打印python版本及位置import sys # 打印 Python 版本 print(Python Version:, sys.version) # 打印 Python 解释器路径 print(Python Executable Path:, sys.executable)python opencv版本及安装位置import cv2 # 打印 OpenCV 版本 print(OpenCV Version:, cv2.__version__) # 打印 OpenCV 安装路径 print(OpenCV Installation Path:, cv2.__file__)pytorch gpuimport torch # 打印 PyTorch 版本 print(PyTorch Version:, torch.__version__) # 检查 CUDA 是否可用即 GPU 是否可调用 print(CUDA Available:, torch.cuda.is_available()) # 如果可用打印 CUDA 版本和 GPU 数量 if torch.cuda.is_available(): print(CUDA Version:, torch.version.cuda) print(Number of GPUs:, torch.cuda.device_count()) print(Current GPU:, torch.cuda.current_device()) print(GPU Name:, torch.cuda.get_device_name(torch.cuda.current_device())) else: print(CUDA is not available. PyTorch will use CPU.)python opencv读取图片import cv2 # 加载预训练的人脸分类器 face_cascade cv2.CascadeClassifier(cv2.data.haarcascades haarcascade_frontalface_alt2.xml) # 加载图片 image_path d:/2.jpg # 替换为你的图片路径 image cv2.imread(image_path) if image is None: print(无法加载图片请检查路径) exit() # 转为灰度图像 gray cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 检测人脸 faces face_cascade.detectMultiScale(gray, scaleFactor1.1, minNeighbors5, minSize(30, 30)) # 绘制检测到的人脸 for (x, y, w, h) in faces: cv2.rectangle(image, (x, y), (x w, y h), (0, 255, 0), 2) # 显示结果 cv2.imshow(Detected Faces, image) cv2.waitKey(0) cv2.destroyAllWindows()c opencv#includeiostream #include opencv2/core/core.hpp #include opencv2/highgui/highgui.hpp using namespace cv; int main() { // 读入一张图片游戏原画 Mat imgimread(D:\\2.jpg); // 创建一个名为 游戏原画窗口 namedWindow(game); // 在窗口中显示游戏原画 imshow(game,img); // 等待6000 ms后窗口自动关闭 waitKey(0); }c helloworld#includeiostream using namespace std; int main() { cout hello world! endl; cin.get();// return 0; }c语言 helloworld#includestdio.h int main() { printf(ok); getchar(); return 0; }opencv新加坡国旗难度比一般国旗高测试import cv2 import numpy as np import math def draw_star(img, center, size, color, thickness-1): 绘制五角星 points [] for i in range(10): angle math.pi * i / 5.0 if i % 2 0: # 外圈顶点 radius size else: # 内圈顶点 radius size * 0.4 x int(center[0] radius * math.cos(angle - math.pi/2)) y int(center[1] radius * math.sin(angle - math.pi/2)) points.append([x, y]) points np.array(points, dtypenp.int32) cv2.fillPoly(img, [points], color) def draw_singapore_flag(): # 创建画布 (比例 2:3) height, width 400, 600 flag np.zeros((height, width, 3), dtypenp.uint8) # 绘制红色上半部分 flag[0:height//2, :] [0, 0, 255] # BGR格式红色 # 绘制白色下半部分 flag[height//2:height, :] [255, 255, 255] # BGR格式白色 # 根据搜索结果月亮和星星应该在左上角区域 # 调整月亮位置和大小 moon_center_x int(width * 0.20) # 距离左边约15% moon_center_y int(height * 0.25) # 距离顶部约25% moon_center (moon_center_x, moon_center_y) # 调整月亮大小 outer_radius int(height * 0.17) # 约为旗帜高度的8% inner_radius int(height * 0.15) # 约为旗帜高度的6% # 绘制外圆白色 cv2.circle(flag, moon_center, outer_radius, (255, 255, 255), -1) # 绘制内圆红色形成弯月形状 # 内圆稍微向右偏移以形成正确的月牙形 inner_offset int(outer_radius * 0.3) inner_center (moon_center[0] inner_offset, moon_center[1]) cv2.circle(flag, inner_center, inner_radius, (0, 0, 255), -1) # 根据搜索结果五颗星星应该排成圆形 star_circle_center_x int(width * 0.28) # 在月亮右边 star_circle_center_y int(height * 0.25) # 与月亮同一水平线 star_circle_radius int(height * 0.08) # 星星圆形排列的半径 star_size int(height * 0.025) # 星星大小 # 计算五颗星星的位置圆形排列 star_positions [] for i