cv2.waitKey(x)

waitKey(x);

第一个参数: 等待x ms,如果在此期间有按键按下,则立即结束并返回按下按键的

ASCII码,否则返回-1

如果x=0,那么无限等待下去,直到有按键按下


waitKey(...)

    waitKey([, delay]) -> retval

    .   @brief Waits for a pressed key.

    .   

    .   The function waitKey waits for a key event infinitely (when \f$\texttt{delay}\leq 0\f$ ) or for delay

    .   milliseconds, when it is positive. Since the OS has a minimum time between switching threads, the

    .   function will not wait exactly delay ms, it will wait at least delay ms, depending on what else is

    .   running on your computer at that time. It returns the code of the pressed key or -1 if no key was

    .   pressed before the specified time had elapsed.

    .   

    .   @note

    .   

    .   This function is the only method in HighGUI that can fetch and handle events, so it needs to be

    .   called periodically for normal event processing unless HighGUI is used within an environment that

    .   takes care of event processing.

    .   

    .   @note

    .   

    .   The function only works if there is at least one HighGUI window created and the window is active.

    .   If there are several HighGUI windows, any of them can be active.

    .   

    .   @param delay Delay in milliseconds. 0 is the special value that means "forever".


import cv2 import os # 读取原始图像 image_path = 'test.jpg' image = cv2.imread(image_path) if image is None: print("无法加载图片,请确保 test.jpg 存在于项目目录中。") exit() # 创建输出文件夹 output_dir = 'processed' os.makedirs(output_dir, exist_ok=True) # 显示原始图像 cv2.imshow("Original", image) cv2.waitKey(500) # 1. 灰度化处理 gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) cv2.imshow("Gray", gray_image) cv2.imwrite(os.path.join(output_dir, 'gray.jpg'), gray_image) cv2.waitKey(500) # 2. 模糊处理(高斯模糊) blur_image = cv2.GaussianBlur(image, (11, 11), 0) cv2.imshow("Blur", blur_image) cv2.imwrite(os.path.join(output_dir, 'blur.jpg'), blur_image) cv2.waitKey(500) # 3. 边缘检测(Canny) edge_image = cv2.Canny(image, 100, 200) cv2.imshow("Edges", edge_image) cv2.imwrite(os.path.join(output_dir, 'edges.jpg'), edge_image) cv2.waitKey(500) # 4. 图像旋转(顺时针45度) (h, w) = image.shape[:2] center = (w // 2, h // 2) rotate_matrix = cv2.getRotationMatrix2D(center, -45, 1.0) rotated_image = cv2.warpAffine(image, rotate_matrix, (w, h)) cv2.imshow("Rotated", rotated_image) cv2.imwrite(os.path.join(output_dir, 'rotated.jpg'), rotated_image) cv2.waitKey(500) # 5. 人脸检测(基于Haar级联分类器) face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') face_image = image.copy() gray_for_face = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray_for_face, scaleFactor=1.1, minNeighbors=5) for (x, y, w, h) in faces: cv2.rectangle(face_image, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.imshow("Face Detection", face_image) cv2.imwrite(os.path.join(output_dir, 'faces.jpg'), face_image) cv2.waitKey(0) cv2.destroyAllWindows()
06-02
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