for user in users_in_train[:10]: print(user) S = model.caliR(train, user, sim_mtx, genres, k, n, para) print('S:', S) recommended_items_in_test = test[user, :].nonzero()[1] print('T:', recommended_items_in_test) DCG = 0 iDCG = 0 temp_A = [] for i in range(len(S)): for j in range(len(recommended_items_in_test)): if S[i] == recommended_items_in_test[j]: temp_A.append(1) # 注意j的取值从0开始 DCG += (1 / np.log2(i + 2)) for k in range(len(temp_A)): # temp_A为空? iDCG += (1 / np.log2(k + 2)) if iDCG != 0: nDCG += (DCG / iDCG)这段代码user为1的时候为什么输出的S为空

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mtx.lock(); // 临界区，只有当前线程能执行 mtx.unlock(); 2. **自动解锁**: 使用 std::unique_lock 可以在作用域结束时自动解锁，避免死锁风险。 cpp std::unique_lock<std::mutex> lock(mtx); /...

mtx.rar_mtx_火山

"mtx.rar_mtx_火山"这个压缩包文件的标题和描述暗示了它可能包含与地质勘探、矿产资源勘查，特别是针对隐伏金属矿、油气构造、推覆体以及火山岩下煤炭资源寻找相关的数据和技术资料。"mtx"可能是某种特定的数据格式...

recalls = [] for user in users_in_train: S, discalibration = model.caliR(train, user, sim_mtx, genres, k, n, para) recommended_items_in_test = test[user, :].nonzero()[1] hit = 0 for item in S: if item in recommended_items_in_test: hit = hit + 1 recall = hit / len(recommended_items_in_test) recalls.append(recall) print(user)

它使用了一个名为 model.caliR 的函数，该函数接受训练数据、用户、相似度矩阵、物品类型、k、n 和参数 para 作为输入，并返回一个包含推荐物品的列表 S 和一个失调值 discalibration。然后，它计算测试集中用户实际...

import numpy as np import cv2 # 准备标定板参数 pattern = (9, 6) #部角点数目 square_size = 25 # 每个棋盘格的边长（单位：毫米） # 准备用于标定的图像路径（替换实际的图像路径） image_paths = [ 'path_to_image1.jpg', 'path_to_image2.jpg', 'path_to_image3.jpg', ... ] # 创建存储角点和物体点的列表 obj_points = [] # 真实世界坐标点 img_points = [] # 图像平面角点 # 准备物体坐标 objp = np.zeros((pattern_size[0] * pattern_size[1], 3), np.float32) objp[:, :2] = np.mgrid[0:pattern_size[0], 0:pattern_size[1]].T.reshape(-1, 2) * square_size for image_path in image_paths: # 读取图像 img = cv2.imread(image_path) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 查找角点 ret, corners = cv2.findChessboardCorners(gray, pattern_size, None) if ret: obj_points.append(objp) img_points.append(corners) # 进行相机标定 ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, gray.shape[::-1], None, None) # 打印相机内参和畸变参数 print("相机内参 (Camera Matrix):\n", mtx) print("\n畸变系数 (Distortion Coefficients):\n", dist) # 保存相机参数 np.save("camera_matrix.npy", mtx) np.save("dist_coeffs.npy", dist)我运行了这个代码，他说pattern_size is not defined

for image_path in image_paths: # 读取图像 img = cv2.imread(image_path) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 查找角点 ret, corners = cv2.findChessboardCorners(gray, pattern, None) if ...

import cv2 import numpy as np # 设置棋盘格大小 grid_size = (6, 9) # 设置棋盘格边长 square_size = 0.03 # 创建棋盘格点的坐标数组 obj_points = np.zeros((grid_size[0] * grid_size[1], 3), np.float32) obj_points[:, :2] = np.mgrid[0:grid_size[0], 0:grid_size[1]].T.reshape(-1, 2) * square_size # 创建空数组来存储拍摄到的棋盘格点的坐标 img_points = [] # 拍摄多张图片，并检测棋盘格点 for i in range(10): # 读取图片 img = cv2.imread(f"image_{i}.jpg") # 将图片转换为灰度图 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 检测棋盘格点 ret, corners = cv2.findChessboardCorners(gray, grid_size, None) # 如果检测到棋盘格点，则将其添加到数组中 if ret: img_points.append(corners) # 在图片上绘制棋盘格点 cv2.drawChessboardCorners(img, grid_size, corners, ret) # 显示图片 cv2.imshow(f"Image {i}", img) # 关闭所有窗口 cv2.destroyAllWindows() # 进行相机标定 ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, gray.shape[::-1], None, None) # 打印相机矩阵和畸变系数 print("Camera matrix:") print(mtx) print("Distortion coefficients:") print(dist)这段代码要想打开一个文件夹怎么改

for filename in os.listdir(folder_path): # 读取图片 img = cv2.imread(os.path.join(folder_path, filename)) # 将图片转换为灰度图 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 检测棋盘格点 ret...

