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* WIP: preparation for JPEG XL support * jxl: add loading support * update jxl abstract * add support for saving jxl (lossless not working) * everything works except setting lossless explicitly * remove unused header * fix wrong quality logic * remove debugging statements * fix lossless encoding * improve support for grayscale images * use JXL instead of JPEGXL everywhere * oops do not make libjxl a requirement * update years * silence some warnings * simplify loader fast path logic * allow python to save jxl and webp * update error message with supported formats * Allow setting image quality in Python The setting is ignored where it does not make sense. * round quality in JPEG saver * improve error message in CMake * add jxl support to imglab * add Davis's suggestion Co-authored-by: Davis E. King <davis685@gmail.com> * Apply suggestions from code review Co-authored-by: Davis E. King <davis685@gmail.com> * make sure grayscale is 8 bit * update abstract: JPEG XL can store grayscale images * add more methods to query basic info from JXL * documentation formatting * Apply Davis' suggestions --------- Co-authored-by: Davis E. King <davis685@gmail.com> |
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CMakeLists.txt | ||
convert_imglab_paths_to_relative | ||
copy_imglab_dataset | ||
README.txt |
imglab is a simple graphical tool for annotating images with object bounding boxes and optionally their part locations. Generally, you use it when you want to train an object detector (e.g. a face detector) since it allows you to easily create the needed training dataset. You can compile imglab with the following commands: cd dlib/tools/imglab mkdir build cd build cmake .. cmake --build . --config Release Note that you may need to install CMake (www.cmake.org) for this to work. On a unix system you can also install imglab into /usr/local/bin by running sudo make install This will make running it more convenient. Next, to use it, lets assume you have a folder of images called /tmp/images. These images should contain examples of the objects you want to learn to detect. You will use the imglab tool to label these objects. Do this by typing the following command: ./imglab -c mydataset.xml /tmp/images This will create a file called mydataset.xml which simply lists the images in /tmp/images. To add bounding boxes to the objects you run: ./imglab mydataset.xml and a window will appear showing all the images. You can use the up and down arrow keys to cycle though the images and the mouse to label objects. In particular, holding the shift key, left clicking, and dragging the mouse will allow you to draw boxes around the objects you wish to detect. Once you finish labeling objects go to the file menu, click save, and then close the program. This will save the object boxes back to mydataset.xml. You can verify this by opening the tool again with: ./imglab mydataset.xml and observing that the boxes are present. imglab can do a few additional things. To see these run: imglab -h and also read the instructions in the About->Help menu.