- 04 Jan, 2015 1 commit
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Vinh Khuc authored
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- 03 Jan, 2015 1 commit
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Vinh Khuc authored
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- 02 Jan, 2015 1 commit
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Davis King authored
on some newer OS X installs.
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- 30 Dec, 2014 3 commits
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Davis King authored
are supposed to since, on some systems, these libraries aren't installed correctly and will cause linker errors if used.
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Davis King authored
incorrect outputs when the requested chip stretched the image unevenly vertically or horizontally. This is because we used the best similarity transform rather than affine transform between the image and the output chip.
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Davis King authored
certain compiler errors you get when accidentally trying to mutate a const image a little easier to understand.
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- 28 Dec, 2014 9 commits
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Davis King authored
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Davis King authored
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Davis King authored
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Davis King authored
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Davis King authored
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Davis King authored
Also just cleaned up a few minor details.
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Davis King authored
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Davis King authored
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Davis King authored
I changed the spec to reflect this.
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- 21 Dec, 2014 5 commits
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Davis King authored
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Davis King authored
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Davis King authored
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Davis King authored
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Davis King authored
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- 20 Dec, 2014 7 commits
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Davis King authored
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Davis King authored
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Davis King authored
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Davis King authored
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Davis King authored
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Davis King authored
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Davis King authored
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- 19 Dec, 2014 1 commit
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Davis King authored
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- 16 Dec, 2014 2 commits
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Davis King authored
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Davis King authored
like cv_image as input.
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- 15 Dec, 2014 2 commits
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Davis King authored
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Davis King authored
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- 13 Dec, 2014 1 commit
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Patrick Snape authored
A little bit hacky, but should be fine. Supports both fhog detectors and the "cached" simple_object_detector. Also, maintains the upscale parameter for testing
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- 11 Dec, 2014 7 commits
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Patrick Snape authored
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Patrick Snape authored
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Patrick Snape authored
This is the biggest change so far. Now, there are two different classes of interface. One where you pass ONLY file paths, and one where you pass ONLY Python objects. The file paths are maintained to keep a matching interface with the C++ examples of dlib. So shape predicition and object detection can be trained using the dlib XML file paths and then serialize the detectors to disk. Shape prediction and object detection can also be trained using numpy arrays and in-memory objects. In this case, the predictor and detector objects are returned from the training functions. To facilitate serializing these objects, they now have a 'save' method. Tetsing follows a similar pattern, in that it can take either XML files are or in-memory objects. I also added back the concept of upsampling during testing to make amends for removing the simple_object_detector_py struct.
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Davis King authored
has valid outputs.
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Patrick Snape authored
Also, removed the saving of the upsample which I missed from before (since I'm not using the struct now). I understand why the upsample was being saved, but I don't necessarily agree it is particularly useful as you should really be upsampling on a case by case basis at test time.
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Patrick Snape authored
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Patrick Snape authored
I also cleaned up a bunch of code. I'm not sure why the simple_object_detector was keeping track of the upsample amount, since it can't even be passed as an argument to the constructor. Therefore, I removed the simple_object_detector_py and the second declaration of the hog object detector. I also changed the view code to optionally take keyword args of color and added a single view of a rectangle. Finally, I added viewing of the shape parts.
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