OpenCV+Python简单实践

一、基础

1.基础函数

读取图片

img = cv.imread('../Resources/Photos/park.jpg')

转灰度图片

gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

高斯滤波

blur = cv.GaussianBlur(img, (7,7), cv.BORDER_DEFAULT)

边缘检测

canny = cv.Canny(blur, 125, 175)

膨胀

dilated = cv.dilate(canny, (7,7), iterations=3)

腐蚀

eroded = cv.erode(dilated, (7,7), iterations=3)

改变图片尺寸

resized = cv.resize(img, (500,500), interpolation=cv.INTER_CUBIC)

裁剪图片

cropped = img[50:200, 200:400]

效果

源代码

import cv2 as cv
import numpy as np

def stackImages(scale, imgArray):
    """
        将多张图像压入同一个窗口显示
        :param scale:float类型，输出图像显示百分比，控制缩放比例，0.5=图像分辨率缩小一半
        :param imgArray:元组嵌套列表，需要排列的图像矩阵
        :return:输出图像
    """
    rows = len(imgArray)
    cols = len(imgArray[0])
    rowsAvailable = isinstance(imgArray[0], list)
    width = imgArray[0][0].shape[1]
    height = imgArray[0][0].shape[0]
    if rowsAvailable:
        for x in range(0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape[:2]:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]),
                                                None, scale, scale)
                if len(imgArray[x][y].shape) == 2: imgArray[x][y] = cv.cvtColor(imgArray[x][y], cv.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank] * rows
        hor_con = [imageBlank] * rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None, scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
        hor = np.hstack(imgArray)
        ver = hor
    return ver


# Read in an image
# 读取图片
img = cv.imread('../Resources/Photos/park.jpg')


# Converting to grayscale
# 转灰度
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)


# Blur
# 高斯滤波
blur = cv.GaussianBlur(img, (7,7), cv.BORDER_DEFAULT)


# Edge Cascade
# Canny算子边缘检测
canny = cv.Canny(blur, 125, 175)


# Dilating the image
# 膨胀
dilated = cv.dilate(canny, (7,7), iterations=3)


# Eroding
# 腐蚀
eroded = cv.erode(dilated, (7,7), iterations=3)


# Resize
# 重新设置图片大小
resized = cv.resize(img, (500,500), interpolation=cv.INTER_CUBIC)


# Cropping
# 剪切图片
cropped = img[50:200, 200:400]


cv.putText(img, "Park", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(gray, "Gray", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(blur, "blur",(0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(canny, "Canny Edges", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(dilated, "Dilated", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(eroded, "Eroded", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(resized, "Resized", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(cropped, "Cropped", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)

imgStack = stackImages(0.5,([img,gray,blur,canny],[dilated,eroded,resized,cropped]))
cv.imshow("imgStack", imgStack)
cv.waitKey(0)

2.找中心

读取图片

img = cv.imread('../Resources/Photos/cats.jpg')

图片变成全黑

blank = np.zeros(img.shape, dtype='uint8')

转为灰度

gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

高斯滤波

blur = cv.GaussianBlur(gray, (5,5), cv.BORDER_DEFAULT)

边缘检测

canny = cv.Canny(blur, 125, 175)

二值化

ret, thresh = cv.threshold(canny, 125, 255, cv.THRESH_BINARY)

找中心

contours, hierarchies = cv.findContours(thresh, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)

绘制

cv.drawContours(blank, contours, -1, (0,0,255), 5)

效果

源代码

#pylint:disable=no-member

import cv2 as cv
import numpy as np


def stackImages(scale, imgArray):
    """
        将多张图像压入同一个窗口显示
        :param scale:float类型，输出图像显示百分比，控制缩放比例，0.5=图像分辨率缩小一半
        :param imgArray:元组嵌套列表，需要排列的图像矩阵
        :return:输出图像
    """
    rows = len(imgArray)
    cols = len(imgArray[0])
    rowsAvailable = isinstance(imgArray[0], list)
    width = imgArray[0][0].shape[1]
    height = imgArray[0][0].shape[0]
    if rowsAvailable:
        for x in range(0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape[:2]:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]),
                                                None, scale, scale)
                if len(imgArray[x][y].shape) == 2: imgArray[x][y] = cv.cvtColor(imgArray[x][y], cv.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank] * rows
        hor_con = [imageBlank] * rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None, scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
        hor = np.hstack(imgArray)
        ver = hor
    return ver

