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Author	SHA1	Message	Date
yanenshuo	d477d24d39	Add a pixel inversion program	3 months ago
Sean McBride	2f57d46df4	chore: Quieter builds	4 years ago
Sean McBride	9728147fe3	chore: makefile cleanup	4 years ago
Sean McBride	3bbad277b3	fix: remove unused arg in printf log	4 years ago
Sean McBride	3060d01025	feat: return bounding box	4 years ago
Sean McBride	8df9f4d8a3	chore: resolve merge conflict	4 years ago
Sean McBride	5d9da0b611	chore: Changes to build in /runtime/tests/sod/	4 years ago
Sean McBride	7780166d30	chore: Update for sledge	4 years ago
phani	8f2ad3f048	globals for multi-sandboxing	5 years ago
phani	3ded1584df	add awsm compilation	5 years ago
phani	1b875f6d70	read from stdin, write to stdout	5 years ago
phani	03f70a904b	makefile update, wasm compile and modify samples for wasm/native compatibility	5 years ago
phani	b8549331bb	makefile update	5 years ago
Symisc Systems	3813bf624d	WebAssembly compilation article link	6 years ago
Symisc Systems	6635a08309	Porting SOD face detector to WebAssembly article	6 years ago
Symisc Systems	35d3d9be5e	Embedded real-time face detection via Realnets ... Real-time frontal face detection via SOD Realnets (~ 5 ms on HD video). This is the built-in realnet embedded face detection model. There is no need to bring an independent model in order to detect faces at real-time. All you need, is to register the model hex array defined in "sod_face_realnet.h" via `sod_realnet_load_model_from_mem()` and use the same C/C++ as for detecting faces via Realnets. This feature is available in the commercial version of the library. You can obtain your commercial license from https://pixlab.io/downloads. Advantages includes: * Multi-core CPU support for all platforms - Up to 3 ~ 10 times faster processing speed. * Built-in (C Code), high performance RealNets frontal face detector. * 75 days of integration & technical assistance. * Royalty-free commercial licenses without any GPL restrictions. * Application source code stays private.	6 years ago
Symisc Systems	e66a795aa6	Added link to the license plate detection article ... https://sod.pixlab.io/articles/license-plate-detection.html	6 years ago
Symisc Systems	f195394e05	Added link to the license plate detection article ... https://sod.pixlab.io/articles/license-plate-detection.html	6 years ago
Symisc Systems	20eb7e5336	SOD Embedded Release 1.1.8	6 years ago
Symisc Systems	66e5301269	Main entry point for the host application	6 years ago
Symisc Systems	79c63059ed	Release note	6 years ago
Symisc Systems	c73b0c6afe	Release note	6 years ago
Symisc Systems	4775957249	Minutiae feature extraction for fingerprint images	6 years ago
Symisc Systems	ee3afd6703	Accompanying header files for reading images (Mem & Disk)	6 years ago
Symisc Systems	0400c50d06	Accompanying header files for reading images (Mem & Disk)	6 years ago
Symisc Systems	c978b7812e	Dual Licensed	6 years ago
Symisc Systems	553d5964fa	Minutiae feature extraction for fingerprint images	6 years ago
Symisc Systems	4c06e35a7c	Usage instruction of the WebAssembly face detector model	6 years ago
Symisc Systems	e8a23ff2f1	Usage instruction of the WebAssembly face detector model	6 years ago
Symisc Systems	f57888689d	Face detection, WebAssemby model for Web apps	6 years ago
Symisc Systems	4a14b23692	Real-time frontal face detection via SOD Realnets (~ 5 ms on HD video) ... Real-time frontal face detection via SOD Realnets (~ 5 ms on HD video)	7 years ago
Symisc Systems	4d168b1ca1	Build badges	7 years ago
Symisc Systems	8255b1e609	SOD Embedded Release 1.1.7	7 years ago
Symisc Systems	7990490c3e	Travis build setup & Makefile template ... SOD does not generally require a Makefile to build. Just drop sod.c and its accompanying header files on your source tree and you are done.	7 years ago
Symisc Systems	66f91e33c9	Badges!	7 years ago
Symisc Systems	62bfa87d17	Main entry point for your host apps	7 years ago
Symisc Systems	3fb5b14431	Accompanying header files for reading images (Mem & Disk)	7 years ago
Symisc Systems	355b2e4bb4	SOD Programming resources	7 years ago
Symisc Systems	b872dd5d38	Frontal License Plate detection without deep-learning	7 years ago
Symisc Systems	fe105fa2c2	Train your Realnets models on the CPU!	7 years ago
Symisc Systems	1f1cb96881	Apply Sobel operator on an input image.	7 years ago
Symisc Systems	5262875b86	RNN text generation via pre-trained model	7 years ago
Symisc Systems	12a2aa2e58	RNN text generation using predefined model	7 years ago
Symisc Systems	de8f7b9085	Template (skeleton) for training RealNets models	7 years ago
Symisc Systems	c68295648f	Perform hough line detection (Generally named Hough Transform) on an input image	7 years ago
Symisc Systems	facdb9a988	Real-Time multi-scale face detection using CNN	7 years ago
Symisc Systems	311842404c	Perform Hilditch thinning on an input image. ... * Skeletonization is useful when we are interested not in the size of the pattern but rather * in the relative position of the strokes in the pattern (Character Recognition, * X, Y Chromosome Recognition, etc.). * The target image must be binary (i.e. images whose pixels have only two possible intensity * value mostly black or white). You can obtain a binary image via sod_canny_edge_image(), * sod_otsu_binarize_image(), sod_binarize_image() or sod_threshold_image().	7 years ago
Symisc Systems	4a9e9ba3e5	Perform grayscale colorspace conversion	7 years ago
Symisc Systems	21fb53743a	Extract a region (crop) from a given image	7 years ago
Symisc Systems	a6f33013bf	CNN multi-class object detection (Pascal VOC)	7 years ago