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基于颜色来检测火焰，将是频频分帧，然后逐帧检测，碧昂切将火焰全画出来，基础编程。(Based on color to test the flame, basic programming )

说明：  很有用的图像显示的源程序，是图像处理方面比较好用的程序！(very useful images of the source, the image processing is more user-friendly procedures.)

输入一组数据，xy值，用来拟合椭圆。只要改变输入数据，该代码就可以直接使用。另附上椭圆拟合算法。(Enter a set of data, xy values, used to fit ellipses. Just change input data, the code can be used directly. Attached on the ellipse fitting algorithm.)

画图源程序-图像数据显示，可以对数据进行时间轴的曲线显示，也可以是空间轴的多维显示 (Paint source- the image data, the data can be curve shows the time axis, it can be multi-dimensional display space axis)

源点定位程序，可用于检测连通域等功能，代码是c/c++的，放心使用。(Source point positioning procedures, can be used to detect other functions connected domain, the code is c/c++, and ease of use.)

基于opencv实时图像的2值化处理，一定要有摄像头(Opencv-based real-time image processing of two values, there must be a camera)

一个简单的虚线类，绘制各种虚线的C++类库，请看效果截图，可绘制出各种彩色的虚线效果。画横向或竖向虚线，以及对角线的虚线框效果，都是可以的，带有一个演示程序。(A simple dashed class, draw various dashed C++ library, see the effects shots, you can draw a variety of color dotted effect. Draw horizontal or vertical dashed line, as well as the effect of diagonal dashed box, are possible with a demo program.)

该源程序可以实现BMP图片的压缩(BMP pictures)

用大整数乘法来计算两个n位二进制数相乘(With large integer multiplication to calculate the multiplication of two n-bit binary number)

：植物种类识别方法主要是根据叶片低维特征进行自动化鉴定。然而，低维特征不能全面描述叶片信息，识别准确率低，本文提 出一种基于多特征降维的植物叶片识别方法。首先通过数字图像处理技术对植物叶片彩色样本图像进行预处理，获得去除颜色、虫洞、 叶柄和背景的叶片二值图像、灰度图像和纹理图像。然后对二值图像提取几何特征和结构特征，对灰度图像提取 Hu不变矩特征、灰 度共生矩阵特征、局部二值模式特征和 Gabor 特征，对纹理图像提取分形维数，共得到 2183 维特征参数。再采用主成分分析与线性 评判分析相结合的方法对叶片多特征进行特征降维，将叶片高维特征数据降到低维空间。使用降维后的训练样本特征数据对支持向量 机分类器进行训练(plant species identification method is mainly based on blade automatic identification of low dimensional characteristics.However, can not fully describe blade low-dimensional feature information, identification accuracy is low, in this paper A kind of plant leaves recognition method based on multiple feature dimension reduction.First by digital image processing technology to the plant leaf color sample image preprocessing, obtain background color removal, wormhole, petioles, and the blades of a binary image, gray image and texture image.Then the binary image to extract the geometric characteristics and characteristics of structure and characteristics of gray image extraction Hu moment invariants, gray co-occurrence matrix feature, local binary pattern features and Gabor, to extract the fractal dimension of texture image, get 2183 d characteristic parameters.By principal component analysis and linear uation analysis method of combining the characteristics of blade more feature dimensi)