代码收藏家 OpenCV Python 深度指南
OpenCV（Open Source Computer Vision Library）是一个开源的计算机视觉库，它包含了大量的图像处理和机器视觉算法。OpenCV支持多种编程语言，包括C++和Python，其中Python接口因其易用性和灵活性而受到广泛欢迎。
本指南将覆盖以下几个方面：
- OpenCV简介
- OpenCV安装与配置
- 图像基础
- 视频处理
- 图像处理
- 颜色空间转换
- 边缘检测
- 图像平滑
- 图像形态学
- 特征检测与描述
- 高级应用
- 人脸识别
- 目标检测
- 性能优化技巧
- 结论
1. OpenCV简介
1.1 开发历史
OpenCV是由Intel在1999年开始的一个项目，后来被Itseez公司接手，目前由其维护。Itseez被Autodesk收购后，OpenCV继续作为开源项目发展。
1.2 主要特性
1.3 架构
核心模块
core：基本运算和数据结构。imgproc：图像处理函数。highgui：GUI操作，如窗口管理、图像和视频捕获。附加模块
features2d：特征检测和描述。objdetect：目标检测。calib3d：相机校准和3D重建。ml：机器学习算法。第三方插件
dnn：深度神经网络支持。stereo：立体视觉。2. OpenCV安装与配置
2.1 Python环境
确保已经安装了Python，并且版本为3.x。
2.2 安装OpenCV
通过pip安装OpenCV的Python接口：
pip install opencv-python
安装额外模块
某些高级功能可能需要额外的模块：
pip install opencv-contrib-python
2.3 测试安装
创建一个简单的Python脚本来测试安装是否成功：
import cv2
# 加载一张图像
image = cv2.imread('path/to/image.jpg')
# 显示图像
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
3. 图像基础
3.1 读取和显示图像
import cv2
import numpy as np
# 读取图像
img = cv2.imread('path/to/image.jpg')
# 显示图像
cv2.imshow('Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
3.2 保存图像
cv2.imwrite('output.jpg', img)
3.3 图像属性
# 获取图像尺寸
height, width, channels = img.shape
# 获取像素值
pixel_value = img[100, 100]
# 修改像素值
img[100, 100] = [255, 0, 0] # 设置为红色
3.4 图像切片
# 获取图像的一部分
roi = img[50:150, 50:150]
3.5 图像缩放
resized_img = cv2.resize(img, (new_width, new_height))
3.6 图像旋转
height, width = img.shape[:2]
center = (width / 2, height / 2)
angle = 45
scale = 1.0
rotation_matrix = cv2.getRotationMatrix2D(center, angle, scale)
rotated_img = cv2.warpAffine(img, rotation_matrix, (width, height))
3.7 图像裁剪与拼接
# 裁剪图像
top_left = (50, 50)
bottom_right = (200, 200)
cropped_image = img[top_left[1]:bottom_right[1], top_left[0]:bottom_right[0]]
# 拼接图像
combined_image = np.hstack((img, cropped_image))
3.8 图像翻转
# 水平翻转
flipped_horizontal = cv2.flip(img, 1)
# 垂直翻转
flipped_vertical = cv2.flip(img, 0)
4. 视频处理
4.1 从文件读取视频
cap = cv2.VideoCapture('path/to/video.mp4')
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
4.2 从摄像头读取视频
cap = cv2.VideoCapture(0) # 使用默认摄像头
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
4.3 视频录制
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi', fourcc, 20.0, (width, height))
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
out.write(frame)
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
out.release()
cv2.destroyAllWindows()
4.4 视频帧率调整
cap = cv2.VideoCapture('path/to/video.mp4')
fps = cap.get(cv2.CAP_PROP_FPS)
print(f"Original FPS: {fps}")
# 调整帧率为30
cap.set(cv2.CAP_PROP_FPS, 30)
# 检查新的帧率
new_fps = cap.get(cv2.CAP_PROP_FPS)
print(f"New FPS: {new_fps}")
5. 图像处理
5.1 颜色空间转换
# RGB to Grayscale
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# RGB to HSV
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# RGB to LAB
lab_img = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
5.2 边缘检测
edges = cv2.Canny(img, threshold1=100, threshold2=200)
5.3 图像平滑
# 使用均值滤波
blur = cv2.blur(img, (5, 5))
# 使用高斯模糊
gaussian_blur = cv2.GaussianBlur(img, (5, 5), 0)
# 使用中值滤波
median_blur = cv2.medianBlur(img, 5)
# 使用双边滤波
bilateral_blur = cv2.bilateralFilter(img, 9, 75, 75)
5.4 图像形态学
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
erosion = cv2.erode(img, kernel, iterations=1)
dilation = cv2.dilate(img, kernel, iterations=1)
opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
5.5 图像分割
# 使用简单的阈值分割
_, thresh = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
# 使用分水岭算法
sure_bg = cv2.dilate(thresh, kernel, iterations=3)
dist_transform = cv2.distanceTransform(thresh, cv2.DIST_L2, 5)
ret, sure_fg = cv2.threshold(dist_transform, 0.7*dist_transform.max(), 255, 0)
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg, sure_fg)