# 读取目标图片
img = cv.imread('../Resources/Photos/cats.jpg')

# 把图片RGB填充为0
blank = np.zeros(img.shape, dtype='uint8')


# 转为灰度图片
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)


# 高斯滤波处理
blur = cv.GaussianBlur(gray, (5,5), cv.BORDER_DEFAULT)


# 边缘检测
canny = cv.Canny(blur, 125, 175)

# 二值化
ret, thresh = cv.threshold(canny, 125, 255, cv.THRESH_BINARY)

# 找到中心
contours, hierarchies = cv.findContours(thresh, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)
print(f'{len(contours)} contour(s) found!')

# 绘制边缘
cv.drawContours(blank, contours, -1, (0,0,255), 5)


cv.putText(img, "Cats", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(gray, "Gray", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(blur, "blur",(0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(canny, "Canny Edges", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(thresh, "thresh", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(blank, "Contours Drawn", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 0), 3)

imgStack = stackImages(0.5,([img,gray,blur],[canny,thresh,blank]))
cv.imshow("imgStack", imgStack)
cv.waitKey(0)

3.修改

把一个区域设置为指定颜色

img_2[200:300, 300:400]=0,0,255

画一个矩形

cv.rectangle(img_3, (0,0), (img_3.shape[1]//2, img_3.shape[0]//2), (0,255,0), thickness=-1)

画一个圆

cv.circle(img_4, (img_4.shape[1]//2, img_4.shape[0]//2), 40, (0,0,255), thickness=-1)

画一条直线

cv.line(img_5, (100,250), (300,400), (255,255,255), thickness=3)

显示字体

cv.putText(img_6, 'HelloWorld', (255,150), cv.FONT_HERSHEY_TRIPLEX, 1.0, (255,255,255), 2)

效果

源码

import cv2 as cv
import numpy as np

def stackImages(scale, imgArray):
    """
        将多张图像压入同一个窗口显示
        :param scale:float类型，输出图像显示百分比，控制缩放比例，0.5=图像分辨率缩小一半
        :param imgArray:元组嵌套列表，需要排列的图像矩阵
        :return:输出图像
    """
    rows = len(imgArray)
    cols = len(imgArray[0])
    rowsAvailable = isinstance(imgArray[0], list)
    width = imgArray[0][0].shape[1]
    height = imgArray[0][0].shape[0]
    if rowsAvailable:
        for x in range(0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape[:2]:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]),
                                                None, scale, scale)
                if len(imgArray[x][y].shape) == 2: imgArray[x][y] = cv.cvtColor(imgArray[x][y], cv.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank] * rows
        hor_con = [imageBlank] * rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None, scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
        hor = np.hstack(imgArray)
        ver = hor
    return ver

# 创造一个黑色背景板
img_1 = np.zeros((500,500,3), dtype='uint8')
# 绘制边缘线便于区分
cv.line(img_1, (0,0), (0,img_1.shape[0]), (255,255,255), thickness=3)
cv.line(img_1, (0,img_1.shape[0]), (img_1.shape[1],img_1.shape[0]), (255,255,255), thickness=3)


# 1. Paint the image a certain colour
# 把一个区域设置为指定颜色
img_2=img_1.copy()
img_2[200:300, 300:400]=0,0,255


# 2. Draw a Rectangle
# 画一个矩形
# 第一个参数为目标图片
# 第二个参数为矩形起始坐标,矩形左上角
# 第三个参数为矩形终止坐标,矩形右下角
# 第四个参数为颜色
# 第五个参数为线条宽度,-1表示填充
img_3=img_2.copy()
cv.rectangle(img_3, (0,0), (img_3.shape[1]//2, img_3.shape[0]//2), (0,255,0), thickness=-1)


# 3. Draw A circle
# 画一个圆
# 第一个参数为目标图片
# 第二个参数为圆心坐标
# 第三个参数为半径
# 第四个参数为颜色
# 第五个参数为线条宽度,-1表示填充
img_4=img_3.copy()
cv.circle(img_4, (img_4.shape[1]//2, img_4.shape[0]//2), 40, (0,0,255), thickness=-1)