using clock_type = std::chrono::system_clock; struct message { clock_type::time_point when; std::function<void()> callback; std::string param; }; class message_loop { public: message_loop(): _stop(false) { // } message_loop(const message_loop&) = delete; message_loop& operator=(const message_loop&) = delete; void run() { while (!_stop) { auto msg = wait_one(); msg.callback(); } } void quit() { post({clock_type::now(), this{ _stop = true; } }); } void post(std::function<void()> callable) { post({clock_type::now(), std::move(callable)}); } void post(std::function<void()> callable, std::chrono::milliseconds delay) { post({clock_type::now() + delay, std::move(callable)}); } private: struct msg_prio_comp { inline bool operator() (const message& a, const message& b) { return a.when > b.when; } }; using queue_type = std::priority_queue<message, std::vector<message>, msg_prio_comp>; std::mutex _mtx; std::condition_variable _cv; queue_type _msgs; bool _stop; void post(message msg) { auto lck = acquire_lock(); _msgs.emplace(std::move(msg)); _cv.notify_one(); } std::unique_lockstd::mutex acquire_lock() { return std::unique_lockstd::mutex(_mtx); } bool idle() const { return _msgs.empty(); } const message& top() const { return _msgs.top(); } message pop() { auto msg = top(); _msgs.pop(); return msg; } message wait_one() { while (true) { auto lck = acquire_lock(); if (idle()) _cv.wait(lck); else if (top().when <= clock_type::now()) return pop(); else { _cv.wait_until(lck, top().when); // 可能是新消息到达，再循环一次看看 } } } }; int main(int argc, char argv[]) { using namespace std; using namespace std::chrono; message_loop pLoop = new message_loop; thread th(pLoop{ pLoop->run(); }); cout << "POST 1"<<endl;; pLoop->post({ cout << "1"<<endl; }); cout << "POST 2"<<endl;; pLoop->post({ cout << "2"<<endl; }, milliseconds(500)); cout << "POST 3"<<endl;; pLoop->post({ cout << "3"<<endl; }); cout << "POST 4"<<endl;; pLoop->post({ cout << "4"<<endl; }, milliseconds(1000)); this_thread::sleep_for(milliseconds(1500)); // pLoop->quit(); cout << "Quit"<<endl; th.join(); cout << "here"<<endl; } 请优化一下，可以传参

可以将 message 结构体中的 param 字段用于传递参数，并相应地修改 post 函数和 wait_one 函数。具体代码如下： ... this_thread::sleep_for(milliseconds(1500)); pLoop->quit(); th.join(); }

void ProducerTask() // 生产者任务 { bool ready_to_exit = false; while (1) { sleep(1); std::unique_lock<std::mutex> lock(instance.pro_mtx); if (instance.pro_item_counter < ProNum) { ++(instance.pro_item_counter); Producer(&instance, instance.pro_item_counter); std::cout << "Producer thread " << std::this_thread::get_id() << " is producing the " << instance.pro_item_counter << "^th item" << std::endl; } else ready_to_exit = true; lock.unlock(); if (ready_to_exit == true) break; }是什么意思

2. 获取 instance 对象的互斥锁 pro_mtx，以确保同时只有一个线程可以修改共享数据； 3. 检查是否还可以继续生产物品，如果可以，则增加 pro_item_counter 计数器的值，调用 Producer 函数生产物品，并输出生产信息...

怎么利用已经保存好的stereo_right_mtx.npy,stereo_right_dist.npy,stereo_left_mtx.npy,stereo_left_dist.npy,R_left2right.npy,T_left2right.npy进行双目矫正

right_mtx = np.load('stereo_right_mtx.npy') right_dist = np.load('stereo_right_dist.npy') left_mtx = np.load('stereo_left_mtx.npy') left_dist = np.load('stereo_left_dist.npy') R_left2right = np.load('...

