---
category: framework
framework: OpenCV
filename: learnopencv.py
contributors:
    - ["Yogesh Ojha", "http://github.com/yogeshojha"]
translators:
    - ["GengchenXU", "https://github.com/GengchenXU"]
lang: zh-cn
---

OpenCV（开源计算机视觉）是一个编程功能库，主要面向实时计算机视觉。最初由英特尔开发，后来由Willow Garage，然后Itseez（后来被英特尔收购）支持。OpenCV 目前支持多种语言，如C++、Python、Java 等

#### 安装

有关在计算机上安装 OpenCV，请参阅这些文章。

* [Windows 安装说明](https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_setup/py_setup_in_windows/py_setup_in_windows.html#install-opencv-python-in-windows)
* [Mac 安装说明](https://medium.com/@nuwanprabhath/installing-opencv-in-macos-high-sierra-for-python-3-89c79f0a246a) (High Sierra)
* [Linux 安装说明](https://www.pyimagesearch.com/2018/05/28/ubuntu-18-04-how-to-install-opencv) （Ubuntu 18.04)

### 在这里，我们将专注于 OpenCV 的 Python 实现

```python
# OpenCV读取图片
import cv2
img = cv2.imread('cat.jpg')

# 显示图片
# imshow() 函数被用来显示图片
cv2.imshow('Image', img)
# 第一个参数是窗口的标题，第二个参数是image
# 如果你得到错误，对象类型为None，你的图像路径可能是错误的。请重新检查图像包
cv2.waitKey(0)
# waitKey() 是一个键盘绑定函数，参数以毫秒为单位。对于GUI事件，必须使用waitKey()函数。

# 保存图片
cv2.imwrite('catgray.png', img)
# 第一个参数是文件名，第二个参数是图像

# 转换图像灰度
gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 从摄像头捕捉视频
cap = cv2.VideoCapture(0)
# 0 是你的相机，如果你有多台相机，你需要输入他们的id
while True:
    # 一帧一帧地获取
    _, frame = cap.read()
    cv2.imshow('Frame', frame)
    # 当用户按下q ->退出
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
# 相机必须释放
cap.release()

# 在文件中播放视频
cap = cv2.VideoCapture('movie.mp4')
while cap.isOpened():
    _, frame = cap.read()
    # 灰度播放视频
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    cv2.imshow('frame', gray)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()

# 在OpenCV中画线
# cv2.line(img, (x,y), (x1,y1), (color->r,g,b->0 to 255), thickness)(注 color颜色rgb参数 thickness粗细)
cv2.line(img, (0, 0), (511, 511), (255, 0, 0), 5)

# 画矩形
# cv2.rectangle(img, (x,y), (x1,y1), (color->r,g,b->0 to 255), thickness)
# 粗细= -1用于填充矩形
cv2.rectangle(img, (384, 0), (510, 128), (0, 255, 0), 3)

# 画圆
# cv2.circle(img, (xCenter,yCenter), radius, (color->r,g,b->0 to 255), thickness)
cv2.circle(img, (200, 90), 100, (0, 0, 255), -1)

# 画椭圆
cv2.ellipse(img, (256, 256), (100, 50), 0, 0, 180, 255, -1)

# 在图像上增加文字
cv2.putText(img, "Hello World!!!", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 2, 255)

# 合成图像
img1 = cv2.imread('cat.png')
img2 = cv2.imread('openCV.jpg')
dst = cv2.addWeighted(img1, 0.5, img2, 0.5, 0)

# 阈值图像
# 二进制阈值
_, thresImg = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
# Adaptive Thresholding
adapThres = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)

# 模糊的形象
# 高斯模糊
blur = cv2.GaussianBlur(img, (5, 5), 0)
# 模糊中值
medianBlur = cv2.medianBlur(img, 5)

# Canny 边缘检测
img = cv2.imread('cat.jpg', 0)
edges = cv2.Canny(img, 100, 200)

# 用Haar Cascades进行人脸检测
# 下载 Haar Cascades 在 https://github.com/opencv/opencv/blob/master/data/haarcascades/
import cv2
import numpy as np
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')

img = cv2.imread('human.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

aces = 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)
    roi_gray = gray[y : y + h, x : x + w]
    roi_color = img[y : y + h, x : x + w]
    eyes = eye_cascade.detectMultiScale(roi_gray)
    for ex, ey, ew, eh in eyes:
        cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)

cv2.imshow('img', img)
cv2.waitKey(0)

cv2.destroyAllWindows()
# destroyAllWindows() destroys all windows.
# 如果您希望销毁特定窗口，请传递您创建的窗口的确切名称。
```

### 进一步阅读：

* Download Cascade from [https://github.com/opencv/opencv/blob/master/data/haarcascades](https://github.com/opencv/opencv/blob/master/data/haarcascades)
* OpenCV 绘图函数 [https://docs.opencv.org/2.4/modules/core/doc/drawing_functions.html](https://docs.opencv.org/2.4/modules/core/doc/drawing_functions.html)
* 最新的语言参考 [https://opencv.org](https://opencv.org)
* 更多的资源 [https://en.wikipedia.org/wiki/OpenCV](https://en.wikipedia.org/wiki/OpenCV)
* 优秀的的 OpenCV 教程
    * [https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html](https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html)
    * [https://realpython.com/python-opencv-color-spaces](https://realpython.com/python-opencv-color-spaces)
    * [https://pyimagesearch.com](https://pyimagesearch.com)
    * [https://www.learnopencv.com](https://www.learnopencv.com)
