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opencl+opencv实现sobel算法
这几天在看opencl编程指南。照着书中的样例实现了sobel算法:
1.结合opencv读取图像,保存到缓冲区中。
2.编写和编译内核。并保存显示处理后的结果。
内核:
const sampler_t sampler = CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST; kernel void sobel_rgb(read_only image2d_t src,write_only image2d_t dst) { int x = (int)get_global_id(0); int y = (int)get_global_id(1); if (x >= get_image_width(src) || y >= get_image_height(src)) return; float4 p00 = read_imagef(src, sampler, (int2)(x - 1, y - 1)); float4 p10 = read_imagef(src, sampler, (int2)(x, y - 1)); float4 p20 = read_imagef(src, sampler, (int2)(x + 1, y - 1)); float4 p01 = read_imagef(src, sampler, (int2)(x - 1, y)); float4 p21 = read_imagef(src, sampler, (int2)(x + 1, y)); float4 p02 = read_imagef(src, sampler, (int2)(x - 1, y + 1)); float4 p12 = read_imagef(src, sampler, (int2)(x, y + 1)); float4 p22 = read_imagef(src, sampler, (int2)(x + 1, y + 1)); float3 gx = -p00.xyz + p20.xyz + 2.0*(p21.xyz - p01.xyz) - p02.xyz + p22.xyz; float3 gy = -p00.xyz + p02.xyz + 2.0*(p21.xyz - p10.xyz) - p20.xyz + p22.xyz; float3 g = native_sqrt(gx*gx + gy*gy); write_imagef(dst,(int2)(x,y),(float4)(g.x,g.y,g.z,1.0f)); } // TODO: Add OpenCL kernel code here.
c++源代码:
// // Book: OpenCL(R) Programming Guide // Authors: Aaftab Munshi, Benedict Gaster, Timothy Mattson, James Fung, Dan Ginsburg // ISBN-10: 0-321-74964-2 // ISBN-13: 978-0-321-74964-2 // Publisher: Addison-Wesley Professional // URLs: http://safari.informit.com/9780132488006/ // http://www.openclprogrammingguide.com // // ImageFilter2D.cpp // // This example demonstrates performing gaussian filtering on a 2D image using // OpenCL // // Requires FreeImage library for image I/O: // http://freeimage.sourceforge.net/ #include <iostream> #include <fstream> #include <sstream> #include <string.h> #include <opencv.hpp> #ifdef __APPLE__ #include <OpenCL/cl.h> #else #include <CL/cl.h> #endif #include "FreeImage.h" /// // Create an OpenCL context on the first available platform using // either a GPU or CPU depending on what is available. // cl_context CreateContext() { cl_int errNum; cl_uint numPlatforms; cl_platform_id firstPlatformId; cl_context context = NULL; // First, select an OpenCL platform to run on. For this example, we // simply choose the first available platform. Normally, you would // query for all available platforms and select the most appropriate one. errNum = clGetPlatformIDs(1, &firstPlatformId, &numPlatforms); if (errNum != CL_SUCCESS || numPlatforms <= 0) { std::cerr << "Failed to find any OpenCL platforms." << std::endl; return NULL; } // Next, create an OpenCL context on the platform. Attempt to // create a GPU-based context, and if that fails, try to create // a CPU-based context. cl_context_properties contextProperties[] = { CL_CONTEXT_PLATFORM, (cl_context_properties)firstPlatformId, 0 }; context = clCreateContextFromType(contextProperties, CL_DEVICE_TYPE_GPU, NULL, NULL, &errNum); if (errNum != CL_SUCCESS) { std::cout << "Could not create GPU context, trying CPU..." << std::endl; context = clCreateContextFromType(contextProperties, CL_DEVICE_TYPE_CPU, NULL, NULL, &errNum); if (errNum != CL_SUCCESS) { std::cerr << "Failed to create an OpenCL GPU or CPU context." << std::endl; return NULL; } } return context; } /// // Create a command queue on the first device available on the // context // cl_command_queue CreateCommandQueue(cl_context