# 4. Draw a line
# 画一条直线
# 第一个参数为目标图片
# 第二个参数为起始坐标
# 第三个参数为终止坐标
# 第四个参数为颜色
# 第五个参数为线条宽度
img_5=img_4.copy()
cv.line(img_5, (100,250), (300,400), (255,255,255), thickness=3)


# 5. Write text
# 在图片上写字
# 第一个参数为目标图片
# 第二个参数为显示内容
# 第三个参数为显示起始坐标
# 第四个参数为字体类型
# 第五个参数为字体大小
# 第六个参数为字体颜色
# 第七个参数为字体线条粗细
img_6=img_5.copy()
cv.putText(img_6, 'HelloWorld', (255,150), cv.FONT_HERSHEY_TRIPLEX, 1.0, (255,255,255), 2)


imgStack = stackImages(0.5,([img_1,img_2,img_3],[img_4,img_5,img_6]))
cv.imshow("imgStack", imgStack)
cv.waitKey(0)

4.读取图片和视频

读取图片

img = cv.imread('../Resources/Photos/cats.jpg')
cv.imshow('Cats', img)

打开视频

capture = cv.VideoCapture('../Resources/Videos/dog.mp4')

读取视频

while True:
    # 循环读取每一帧
    isTrue, frame = capture.read()
    
    # if cv.waitKey(20) & 0xFF==ord('d'):
    # This is the preferred way - if `isTrue` is false (the frame could 
    # not be read, or we're at the end of the video), we immediately
    # break from the loop.
    # 如果没有读取成功或者读取完毕直接退出
    # 或者直接按下键盘的d退出
    if isTrue:    
        cv.imshow('Video', frame)
        if cv.waitKey(20) & 0xFF==ord('d'):
            break            
    else:
        break

效果

源代码

#pylint:disable=no-member

import cv2 as cv

img = cv.imread('../Resources/Photos/cats.jpg')
cv.imshow('Cats', img)

cv.waitKey(0)

# Reading Videos
# 打开视频
capture = cv.VideoCapture('../Resources/Videos/dog.mp4')

while True:
    # 循环读取每一帧
    isTrue, frame = capture.read()
    
    # if cv.waitKey(20) & 0xFF==ord('d'):
    # This is the preferred way - if `isTrue` is false (the frame could 
    # not be read, or we're at the end of the video), we immediately
    # break from the loop.
    # 如果没有读取成功或者读取完毕直接退出
    # 或者直接按下键盘的d退出
    if isTrue:    
        cv.imshow('Video', frame)
        if cv.waitKey(20) & 0xFF==ord('d'):
            break            
    else:
        break
# 释放
capture.release()
# 关闭窗口
cv.destroyAllWindows()

5.二值化

读取图片

img = cv.imread('../Resources/Photos/cats.jpg')

转灰度

gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

二值化

threshold, thresh = cv.threshold(gray, 150, 255, cv.THRESH_BINARY )

threshold, thresh_inv = cv.threshold(gray, 150, 255, cv.THRESH_BINARY_INV )

adaptive_thresh1 = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY_INV, 55, 1)

adaptive_thresh2 = cv.adaptiveThreshold(gray, 255,  cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY_INV, 55, 1)

效果

源码

#pylint:disable=no-member

import cv2 as cv
import numpy as np

def stackImages(scale, imgArray):
    """
        将多张图像压入同一个窗口显示
        :param scale:float类型，输出图像显示百分比，控制缩放比例，0.5=图像分辨率缩小一半
        :param imgArray:元组嵌套列表，需要排列的图像矩阵
        :return:输出图像
    """
    rows = len(imgArray)
    cols = len(imgArray[0])
    rowsAvailable = isinstance(imgArray[0], list)
    width = imgArray[0][0].shape[1]
    height = imgArray[0][0].shape[0]
    if rowsAvailable:
        for x in range(0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape[:2]:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]),
                                                None, scale, scale)
                if len(imgArray[x][y].shape) == 2: imgArray[x][y] = cv.cvtColor(imgArray[x][y], cv.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank] * rows
        hor_con = [imageBlank] * rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None, scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
        hor = np.hstack(imgArray)
        ver = hor
    return ver