ret, markers = cv2.connectedComponents(sure_fg)
markers = markers + 1
markers[unknown==255] = 0
markers = cv2.watershed(img, markers)
img[markers == -1] = [255, 0, 0]
5.6 图像融合
# 使用加权融合
alpha = 0.5
beta = 1 - alpha
merged_image = cv2.addWeighted(img1, alpha, img2, beta, 0)
6. 特征检测与描述
6.1 SIFT (Scale-Invariant Feature Transform)
sift = cv2.SIFT_create()
keypoints, descriptors = sift.detectAndCompute(img, None)
6.2 SURF (Speeded Up Robust Features)
surf = cv2.SURF_create(400)
keypoints, descriptors = surf.detectAndCompute(img, None)
6.3 ORB (Oriented FAST and Rotated BRIEF)
orb = cv2.ORB_create()
keypoints, descriptors = orb.detectAndCompute(img, None)
6.4 特征匹配
# 创建BFMatcher对象
bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
# 匹配描述符
matches = bf.match(descriptors1, descriptors2)
# 排序匹配结果
matches = sorted(matches, key=lambda x: x.distance)
# 绘制匹配结果
result_img = cv2.drawMatches(img1, keypoints1, img2, keypoints2, matches[:10], None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
6.5 特征点追踪
# 光流追踪
prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
next_gray = cv2.cvtColor(next_frame, cv2.COLOR_BGR2GRAY)
# 初始化特征点
prev_pts = cv2.goodFeaturesToTrack(prev_gray, maxCorners=100, qualityLevel=0.3, minDistance=7)
# 计算光流
next_pts, status, err = cv2.calcOpticalFlowPyrLK(prev_gray, next_gray, prev_pts, None)
# 追踪有效特征点
good_new = next_pts[status == 1]
good_old = prev_pts[status == 1]
6.6 特征点可视化
# 在图像上绘制特征点
for i, (new, old) in enumerate(zip(good_new, good_old)):
a, b = new.ravel()
c, d = old.ravel()
mask = cv2.line(mask, (a, b), (c, d), (0, 255, 0), 2)
frame = cv2.circle(frame, (a, b), 5, (0, 0, 255), -1)
img = cv2.add(frame, mask)
7. 高级应用
7.1 人脸识别
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
7.2 目标检测
# 使用YOLO v3
net = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')
blob = cv2.dnn.blobFromImage(img, 1/255, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
outs = net.forward(net.getUnconnectedOutLayersNames())
class_ids = []
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
center_x, center_y, w, h = (detection[0:4] * np.array([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])).astype('int')
x, y = center_x - w // 2, center_y - h // 2
boxes.append([x, y, w, h])
confidences.append(float(confidence))
class_ids.append(class_id)
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
colors = np.random.uniform(0, 255, size=(len(class_ids), 3))
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]])
color = colors[i]
cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
cv2.putText(img, label, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
7.3 深度估计
# 使用立体匹配
stereo = cv2.StereoBM_create(numDisparities=16, blockSize=15)
disparity = stereo.compute(left_img, right_img)
depth = cv2.filterSpeckles(disparity, -1, 50, 2000)
8. 性能优化技巧
8.1 使用NumPy进行高效操作
# NumPy操作通常比OpenCV操作更快
mean = np.mean(img, axis=(0, 1))
8.2 减少不必要的内存复制
# 直接访问图像数据而不是复制
roi = img[50:150, 50:150, :]
8.3 并行处理
from concurrent.futures import ThreadPoolExecutor
def process_frame(frame):
# 处理单个帧
pass
frames = [...] # 假设这是一个包含多个帧的列表
with ThreadPoolExecutor(max_workers=4) as executor:
results = [executor.submit(process_frame, frame) for frame in frames]
processed_frames = [future.result() for future in results]
8.4 GPU加速
# 使用cuDNN进行GPU加速
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
8.5 缓存中间结果
# 缓存计算结果
cache = {}
def get_processed_image(image_name):
if image_name in cache:
return cache[image_name]
else:
img = cv2.imread(image_name)
processed_img = ... # 处理图像
cache[image_name] = processed_img
return processed_img
9. 结论
本文深入介绍了OpenCV的基本使用方法和一些高级功能。OpenCV是一个非常强大的工具，适用于各种计算机视觉任务。通过学习本文介绍的内容，您可以开始探索OpenCV的强大功能，并将它们应用到实际项目中。
作者：极客代码
