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Code Issues Releases Wiki Activity

fixing facerecognizer tutorials and interface

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This commit is contained in:
StevenPuttemans
2015-07-27 15:27:52 +02:00
parent aa11ac48f1
commit 076f53d667
14 changed files with 59 additions and 1202 deletions
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18
modules/face/tutorials/face_tutorial.markdown
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@@ -246,7 +246,7 @@ every source code listing is commented in detail, so you should have no problems
The source code for this demo application is also available in the src folder coming with this
documentation:
@include src/facerec_eigenfaces.cpp
@include face/samples/facerec_eigenfaces.cpp
I've used the jet colormap, so you can see how the grayscale values are distributed within the
specific Eigenfaces. You can see, that the Eigenfaces do not only encode facial features, but also
@@ -263,8 +263,8 @@ let's see how many Eigenfaces are needed for a good reconstruction. I'll do a su
for(int num_components = 10; num_components < 300; num_components+=15) {
// slice the eigenvectors from the model
Mat evs = Mat(W, Range::all(), Range(0, num_components));
Mat projection = subspaceProject(evs, mean, images[0].reshape(1,1));
Mat reconstruction = subspaceReconstruct(evs, mean, projection);
Mat projection = LDA::subspaceProject(evs, mean, images[0].reshape(1,1));
Mat reconstruction = LDA::subspaceReconstruct(evs, mean, projection);
// Normalize the result:
reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
// Display or save:
@@ -370,7 +370,7 @@ given by:
The source code for this demo application is also available in the src folder coming with this
documentation:
@include src/facerec_fisherfaces.cpp
@include face/samples/facerec_fisherfaces.cpp
For this example I am going to use the Yale Facedatabase A, just because the plots are nicer. Each
Fisherface has the same length as an original image, thus it can be displayed as an image. The demo
@@ -398,8 +398,8 @@ Fisherfaces describes:
for(int num_component = 0; num_component < min(16, W.cols); num_component++) {
// Slice the Fisherface from the model:
Mat ev = W.col(num_component);
Mat projection = subspaceProject(ev, mean, images[0].reshape(1,1));
Mat reconstruction = subspaceReconstruct(ev, mean, projection);
Mat projection = LDA::subspaceProject(ev, mean, images[0].reshape(1,1));
Mat reconstruction = LDA::subspaceReconstruct(ev, mean, projection);
// Normalize the result:
reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
// Display or save:
@@ -528,7 +528,7 @@ Patterns Histograms*.
The source code for this demo application is also available in the src folder coming with this
documentation:
@include src/facerec_lbph.cpp
@include face/samples/facerec_lbph.cpp
Conclusion {#tutorial_face_conclusion}
----------
@@ -658,7 +658,7 @@ at/s17/3.pgm;1
Here is the script, if you can't find it:
@verbinclude face/samples/src/create_csv.py
@verbinclude face/samples/etc/create_csv.py
### Aligning Face Images {#tutorial_face_appendix_align}
@@ -677,7 +677,7 @@ where:
If you are using the same *offset_pct* and *dest_sz* for your images, they are all aligned at the
eyes.
@verbinclude face/samples/src/crop_face.py
@verbinclude face/samples/etc/crop_face.py
Imagine we are given [this photo of Arnold
Schwarzenegger](http://en.wikipedia.org/wiki/File:Arnold_Schwarzenegger_edit%28ws%29.jpg), which is