in range(5): angle (i * 2 * math.pi / 5) - math.pi/2 # 从顶部开始逆时针 star_x star_circle_center_x int(star_circle_radius * math.cos(angle)) star_y star_circle_center_y int(star_circle_radius * math.sin(angle)) star_positions.append((star_x, star_y)) # 绘制五颗白色星星 for pos in star_positions: draw_star(flag, pos, star_size, (255, 255, 255)) return flag # 创建并显示新加坡国旗 singapore_flag draw_singapore_flag() # 显示国旗 cv2.imshow(Singapore Flag - Corrected, singapore_flag) cv2.waitKey(0) cv2.destroyAllWindows() # 可选保存图片 cv2.imwrite(singapore_flag_corrected.png, singapore_flag) print(修正后的新加坡国旗已保存为 singapore_flag_corrected.png)open3d python 测试import open3d as o3d import numpy as np # 生成球形点云 num_points 10000 radius 30 theta np.random.uniform(0, np.pi, num_points) # 天顶角 phi np.random.uniform(0, 2*np.pi, num_points) # 方位角 # 将球坐标转换为笛卡尔坐标 x radius * np.sin(theta) * np.cos(phi) y radius * np.sin(theta) * np.sin(phi) z radius * np.cos(theta) # 将坐标组合成点云数据 points np.column_stack((x, y, z)) # 创建点云对象 pcd o3d.geometry.PointCloud() pcd.points o3d.utility.Vector3dVector(points) # 可视化点云 o3d.visualization.draw_geometries([pcd])import open3d as o3d import numpy as np # 读取PCD文件 pcd o3d.io.read_point_cloud(d:/rabbit.pcd) # 可视化点云 o3d.visualization.draw_geometries([pcd]) # 获取点云数据 points np.asarray(pcd.points) # 获取点坐标 colors np.asarray(pcd.colors) # 如果有颜色信息的话 normals np.asarray(pcd.normals) # 如果有法向量信息的话 # 打印基本信息 print(f点的数量: {len(pcd.points)}) print(f点云维度: {points.shape})摄像头或视频import cv2 # 加载预训练的人脸分类器 face_cascade cv2.CascadeClassifier(cv2.data.haarcascades haarcascade_frontalface_alt2.xml) # 打开摄像头 cap cv2.VideoCapture(0) # 0 是默认摄像头若有多个摄像头可以调整为 1, 2 等 if not cap.isOpened(): print(无法打开摄像头) exit() while True: # 读取每一帧 ret, frame cap.read() if not ret: print(无法读取视频帧) break # 转为灰度图像 gray cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 检测人脸 faces face_cascade.detectMultiScale(gray, scaleFactor1.1, minNeighbors5, minSize(30, 30)) # 绘制检测到的人脸 for (x, y, w, h) in faces: cv2.rectangle(frame, (x, y), (x w, y h), (0, 255, 0), 2) # 显示结果 cv2.imshow(Face Detection, frame) # 按 q 键退出 if cv2.waitKey(1) 0xFF ord(q): break # 释放摄像头资源并关闭所有窗口 cap.release() cv2.destroyAllWindows()qt cmake opencv 只需要添加以下内容 #opencv 设置 set(OpenCV_DIR D:/opencv4.14/build/x64/vc16/lib) find_package(OpenCV REQUIRED) target_include_directories(testopen PRIVATE ${OpenCV_INCLUDE_DIRS}) target_link_libraries(testopen PRIVATE ${OpenCV_LIBS})#包含路径 INCLUDEPATH E:\opencv\opencv4.5.4_mingw730_64_qt5.14.2\include #库文件 LIBS E:\opencv\opencv4.5.4_mingw730_64_qt5.14.2\x64\mingw\lib\libopencv*.a #include mainwindow.h #include ui_mainwindow.h #include QFileDialog #include QMessageBox #include QPixmap #include opencv2/opencv.hpp #include opencv2/imgproc.hpp using namespace cv; // 引入opencv的命名空间 using namespace std; MainWindow::MainWindow(QWidget *parent) : QMainWindow(parent) , ui(new Ui::MainWindow) { ui-setupUi(this); } MainWindow::~MainWindow() { delete ui; } void MainWindow::on_pushButton_clicked() { QString filename QFileDialog::getOpenFileName(this, 打开图像文件, C:/Users, Image Files (*.bmp;*.png;*.jpg)); if (filename.isEmpty()) { QMessageBox::information(this, 提示, 文件打开失败1!); return; } Mat img_input; img_input cv::imread(filename.toLocal8Bit().toStdString()); if (img_input.empty()) { QMessageBox::information(this, 