.
    warp = vpi.WarpMap(vpi.WarpGrid(input.size))
  3. Interpret warp map coordinates as a numpy array. No copies are made. The numpy array has shape (H,W,2) and is a view of the warp map. The shape transposition to (2,W,H) is just to have the x and y coordinates in the arrays wx and wy respectively. This makes coordinates' manipulation easier and more efficient.
    wx,wy = np.asarray(warp).transpose(2,1,0)
  4. Generate a custom mapping suitable to render an equirectangular panorama with the "Little Planet" effect. A similar approach is also taken when performing lens distortion correction for a lens whose distortion model doesn't match the ones provided by VPI. See Lens Distortion Correction for more details.
    x = wx-input.width/2
    y = wy-input.height/2
    R = input.height/8 # planet radius
    r = np.sqrt(x*x + y*y)
    theta = np.pi + np.arctan2(y,x)
    phi = np.pi/2 - 2*np.arctan2(r, 2*R)
    wx[:] = np.fmod((theta + np.pi)/(2*np.pi)*(input.width-1), input.width-1)
    wy[:] = (phi + np.pi/2) / np.pi*(input.height-1)
  5. Executes the algorithm, it'll transform the VPI image input using the warp map created, writing the result into the output VPI image.
    with vpi.Backend.CUDA:
    output = input.remap(warp)
  1. Initialization phase
    1. Include the header that defines the Remap functions and VPIWarpMap definition.
      #include <vpi/WarpMap.h>
      #include <vpi/algo/Remap.h>
      Declares functions that implement the Remap algorithm.
      Declares functions that implement the WarpMap structure and related functions.
    2. Define the input image object.
      VPIImage input = /*...*/;
      struct VPIImageImpl * VPIImage
      A handle to an image.
      Definition: Types.h:215
    3. Create the output image. In this particular case both input and output have same dimensions, but this is not required. The formats must match, though.
      int32_t w, h;
      vpiImageGetSize(input, &w, &h);
      vpiImageGetFormat(input, &type);
      VPIImage output;
      vpiImageCreate(w, h, type, 0, &output);
      Pre-defined image formats.
      Definition: ImageFormat.h:99
      VPIStatus vpiImageCreate(int32_t width, int32_t height, VPIImageFormat fmt, uint32_t flags, VPIImage *img)
      Create an empty image instance with the specified flags.
      VPIStatus vpiImageGetFormat(VPIImage img, VPIImageFormat *format)
      Get the image format.
      VPIStatus vpiImageGetSize(VPIImage img, int32_t *width, int32_t *height)
      Get the image size in pixels.
    4. Create the stream where the algorithm will be submitted for execution.
      VPIStream stream;
      vpiStreamCreate(0, &stream);
      struct VPIStreamImpl * VPIStream
      A handle to a stream.
      Definition: Types.h:209
      VPIStatus vpiStreamCreate(uint32_t flags, VPIStream *stream)
      Create a stream instance.
    5. Create an dense warp map.
      memset(&map, 0, sizeof(map));
      map.grid.regionWidth[0] = w;
      map.grid.regionHeight[0] = h;
      map.grid.horizInterval[0] = 1;
      map.grid.vertInterval[0] = 1;
      int8_t numHorizRegions
      Number of regions horizontally.
      Definition: WarpGrid.h:158
      VPIWarpGrid grid
      Warp grid control point structure definition.
      Definition: WarpMap.h:91
      int16_t horizInterval[VPI_WARPGRID_MAX_HORIZ_REGIONS_COUNT]
      Horizontal spacing between control points within a given region.
      Definition: WarpGrid.h:163
      int8_t numVertRegions
      Number of regions vertically.
      Definition: WarpGrid.h:159
      int16_t vertInterval[VPI_WARPGRID_MAX_VERT_REGIONS_COUNT]
      Vertical spacing between control points within a given region.
      Definition: WarpGrid.h:165
      Width of each region.
      Definition: WarpGrid.h:161
      int16_t regionHeight[VPI_WARPGRID_MAX_VERT_REGIONS_COUNT]
      Height of each region.
      Definition: WarpGrid.h:162
      VPIStatus vpiWarpMapAllocData(VPIWarpMap *warpMap)
      Allocates the warp map's control point array for a given warp grid.
      Defines the mapping between input and output images' pixels.
      Definition: WarpMap.h:88
