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Open Source Computer Vision Library,OpenCV于1999年由Intel建立,如今由Willow Garage提供支持。OpenCV是一个基于BSD许可(开源)发行的跨平台计算机视觉库,可以运行在Linux、Windows、MacOS操作系统上。它轻量而且高效——由一系列 C 函数和少量C++类构成,同时提供了Python、Ruby、MATLAB等语言的接口,实现了图像处理和计算机视觉方面的很多通用算法。
pip install opencv-python -i http://pypi.douban.com/simple --trusted-host pypi.douban.com pip install opencv-contrib-python -i http://pypi.douban.com/simple --trusted-host pypi.douban.com pip install pytesseract -i http://pypi.douban.com/simple --trusted-host pypi.douban.com
安装opencv的扩展模块
import cv2 as cv def get_image_info(image): print(type(image)) # <class 'numpy.ndarray'> print(image.shape) # 高度 宽度 通道数 print(image.size) # 像素大小 print(image.dtype) # 数据类型 src = cv.imread(r'D:\python\pycharm2020\test\004.jpg') cv.imshow("input image", src) get_image_info(src) cv.waitKey(0) cv.destroyAllWindows()
运行结果如下:
<class 'numpy.ndarray'> (500, 500, 3) 750000 uint8
import cv2 as cv def read_video(): cap = cv.VideoCapture(r'D:\beauty\video\test.mp4') while True: ret, frame = cap.read() if ret == False: break cv.imshow('video', frame) cv.waitKey(20) read_video()
import cv2 as cv # 调用笔记本内置镜头 cap = cv.VideoCapture(0, cv.CAP_DSHOW) while True: ret, frame = cap.read() frame = cv.flip(frame, 1) cv.imshow('video', frame) # 显示镜头捕获的每一帧 if cv.waitKey(100) & 0xff == ord('q'): # 按q退出 break cap.release() cv.destroyAllWindows()
释放摄像头对象时错误:
SourceReaderCB::~SourceReaderCB terminating async callback
解决方法:
cap = cv.VideoCapture(0, cv.CAP_DSHOW)
import cv2 as cv def color_space_transform(img): gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) cv.imshow('gray', gray) hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV) cv.imshow('hsv', hsv) hls = cv.cvtColor(img, cv.COLOR_BGR2HLS) cv.imshow('hls', hls) YCrCb = cv.cvtColor(img, cv.COLOR_BGR2YCrCb) cv.imshow('YCrCb', YCrCb) yuv = cv.cvtColor(img, cv.COLOR_BGR2YUV) cv.imshow('yuv', yuv) src = cv.imread(r'D:\python\pycharm2020\test\004.jpg') src = cv.resize(src, None, fx=0.5, fy=0.5) cv.imshow('src', src) color_space_transform(src) cv.waitKey(0) cv.destroyAllWindows()
运行效果如下:
函数的参数意义:第一个参数为原数组,可以为单通道,多通道。第二个参数为下界,第三个参数为上界 mask = cv2.inRange(hsv, lower_blue, upper_blue)
而在lower_blue~upper_blue之间的值变成255 (255代表白色)
即:opencv的inRange函数可提取特定颜色,使特定颜色变为白色,其他颜色变为黑色,从而实现图像的二值化处理。
HSV颜色对应的RGB分量范围表如下:(这里是三通道的)
测试所用图像如下:
追踪绿色,代码如下:
import cv2 as cv import numpy as np def tracking_colors(image): hsv = cv.cvtColor(image, cv.COLOR_BGR2HSV) # 追踪绿色 lower_hsv = np.array([35, 43, 46]) upper_hsv = np.array([77, 255, 255]) mask = cv.inRange(hsv, lowerb=lower_hsv, upperb=upper_hsv) cv.imshow('mask', mask) cv.waitKey(0) cv.destroyAllWindows() src = cv.imread(r'D:\python\pycharm2020\test\001.jpg') tracking_colors(src)
运行效果如下:
追踪蓝色,代码如下:
