软件研制任务书stdt
时间: 2023-07-28 09:02:20 浏览: 100
软件研制任务书(Software Development Task Description, STDt),是编写软件时必须遵循的一份文件,用于明确软件开发的目标、需求、时间和资源等。
STDt主要包括以下几个方面的内容:
1.项目背景和目标:介绍软件开发项目的背景、目标和意义,让开发团队对项目有全面的了解和认知。
2.需求分析:明确软件的功能、性能、可靠性和用户界面等各方面的需求,这是软件开发的基础,开发团队应根据需求进行详细设计和编码。
3.开发计划:列出软件开发的具体时间进度,确定开发的各个阶段和里程碑,以确保开发工作按时进行。
4.资源要求:明确开发所需的人力、物力和财力资源,包括开发人员的专业背景、硬件设备的配置以及开发所需软件工具的使用等。
5.风险管理:识别潜在的风险,并制定相应的应对措施,确保项目的顺利推进。可能的风险包括技术上的难点、资源不足、需求变更等。
6.测试计划:规划软件开发过程中的测试活动,包括单元测试、集成测试和系统测试等,保证软件的质量和稳定性。
7.交付标准与时间表:设定软件开发的交付标准和时间表,明确项目完成后的验收标准和交付日期。
软件研制任务书是软件开发过程中的重要文件,用于规范和指导开发团队的工作,确保项目的顺利进行并达到预期目标。在实际开发中,开发人员应遵循任务书提供的指导,有序地进行需求分析、设计、编码、测试和交付等环节。通过遵循任务书,可以提高软件的质量、减少开发风险,并提高项目的成功率。
相关问题
请使用C++和OpenGL编写导入obj模型文件并为模型贴图的代码,模型文件名为stdT.obj
好的,以下是一个简单的导入.obj模型文件并为模型贴图的C++和OpenGL代码示例:
```c++
#include <GL/glut.h>
#include <iostream>
#include <fstream>
#include <vector>
#include <cstring>
using namespace std;
// 存储顶点坐标的向量
vector<float> vertices;
// 存储纹理坐标的向量
vector<float> texcoords;
// 加载.obj模型文件
bool loadOBJ(const char* filename) {
ifstream ifs(filename);
if (!ifs) {
cerr << "Error: cannot open file " << filename << endl;
return false;
}
// 解析文件
char buf[256];
while (ifs.getline(buf, 256)) {
if (strncmp(buf, "v ", 2) == 0) {
float x, y, z;
sscanf(buf + 2, "%f %f %f", &x, &y, &z);
vertices.push_back(x);
vertices.push_back(y);
vertices.push_back(z);
} else if (strncmp(buf, "vt ", 3) == 0) {
float u, v;
sscanf(buf + 3, "%f %f", &u, &v);
texcoords.push_back(u);
texcoords.push_back(v);
} else if (strncmp(buf, "f ", 2) == 0) {
int v1, t1, v2, t2, v3, t3;
sscanf(buf + 2, "%d/%d %d/%d %d/%d", &v1, &t1, &v2, &t2, &v3, &t3);
// 将三个顶点加入OpenGL的顶点数组中
glVertex3f(vertices[(v1 - 1) * 3], vertices[(v1 - 1) * 3 + 1], vertices[(v1 - 1) * 3 + 2]);
glVertex3f(vertices[(v2 - 1) * 3], vertices[(v2 - 1) * 3 + 1], vertices[(v2 - 1) * 3 + 2]);
glVertex3f(vertices[(v3 - 1) * 3], vertices[(v3 - 1) * 3 + 1], vertices[(v3 - 1) * 3 + 2]);
// 将三个纹理坐标加入OpenGL的纹理坐标数组中
glTexCoord2f(texcoords[(t1 - 1) * 2], texcoords[(t1 - 1) * 2 + 1]);
glTexCoord2f(texcoords[(t2 - 1) * 2], texcoords[(t2 - 1) * 2 + 1]);
glTexCoord2f(texcoords[(t3 - 1) * 2], texcoords[(t3 - 1) * 2 + 1]);
}
}
ifs.close();
return true;
}
void display() {
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);
glMatrixMode(GL_MODELVIEW);
glLoadIdentity();
gluLookAt(0.0, 0.0, 5.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0);
// 加载纹理
GLuint texture;
glGenTextures(1, &texture);
glBindTexture(GL_TEXTURE_2D, texture);
