OPEN CV系列一：图像的基本操作

## 图像数据读取

import cv2
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

 

img = cv2.imread('0.jpg')  # 读取图像 opencv读取的是BGR格式

 

 

# 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表示按键盘任意键终止
cv2.waitKey(0)
cv2.destroyAllWindows()  # 销毁窗口

 

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

 

#  cv2.IMREAD_COLOR: 彩色图像
#  cv2.IMREAD_GRAYSCALE: 灰度图像

 

img = cv2.imread('0.jpg', cv2.IMREAD_GRAYSCALE)  # 读取为灰度图像
cv_show('cat', img)

 

cv2.imwrite('cat.jpg', img)  # 图像保存

 

type(img)  # 图像格式

 

## 视频数据读取

import cv2
vc = cv2.VideoCapture('1.mp4')  # 视频流

 

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

 

while open:  # 读取视频一帧一帧读图像
    rel, frame = vc.read()
    if frame is None:
        break
    if rel == True:
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # 变成灰度图像
        cv2.imshow('result', gray)
        if cv2.waitKey(100) & 0xFF == 27:  # 等100毫秒播一帧
            break
vc.release()
cv2.destroyAllWindows()

 

### 截取部分图片数据

import cv2
img = cv2.imread('0.jpg')
cat = img[200:400, 200:400]
cv2.imshow('cat', cat)
cv2.waitKey(0)
cv2.destroyAllWindows()

 

 ### 颜色通道提取

b, g, r = cv2.split(img)  # 切分

img = cv2.merge((b, g, r))  # 合并

 

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

img = cv2.imread('0.jpg')
cv_show('cat', img)
# 只保留b
cur_img = img.copy()
cur_img[:, :, 0] = 0
cur_img[:, :, 1] = 0
cv_show('G', cur_img)
# 只保留g
cur_img = img.copy()
cur_img[:, :, 0] = 0
cur_img[:, :, 2] = 0
cv_show('B', cur_img)
# 只保留r
cur_img = img.copy()
cur_img[:, :, 1] = 0
cur_img[:, :, 2] = 0
cv_show('R', cur_img)

 

### 边界填充

 

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)  # 复制最边缘像素
reflect = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_REFLECT) # 反射法
reflect101 = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_REFLECT_101)  # 反射法
wrap = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_WRAP) # 外包装法
constant = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_CONSTANT, value=0)  # 常量法

 

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

 

### 数值计算

 

img = cv2.imread('0.jpg')
cv_show('cat', img)
img_cat = img + 10 # 每个像素点的值+10  值超过255后 经过对256取余得到
cv_show('cat+10', img_cat)

 

img_3 = img + img_cat
cv_show('img_3', img_3)  # 越界对256取余

 

img_4 = cv2.add(img, img_cat)
cv_show('img_4', img_4)  # 越界取255

 

### 图像融合

img_dog = cv2.imread('1.jpg')
# cv_show('img_Dog', img_dog)
print(img_dog.shape)
img_dog_1 = cv2.resize(img_dog, (500, 314))
print(img_dog_1.shape)
cv_show('img_dog_1', img_dog_1)

 

img_dog_2 = cv2.resize(img_dog, (0, 0), fx=3, fy=1)  # x 3倍 y 1倍
cv_show('img_dog_2', img_dog_2)

 

img_cat = cv2.imread('0.jpg')
res = cv2.addWeighted(img_cat, 0.8, img_dog_1, 0.6, 0)  # 权重 权重 偏置值
cv_show('res', res)