Opencv使用cuda实现图像处理

main.py

python 复制代码
import os
import cv2
print(f'OpenCV: {cv2.__version__} for python installed and working')
image = cv2.imread('bus.jpg')
if image is None:
    print("无法加载图像1")
print(cv2.cuda.getCudaEnabledDeviceCount())
cv2.cuda.setDevice(0)
cv2.cuda.printCudaDeviceInfo(0)
image_gpu = cv2.cuda_GpuMat()
image_gpu.upload(image)
screenshot = cv2.cuda.cvtColor(image_gpu, cv2.COLOR_RGB2BGR)
screenshot = cv2.cuda.resize(image_gpu, (400, 400))
result_cpu = screenshot.download()
print("图像宽度: ", image.shape)
print("数据类型:", image.dtype)
print("在CPU下,原始图像格式:", type(image))
print("在GPU下,处理后的图像格式:", type(screenshot))
print("在CPU下,处理后的图像格式:", type(result_cpu))
print("图像宽度: ", result_cpu.shape)
print("数据类型:", result_cpu.dtype)
if result_cpu is None:
    print("无法加载图像2")
else:
    cv2.imshow("Window", result_cpu)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

main.cpp

cpp 复制代码
#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/cudaimgproc.hpp>
int main(int argc, char* argv[])
{
	cv::Mat h_img1 = cv::imread("bus.jpg");
	cv::cuda::GpuMat d_result1, d_result2, d_result3, d_result4, d_img1;
	d_img1.upload(h_img1);
	cv::cuda::cvtColor(d_img1, d_result1, cv::COLOR_BGR2GRAY);
	cv::cuda::cvtColor(d_img1, d_result2, cv::COLOR_BGR2RGB);
	cv::cuda::cvtColor(d_img1, d_result3, cv::COLOR_BGR2HSV);
	cv::cuda::cvtColor(d_img1, d_result4, cv::COLOR_BGR2YCrCb);
	cv::Mat h_result1, h_result2, h_result3, h_result4;
	d_result1.download(h_result1);
	d_result2.download(h_result2);
	d_result3.download(h_result3);
	d_result4.download(h_result4);
	cv::imshow("Gray处理结果:", h_result1);
	cv::imshow("RGB处理结果:", h_result2);
	cv::imshow("HSV处理结果:", h_result3);
	cv::imshow("YCrCb处理结果:", h_result4);
	cv::waitKey(0);
	return 0;
}

CMakeLists.txt

bash 复制代码
cmake_minimum_required(VERSION 3.10)
project(OpenCVCuda LANGUAGES CXX)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set("OpenCV_DIR" "E:\\Opencv gpu\\newbuild\\install")
set(OpenCV_INCLUDE_DIRS ${OpenCV_DIR}\\include)
set(OpenCV_LIB_DEBUG ${OpenCV_DIR}\\x64\\vc17\\lib\\opencv_world470d.lib) 
set(OpenCV_LIB_RELEASE ${OpenCV_DIR}\\x64\\vc17\\lib\\opencv_world470.lib)   
set(CMAKE_CXX_STANDARD 14)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_CUDA_ARCHITECTURES 86)		
find_package(CUDA REQUIRED)			
enable_language(CUDA)  
include_directories(${OpenCV_INCLUDE_DIRS}) 
link_directories(${OpenCV_LIB_DIRS})  
find_package(OpenCV QUIET)	
link_libraries(${OpenCV_LIBS})
add_executable(OpenCVCuda main.cpp)
target_link_libraries(${PROJECT_NAME} 
    $<$<CONFIG:Debug>:${OpenCV_LIB_DEBUG}>
    $<$<CONFIG:Release>:${OpenCV_LIB_RELEASE}>
)
target_compile_features(OpenCVCuda PRIVATE cxx_std_14)
相关推荐
AI科技星7 分钟前
垂直原理:宇宙的沉默法则与万物运动的终极源头
android·服务器·数据结构·数据库·人工智能
开始学AI17 分钟前
ChatClimate:让对话式人工智能立足于气候科学
人工智能
学习路上_write30 分钟前
神经网络初次学习收获
人工智能·python
zstar-_31 分钟前
DeepSeek-OCR可能成为开启新时代的钥匙
人工智能·ocr
墨利昂42 分钟前
自然语言处理NLP的数据预处理:从原始文本到模型输入(MindSpore版)
人工智能·自然语言处理
wb043072011 小时前
如何开发一个 IDEA 插件通过 Ollama 调用大模型为方法生成仙侠风格的注释
人工智能·语言模型·kotlin·intellij-idea
apocalypsx1 小时前
深度学习-卷积神经网络基础
人工智能·深度学习·cnn
Aevget1 小时前
界面控件DevExpress WPF v25.2新功能预览 - 聚焦AI功能提升
人工智能·wpf·界面控件·devexpress·ui开发·.net 10
F_D_Z1 小时前
扩散模型对齐:DMPO 让模型更懂人类偏好
人工智能·扩散模型·kl散度·双阶段训练·散度最小化偏好优化
ezl1fe1 小时前
第一篇:把任意 HTTP API 一键变成 Agent 工具
人工智能·后端·算法