图像基本操作
一、数据读取-图像
- cv2.IMREAD_COLOR：彩色图像
- cv2.IMREAD_GRAYSCALE：灰度图像
import cv2 #opencv读取的格式是BGR
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
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()
#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()
#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(10000)
cv2.destroyAllWindows()
#保存
cv2.imwrite('mycat.png',img)
type(img) #numpy.ndarray
img.size #207000
img.dtype #dtype('uint8')
二、数据读取-视频
- cv2.VideoCapture可以捕获摄像头，用数字来控制不同的设备，例如0,1。
- 如果是视频文件，直接指定好路径即可。
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()
三、截取部分图像数据
img=cv2.imread('cat.jpg')
cat=img[0:50,0:200]
cv_show('cat',cat)
四、颜色通道提取
b,g,r=cv2.split(img)
img=cv2.merge((b,g,r))
img.shape
# 只保留R
cur_img = img.copy()
cur_img[:,:,0] = 0
cur_img[:,:,1] = 0
cv_show('R',cur_img)
# 只保留G
cur_img = img.copy()
cur_img[:,:,0] = 0
cur_img[:,:,2] = 0
cv_show('G',cur_img)
# 只保留B
cur_img = img.copy()
cur_img[:,:,1] = 0
cur_img[:,:,2] = 0
cv_show('B',cur_img)
五、边界填充
- BORDER_REPLICATE：复制法，也就是复制最边缘像素。
- BORDER_REFLECT：反射法，对感兴趣的图像中的像素在两边进行复制例如：fedcba|abcdefgh|hgfedcb
- BORDER_REFLECT_101：反射法，也就是以最边缘像素为轴，对称，gfedcb|abcdefgh|gfedcba
- BORDER_WRAP：外包装法cdefgh|abcdefgh|abcdefg
- BORDER_CONSTANT：常量法，常数值填充。
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)
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()
六、数值计算
In:
img_cat=cv2.imread('cat.jpg')
img_dog=cv2.imread('dog.jpg')
img_cat2= img_cat +10
img_cat[:5,:,0]
Out:
array([[142, 146, 151, ..., 156, 155, 154],
[107, 112, 117, ..., 155, 154, 153],
[108, 112, 118, ..., 154, 153, 152],
[139, 143, 148, ..., 156, 155, 154],
[153, 158, 163, ..., 160, 159, 158]], dtype=uint8)
In:
img_cat2[:5,:,0]
Out:
array([[152, 156, 161, ..., 166, 165, 164],
[117, 122, 127, ..., 165, 164, 163],
[118, 122, 128, ..., 164, 163, 162],
[149, 153, 158, ..., 166, 165, 164],
[163, 168, 173, ..., 170, 169, 168]], dtype=uint8)
In:
#相当于% 256
(img_cat + img_cat2)[:5,:,0]
Out:
array([[ 38, 46, 56, ..., 66, 64, 62],
[224, 234, 244, ..., 64, 62, 60],
[226, 234, 246, ..., 62, 60, 58],
[ 32, 40, 50, ..., 66, 64, 62],
[ 60, 70, 80, ..., 74, 72, 70]], dtype=uint8)
In:
cv2.add(img_cat,img_cat2)[:5,:,0]
Out:
array([[255, 255, 255, ..., 255, 255, 255],
[224, 234, 244, ..., 255, 255, 255],
[226, 234, 246, ..., 255, 255, 255],
[255, 255, 255, ..., 255, 255, 255],
[255, 255, 255, ..., 255, 255, 255]], dtype=uint8)
七、图像融合
img_cat.shape #(414, 500, 3)
img_dog = cv2.resize(img_dog, (500, 414))
img_dog.shape #(414, 500, 3)
#y=ax+by+c
res = cv2.addWeighted(img_cat, 0.4, img_dog, 0.6, 0)
plt.imshow(res)
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)
