本文主要是介绍使用CV_CUDA对图像进行Crop和Resize，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
可能是我的使用方式不对，直接调用C++ OpenCV api比用CV_CUDA快很多。
/** SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.* SPDX-License-Identifier: Apache-2.0** Licensed under the Apache License, Version 2.0 (the "License");* you may not use this file except in compliance with the License.* You may obtain a copy of the License at** http://www.apache.org/licenses/LICENSE-2.0** Unless required by applicable law or agreed to in writing, software* distributed under the License is distributed on an "AS IS" BASIS,* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.* See the License for the specific language governing permissions and* limitations under the License.*/#include <common/NvDecoder.h>
#include <common/TestUtils.h>
#include <cuda_runtime_api.h>
#include <cvcuda/OpCustomCrop.hpp>
#include <cvcuda/OpResize.hpp>
#include <getopt.h>
#include <cmath>
#include <opencv2/opencv.hpp>
#include <nvcv/Image.hpp>
#include <nvcv/Tensor.hpp>#include <chrono>
using namespace std;
using namespace chrono;
/*** @brief Crop and Resize sample app.** The Crop and Resize is a simple pipeline which demonstrates usage of* CVCuda Tensor along with a few operators.** Input Batch Tensor -> Crop -> Resize -> WriteImage*//*** @brief Utility to show usage of sample app***/
void showUsage()
{std::cout << "usage: ./nvcv_cropandresize_app -i <image file path or image directory -b <batch size>" << std::endl;
}/*** @brief Utility to parse the command line arguments***/
int ParseArgs(int argc, char *argv[], std::string &imagePath, uint32_t &batchSize)
{static struct option long_options[] = {{ "help", no_argument, 0, 'h'},{"imagePath", required_argument, 0, 'i'},{ "batch", required_argument, 0, 'b'},{ 0, 0, 0, 0}};int long_index = 0;int opt = 0;while ((opt = getopt_long(argc, argv, "hi:b:", long_options, &long_index)) != -1){switch (opt){case 'h':showUsage();return -1;break;case 'i':imagePath = optarg;break;case 'b':batchSize = std::stoi(optarg);break;case ':':showUsage();return -1;default:break;}}std::ifstream imageFile(imagePath);if (!imageFile.good()){showUsage();std::cerr << "Image path '" + imagePath + "' does not exist\n";return -1;}return 0;
}int main(int argc, char *argv[])
{// Default parametersstd::string imagePath = "test.jpg";uint32_t batchSize = 1;cv::Mat imgMat = cv::imread(imagePath);// Parse the command line paramaters to override the default parametersint retval = ParseArgs(argc, argv, imagePath, batchSize);if (retval != 0){return retval;}// NvJpeg is used to decode the images to the color format required.// Since we need a contiguous buffer for batched input, a buffer is// preallocated based on the maximum image dimensions and batch size// for NvJpeg to write into.// Note : The maximum input image dimensions needs to be updated in case// of testing with different test imagesint maxImageWidth = 1920;int maxImageHeight = 1080;int maxChannels = 3;// tag: Create the cuda streamcudaStream_t stream;CHECK_CUDA_ERROR(cudaStreamCreate(&stream));// tag: Allocate input tensor// Allocating memory for RGBI input image batch of uint8_t data type// without padding since NvDecode utility currently doesnt support// Padded buffers.nvcv::TensorDataStridedCuda::Buffer inBuf;inBuf.strides[3] = sizeof(uint8_t);inBuf.strides[2] = maxChannels * inBuf.strides[3];inBuf.strides[1] = maxImageWidth * inBuf.strides[2];inBuf.strides[0] = maxImageHeight * inBuf.strides[1];CHECK_CUDA_ERROR(cudaMallocAsync(&inBuf.basePtr, batchSize * inBuf.strides[0], stream));// tag: Tensor Requirements// Calculate the requirements for the RGBI uint8_t Tensor which include// pitch bytes, alignment, shape and tensor layoutnvcv::Tensor::Requirements inReqs= nvcv::Tensor::CalcRequirements(batchSize, {maxImageWidth, maxImageHeight}, nvcv::FMT_RGB8);// Create a tensor buffer to store the data pointer and pitch bytes for each planenvcv::TensorDataStridedCuda inData(nvcv::TensorShape{inReqs.shape, inReqs.rank, inReqs.layout},nvcv::DataType{inReqs.dtype}, inBuf);// TensorWrapData allows for interoperation of external tensor representations with CVCUDA Tensor.nvcv::Tensor inTensor = nvcv::TensorWrapData(inData);// tag: Image Loading// NvJpeg is used to load the images to create a batched input device buffer.uint8_t *gpuInput = reinterpret_cast<uint8_t *>(inBuf.basePtr);CHECK_CUDA_ERROR(cudaMemcpyAsync(gpuInput, imgMat.data, inBuf.strides[0], cudaMemcpyHostToDevice));// The total images is set to the same value as batch size for testinguint32_t totalImages = batchSize;// Format in which the decoded output will be saved//nvjpegOutputFormat_t outputFormat = NVJPEG_OUTPUT_RGBI;//NvDecode(imagePath, batchSize, totalImages, outputFormat, gpuInput);// tag: The input buffer is now ready to be used by the operators// Set parameters for Crop and Resize// ROI dimensions to crop in the input imageint cropX = 150;int cropY = 50;int cropWidth = 800;int cropHeight = 1000;// Set the resize dimensionsint resizeWidth = 1600;int resizeHeight = 2000;// Initialize the CVCUDA ROI structNVCVRectI crpRect = {cropX, cropY, cropWidth, cropHeight};cv::Rect Rect(cropX, cropY, cropWidth, cropHeight);auto t1=std::chrono::steady_clock::now();// 裁剪图像cv::Mat cropImg = imgMat(Rect);// 调整图像大小cv::resize(cropImg, cropImg, cv::Size(resizeWidth, resizeHeight));auto t2=std::chrono::steady_clock::now();double dr_ms=std::chrono::duration<double,std::milli>(t2-t1).count();std::cout << "opencv costs: " << dr_ms << "ms" << std::endl;// tag: Allocate Tensors for Crop and Resize// Create a CVCUDA Tensor based on the crop window size.nvcv::Tensor cropTensor(batchSize, {cropWidth, cropHeight}, nvcv::FMT_RGB8);// Create a CVCUDA Tensor based on resize dimensionsnvcv::Tensor resizedTensor(batchSize, {resizeWidth, resizeHeight}, nvcv::FMT_RGB8);// tag: Initialize operators for Crop and Resizecvcuda::CustomCrop cropOp;cvcuda::Resize resizeOp;cudaEvent_t start, stop;cudaEventCreate(&start);cudaEventCreate(&stop);cudaEventRecord(start);// tag: Executes the CustomCrop operation on the given cuda streamcropOp(stream, inTensor, cropTensor, crpRect);// Resize operator can now be enqueued into the same streamresizeOp(stream, cropTensor, resizedTensor, NVCV_INTERP_LINEAR);// tag: Profile sectioncudaEventRecord(stop);cudaEventSynchronize(stop);float operatorms = 0;cudaEventElapsedTime(&operatorms, start, stop);std::cout << "Time for Crop and Resize : " << operatorms << " ms" << std::endl;// tag: Copy the buffer to CPU and write resized image into .bmp fileWriteRGBITensor(resizedTensor, stream);// tag: Clean upCHECK_CUDA_ERROR(cudaStreamDestroy(stream));// tag: End of Sample
}输出
opencv costs: 3.16336ms
Time for Crop and Resize : 200.148 ms
Writing to ./cvcudatest_0.jpg 4800 1600 2000这篇关于使用CV_CUDA对图像进行Crop和Resize的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