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_REPEAT);
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_REPEAT);
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR);
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR);
int width, height;
unsigned char* image = SOIL_load_image("texture.jpg", &width, &height, 0, SOIL_LOAD_RGB);
if (!image) {
cerr << "Error: cannot load texture file" << endl;
exit(1);
}
glTexImage2D(GL_TEXTURE_2D, 0, GL_RGB, width, height, 0, GL_RGB, GL_UNSIGNED_BYTE, image);
SOIL_free_image_data(image);
// 绘制模型
glBegin(GL_TRIANGLES);
glColor3f(1.0, 1.0, 1.0);
if (!loadOBJ("stdT.obj")) {
cerr << "Error: cannot load OBJ file" << endl;
exit(1);
}
glEnd();
glFlush();
}
void reshape(int w, int h) {
glViewport(0, 0, w, h);
glMatrixMode(GL_PROJECTION);
glLoadIdentity();
gluPerspective(60.0, (GLfloat)w / (GLfloat)h, 1.0, 100.0);
}
int main(int argc, char** argv) {
glutInit(&argc, argv);
glutInitDisplayMode(GLUT_SINGLE | GLUT_RGB | GLUT_DEPTH);
glutInitWindowSize(500, 500);
glutCreateWindow("OBJ Model Viewer");
glutDisplayFunc(display);
glutReshapeFunc(reshape);
glEnable(GL_DEPTH_TEST);
glutMainLoop();
return 0;
}
```
请确保在编译和运行代码之前,已经安装了OpenGL和SOIL(Simple OpenGL Image Library)库。此外,还需要将纹理文件texture.jpg和模型文件stdT.obj放在同一目录下。
traceH = hilbert(trace); clear trace traceH = traceH.*conj(traceH(:,1)); GAPL = 0.4; moveN = round(8/GAPL); [output] = moveVectorMean2(traceH,moveN); traceH=output; clear output amp = abs(traceH); [M,N] = size(amp); gapD = round(4/GAPL); [output] = spacephsed(traceH,gapD); clear traceH phaOutput = angle(output); clear output phaseUnw(:,:) = unwrap(squeeze(phaOutput(:,:))')'; clear phaOutput [~,axisTp,outputtp] = denoiseFunc(5,500,phaseUnw,fd,fs,GAPL); % 慢轴,快轴 clear phaseUnw [~,axisT,outputint] = denoiseFunc(5,490,amp,fd,fs,GAPL); % 慢轴,快轴 clear amp for i=1:1:floor(length(outputtp(:,1))/10) phase(i,:)=mean(outputtp((i-1)*10+1:i*10,:)); end clear outputtp for i=1:1:floor(length(outputint(:,1))/10) intensity(i,:)=mean(outputint((i-1)*10+1:i*10,:)); end clear outputint figure for i=1:1:22 plot(axisTp,phase(i,:)/10*10+i*4); hold on end hold off figure for i=1:1:22 plot(axisT,intensity(i,:)/10*10+i*4); hold on end hold off % for i=1:1:floor(length(intensity(1,:))/4000) % stdp(:,i)=var(phase(:,(i-1)*4000+1:i*4000),0,2); % stdt(:,i)=var(intensity(:,(i-1)*4000+1:i*4000),0,2); % end % for i=1:1:29 % positionp(i)=find(stdp(:,i)==max(stdp(:,i))); % positioni(i)=find(stdt(:,i)==max(stdt(:,i))); % end %%将数据切为不同帧 for i=1:1:10 trandatap(:,:,i)=phase(1:8,(i-1)*4000+1:(i)*4000); trandatai(:,:,i)=intensity(1:8,(i-1)*4000+1:(i)*4000); end %%删除信噪比较差的数据。