01_opencv_python_基本图像处理

import cv2  # opencv读取的格式是BGR
import matplotlib.pyplot as plt
import numpy as np 
%matplotlib inline 

# img读入的本质上就是数组
img = cv2.imread(‘cat.jpg‘)

#图像的显示,也可以创建多个窗口
cv2.imshow(‘image‘,img) 
# 等待时间，毫秒级，0表示任意键终止
cv2.waitKey(0) 
cv2.destroyAllWindows()

def cv_show(name,img):
    cv2.imshow(name,img) 
    cv2.waitKey(0) 
    cv2.destroyAllWindows()

print(img.shape)  # 图片形状(h, w, c)

# 读取灰度图 -->通道数 c == 1
img=cv2.imread(‘cat.jpg‘,cv2.IMREAD_GRAYSCALE)
img

# 看看图片的类型  numpy.ndarray
type(img)

# 图片的size = h * w * c
img.size

# 图片中数据的类型 比如uint8 float32
img.dtype

# （‘路径/保存图片名字‘, 要保存的图片）
cv2.imwrite(‘mycat.png‘, img)  

vc = cv2.VideoCapture(‘test.mp4‘)

# 检查是否打开正确
if vc.isOpened(): 
    oepn, frame = vc.read()
else:
    open = False

while open:
    ret, frame = vc.read()
    if frame is None:
        break
    if ret == True:
        gray = cv2.cvtColor(frame,  cv2.COLOR_BGR2GRAY)
        cv2.imshow(‘result‘, gray)
        if cv2.waitKey(100) & 0xFF == 27:
            break
vc.release()
cv2.destroyAllWindows()

import cv2#导入opencv包
 
video=cv2.VideoCapture(0)#打开摄像头
 
fourcc = cv2.VideoWriter_fourcc(*‘XVID‘)#视频存储的格式
fps = video.get(cv2.CAP_PROP_FPS)#帧率
#视频的宽高
size = (int(video.get(cv2.CAP_PROP_FRAME_WIDTH)),         int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)))
out = cv2.VideoWriter(‘video.avi‘, fourcc, fps, size)#视频存储
 
while out.isOpened():
    ret,img=video.read()#开始使用摄像头读数据，返回ret为true，img为读的图像
    if ret is False:#ret为false则关闭
        exit()
    cv2.namedWindow(‘video‘,cv2.WINDOW_AUTOSIZE)#创建一个名为video的窗口
    cv2.imshow(‘video‘,img)#将捕捉到的图像在video窗口显示
    out.write(img)#将捕捉到的图像存储
    #按esc键退出程序
    if cv2.waitKey(1) & 0xFF ==27:
        video.release()#关闭摄像头
        break

img=cv2.imread(‘cat.jpg‘)

cat=img[0:100,0:200]
cv_show(‘cat‘,cat)

def color_space_demo(src):
    img = cv2.imread(src)
    # 转成灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    cv_show("gray", gray)
    # 转成HSV
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    cv_show(‘hsv‘, hsv)
    # hsv转成rgb(bgr)
    img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
    cv_show(‘hcv2img‘, img)
    # yuv...也可以转

def extrace_object_demo(src):
    img = cv2.imread(src)  # 通道数是3
    # print(img.shape)
    img_binary = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # 通道数是 1
    # print(img_binary.shape)
    
    # 1.将RGB转换成HSV色彩空间
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    # print(hsv.shape)
    
    # 2.定义数组，说明你要提取（过滤）的颜色目标
    # 三通道，所以是三个参数
    # 红色
    lower_hsv_r = np.array([156, 43, 46])
    upper_hsv_r = np.array([180, 255, 255])   
    
    # 3.进行过滤，提取，得到二值图像
    mask_red = cv2.inRange(hsv, lower_hsv_r, upper_hsv_r)  # 通道数是 1 
    # print(mask_red.shape)
    
    # 4.展示成果
    cv_show(‘original‘, img)
    cv_show(‘mask_red‘, mask_red)
    
    # 5.合并展示
    res = np.hstack((img_binary, mask_red))
    cv_show("hastck", res)
    
    return mask_red

# 提取
b,g,r=cv2.split(img)
print(b.shape)
# 合并
img = cv2.merge((b,g,r))
print(img.shape)

# 只保留R
cur_img = img.copy()
cur_img[:,:,0] = 0
cur_img[:,:,1] = 0
cv_show(‘R‘,cur_img)

top_size,bottom_size,left_size,right_size = (50,50,50,50)

replicate = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_REPLICATE)
reflect = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size,cv2.BORDER_REFLECT)
reflect101 = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, cv2.BORDER_REFLECT_101)
wrap = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, cv2.BORDER_WRAP)
constant = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size,cv2.BORDER_CONSTANT, value=0)

import matplotlib.pyplot as plt
plt.subplot(231), plt.imshow(img, ‘gray‘), plt.title(‘ORIGINAL‘)
plt.subplot(232), plt.imshow(replicate, ‘gray‘), plt.title(‘REPLICATE‘)
plt.subplot(233), plt.imshow(reflect, ‘gray‘), plt.title(‘REFLECT‘)
plt.subplot(234), plt.imshow(reflect101, ‘gray‘), plt.title(‘REFLECT_101‘)
plt.subplot(235), plt.imshow(wrap, ‘gray‘), plt.title(‘WRAP‘)
plt.subplot(236), plt.imshow(constant, ‘gray‘), plt.title(‘CONSTANT‘)

plt.show()

img_cat=cv2.imread(‘cat.jpg‘)
img_dog=cv2.imread(‘dog.jpg‘)

img_cat + img_dog
# ValueError: operands could not be broadcast together with shapes (414,500,3) (429,499,3) 

# 将狗狗的图片和猫猫的图片resize一样
img_dog = cv2.resize(img_dog, (500, 414))

# 选择融合的权重
res = cv2.addWeighted(img_cat, 0.4, img_dog, 0.6, 0)

# 展示结果
plt.imshow(res)

# 将猫猫的图片放大
res = cv2.resize(img, (0, 0), fx=4, fy=4)
plt.imshow(res)

res = cv2.resize(img, (0, 0), fx=1, fy=3)
plt.imshow(res)