Template Matching

Overview

Template matching is a method for finding the best matched location of a template image inside a larger image. We use the normalized cross-corrleation to compute the best match.

The following is an example showing a source image on the left with the template image in the middle. The matched score of output image is shown in the right.

Input	Template	Output score converted to U8

Implementation

The algorithm computes the normalized cross correlation (score) for every possible location of the template inside the source image. The location with the highest score is chosen as the best matching location between source and template image.

The cross correlation is calculated as follows:

\begin{align*} R(x, y) &= \frac { \sum_{x\prime, y\prime}( (T(x\prime, y\prime) - I(x+x\prime, y+y\prime)) \cdot M(x\prime, y\prime))^2 } { \sqrt{ \sum_{x\prime, y\prime} (T(x\prime, y\prime) \cdot M(x\prime, y\prime))^2 \cdot \sum_{x\prime, y\prime} (I(x+x\prime, y+y\prime) \cdot M(x\prime, y\prime))^2} } \end{align*}

where:
\(T(x\prime, y\prime)\) refers to the template image.
\(I(x+x\prime, y+y\prime)\) refers to the source image.
\(M(x\prime, y\prime)\) refers to the mask.
\(x\prime\) and \(y\prime\) are the pixel coordinates inside the template image.

If the source image has a resolution W \(\times\) H and template image w \(\times\) h, then the output image has a resolution (W - w + 1) \(\times\) (H - h + 1). If the template exceeds a size of 400 pixels, the convolution will be computed in Fourier space, otherwise in original space.

C API functions

For list of limitations, constraints and backends that implements the algorithm, consult reference documentation of the following functions:

Function	Description
vpiCreateTemplateMatching	Creates payload for vpiSubmitTemplateMatching.
vpiTemplateMatchingSetSourceImage	Set the source image.
vpiTemplateMatchingSetTemplateImage	Set the template image.
vpiSubmitTemplateMatching	Runs the template matching algorithm with provided template.

Attention

This algorithm requires that the below libraries be to be installed in the system:

• libnppc.so.11
• libnppial.so.11
• libnppidei.so.11
• libnppist.so.11

Usage

Language: C/C++ Python

1. Import VPI module

   import vpi

2. Match the template image in the input image by using CUDA backend. It's using the normalized cross-correlation.

   with vpi.Backend.CUDA:
       output = vpi.templateMatching(input, templ)

1. Initialization phase
   1. Include the header that defines the template matching function.

      #include <vpi/algo/TemplateMatching.h>

   2. Define the input image object.

          VPIImage input = /*...*/;

   3. Define the template image object.

          VPIImage templ = /*...*/;

   4. Create an output image with the required size and format.

          int32_t srcW, srcH;
          vpiImageGetSize(input, &srcW, &srcH);
       
          int32_t templW, templH;
          vpiImageGetSize(templ, &templW, &templH);
       
          VPIImage output;
          vpiImageCreate(srcW - templW + 1, srcH - templH + 1, VPI_IMAGE_FORMAT_F32, 0, &output);

   5. Create the stream where the algorithm will be submitted for execution.

          VPIStream stream;
          vpiStreamCreate(0, &stream);

   6. Create the payload that will contain all temporary buffers needed for processing. It'll be created on the CUDA backend.

          VPIPayload payload;
          vpiCreateTemplateMatching(VPI_BACKEND_CUDA, srcW, srcH, &payload);

2. Processing phase
   1. Set the input image.

          vpiTemplateMatchingSetSourceImage(stream, VPI_BACKEND_CUDA, payload, input);

   2. Set the template image.

          vpiTemplateMatchingSetTemplateImage(stream, VPI_BACKEND_CUDA, payload, templ, NULL);

   3. Submit the algorithm to the stream along with all parameters. It'll be executed by the CUDA algorithm.

          vpiSubmitTemplateMatching(stream, VPI_BACKEND_CUDA, payload, output, VPI_TEMPLATE_MATCHING_NCC);

   4. Optionally, wait until the processing is done.

          vpiStreamSync(stream);

3. Cleanup phase
   1. Free resources held by the stream and the input and output images.

          vpiStreamDestroy(stream);
          vpiImageDestroy(input);
          vpiImageDestroy(templ);
          vpiImageDestroy(output);

Consult the Template Matching for a complete example.

For more information, see Template Matching Algorithm in the "C API Reference" section of VPI - Vision Programming Interface.

Performance

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.

clear filters Device: Jetson AGX Orin  -  Streams: 1 2 4 8

References

1. Briechle, K., & Hanebeck, U. D. (2001, March). Template matching using fast normalized cross correlation. In Optical Pattern Recognition XII (Vol. 4387, pp. 95-102). SPIE.
2. Chen, C. S., Huang, C. L., Yeh, C. W., & Chang, W. C. (2016). An accelerating CPU based correlation-based image alignment for real-time automatic optical inspection. Computers & Electrical Engineering, 49, 207-220.