ios 截屏方法

1.
        UIGraphicsBeginImageContextWithOptions(pageView.page.bounds.size, YES, zoomScale);
        [pageView.page.layer renderInContext:UIGraphicsGetCurrentContext()];
        UIImage *uiImage = UIGraphicsGetImageFromCurrentImageContext();
        UIGraphicsEndImageContext();
2.
- (UIImage *) glToUIImage {
       DWScrollView *pageView = [self getActivePageView];
       pageView.page.backgroundColor = [UIColor clearColor];
       // self.backgroundColor=[UIColor clearColor];
       NSInteger myDataLength = 320 * 308 * 4;
    
        // allocate array and read pixels into it.
        GLubyte *buffer = (GLubyte *) malloc(myDataLength);
        glReadPixels(0, 0, 320, 308, GL_RGBA, GL_UNSIGNED_BYTE, buffer);
    
        // gl renders "upside down" so swap top to bottom into new array.
        // there's gotta be a better way, but this works.
        GLubyte *buffer2 = (GLubyte *) malloc(myDataLength);
    
        for(int y = 0; y <308; y++)
        {
            for(int x = 0; x <320 * 4; x++)
            {
                if(buffer[y* 4 * 320 + x]==0)
                    buffer2[(307 - y) * 320 * 4 + x]=1;
                else
                    buffer2[(307 - y) * 320 * 4 + x] = buffer[y* 4 * 320 + x];
            }
        }
    
    // make data provider with data.
    CGDataProviderRef provider = CGDataProviderCreateWithData(NULL, buffer2, myDataLength, NULL);
    
    // prep the ingredients
    int bitsPerComponent = 8;
    int bitsPerPixel = 32;
    int bytesPerRow = 4 * 320;
    CGColorSpaceRef colorSpaceRef = CGColorSpaceCreateDeviceRGB();
    CGBitmapInfo bitmapInfo = kCGBitmapByteOrderDefault;
    CGColorRenderingIntent renderingIntent = kCGRenderingIntentDefault;
    
    // make the cgimage
    CGImageRef
imageRef = CGImageCreate(320, 308, bitsPerComponent, bitsPerPixel, 
bytesPerRow, colorSpaceRef, bitmapInfo, provider, NULL, NO, 
renderingIntent);
    
    // then make the uiimage from that
    UIImage *myImage = [UIImage imageWithCGImage:imageRef];
    UIImageWriteToSavedPhotosAlbum(myImage, nil, nil, nil);
    return myImage;
}

3.
// get screen
- (void)grabScreen {
    unsigned char buffer[320*480*4];
    glReadPixels(0,0,320,480,GL_RGBA,GL_UNSIGNED_BYTE,&buffer);
    
    CGDataProviderRef ref = CGDataProviderCreateWithData(NULL, &buffer, 320*480*4, NULL);
    CGImageRef
iref = 
CGImageCreate(320,480,8,32,320*4,CGColorSpaceCreateDeviceRGB(),kCGBitmapByteOrderDefault,ref,NULL,true,kCGRenderingIntentDefault);
    CGFloat width = CGImageGetWidth(iref);
    CGFloat height = CGImageGetHeight(iref);
    size_t length = width*height*4;
    uint32_t *pixels = (uint32_t *)malloc(length);
    CGContextRef
context = CGBitmapContextCreate(pixels, width, height, 8, 320*4, 
CGImageGetColorSpace(iref), kCGImageAlphaLast | 
kCGBitmapByteOrder32Big);
    CGContextTranslateCTM(context, 0.0, height);
    CGContextScaleCTM(context, 1.0, -1.0);
    CGContextDrawImage(context, CGRectMake(0.0, 0.0, width, height), iref);
    CGImageRef outputRef = CGBitmapContextCreateImage(context);
    UIImage *outputImage = [UIImage imageWithCGImage:outputRef];
    
    UIImageWriteToSavedPhotosAlbum(outputImage, nil, nil, nil); 
    
    CGContextRelease(context);
    CGImageRelease(iref);
    CGDataProviderRelease(ref);

