Python中OpenCV绑定库的使用方法详解
作者:code_shenbing
OpenCV是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,本文将详细介绍Python中OpenCV绑定库的使用方法,并提供丰富的示例代码,需要的朋友可以参考下
引言
OpenCV是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。本文将详细介绍Python中OpenCV绑定库的使用方法,并提供丰富的示例代码。
一、安装OpenCV
首先需要安装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"图像大小: {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RGB rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # 显示结果 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)) # 5x5核大小 cv2.imshow('Blurred', blurred) cv2.waitKey(0)
2. 高斯模糊
gaussian = cv2.GaussianBlur(img, (5, 5), 0) # 核大小5x5,标准差0 cv2.imshow('Gaussian', gaussian) cv2.waitKey(0)
3. 中值模糊
median = cv2.medianBlur(img, 5) # 核大小5 cv2.imshow('Median', median) cv2.waitKey(0)
4. 双边滤波
bilateral = cv2.bilateralFilter(img, 9, 75, 75) # 核大小9,颜色和空间sigma cv2.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方向 grad_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3) # y方向 # 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) # 结果可视化 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-python sift = 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 = 1 index_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) # 绘制匹配结果 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、高级示例: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_array img = 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]
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