# 读取图片
img = cv.imread('../Resources/Photos/cats.jpg')
# img = cv.imread('img_2.png')


# 转灰度
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)



# Simple Thresholding
# 二值化
# 第一个参数为目标图片
# 第二个参数为 阈值 threshold
# 第三参数为 最大值 maxval
# 第四个参数为类型
# THRESH_BINARY     = 0, 大于thresh 为 maxval,否则为 0
# THRESH_BINARY_INV = 1, 大于thresh 为 0,否则为 maxval
# THRESH_TRUNC      = 2, 大于thresh 为 thresh,否则为 0
# THRESH_TOZERO     = 3, 大于thresh  不变,否则为 0
# THRESH_TOZERO_INV = 4, 大于thresh 为 0,否则 不变
# THRESH_MASK       = 7,
# THRESH_OTSU       = 8, 使用OTSU阈值
# THRESH_TRIANGLE   = 16 使用三角形算法阈值

threshold, thresh = cv.threshold(gray, 150, 255, cv.THRESH_BINARY )


# 二值化
threshold, thresh_inv = cv.threshold(gray, 150, 255, cv.THRESH_BINARY_INV )


# Adaptive Thresholding
# 阈值自适应二值化
adaptive_thresh1 = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY_INV, 55, 1)
adaptive_thresh2 = cv.adaptiveThreshold(gray, 255,  cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY_INV, 55, 1)


cv.putText(img, "Cats", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(gray, "Gray", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(thresh, "thresh",(0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(thresh_inv, "thresh_inv", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(adaptive_thresh1, "adaptive_thresh1", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(adaptive_thresh2, "adaptive_thresh1", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 0), 3)

imgStack = stackImages(0.5,([img,gray,thresh],[thresh_inv,adaptive_thresh1,adaptive_thresh2]))
cv.imshow("imgStack", imgStack)
cv.waitKey(0)

6.变换

读取图片

img = cv.imread('../Resources/Photos/park.jpg')

缩放

def translate(img, x, y):
    transMat = np.float32([[1,0,x],[0,1,y]])
    dimensions = (img.shape[1], img.shape[0])
    return cv.warpAffine(img, transMat, dimensions)

旋转

def rotate(img, angle, rotPoint=None):
    (height,width) = img.shape[:2]

    if rotPoint is None:
        rotPoint = (width//2,height//2)
    
    rotMat = cv.getRotationMatrix2D(rotPoint, angle, 1.0)
    dimensions = (width,height)

    return cv.warpAffine(img, rotMat, dimensions)

效果

源代码

#pylint:disable=no-member

import cv2 as cv
import numpy as np

def stackImages(scale, imgArray):
    """
        将多张图像压入同一个窗口显示
        :param scale:float类型，输出图像显示百分比，控制缩放比例，0.5=图像分辨率缩小一半
        :param imgArray:元组嵌套列表，需要排列的图像矩阵
        :return:输出图像
    """
    rows = len(imgArray)
    cols = len(imgArray[0])
    rowsAvailable = isinstance(imgArray[0], list)
    width = imgArray[0][0].shape[1]
    height = imgArray[0][0].shape[0]
    if rowsAvailable:
        for x in range(0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape[:2]:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]),
                                                None, scale, scale)
                if len(imgArray[x][y].shape) == 2: imgArray[x][y] = cv.cvtColor(imgArray[x][y], cv.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank] * rows
        hor_con = [imageBlank] * rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None, scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
        hor = np.hstack(imgArray)
        ver = hor
    return ver

img = cv.imread('../Resources/Photos/park.jpg')
#cv.imshow('Park', img)

# Translation
def translate(img, x, y):
    transMat = np.float32([[1,0,x],[0,1,y]])
    dimensions = (img.shape[1], img.shape[0])
    return cv.warpAffine(img, transMat, dimensions)

# -x --> Left
# -y --> Up
# x --> Right
# y --> Down

# 缩放
translated = translate(img, -100, 100)
#cv.imshow('Translated', translated)

# Rotation
def rotate(img, angle, rotPoint=None):
    (height,width) = img.shape[:2]

    if rotPoint is None:
        rotPoint = (width//2,height//2)
    
    rotMat = cv.getRotationMatrix2D(rotPoint, angle, 1.0)
    dimensions = (width,height)

    return cv.warpAffine(img, rotMat, dimensions)

# 旋转
rotated = rotate(img, -45)
#cv.imshow('Rotated', rotated)

rotated_rotated = rotate(img, -90)
#cv.imshow('Rotated Rotated', rotated_rotated)