提示, 文件打开失败2!); return; } cv::Mat temp; cv::cvtColor(img_input, temp, cv::COLOR_BGR2RGB); namedWindow(Display window,WINDOW_AUTOSIZE); imshow(Display window,img_input); waitKey(0); MainWindow w; w.show(); }qt .pro opencvqt中配置路径 E:\opencvMK\QT653_MSVC2019_VS2022_Opencv4.10\install_QT653_MSVC2019_VS2022_Opencv4.10\x64\vc17\lib win32:CONFIG(debug, debug|release): LIBS -LD:/Download/install_QT653_MSVC2019_VS2022_Opencv4.10/x64/vc17/lib/ -lopencv_aruco4100d -lopencv_bgsegm4100d -lopencv_bioinspired4100d -lopencv_calib3d4100d -lopencv_ccalib4100d -lopencv_core4100d -lopencv_cvv4100d -lopencv_datasets4100d -lopencv_dnn4100d -lopencv_dnn_objdetect4100d -lopencv_dnn_superres4100d -lopencv_dpm4100d -lopencv_face4100d -lopencv_features2d4100d -lopencv_flann4100d -lopencv_fuzzy4100d -lopencv_gapi4100d -lopencv_hfs4100d -lopencv_highgui4100d -lopencv_imgcodecs4100d -lopencv_imgproc4100d -lopencv_img_hash4100d -lopencv_intensity_transform4100d -lopencv_line_descriptor4100d -lopencv_mcc4100d -lopencv_ml4100d -lopencv_objdetect4100d -lopencv_optflow4100d -lopencv_phase_unwrapping4100d -lopencv_photo4100d -lopencv_plot4100d -lopencv_quality4100d -lopencv_rapid4100d -lopencv_reg4100d -lopencv_rgbd4100d -lopencv_saliency4100d -lopencv_shape4100d -lopencv_signal4100d -lopencv_stereo4100d -lopencv_stitching4100d -lopencv_structured_light4100d -lopencv_superres4100d -lopencv_surface_matching4100d -lopencv_text4100d -lopencv_tracking4100d -lopencv_video4100d -lopencv_videoio4100d -lopencv_videostab4100d -lopencv_wechat_qrcode4100d -lopencv_xfeatures2d4100d -lopencv_ximgproc4100d -lopencv_xobjdetect4100d -lopencv_xphoto4100d else:win32:CONFIG(release, debug|release): LIBS -LD:/Download/install_QT653_MSVC2019_VS2022_Opencv4.10/x64/vc17/lib/ -lopencv_aruco4100 -lopencv_bgsegm4100 -lopencv_bioinspired4100 -lopencv_calib3d4100 -lopencv_ccalib4100 -lopencv_core4100 -lopencv_cvv4100 -lopencv_datasets4100 -lopencv_dnn4100 -lopencv_dnn_objdetect4100 -lopencv_dnn_superres4100 -lopencv_dpm4100 -lopencv_face4100 -lopencv_features2d4100 -lopencv_flann4100 -lopencv_fuzzy4100 -lopencv_gapi4100 -lopencv_hfs4100 -lopencv_highgui4100 -lopencv_imgcodecs4100 -lopencv_imgproc4100 -lopencv_img_hash4100 -lopencv_intensity_transform4100 -lopencv_line_descriptor4100 -lopencv_mcc4100 -lopencv_ml4100 -lopencv_objdetect4100 -lopencv_optflow4100 -lopencv_phase_unwrapping4100 -lopencv_photo4100 -lopencv_plot4100 -lopencv_quality4100 -lopencv_rapid4100 -lopencv_reg4100 -lopencv_rgbd4100 -lopencv_saliency4100 -lopencv_shape4100 -lopencv_signal4100 -lopencv_stereo4100 -lopencv_stitching4100 -lopencv_structured_light4100 -lopencv_superres4100 -lopencv_surface_matching4100 -lopencv_text4100 -lopencv_tracking4100 -lopencv_video4100 -lopencv_videoio4100 -lopencv_videostab4100 -lopencv_wechat_qrcode4100 -lopencv_xfeatures2d4100 -lopencv_ximgproc4100 -lopencv_xobjdetect4100 -lopencv_xphoto4100 INCLUDEPATH D:/Download/install_QT653_MSVC2019_VS2022_Opencv4.10/include DEPENDPATH D:/Download/install_QT653_MSVC2019_VS2022_Opencv4.10/includeconda 主环境中的 jupyter 使用虚拟环境的 jupyter 核心测试以下新虚拟环境conda create -n p39 python3.9.19conda activate p39pip install ipykernelpython -m ipykernel install --user --name p39 --display-name p39一般进行以上步聚就可以了,如果 还不行,在虚拟环境中再安装 jupyter