Projects

Yayi

Mathematical morphology framework written in C++.

ctypes-opencv-ml

ctypes-opencv-ml is a subpackage of Python package ctypes-opencv. It provides Python access to the functionality of Intel's Open Source Computer Vision Library (OpenCV)'s machine learning (ML) component. Currently, the built-in ctypes package of Python does not support wrapping C++ classes ... [More] and their functions. Thus, wrapping OpenCV's ML component using ctypes alone is not possible. This package wraps the component using both ctypes and Boost.Python, offering the best features of both worlds. Bugs and CommentaryPlease send information on issues of usage to Minh-Tri Pham , post a message to ctypes-opencv's discussion group, or create an issue in the Issues pannel. [Less]

tspengine

LSTest Solution is a test tool for handset， it can send at command，receive result from handset ，reconginze image ，and so on ，tspengine is a part of it。

golden-huffman

C++ implementaion of multiple compressing method, using template. 计划完成一个C++实现各种常用文本压缩算法的库. 会尽可能的优化性能和减少占用空间。采用模板类template增强复用，如支持基于char和基于word的huffman,范式huffman算法(spporting both ... [More] char and word based 强调实验的特性，会详细输出中间过程，如打印具体的huffan tree. 进展11.30 完成了英文文本基于分词的范式huffman完全无损的压缩解压缩。 对于24M的一个测试英文文本用普通的基于字节的压缩可压缩到13M， 而基于分词的压缩当前测试是9.5M,gzip默认选项压缩到7.6M 如果改进分词或者是对于更大的英文文本(这个测试文本中符号比较多稍微影响效果) 基于词的压缩能取得更好的效果。 下一步，改进分词，改进速度，尝试中文分词压缩，或者混合文本... golden_huffman1.1 Table based canonical huff decoding is quick. allen:~/study/data_structure/golden-huffman/build/bin$ du -h 5big.log 116M 5big.log 1.Normal canonical huff decoding(char based) allen:~/study/data_structure/golden-huffman/build/bin$ time ./utest 5big.log.crs2 4 real 0m5.287s user 0m2.500s sys 0m2.572s 2.Table based canonical huff decoding(char based) allen:~/study/data_structure/golden-huffman/build/bin$ time ./utest 5big.log.crs2 6 real 0m3.621s user 0m2.132s sys 0m1.428s 3.gzip decoding allen:~/study/data_structure/golden-huffman/build/bin$ time gzip -d 5big.log.gz real 0m5.970s user 0m1.868s sys 0m1.532s golden_huffman1.0 * Finished character(byte) normal huffman compressing decomressing method. * Using boost.python, pygraphviz printing the huff tree to dot file. * Finshed character(byte) canonical huffman compressing and decompressing. * TODO try to optimize decompressing process of byte canonical huffman. * TODO dictionary,word based huffman and canonical huffman. allen:~/study/data_structure/golden-huffman/build/bin$ time ./utest [==========] Running 2 tests from 1 test case. [----------] Global test environment set-up. [----------] 2 tests from canonical_huff_char [ RUN ] canonical_huff_char.compress_perf [ OK ] canonical_huff_char.compress_perf (1037 ms) [ RUN ] canonical_huff_char.decomress_perf [ OK ] canonical_huff_char.decomress_perf (912 ms) [----------] 2 tests from canonical_huff_char (1950 ms total) [----------] Global test environment tear-down [==========] 2 tests from 1 test case ran. (1951 ms total) [ PASSED ] 2 tests. real 0m1.975s user 0m1.280s sys 0m0.676s allen:~/study/data_structure/golden-huffman/build/bin$ du -h big.log 24M big.log allen:~/study/data_structure/golden-huffman/build/bin$ du -h big.log.crs2 13M big.log.crs2 allen:~/study/data_structure/golden-huffman/build/bin$ du -h big.log.crs2.de 24M big.log.crs2.de allen:~/study/data_structure/golden-huffman/build/bin$ diff big.log big.log.crs2.de allen:~/study/data_structure/golden-huffman/build/bin$ time gzip big.log real 0m3.607s user 0m3.348s sys 0m0.136s allen:~/study/data_structure/golden-huffman/build/bin$ du -h big.log.gz 7.9M big.log.gz allen:~/study/data_structure/golden-huffman/build/bin$ time gzip -d big.log.gz real 0m0.742s user 0m0.228s sys 0m0.488s [Less]