# Resizing
#
resized = cv.resize(img, (500,500), interpolation=cv.INTER_CUBIC)
#cv.imshow('Resized', resized)

# Flipping
# 旋转
flip = cv.flip(img, -1)
#cv.imshow('Flip', flip)

# Cropping
cropped = img[200:400, 300:400]
#cv.imshow('Cropped', cropped)


cv.putText(img, "img", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(translated, "translated", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(rotated, "rotated",(0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(rotated_rotated, "rotated_rotated", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(resized, "resized", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 3.0, (255, 0, 0), 3)
cv.putText(flip, "flip", (0, 100), cv.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 0), 3)

imgStack = stackImages(0.5,([img,translated,rotated],[rotated_rotated,resized,flip]))
cv.imshow("imgStack", imgStack)
cv.waitKey(0)

二、进阶

1.图片基本运算

代码

#pylint:disable=no-member

import cv2 as cv
import numpy as np
def stackImages(scale, imgArray):
    """
        将多张图像压入同一个窗口显示
        :param scale:float类型，输出图像显示百分比，控制缩放比例，0.5=图像分辨率缩小一半
        :param imgArray:元组嵌套列表，需要排列的图像矩阵
        :return:输出图像
    """
    rows = len(imgArray)
    cols = len(imgArray[0])

    rowsAvailable = isinstance(imgArray[0], list)

    # 用空图片补齐
    for i in range(rows):
        tmp = cols - len(imgArray[i])
        for j in range(tmp):
            img = np.zeros((imgArray[0][0].shape[0], imgArray[0][0].shape[1]), dtype='uint8')
            imgArray[i].append(img)

    # 判断维数
    if rows>=2:
        width = imgArray[0][0].shape[1]
        height = imgArray[0][0].shape[0]

    else:
        width = imgArray[0].shape[1]
        height = imgArray[0].shape[0]

    if rowsAvailable:
        for x in range(0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape[:2]:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]),
                                                None, scale, scale)
                if len(imgArray[x][y].shape) == 2:
                    imgArray[x][y] = cv.cvtColor(imgArray[x][y], cv.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank] * rows
        hor_con = [imageBlank] * rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None, scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv.cvtColor(imgArray[x], cv.COLOR_GRAY2BGR)
        hor = np.hstack(imgArray)
        ver = hor
    return ver

blank = np.zeros((400,400), dtype='uint8')

rectangle = cv.rectangle(blank.copy(), (30,30), (370,370), 255, -1)
circle = cv.circle(blank.copy(), (200,200), 200, 255, -1)


# bitwise AND --> intersecting regions
# 与运算
bitwise_and = cv.bitwise_and(rectangle, circle)
#cv.imshow('Bitwise AND', bitwise_and)

# bitwise OR --> non-intersecting and intersecting regions
# 或运算
bitwise_or = cv.bitwise_or(rectangle, circle)
#cv.imshow('Bitwise OR', bitwise_or)

# bitwise XOR --> non-intersecting regions
# 异或运算
bitwise_xor = cv.bitwise_xor(rectangle, circle)
#cv.imshow('Bitwise XOR', bitwise_xor)

# bitwise NOT
# 非运算
bitwise_not = cv.bitwise_not(circle)
#cv.imshow('Circle NOT', bitwise_not)

imgStack = stackImages(0.5, ([rectangle,circle,bitwise_and],[bitwise_or,bitwise_xor,bitwise_not]))
cv.imshow("imgStack", imgStack)
cv.waitKey(0)
cv.waitKey(0)

效果

2.滤波

代码

#pylint:disable=no-member

import cv2 as cv
import numpy as np
def stackImages(scale, imgArray):
    """
        将多张图像压入同一个窗口显示
        :param scale:float类型，输出图像显示百分比，控制缩放比例，0.5=图像分辨率缩小一半
        :param imgArray:元组嵌套列表，需要排列的图像矩阵
        :return:输出图像
    """
    rows = len(imgArray)
    cols = len(imgArray[0])

    rowsAvailable = isinstance(imgArray[0], list)

    # 用空图片补齐
    for i in range(rows):
        tmp = cols - len(imgArray[i])
        for j in range(tmp):
            img = np.zeros((imgArray[0][0].shape[0], imgArray[0][0].shape[1]), dtype='uint8')
            imgArray[i].append(img)