# # 多线程实例3 method = [pieState] # 字典 self.thread_s1 = {} self.worker_s1 = {} for i in range(len(method)): self.thread_s1[i] = 'thread' + str(i) self.worker_s1[i] = 'worker' + str(i) for j in range(len(method)): self.thread_s1[j] = QThread() self.worker_s1[j] = Worker() self.worker_s1[j].moveToThread(self.thread_s1[j]) self.thread_s1[j].started.connect(self.worker_s1[j].run) self.worker_s1[j].finished.connect(self.thread_s1[j].quit) self.worker_s1[j].finished.connect(self.worker_s1[j].deleteLater) self.thread_s1[j].finished.connect(self.thread_s1[j].deleteLater) self.worker_s1[j].finished.connect(method[j]) # Start the thread self.thread_s1[j].start() #调用函数 # pieState() except Exception as alarm: message = QMessageBox.warning(stack1,'警告','数据库没有数据',QMessageBox.Ok)报警： return affine_transform(points, mtx) RuntimeError: In affine_transform: Invalid vertices array. 为什么没有触发try except机制

根据您提供的代码片段，我看不出来为什么try except机制没有被触发。但是，我注意到您在except语句块中使用了QMessageBox来弹出...另外，您可以在except语句块中添加一些print语句来输出异常信息，以便更好地调试问题。

运行#!/usr/bin/env python2.7 -- coding: UTF-8 -- import numpy as np import cv2 准备标定板参数 pattern = (9, 6) # 部角点数目 square_size = 25 # 每个棋盘格的边长（单位：毫米）准备用于标定的图像路径（替换实际的图像路径） image_paths = [ 'pictures1.jpg', 'pictures2.jpg', 'pictures3.jpg', ] 创建存储角点和物体点的列表 obj_points = [] # 真实世界坐标点 img_points = [] # 图像平面角点准备物体坐标 objp = np.zeros((pattern[0] * pattern[1], 3), np.float32) objp[:, :2] = np.mgrid[0:pattern[0], 0:pattern[1]].T.reshape(-1, 2) * square_size for image_path in image_paths: # 读取图像 img = cv2.imread(image_path) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 查找角点 ret, corners = cv2.findChessboardCorners(gray, pattern, None) if ret: obj_points.append(objp) img_points.append(corners) 进行相机标定 ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, gray.shape[::-1], None, None) 打印相机内参和畸变参数 print("相机内参 (Camera Matrix):\n", mtx) print("\n畸变系数 (Distortion Coefficients):\n", dist) 保存相机参数 np.save("camera_matrix.npy", mtx) np.save("dist_coeffs.npy", dist) 后显示gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) cv2.error: /build/opencv-XDqSFW/opencv-3.2.0+dfsg/modules/imgproc/src/color.cpp:9748: error: (-215) scn == 3 || scn == 4 in function cvtColor 会报错是因为图片通道数并非4，更改代码使它先将图片通道数变为4然后运行上述代码要求的功能

for image_path in image_paths: # 将图片通道数变为4 converted_image_path = convert_image_channels(image_path) # 读取图像 img = cv2.imread(converted_image_path) gray = cv2.cvtColor(img, cv2.COLOR_...

import cv2 import numpy as np chessboard_size = (7,10) # 棋盘格行列数 objp = np.zeros((np.prod(chessboard_size), 3), np.float32) # 内部点的坐标 objp[:, :2] = np.mgrid[0:chessboard_size[0], 0:chessboard_size[1]].T.reshape(-1, 2) img = cv2.imread('C:\yingxiang\OpenCV_Chessboard.png') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) ret, corners = cv2.findChessboardCorners(gray, chessboard_size, None) if ret == True: cv2.drawChessboardCorners(img, chessboard_size, corners, ret) ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera([objp], [corners], gray.shape[::-1], None, None) # 打印相机内部参数和畸变系数 print("相机内部参数：") print(mtx) print("畸变系数：") print(dist) # 打印外部参数 print("旋转向量：") print(rvecs) print("平移向量：") print(tvecs)这段代码为什么打印不出需要的参数

这段代码可能无法打印出相机的内部参数、畸变系数和外部参数，主要原因是图像路径设置不正确。在 Windows 操作系统中，路径中的反斜杠 \ 会被解释为转义字符，因此需要使用双反斜杠 \\ 或者单斜杠 / 来表示...