context, cl_device_id *device) { cl_int errNum; cl_device_id *devices; cl_command_queue commandQueue = NULL; size_t deviceBufferSize = -1; // First get the size of the devices buffer errNum = clGetContextInfo(context, CL_CONTEXT_DEVICES, 0, NULL, &deviceBufferSize); if (errNum != CL_SUCCESS) { std::cerr << "Failed call to clGetContextInfo(...,GL_CONTEXT_DEVICES,...)"; return NULL; } if (deviceBufferSize <= 0) { std::cerr << "No devices available."; return NULL; } // Allocate memory for the devices buffer devices = new cl_device_id[deviceBufferSize / sizeof(cl_device_id)]; errNum = clGetContextInfo(context, CL_CONTEXT_DEVICES, deviceBufferSize, devices, NULL); if (errNum != CL_SUCCESS) { std::cerr << "Failed to get device IDs"; return NULL; } // In this example, we just choose the first available device. In a // real program, you would likely use all available devices or choose // the highest performance device based on OpenCL device queries commandQueue = clCreateCommandQueue(context, devices[0], 0, NULL); if (commandQueue == NULL) { std::cerr << "Failed to create commandQueue for device 0"; return NULL; } *device = devices[0]; delete[] devices; return commandQueue; } /// // Create an OpenCL program from the kernel source file // cl_program CreateProgram(cl_context context, cl_device_id device, const char* fileName) { cl_int errNum; cl_program program; std::ifstream kernelFile(fileName, std::ios::in); if (!kernelFile.is_open()) { std::cerr << "Failed to open file for reading: " << fileName << std::endl; return NULL; } std::ostringstream oss; oss << kernelFile.rdbuf(); std::string srcStdStr = oss.str(); const char *srcStr = srcStdStr.c_str(); program = clCreateProgramWithSource(context, 1, (const char**)&srcStr, NULL, NULL); if (program == NULL) { std::cerr << "Failed to create CL program from source." << std::endl; return NULL; } errNum = clBuildProgram(program, 0, NULL, NULL, NULL, NULL); if (errNum != CL_SUCCESS) { // Determine the reason for the error char buildLog[16384]; clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_LOG, sizeof(buildLog), buildLog, NULL); std::cerr << "Error in kernel: " << std::endl; std::cerr << buildLog; clReleaseProgram(program); return NULL; } return program; } /// // Cleanup any created OpenCL resources // void Cleanup(cl_context context, cl_command_queue commandQueue, cl_program program, cl_kernel kernel, cl_mem imageObjects[2], cl_sampler sampler) { for (int i = 0; i < 2; i++) { if (imageObjects[i] != 0) clReleaseMemObject(imageObjects[i]); } if (commandQueue != 0) clReleaseCommandQueue(commandQueue); if (kernel != 0) clReleaseKernel(kernel); if (program != 0) clReleaseProgram(program); if (sampler != 0) clReleaseSampler(sampler); if (context != 0) clReleaseContext(context); } /// // Load an image using the FreeImage library and create an OpenCL // image out of it // cl_mem LoadImage(cl_context context, char *fileName, int &width, int &height) { //FREE_IMAGE_FORMAT format = FreeImage_GetFileType(fileName, 0); //FIBITMAP* image = FreeImage_Load(format, fileName); //// Convert to 32-bit image //FIBITMAP* temp = image; //image = FreeImage_ConvertTo32Bits(image); //FreeImage_Unload(temp); //width = FreeImage_GetWidth(image); //height = FreeImage_GetHeight(image); /*char *buffer = new char[width * height * 4]; memcpy(buffer, FreeImage_GetBits(image), width * height * 4); FreeImage_Unload(image);*/ cv::Mat