    # 判断维数
    if rows>=2:
        width = imgArray[0][0].shape[1]
        height = imgArray[0][0].shape[0]

    else:
        width = imgArray[0].shape[1]
        height = imgArray[0].shape[0]

    if rowsAvailable:
        for x in range(0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape[:2]:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]),
                                                None, scale, scale)
                if len(imgArray[x][y].shape) == 2:
                    imgArray[x][y] = cv.cvtColor(imgArray[x][y], cv.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank] * rows
        hor_con = [imageBlank] * rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None, scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv.cvtColor(imgArray[x], cv.COLOR_GRAY2BGR)
        hor = np.hstack(imgArray)
        ver = hor
    return ver
img = cv.imread('../Resources/Photos/cats.jpg')

# Averaging
# 均值滤波
average = cv.blur(img, (3,3))

# Gaussian Blur
# 高斯滤波
gauss = cv.GaussianBlur(img, (3,3), 0)

# Median Blur
# 中值滤波
median = cv.medianBlur(img, 3)

# Bilateral
# 双边滤波
bilateral = cv.bilateralFilter(img, 10, 35, 25)


imgStack =stackImages(0.5,([img,average,gauss],[median,bilateral]))
cv.imshow("imgStack",imgStack)
cv.waitKey(0)

效果

3.颜色转换

代码

#pylint:disable=no-member

import cv2 as cv
import matplotlib.pyplot as plt
import numpy as np

def stackImages(scale, imgArray):
    """
        将多张图像压入同一个窗口显示
        :param scale:float类型，输出图像显示百分比，控制缩放比例，0.5=图像分辨率缩小一半
        :param imgArray:元组嵌套列表，需要排列的图像矩阵
        :return:输出图像
    """
    rows = len(imgArray)
    cols = len(imgArray[0])

    rowsAvailable = isinstance(imgArray[0], list)

    # 用空图片补齐
    for i in range(rows):
        tmp = cols - len(imgArray[i])
        for j in range(tmp):
            img = np.zeros((imgArray[0][0].shape[0], imgArray[0][0].shape[1]), dtype='uint8')
            imgArray[i].append(img)

    # 判断维数
    if rows>=2:
        width = imgArray[0][0].shape[1]
        height = imgArray[0][0].shape[0]

    else:
        width = imgArray[0].shape[1]
        height = imgArray[0].shape[0]

    if rowsAvailable:
        for x in range(0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape[:2]:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]),
                                                None, scale, scale)
                if len(imgArray[x][y].shape) == 2:
                    imgArray[x][y] = cv.cvtColor(imgArray[x][y], cv.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank] * rows
        hor_con = [imageBlank] * rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None, scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv.cvtColor(imgArray[x], cv.COLOR_GRAY2BGR)
        hor = np.hstack(imgArray)
        ver = hor
    return ver

img = cv.imread('../Resources/Photos/park.jpg')


# BGR to Grayscale
# BGR转灰度
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)


# BGR to HSV
# BGR转HSV
hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)


# BGR to L*a*b
# BGR转Lab
lab = cv.cvtColor(img, cv.COLOR_BGR2LAB)


# BGR to RGB
# BGR格式转RGB格式
rgb = cv.cvtColor(img, cv.COLOR_BGR2RGB)


# HSV to BGR
# HSV转BGR
lab_bgr = cv.cvtColor(lab, cv.COLOR_LAB2BGR)


imgStack =stackImages(0.5,([img,gray,hsv],[lab,rgb,lab_bgr]))
cv.imshow("imgStack",imgStack)
cv.waitKey(0)

效果

4.梯度计算

gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
cv.imshow('Gray', gray)

拉普拉斯算子

# Laplacian
# 拉普拉斯算子
lap = cv.Laplacian(gray, cv.CV_64F)
lap = np.uint8(np.absolute(lap))
cv.imshow('Laplacian', lap)

Sobel算子

# x方向
sobelx = cv.Sobel(gray, cv.CV_64F, 1, 0)

# y方向
sobely = cv.Sobel(gray, cv.CV_64F, 0, 1)

# 合方向
combined_sobel = cv.bitwise_or(sobelx, sobely)

边缘检测

# 边缘检测
canny = cv.Canny(gray, 150, 175)
cv.imshow('Canny', canny)

5.直方图

灰度直方图

img = cv.imread('../Resources/Photos/cats.jpg')

blank = np.zeros(img.shape[:2], dtype='uint8')

gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
cv.imshow('Gray', gray)

mask = cv.circle(blank, (img.shape[1]//2,img.shape[0]//2), 100, 255, -1)


