opencv-python库Python中OpenCV绑定库的使用方法详解

引言

OpenCV一个开源的计算机视觉库，广泛应用于图像处理和计算机视觉领域。Python通过cv2模块提供了对OpenCV的绑定，使得开发者可以方便地使用Python进行图像处理和计算机视觉任务。这篇文章小编将详细介绍Python中OpenCV绑定库的使用技巧，并提供丰富的示例代码。

一、安装OpenCV

开头来说需要安装OpenCV库：

pip install opencv-python

如果需要额外的功能（如SIFT、SURF等专利算法），可以安装：

pip install opencv-contrib-python

二、基本图像操作

1. 读取和显示图像

import cv2 读取图像img = cv2.imread(‘image.jpg’) 默认BGR格式显示图像cv2.imshow(‘Image’, img) 等待按键并关闭窗口cv2.waitKey(0)cv2.destroyAllWindows()

2. 保存图像

cv2.imwrite(‘output.jpg’, img) 保存为JPEG格式

3. 获取图像信息

print(f”图像形状: img.shape}”) (高度, 宽度, 通道数)print(f”图像大致: img.size} 字节”)print(f”图像数据类型: img.dtype}”) 通常是uint8

三、图像基本处理

1. 颜色空间转换

BGR转灰度gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) BGR转RGBrgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 显示结局cv2.imshow(‘Gray’, gray)cv2.imshow(‘RGB’, rgb)cv2.waitKey(0)

2. 图像缩放

缩放到指定尺寸resized = cv2.resize(img, (300, 200)) (宽度, 高度) 按比例缩放scale_percent = 50 缩放到50%width = int(img.shape[1] * scale_percent / 100)height = int(img.shape[0] * scale_percent / 100)resized = cv2.resize(img, (width, height)) cv2.imshow(‘Resized’, resized)cv2.waitKey(0)

3. 图像裁剪

裁剪图像 (y1:y2, x1:x2)cropped = img[100:400, 200:500] cv2.imshow(‘Cropped’, cropped)cv2.waitKey(0)

4. 图像旋转

获取图像中心(h, w) = img.shape[:2]center = (w // 2, h // 2) 旋转矩阵M = cv2.getRotationMatrix2D(center, 45, 1.0) 旋转45度，缩放1.0 应用旋转rotated = cv2.warpAffine(img, M, (w, h)) cv2.imshow(‘Rotated’, rotated)cv2.waitKey(0)

四、图像滤波

1. 均值模糊

blurred = cv2.blur(img, (5, 5)) 5×5核大致cv2.imshow(‘Blurred’, blurred)cv2.waitKey(0)

2. 高斯模糊

gaussian = cv2.GaussianBlur(img, (5, 5), 0) 核大致5×5，标准差0cv2.imshow(‘Gaussian’, gaussian)cv2.waitKey(0)

3. 中值模糊

median = cv2.medianBlur(img, 5) 核大致5cv2.imshow(‘Median’, median)cv2.waitKey(0)

4. 双边滤波

bilateral = cv2.bilateralFilter(img, 9, 75, 75) 核大致9，颜色和空间sigmacv2.imshow(‘Bilateral’, bilateral)cv2.waitKey(0)

五、边缘检测

1. Canny边缘检测

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)edges = cv2.Canny(gray, 100, 200) 阈值100和200 cv2.imshow(‘Edges’, edges)cv2.waitKey(0)

2. Sobel算子

grad_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3) x路线grad_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3) y路线合并梯度abs_grad_x = cv2.convertScaleAbs(grad_x)abs_grad_y = cv2.convertScaleAbs(grad_y)grad = cv2.addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0) cv2.imshow(‘Sobel’, grad)cv2.waitKey(0)

六、形态学操作

1. 膨胀和腐蚀

二值化图像_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY) 定义核kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) 膨胀dilated = cv2.dilate(binary, kernel, iterations=1) 腐蚀eroded = cv2.erode(binary, kernel, iterations=1) cv2.imshow(‘Dilated’, dilated)cv2.imshow(‘Eroded’, eroded)cv2.waitKey(0)

2. 开运算和闭运算

开运算（先腐蚀后膨胀）opening = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel) 闭运算（先膨胀后腐蚀）closing = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel) cv2.imshow(‘Opening’, opening)cv2.imshow(‘Closing’, closing)cv2.waitKey(0)