import numpy as np from scipy.stats import bartlett from scipy.linalg import toeplitz from scipy.stats import chi2 def kmo(dataset): corr_mtx = np.corrcoef(dataset.T) corr_inv = np.linalg.inv(corr_mtx) n_vars = dataset.shape[1] kmo_num = np.sum(np.square(corr_inv)) - n_vars kmo_denom = kmo_num + np.sum(np.square(toeplitz(np.arange(1, n_vars + 1)))) kmo_val = kmo_num / kmo_denom return kmo_val # 使用示例数据进行KMO检验 data = np.random.rand(100, 5) # 替换为您自己的数据集 kmo_value = kmo(data) print("KMO值:", kmo_value)

void ConsumerTask() // 消费者任务 { bool ready_to_exit = false; while (1) { sleep(1); std::unique_lock<std::mutex> lock(instance.con_mtx); if (instance.con_item_counter < ProNum) { int item = Consumer(&instance); ++(instance.con_item_counter); std::cout << "Consumer thread " << std::this_thread::get_id() << " is consuming the " << item << "^th item" << std::endl; } else ready_to_exit = true; lock.unlock(); if (ready_to_exit == true) break; } std::cout << "Consumer thread " << std::this_thread::get_id() << " is exiting..." << std::endl; }是什么意思

2. 获取 instance 对象的互斥锁 con_mtx，以确保同时只有一个线程可以修改共享数据； 3. 检查是否还有剩余的物品可以消费，如果有，则调用 Consumer 函数从共享缓冲区中取出物品，并输出消费信息； 4. 增加 con_item...

import cv2 import numpy as np chessboard_size = (9,6) # 棋盘格行列数 objp = np.zeros((np.prod(chessboard_size), 3), np.float32) # 内部点的坐标 objp[:, :2] = np.mgrid[0:chessboard_size[0], 0:chessboard_size[1]].T.reshape(-1, 2) img = cv2.imread('C:/yingxiang/biaoding.png') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) ret, corners = cv2.findChessboardCorners(gray, chessboard_size, None) if ret == True: cv2.drawChessboardCorners(img, chessboard_size, corners, ret) ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera([objp], [corners], gray.shape[::-1], None, None) # 打印相机内部参数和畸变系数 print("相机内部参数：") print(mtx) print("畸变系数：") print(dist) # 打印外部参数 print("旋转向量：") print(rvecs) print("平移向量：") print(tvecs)这段代码怎么改可以打印成正常数字形式

print(mtx) print("畸变系数：") np.set_printoptions(precision=4, suppress=True) print(dist) # 打印外部参数 print("旋转向量：") np.set_printoptions(precision=4, suppress=True) print(rvecs)...

import cv2 import numpy as np chessboard_size = (9,6) # 棋盘格行列数 objp = np.zeros((np.prod(chessboard_size), 3), np.float32) # 内部点的坐标 objp[:, :2] = np.mgrid[0:chessboard_size[0], 0:chessboard_size[1]].T.reshape(-1, 2) img = cv2.imread('C:/yingxiang/biaoding.png') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) ret, corners = cv2.findChessboardCorners(gray, chessboard_size, None) if ret == True: cv2.drawChessboardCorners(img, chessboard_size, corners, ret) ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera([objp], [corners], gray.shape[::-1], None, None) # 打印相机内部参数和畸变系数 print("相机内部参数：") np.set_printoptions(precision=4, suppress=True) print(mtx) print("畸变系数：") np.set_printoptions(precision=4, suppress=True) print(dist) # 打印外部参数 print("旋转向量：") np.set_printoptions(precision=4, suppress=True) print(rvecs) print("平移向量：") np.set_printoptions(precision=4, suppress=True) print(tvecs)这段代码如何将旋转向量和平移向量改为旋转矩阵和平移矩阵

print(mtx) print("畸变系数：") np.set_printoptions(precision=4, suppress=True) print(dist) # 将旋转向量转换为旋转矩阵 R, _ = cv2.Rodrigues(rvecs[0]) # 打印旋转矩阵和平移矩阵 print(...

使用多线程加速处理这一段for (int i = 0; i < m; i++) { edges = Extract1DEdgeCircle.Get1DEdge(RoiMat, m_vpdEquinoxPoints[i], m_dMeasureLength, m_dMeasureHeight, m_vdMeasureAngle[i] , m_dSigma, m_nThresholdCircle, m_nTranslationCircle == 1 ? Translation::Poisitive : Translation::Negative, Selection::Strongest); for (int i = 0; i < edges.size(); i++) { m_vpdEdgePoints.push_back(edges[i].m_pdEdgePoint); m_vdEdgeGradient.push_back(edges[i].m_dGradient); } }

可以使用多线程的方式将这个for循环中的任务分配给多个线程处理，从而加速处理过程。具体实现方式可以使用C++11中的std::thread库进行线程的创建和管理。将for循环的迭代次数分配给多个线程处理，每个线程处理一...

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