# GRayscale histogram
gray_hist = cv.calcHist([gray], [0], mask, [256], [0,256] )

plt.figure()
plt.title('Grayscale Histogram')
plt.xlabel('Bins')
plt.ylabel('# of pixels')
plt.plot(gray_hist)
plt.xlim([0,256])
plt.show()

效果

颜色直方图
代码

img = cv.imread('../Resources/Photos/cats.jpg')

blank = np.zeros(img.shape[:2], dtype='uint8')

gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
cv.imshow('Gray', gray)

mask = cv.circle(blank, (img.shape[1]//2,img.shape[0]//2), 100, 255, -1)

masked = cv.bitwise_and(img,img,mask=mask)


# Colour Histogram

plt.figure()
plt.title('Colour Histogram')
plt.xlabel('Bins')
plt.ylabel('# of pixels')
colors = ('b', 'g', 'r')
for i,col in enumerate(colors):
    hist = cv.calcHist([img], [i], mask, [256], [0,256])
    plt.plot(hist, color=col)
    plt.xlim([0,256])

plt.show()

cv.waitKey(0)

效果

6.掩饰

代码

#pylint:disable=no-member

import cv2 as cv
import numpy as np
def stackImages(scale, imgArray):
    """
        将多张图像压入同一个窗口显示
        :param scale:float类型，输出图像显示百分比，控制缩放比例，0.5=图像分辨率缩小一半
        :param imgArray:元组嵌套列表，需要排列的图像矩阵
        :return:输出图像
    """
    rows = len(imgArray)
    cols = len(imgArray[0])

    rowsAvailable = isinstance(imgArray[0], list)

    # 用空图片补齐
    for i in range(rows):
        tmp = cols - len(imgArray[i])
        for j in range(tmp):
            img = np.zeros((imgArray[0][0].shape[0], imgArray[0][0].shape[1]), dtype='uint8')
            imgArray[i].append(img)

    # 判断维数
    if rows>=2:
        width = imgArray[0][0].shape[1]
        height = imgArray[0][0].shape[0]

    else:
        width = imgArray[0].shape[1]
        height = imgArray[0].shape[0]

    if rowsAvailable:
        for x in range(0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape[:2]:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]),
                                                None, scale, scale)
                if len(imgArray[x][y].shape) == 2:
                    imgArray[x][y] = cv.cvtColor(imgArray[x][y], cv.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank] * rows
        hor_con = [imageBlank] * rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None, scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv.cvtColor(imgArray[x], cv.COLOR_GRAY2BGR)
        hor = np.hstack(imgArray)
        ver = hor
    return ver

img = cv.imread('../Resources/Photos/cats 2.jpg')

blank = np.zeros(img.shape[:2], dtype='uint8')

# 画圆
circle = cv.circle(blank.copy(), (img.shape[1]//2 + 45,img.shape[0]//2), 100, 255, -1)

# 画矩形
rectangle = cv.rectangle(blank.copy(), (30,30), (370,370), 255, -1)

# 与运算得到半圆
weird_shape = cv.bitwise_and(circle,rectangle)


# 与图片与运算得到目标区域
masked = cv.bitwise_and(img,img,mask=weird_shape)


imgStack =stackImages(0.5,([img,blank],[circle,rectangle],[weird_shape,masked]))
cv.imshow("imgStack",imgStack)
cv.waitKey(0)

效果

7.视频缩放

代码

#pylint:disable=no-member

import cv2 as cv

# img = cv.imread('../Resources/Photos/cat.jpg')
# cv.imshow('Cat', img)


# 缩放
def rescaleFrame(frame, scale=0.75):
    # Images, Videos and Live Video
    width = int(frame.shape[1] * scale)
    height = int(frame.shape[0] * scale)

    dimensions = (width,height)

    return cv.resize(frame, dimensions, interpolation=cv.INTER_AREA)

# 改变视频宽度和高度比例
def changeRes(width,height):
    # Live video
    capture.set(3,width)
    capture.set(4,height)
    
# Reading Videos
# 读取视频
capture = cv.VideoCapture('../Resources/Videos/dog.mp4')

while True:
    isTrue, frame = capture.read()

    frame_resized = rescaleFrame(frame)
    