4.
CGImageRef UIGetScreenImage();
void SaveScreenImage(NSString *path)
{
    NSAutoreleasePool *pool = [[NSAutoreleasePool alloc] init];
    CGImageRef cgImage = UIGetScreenImage();
        void *imageBytes = NULL;
        if (cgImage == NULL) {
                CGColorSpaceRef colorspace = CGColorSpaceCreateDeviceRGB();
                imageBytes = malloc(320 * 480 * 4);
                CGContextRef
context = CGBitmapContextCreate(imageBytes, 320, 480, 8, 320 * 4, 
colorspace, kCGImageAlphaNoneSkipFirst | kCGBitmapByteOrder32Big);
                CGColorSpaceRelease(colorspace);
                for (UIWindow *window in [[UIApplication sharedApplication] windows]) {
                        CGRect bounds = [window bounds];
                        CALayer *layer = [window layer];
                        CGContextSaveGState(context);
                        if ([layer contentsAreFlipped]) {
                                CGContextTranslateCTM(context, 0.0f, bounds.size.height);
                                CGContextScaleCTM(context, 1.0f, -1.0f);
                        }
                        [layer renderInContext:(CGContextRef)context];
                        CGContextRestoreGState(context);
                }
                cgImage = CGBitmapContextCreateImage(context);
                CGContextRelease(context);
        }
    NSData *pngData = UIImagePNGRepresentation([UIImage imageWithCGImage:cgImage]);
    CGImageRelease(cgImage);
        if (imageBytes)
                free(imageBytes);
    [pngData writeToFile:path atomically:YES];
    [pool release];
}

5.
  + (UIImage *)imageWithScreenContents
{
     CGImageRef cgScreen = UIGetScreenImage();
     if (cgScreen) {
         UIImage *result = [UIImage imageWithCGImage:cgScreen];
         CGImageRelease(cgScreen);
         return result;
     }
     return nil;



在程序中如何把两张图片合成为一张图片   
- (UIImage *)addImage:(UIImage *)image1 toImage:(UIImage *)image2 {  
    UIGraphicsBeginImageContext(image1.size);  

    // Draw image1  
    [image1 drawInRect:CGRectMake(0, 0, image1.size.width, image1.size.height)];  

    // Draw image2  
    [image2 drawInRect:CGRectMake(0, 0, image2.size.width, image2.size.height)];  

    UIImage *resultingImage = UIGraphicsGetImageFromCurrentImageContext();  

    UIGraphicsEndImageContext();  

    return resultingImage;  

}


原文:http://www.cnblogs.com/pengyingh/articles/2466955.html

(Kriging_NSGA2)克里金模型结合多目标遗传算法求最优因变量及对应的最佳自变量组合研究(Matlab代码实现)内容概要:本文介绍了克里金模型(Kriging)与多目标遗传算法NSGA-II相结合的方法,用于求解最优因变量及其对应的最佳自变量组合,并提供了完整的Matlab代码实现。该方法首先利用克里金模型构建高精度的代理模型,逼近复杂的非线性系统响应,减少计算成本;随后结合NSGA-II算法进行多目标优化,搜索帕累托前沿解集,从而获得多个最优折衷方案。文中详细阐述了代理模型构建、算法集成流程及参数设置,适用于工程设计、参数反演等复杂优化问题。此外,文档还展示了该方法在SCI一区论文中的复现应用,体现了其科学性与实用性。; 适合人群:具备一定Matlab编程基础,熟悉优化算法和数值建模的研究生、科研人员及工程技术人员,尤其适合从事仿真优化、实验设计、代理模型研究的相关领域工作者。; 使用场景及目标:①解决高计算成本的多目标优化问题,通过代理模型降低仿真次数;②在无法解析求导或函数高度非线性的情况下寻找最优变量组合;③复现SCI高水平论文中的优化方法,提升科研可信度与效率;④应用于工程设计、能源系统调度、智能制造等需参数优化的实际场景。; 阅读建议:建议读者结合提供的Matlab代码逐段理解算法实现过程,重点关注克里金模型的构建步骤与NSGA-II的集成方式,建议自行调整测试函数或实际案例验证算法性能,并配合YALMIP等工具包扩展优化求解能力。
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