Python interface using OpenCV method
This time I will bring you how to use OpenCV with the Python interface. What are the precautions for using OpenCV with the Python interface? The following is a practical case, let’s take a look.
1. Configure OpenCV in Anaconda2
Decompress opencv, add system environment variables, computer-->right-click properties--> Advanced system settings-->Environment variables-->System variables-->Edit path-->Add F:\Program Files (x86)\opencv-3.2.0-vc14\build \x64\vc14\bin
Copy opencv/build/python/2.7/x64/cv2.pyd to Anaconda2/Lib/Site-packages/Note: You can see it from python/2.7 above It turns out that the official python interface of opencv only supports the Anaconda2 version. If you are installing Anaconda3, you can open cmd and then execute conda install -c https://conda.anaconda.org/menpo opencv3;You can also refer to this article to configure Anaconda3
Open ipython and test itimport cv2 print(cv2.version)
2. Basic knowledge of OpenCV
1. Reading, displaying and writing images
import cv2
import matplotlib.pyplot as plt
# 读取图像，第二个参数可以为1(默认读入彩图, 可省略), 0(以灰度图读入)
im = cv2.imread('empire.jpg', 1) # 函数imread()返回图像为一个标准的 NumPy 数组
h,w = im.shape[:2]
print h,w
# 显示图像，第一个参数是窗口的名字，其次才是我们的图像，窗口会自动调整为图像大小。
cv2.imshow('image', img)
cv2.waitKey(0) # 为防止图像一闪而过，无限期的等待键盘输入
cv2.destroyAllWindows() # 关闭所有图像
# 保存图像(必须设置保存图像的路径和扩展名)
cv2.imwrite('result.png', im)
# 使用 plt 显示图像(可显示像素坐标及像素值)、保存图像
plt.imshow(im, cmap='gray', interpolation='bicubic')
plt.show()
plt.savefig('figpath.png', bbox_inches='tight')2. Color space conversion
In OpenCV, images It is not stored in traditional RGB color channels, but in BGR order (that is, the reverse order of RGB). The default when reading images is BGR, but there are some conversion functions available. Color space conversion can be achieved using the function cvtColor().# 1.使用opencv读取并创建灰度图像，按 BGR 顺序
im = cv2.imread('empire.jpg')
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
# 2.使用matplotlib.image 读入并创建灰度图像，按 RGB 顺序
import matplotlib.image as mpl_img
im = mpl_img.imread('empire.jpg')
gray = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
# Note: 注意1和2的区别在颜色转换代码
# 常用：cv2.COLOR_BGR2RGB、cv2.COLOR_GRAY2BGR、cv2.COLOR_BGR2HSV3. Draw straight lines, rectangles, circles, polygons (curves) on the image
Draw straight lines: cv2.line()import cv2
# 读取图像，按 BGR 顺序
img = cv2.imread('empire.jpg')
# 传入图像、起点坐标、终点坐标、线的颜色(color)、线的厚度(thickness)
# color : Color of the shape. for BGR, pass it as a tuple, eg: (255,0,0) for blue. For grayscale, just pass the scalar value.
# thickness : if -1 is passed for closed figures like circles, it will fill the shape, default thickness = 1.
img = cv2.line(img, (0, 0), (511, 511), (255, 0, 0), 5)# 需要传入图像、左上角顶点坐标、右下角顶点坐标、颜色、线宽 img = cv2.rectangle(img, (384, 0), (510, 128), (0, 255, 0), 3)
# 需要传入图像、圆的中心点坐标、半径、颜色、线宽 img = cv2.circle(img, (447, 63), 63, (0, 0, 255), -1) # If -1 is passed for closed figures like circles, it will fill the shape. default thickness = 1
# 数组的数据类型必须为int32，若知道曲线方程，可以生成一堆点，就可以画出曲线来啦 pts = np.array([[10,5],[20,30],[70,20],[50,10]], np.int32) # 第一个参数为-1, 表明这一维的长度(点的数量)是根据后面的维度的计算出来的 pts = pts.reshape((-1,1,2)) # 如果第三个参数是False，我们得到的多边形是不闭合的(首尾不相连) img = cv2.polylines(img, [pts], True, (0, 255, 255))
font = cv2.FONT_HERSHEY_SIMPLEX # 第 3~6 个参数为：bottom-left corner where data starts、font size、color、thickness cv2.putText(img,'OpenCV',(10,500), font, 4, (255, 255, 255), 2, cv2.LINE_AA)
4. Basic operations of the image
Get and modify the pixel valueimport cv2
import numpy as np
img = cv2.imread('messi5.jpg')
px = img[100, 100]
print px
[57 63 68]
# accessing only blue pixel
blue = img[100, 100, 0]
print blue
57
# modify the pixel
img[100, 100] = [255, 255, 255]
print img[100, 100]
[255 255 255]
# channel 2 所有值置为0
img[:, :, 2] = 0img = cv2.imread('messi5.jpg')
print img.shape
(960L, 1280L, 3L)
print img.size
3686400
print img.dtype
uint8img = cv2.imread('messi5.jpg')
# select the ball and copy it to another region
ball = img[280:340, 330:390] # 注意：340和390取不到
img[273:333, 100:160] = ballPython opencv detects and extracts the target color
How does Python write the data in the data frame to the database
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