    # 原视频
    cv.imshow('Video', frame)
    # 缩放后视频
    cv.imshow('Video Resized', frame_resized)

    if cv.waitKey(20) & 0xFF==ord('d'):
        break

capture.release()
cv.destroyAllWindows()

效果

8.通道分离与合并

代码

#pylint:disable=no-member

import cv2 as cv
import numpy as np
def stackImages(scale, imgArray):
    """
        将多张图像压入同一个窗口显示
        :param scale:float类型，输出图像显示百分比，控制缩放比例，0.5=图像分辨率缩小一半
        :param imgArray:元组嵌套列表，需要排列的图像矩阵
        :return:输出图像
    """
    rows = len(imgArray)
    cols = len(imgArray[0])

    rowsAvailable = isinstance(imgArray[0], list)

    # 用空图片补齐
    for i in range(rows):
        tmp = cols - len(imgArray[i])
        for j in range(tmp):
            img = np.zeros((imgArray[0][0].shape[0], imgArray[0][0].shape[1]), dtype='uint8')
            imgArray[i].append(img)

    # 判断维数
    if rows>=2:
        width = imgArray[0][0].shape[1]
        height = imgArray[0][0].shape[0]

    else:
        width = imgArray[0].shape[1]
        height = imgArray[0].shape[0]

    if rowsAvailable:
        for x in range(0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape[:2]:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]),
                                                None, scale, scale)
                if len(imgArray[x][y].shape) == 2:
                    imgArray[x][y] = cv.cvtColor(imgArray[x][y], cv.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank] * rows
        hor_con = [imageBlank] * rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None, scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv.cvtColor(imgArray[x], cv.COLOR_GRAY2BGR)
        hor = np.hstack(imgArray)
        ver = hor
    return ver

img = cv.imread('../Resources/Photos/park.jpg')


blank = np.zeros(img.shape[:2], dtype='uint8')

# 把图片通道进行分离
b,g,r = cv.split(img)

# 通道合并
# 只保留蓝色
blue = cv.merge([b,blank,blank])
# 只保留绿色
green = cv.merge([blank,g,blank])
# 只保留红色
red = cv.merge([blank,blank,r])

print(img.shape)
print(b.shape)
print(g.shape)
print(r.shape)

# 通道合并,恢复原图
merged = cv.merge([b,g,r])


imgStack =stackImages(0.5,([img,blue,green],[red,merged]))
cv.imshow("imgStack",imgStack)
cv.waitKey(0)

效果

三、练习

1.转灰度

代码

img = cv.imread('../Resources/Photos/park.jpg')
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
cv.imshow('Gray', gray)

效果

2.RGB转HSV

代码

hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)

效果

3.RGB转HSI

实现函数

def rgb2hsi(image):
    b, g, r = cv.split(image)                    # 读取通道
    r = r / 255.0                                # 归一化
    g = g / 255.0
    b = b / 255.0
    eps = 1e-6                                   # 防止除零

    img_i = (r + g + b) / 3                      # I分量

    img_h = np.zeros(r.shape, dtype=np.float32)
    img_s = np.zeros(r.shape, dtype=np.float32)
    min_rgb = np.zeros(r.shape, dtype=np.float32)
    # 获取RGB中最小值
    min_rgb = np.where((r <= g) & (r <= b), r, min_rgb)
    min_rgb = np.where((g <= r) & (g <= b), g, min_rgb)
    min_rgb = np.where((b <= g) & (b <= r), b, min_rgb)
    img_s = 1 - 3*min_rgb/(r+g+b+eps)                                            # S分量

    num = ((r-g) + (r-b))/2
    den = np.sqrt((r-g)**2 + (r-b)*(g-b))
    theta = np.arccos(num/(den+eps))
    img_h = np.where((b-g) > 0, 2*np.pi - theta, theta)                           # H分量
    img_h = np.where(img_s == 0, 0, img_h)

    img_h = img_h/(2*np.pi)                                                       # 归一化
    temp_s = img_s - np.min(img_s)
    temp_i = img_i - np.min(img_i)
    img_s = temp_s/np.max(temp_s)
    img_i = temp_i/np.max(temp_i)

    image_hsi = cv.merge([img_h, img_s, img_i])
    return image_hsi

效果

三、总结

图像不好学哦，还是得多多借助工具实现。

四、参考

https://blog.youkuaiyun.com/qq_44926189/article/details/121131241