import cv2 as cv import numpy as np def tracking_colors(image): hsv = cv.cvtColor(image, cv.COLOR_BGR2HSV) # 追踪蓝色 lower_hsv = np.array([100, 43, 46]) upper_hsv = np.array([124, 255, 255]) mask = cv.inRange(hsv, lowerb=lower_hsv, upperb=upper_hsv) cv.imshow('mask', mask) cv.waitKey(0) cv.destroyAllWindows() src = cv.imread(r'D:\python\pycharm2020\test\001.jpg') tracking_colors(src)
运行效果如下:
通道分离与合并
import cv2 as cv src = cv.imread(r'D:\python\pycharm2020\test\004.jpg') src = cv.resize(src, None, fx=0.5, fy=0.5) b, g, r = cv.split(src) # 分离 cv.imshow('blue', b) cv.imshow('green', g) cv.imshow('red', r) cv.imshow('src', src) cv.waitKey(0) cv.destroyAllWindows()
运行效果如下:
import cv2 as cv src = cv.imread(r'D:\python\pycharm2020\test\004.jpg') b, g, r = cv.split(src) src[:, :, 2] = 0 # 修改了的图片 cv.imshow('changed src', src) src1 = cv.merge((b, g, r)) # 合并得到原来的图片 cv.imshow('merged src', src1) cv.waitKey(0)
运行效果如下:
算数运算:
像素的算术运算涉及加减乘除等基本运算(要进行算术运算,两张图片的shape必须一样)
实例如下:
import cv2 as cv def add_demo(m1, m2): # 像素的加运算 dst = cv.add(m1, m2) cv.imshow("add_demo", dst) def subtract_demo(m1, m2): # 像素的减运算 dst = cv.subtract(m1, m2) cv.imshow("subtract_demo", dst) def divide_demo(m1, m2): # 像素的除法运算 dst = cv.divide(m1, m2) cv.imshow("divide_demo", dst) def multiply_demo(m1, m2): # 像素的乘法运算 dst = cv.multiply(m1, m2) cv.imshow("multiply_demo", dst) src1 = cv.imread(r'D:\python\pycharm2020\test\007.png') src2 = cv.imread(r'D:\python\pycharm2020\test\008.png') cv.imshow('image1', src1) cv.imshow('image2', src2) # 像素的算术运算(加、减、乘、除) 两张图片必须shape一致 add_demo(src1, src2) subtract_demo(src1, src2) divide_demo(src1, src1) multiply_demo(src1, src2) cv.waitKey(0) cv.destroyAllWindows()
运行效果如下:
像素的逻辑运算:
像素的逻辑运算涉及与、或、非、异或等基本运算(要进行逻辑运算,两张图片的shape必须一样)
实例如下:
import cv2 as cv def and_demo(m1, m2): # 与运算 每个像素点每个通道的值按位与 dst = cv.bitwise_and(m1, m2) cv.imshow("and_demo", dst) def or_demo(m1, m2): # 或运算 每个像素点每个通道的值按位或 dst = cv.bitwise_or(m1, m2) cv.imshow("or_demo", dst) def not_demo(m1): # 非运算 每个像素点每个通道的值按位取反 dst = cv.bitwise_not(m1) cv.imshow("not_demo", dst) src1 = cv.imread(r'D:\python\pycharm2020\test\007.png') src2 = cv.imread(r'D:\python\pycharm2020\test\008.png') cv.imshow('image1', src1) cv.imshow('image2', src2) # 像素的逻辑运算(与、或、非) 两张图片必须shape一致 and_demo(src1, src2) or_demo(src1, src2) not_demo(src1) cv.waitKey(0) cv.destroyAllWindows()
运行效果如下:
调节图片对比度和亮度:
简单测试如下:
import cv2 as cv import numpy as np def adjust_brightness_image(image, c, b): # 第2个参数rario为对比度 第3个参数b为亮度 height, width, channels = image.shape blank = np.zeros([height, width, channels], image.dtype) # 新建的一张全黑图片和img1图片shape类型一样,元素类型也一样 dst = cv.addWeighted(image, c, blank, 1 - c, b) cv.imshow("adjust_contrast_brightness", dst) src = cv.imread(r'D:\python\pycharm2020\test\004.jpg') cv.imshow("first", src) # 调节图片对比度和亮度 contrast and brightness (1.2, 100) (2.5, 0) (0.5 10) adjust_brightness_image(src, 1.2, 100) cv.waitKey(0) cv.destroyAllWindows()
运行效果如下:
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