(先把数据图像画图,然后筛选) trainphase=trandatap; traintensity=trandatai; trainphase(:,:,27:29)=[]; trainphase(:,:,1:2)=[]; trainphase(:,:,16)=[]; trainphase(:,:,14)=[]; trainphase(:,:,10)=[]; trainphase(:,:,7:8)=[]; trainphase(:,:,4)=[]; trainphase(:,:,1)=[]; traintensity(:,:,27:29)=[]; traintensity(:,:,1:2)=[]; traintensity(:,:,16)=[]; traintensity(:,:,14)=[]; traintensity(:,:,10)=[]; traintensity(:,:,7:8)=[]; traintensity(:,:,4)=[]; traintensity(:,:,1)=[];
traceH = hilbert(trace);
clear trace
traceH = traceH.*conj(traceH(:,1));
GAPL = 0.4;
moveN = round(8/GAPL);
[output] = moveVectorMean2(traceH,moveN);
traceH=output;
clear output
amp = abs(traceH);
[M,N] = size(amp);
gapD = round(4/GAPL);
[output] = spacephsed(traceH,gapD);
clear traceH
phaOutput = angle(output);
clear output
phaseUnw(:,:) = unwrap(squeeze(phaOutput(:,:))')';
clear phaOutput
[~,axisTp,outputtp] = denoiseFunc(5,500,phaseUnw,fd,fs,GAPL); % Slow axis, Fast axis
clear phaseUnw
[~,axisT,outputint] = denoiseFunc(5,490,amp,fd,fs,GAPL); % Slow axis, Fast axis
clear amp
for i=1:1:floor(length(outputtp(:,1))/10)
phase(i,:)=mean(outputtp((i-1)*10+1:i*10,:));
end
clear outputtp
for i=1:1:floor(length(outputint(:,1))/10)
intensity(i,:)=mean(outputint((i-1)*10+1:i*10,:));
end
clear outputint
figure
for i=1:1:22
plot(axisTp,phase(i,:)/10*10+i*4);
hold on
end
hold off
figure
for i=1:1:22
plot(axisT,intensity(i,:)/10*10+i*4);
hold on
end
hold off
% for i=1:1:floor(length(intensity(1,:))/4000)
% stdp(:,i)=var(phase(:,(i-1)*4000+1:i*4000),0,2);
% stdt(:,i)=var(intensity(:,(i-1)*4000+1:i*4000),0,2);
% end
% for i=1:1:29
% positionp(i)=find(stdp(:,i)==max(stdp(:,i)));
% positioni(i)=find(stdt(:,i)==max(stdt(:,i)));
% end
%% Split the data into different frames
for i=1:1:10
trandatap(:,:,i)=phase(1:8,(i-1)*4000+1:(i)*4000);
trandatai(:,:,i)=intensity(1:8,(i-1)*4000+1:(i)*4000);
end
%% Remove data with poor signal-to-noise ratio (plot the data and then select)
trainphase=trandatap;
traintensity=trandatai;
trainphase(:,:,27:29)=[];
trainphase(:,:,1:2)=[];
trainphase(:,:,16)=[];
trainphase(:,:,14)=[];
trainphase(:,:,10)=[];
trainphase(:,:,7:8)=[];
trainphase(:,:,4)=[];
trainphase(:,:,1)=[];
traintensity(:,:,27:29)=[];
traintensity(:,:,1:2)=[];
traintensity(:,:,16)=[];
traintensity(:,:,14)=[];
traintensity(:,:,10)=[];
traintensity(:,:,7:8)=[];
traintensity(:,:,4)=[];
traintensity(:,:,1)=[];