image1 = cv::imread(fileName); width = image1.cols; height = image1.rows; char *buffer = new char[width * height * 4]; int w = 0; for (int v = height - 1; v >= 0; v--) { for (int u = 0; u <width; u++) { buffer[w++] = image1.at<cv::Vec3b>(v, u)[0]; buffer[w++] = image1.at<cv::Vec3b>(v, u)[1]; buffer[w++] = image1.at<cv::Vec3b>(v, u)[2]; w++; } } // Create OpenCL image cl_image_format clImageFormat; clImageFormat.image_channel_order = CL_RGBA; clImageFormat.image_channel_data_type = CL_UNORM_INT8; cl_int errNum; cl_mem clImage; clImage = clCreateImage2D(context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR, &clImageFormat, width, height, 0, buffer, &errNum); if (errNum != CL_SUCCESS) { std::cerr << "Error creating CL image object" << std::endl; return 0; } return clImage; } /// // Save an image using the FreeImage library // bool SaveImage(char *fileName, char *buffer, int width, int height) { FREE_IMAGE_FORMAT format = FreeImage_GetFIFFromFilename(fileName); FIBITMAP *image = FreeImage_ConvertFromRawBits((BYTE*)buffer, width, height, width * 4, 32, 0xFF000000, 0x00FF0000, 0x0000FF00); return FreeImage_Save(format, image, fileName); } /// // Round up to the nearest multiple of the group size // size_t RoundUp(int groupSize, int globalSize) { int r = globalSize % groupSize; if (r == 0) { return globalSize; } else { return globalSize + groupSize - r; } } /// // main() for HelloBinaryWorld example // int main(int argc, char** argv) { cl_context context = 0; cl_command_queue commandQueue = 0; cl_program program = 0; cl_device_id device = 0; cl_kernel kernel = 0; cl_mem imageObjects[2] = { 0, 0 }; cl_sampler sampler = 0; cl_int errNum; /*if (argc != 3) { std::cerr << "USAGE: " << argv[0] << " <inputImageFile> <outputImageFiles>" << std::endl; return 1; }*/ // Create an OpenCL context on first available platform context = CreateContext(); if (context == NULL) { std::cerr << "Failed to create OpenCL context." << std::endl; return 1; } // Create a command-queue on the first device available // on the created context commandQueue = CreateCommandQueue(context, &device); if (commandQueue == NULL) { Cleanup(context, commandQueue, program, kernel, imageObjects, sampler); return 1; } // Make sure the device supports images, otherwise exit cl_bool imageSupport = CL_FALSE; clGetDeviceInfo(device, CL_DEVICE_IMAGE_SUPPORT, sizeof(cl_bool), &imageSupport, NULL); if (imageSupport != CL_TRUE) { std::cerr << "OpenCL device does not support images." << std::endl; Cleanup(context, commandQueue, program, kernel, imageObjects, sampler); return 1; } // Load input image from file and load it into // an OpenCL image object int width, height; char *src0 = "C:/Users/jiang/Desktop/image/tu1.jpg"; imageObjects[0] = LoadImage(context, src0, width, height); if (imageObjects[0] == 0) { std::cerr << "Error loading: " << std::string(src0) << std::endl; Cleanup(context, commandQueue, program, kernel, imageObjects, sampler); return 1; } // Create ouput image object cl_image_format clImageFormat; clImageFormat.image_channel_order = CL_RGBA; clImageFormat.image_channel_data_type = CL_UNORM_INT8; imageObjects[1] = clCreateImage2D(context, CL_MEM_WRITE_ONLY, &clImageFormat, width, height, 0, NULL, &errNum); if (errNum != CL_SUCCESS) { std::cerr << "Error creating CL output image object." << std::endl; Cleanup(context, commandQueue, program, kernel, imageObjects, sampler); return 1; } // Create