七、特征检测与匹配

1. Harris角点检测

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) Harris角点检测corners = cv2.cornerHarris(gray, 2, 3, 0.04) 结局可视化img_corners = img.copy()img_corners[corners > 0.01 * corners.max()] = [0, 0, 255] cv2.imshow(‘Harris Corners’, img_corners)cv2.waitKey(0)

2. SIFT特征检测

确保安装了opencv-contrib-pythonsift = cv2.SIFT_create() 检测关键点和描述符keypoints, descriptors = sift.detectAndCompute(gray, None) 绘制关键点img_sift = cv2.drawKeypoints(img, keypoints, None, color=(0, 255, 0)) cv2.imshow(‘SIFT Keypoints’, img_sift)cv2.waitKey(0)

3. 特征匹配

读取第二张图像img2 = cv2.imread(‘image2.jpg’)gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) 检测关键点和描述符keypoints2, descriptors2 = sift.detectAndCompute(gray2, None) 使用FLANN匹配器FLANN_INDEX_KDTREE = 1index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)search_params = dict(checks=50)flann = cv2.FlannBasedMatcher(index_params, search_params) matches = flann.knnMatch(descriptors, descriptors2, k=2) 应用比率测试good = []for m, n in matches: if m.distance < 0.7 * n.distance: good.append(m) 绘制匹配结局img_matches = cv2.drawMatches(img, keypoints, img2, keypoints2, good, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS) cv2.imshow(‘Feature Matches’, img_matches)cv2.waitKey(0)

八、视频处理

1. 读取和显示视频

cap = cv2.VideoCapture(‘video.mp4’) 或使用0读取摄像头 while cap.isOpened(): ret, frame = cap.read() if not ret: break cv2.imshow(‘Video’, frame) if cv2.waitKey(25) & 0xFF == ord(‘q’): break cap.release()cv2.destroyAllWindows()

2. 视频写入

cap = cv2.VideoCapture(0) 读取摄像头fourcc = cv2.VideoWriter_fourcc(*’XVID’)out = cv2.VideoWriter(‘output.avi’, fourcc, 20.0, (640, 480)) while cap.isOpened(): ret, frame = cap.read() if not ret: break 处理帧（例如转换为灰度） gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) out.write(cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)) 需要转换回BGR cv2.imshow(‘Video’, frame) if cv2.waitKey(1) & 0xFF == ord(‘q’): break cap.release()out.release()cv2.destroyAllWindows()

九、图像分割

1. 阈值分割

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 固定阈值_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY) 自适应阈值thresh_adapt = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2) cv2.imshow(‘Threshold’, thresh)cv2.imshow(‘Adaptive Threshold’, thresh_adapt)cv2.waitKey(0)

2. 轮廓检测

二值化图像_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY) 查找轮廓contours, _ = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) 绘制轮廓img_contours = img.copy()cv2.drawContours(img_contours, contours, -1, (0, 255, 0), 2) cv2.imshow(‘Contours’, img_contours)cv2.waitKey(0)

十、高质量示例：人脸检测

加载预训练的人脸检测模型face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + ‘haarcascade_frontalface_default.xml’) 读取图像img = cv2.imread(‘face.jpg’)gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 检测人脸faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)) 绘制矩形框for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2) cv2.imshow(‘Face Detection’, img)cv2.waitKey(0)

十一、性能优化技巧

??使用NumPy操作替代循环??：

不推荐for i in range(rows): for j in range(cols): img[i,j] = [255, 255, 255] if some_condition else [0, 0, 0] 推荐condition = some_condition_arrayimg = np.where(condition[…, None], [255, 255, 255], [0, 0, 0])

??使用inRange进行颜色分割??：

创建掩膜lower = np.array([0, 100, 100])upper = np.array([10, 255, 255])mask = cv2.inRange(hsv_img, lower, upper)

使用积分图像加速计算??：

计算积分图像integral = cv2.integral(gray) 快速计算矩形区域和sum_rect = integral[x2,y2] – integral[x1-1,y2] – integral[x2,y1-1] + integral[x1-1,y1-1]

以上就是Python中OpenCV绑定库的使用技巧详解的详细内容，更多关于Python OpenCV绑定库使用的资料请关注风君子博客其它相关文章！

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