    6. Generate a custom mapping suitable to render an equirectangular panorama as a little planet. vpiWarpMapGenerateIdentity will fill the warp map with an identity mapping, i.e., all control points' position will match their position in the output image. Once this is done, the code loops through all control points and using the output coordinate, calculates the corresponding coordinate in the input image. This approach is also taken when performing lens distortion correction for a lens whose distortion model doesn't match the ones provided by VPI. See Lens Distortion Correction for more details.
      int i;
      for (i = 0; i < map.numVertPoints; ++i)
      VPIKeypoint *row = (VPIKeypoint *)((uint8_t *)map.keypoints + map.pitchBytes * i);
      int j;
      for (j = 0; j < map.numHorizPoints; ++j)
      float x = row[j].x - w / 2.0f;
      float y = row[j].y - h / 2.0f;
      const float R = h / 8.0f; /* planet radius */
      const float r = sqrtf(x * x + y * y);
      float theta = M_PI + atan2f(y, x);
      float phi = M_PI / 2 - 2 * atan2f(r, 2 * R);
      row[j].x = fmod((theta + M_PI) / (2 * M_PI) * (w - 1), w - 1);
      row[j].y = (phi + M_PI / 2) / M_PI * (h - 1);
      float y
      Keypoint's y coordinate.
      Definition: Types.h:276
      float x
      Keypoint's x coordinate.
      Definition: Types.h:275
      Stores a keypoint coordinate.
      Definition: Types.h:274
      VPIKeypoint * keypoints
      Pointer to an array with control point positions in the input image corresponding to those in the out...
      Definition: WarpMap.h:115
      int32_t pitchBytes
      Number of bytes between one control point and the one immediately below.
      Definition: WarpMap.h:106
      int16_t numHorizPoints
      Number of points horizontally.
      Definition: WarpMap.h:96
      int16_t numVertPoints
      Number of points vertically.
      Definition: WarpMap.h:100
      VPIStatus vpiWarpMapGenerateIdentity(VPIWarpMap *warpMap)
      Fills the given warp map with an identity mapping.
    7. Create the algorithm payload with the created mapping. The payload is created in the CUDA backend.
      VPIPayload warp;
      struct VPIPayloadImpl * VPIPayload
      A handle to an algorithm payload.
      Definition: Types.h:227
      VPIStatus vpiCreateRemap(uint32_t backends, const VPIWarpMap *warpMap, VPIPayload *payload)
      Create a payload for Remap algorithm.
      CUDA backend.
      Definition: Types.h:93
  2. Processing phase
    1. Submit the algorithm to the stream, along with all parameters. The algorithm will be executed by the backend where the payload was created, CUDA.
      VPIStatus vpiSubmitRemap(VPIStream stream, uint32_t backend, VPIPayload payload, VPIImage input, VPIImage output, VPIInterpolationType interp, VPIBorderExtension border, uint32_t flags)
      Submits the Remap operation to the stream associated with the payload.
      All pixels outside the image are considered to be zero.
      Definition: Types.h:237
      Linear interpolation.
      Definition: Interpolation.h:93
    2. Optionally, wait until the processing is done.
      VPIStatus vpiStreamSync(VPIStream stream)
      Blocks the calling thread until all submitted commands in this stream queue are done (queue is empty)...
  3. Cleanup phase
    1. Free resources held by the stream, the payload, the warp map and the input and output images.
      void vpiImageDestroy(VPIImage img)
      Destroy an image instance.
      void vpiPayloadDestroy(VPIPayload payload)
      Deallocates the payload object and all associated resources.
      void vpiStreamDestroy(VPIStream stream)
      Destroy a stream instance and deallocate all HW resources.
      void vpiWarpMapFreeData(VPIWarpMap *warpMap)
      Deallocates the warp map control points allocated by vpiWarpMapAllocData.

For more information, see Remap in the "API Reference" section of VPI - Vision Programming Interface.

Limitations and Constraints

Constraints for specific backends supersede the ones specified for all backends.

All Backends



  • Not implemented


For information on how to use the performance table below, see Algorithm Performance Tables.
Before comparing measurements, consult Comparing Algorithm Elapsed Times.
For further information on how performance was benchmarked, see Performance Benchmark.