sampler for sampling image object sampler = clCreateSampler(context, CL_FALSE, // Non-normalized coordinates CL_ADDRESS_CLAMP_TO_EDGE, CL_FILTER_NEAREST, &errNum); if (errNum != CL_SUCCESS) { std::cerr << "Error creating CL sampler object." << std::endl; Cleanup(context, commandQueue, program, kernel, imageObjects, sampler); return 1; } // Create OpenCL program //program = CreateProgram(context, device, "ImageFilter2D.cl"); program = CreateProgram(context, device, "Sobel.cl"); if (program == NULL) { Cleanup(context, commandQueue, program, kernel, imageObjects, sampler); return 1; } // Create OpenCL kernel kernel = clCreateKernel(program, "sobel_rgb", NULL); if (kernel == NULL) { std::cerr << "Failed to create kernel" << std::endl; Cleanup(context, commandQueue, program, kernel, imageObjects, sampler); return 1; } // Set the kernel arguments errNum = clSetKernelArg(kernel, 0, sizeof(cl_mem), &imageObjects[0]); errNum |= clSetKernelArg(kernel, 1, sizeof(cl_mem), &imageObjects[1]); /*errNum |= clSetKernelArg(kernel, 2, sizeof(cl_sampler), &sampler); errNum |= clSetKernelArg(kernel, 3, sizeof(cl_int), &width); errNum |= clSetKernelArg(kernel, 4, sizeof(cl_int), &height);*/ if (errNum != CL_SUCCESS) { std::cerr << "Error setting kernel arguments." << std::endl; Cleanup(context, commandQueue, program, kernel, imageObjects, sampler); return 1; } size_t localWorkSize[2] = { 16, 16 }; size_t globalWorkSize[2] = { RoundUp(localWorkSize[0], width), RoundUp(localWorkSize[1], height) }; // Queue the kernel up for execution errNum = clEnqueueNDRangeKernel(commandQueue, kernel, 2, NULL, globalWorkSize, localWorkSize, 0, NULL, NULL); if (errNum != CL_SUCCESS) { std::cerr << "Error queuing kernel for execution." << std::endl; Cleanup(context, commandQueue, program, kernel, imageObjects, sampler); return 1; } // Read the output buffer back to the Host char *buffer = new char[width * height * 4]; size_t origin[3] = { 0, 0, 0 }; size_t region[3] = { width, height, 1 }; errNum = clEnqueueReadImage(commandQueue, imageObjects[1], CL_TRUE, origin, region, 0, 0, buffer, 0, NULL, NULL); if (errNum != CL_SUCCESS) { std::cerr << "Error reading result buffer." << std::endl; Cleanup(context, commandQueue, program, kernel, imageObjects, sampler); return 1; } std::cout << std::endl; std::cout << "Executed program succesfully." << std::endl; //memset(buffer, 0xff, width * height * 4); // Save the image out to disk char *saveImage = "C:/Users/jiang/Desktop/image/tu2.jpg"; //std::cout << buffer << std::endl; cv::Mat imageColor = cv::imread(src0); cv::Mat imageColor2; imageColor2.create(imageColor.rows, imageColor.cols, imageColor.type()); int w = 0; for (int v = imageColor2.rows-1; v >=0; v--) { for (int u =0 ; u <imageColor2.cols; u++) { imageColor2.at<cv::Vec3b>(v, u)[0] = buffer[w++]; imageColor2.at<cv::Vec3b>(v, u)[1] = buffer[w++]; imageColor2.at<cv::Vec3b>(v, u)[2] = buffer[w++]; w++; } } cv::imshow("image", imageColor2); cv::imwrite(saveImage, imageColor2); cv::waitKey(0); /*if (!SaveImage(saveImage, buffer, width, height)) { std::cerr << "Error writing output image: " << saveImage<< std::endl; Cleanup(context, commandQueue, program, kernel, imageObjects, sampler); delete[] buffer; return 1; }*/ delete[] buffer; Cleanup(context, commandQueue, program, kernel, imageObjects, sampler); return 0; }
opencl+opencv实现sobel算法
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