01 计算机视觉-opencv图像基本操作

计算机视觉-opencv图像基本操作

1 数据读取-图像

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

import cv2
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
#读取彩色图像
img = cv2.imread("cat.jpg")

img

array([[[142, 151, 160],
        [146, 155, 164],
        [151, 160, 170],
        ...,
        [156, 172, 185],
        [155, 171, 184],
        [154, 170, 183]],

       [[108, 117, 126],
        [112, 123, 131],
        [118, 127, 137],
        ...,
        [155, 171, 184],
        [154, 170, 183],
        [153, 169, 182]],

       [[108, 119, 127],
        [110, 123, 131],
        [118, 128, 138],
        ...,
        [156, 169, 183],
        [155, 168, 182],
        [154, 167, 181]],

       ...,

       [[162, 186, 198],
        [157, 181, 193],
        [142, 166, 178],
        ...,
        [181, 204, 206],
        [170, 193, 195],
        [149, 172, 174]],

       [[140, 164, 176],
        [147, 171, 183],
        [139, 163, 175],
        ...,
        [169, 187, 188],
        [125, 143, 144],
        [106, 124, 125]],

       [[154, 178, 190],
        [154, 178, 190],
        [121, 145, 157],
        ...,
        [183, 198, 200],
        [128, 143, 145],
        [127, 142, 144]]], dtype=uint8)

# 图像的显示
cv2.imshow("image",img)
# 等待时间 毫秒级 0表示任意键终止
cv2.waitKey(0)
cv2.destroyAllWindows()

plt.imshow(img)

<matplotlib.image.AxesImage at 0x22212b762b0>

# 直接定义一个opencv显示的函数
def cv_show(name,img):
    cv2.imshow(name,img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# 看一下图片的维度 h w c
img.shape

(414, 500, 3)

# 读取灰度图像
img = cv2.imread("cat.jpg",cv2.IMREAD_GRAYSCALE)
img

array([[153, 157, 162, ..., 174, 173, 172],
       [119, 124, 129, ..., 173, 172, 171],
       [120, 124, 130, ..., 172, 171, 170],
       ...,
       [187, 182, 167, ..., 202, 191, 170],
       [165, 172, 164, ..., 185, 141, 122],
       [179, 179, 146, ..., 197, 142, 141]], dtype=uint8)

img.shape

(414, 500)

cv_show("image",img)

# 保存图像
cv2.imwrite("mycat.png",img)

True

# 看一下读取图像的类型
type(img)

numpy.ndarray

# 图像的大小
img.size

207000

# 图像的类型
img.dtype

dtype('uint8')

2 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():
    open,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(10) & 0xFF == 27:
            break
vc.release()
cv2.destroyAllWindows()

3 截取部分图像数据

img = cv2.imread("cat.jpg")
cat = img[0:50,0:200]
cv_show("cat",cat)
plt.imshow(cat)

<matplotlib.image.AxesImage at 0x24003b735c0>

4 颜色通道提取

b,g,r = cv2.split(img)

b

array([[142, 146, 151, ..., 156, 155, 154],
       [108, 112, 118, ..., 155, 154, 153],
       [108, 110, 118, ..., 156, 155, 154],
       ...,
       [162, 157, 142, ..., 181, 170, 149],
       [140, 147, 139, ..., 169, 125, 106],
       [154, 154, 121, ..., 183, 128, 127]], dtype=uint8)

b.shape

(414, 500)

img = cv2.merge((b,g,r))
img.shape

(414, 500, 3)

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

<matplotlib.image.AxesImage at 0x24003c105f8>

# 只保留G
cur_img = img.copy()
cur_img[:,:,0] = 0
cur_img[:,:,2] = 0
cv_show("G",cur_img)
plt.imshow(cur_img)

<matplotlib.image.AxesImage at 0x24003c7fc18>

# 只保留B
cur_img = img.copy()
cur_img[:,:,1] = 0
cur_img[:,:,2] = 0
cv_show("B",cur_img)
plt.imshow(cur_img)

<matplotlib.image.AxesImage at 0x24003ce7358>

5 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)

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()

6 数值计算

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

img_cat2 = img_cat + 10
img_cat[:5,:,0]

array([[142, 146, 151, ..., 156, 155, 154],
       [108, 112, 118, ..., 155, 154, 153],
       [108, 110, 118, ..., 156, 155, 154],
       [139, 141, 148, ..., 156, 155, 154],
       [153, 156, 163, ..., 160, 159, 158]], dtype=uint8)

img_cat2[:5,:,0]

array([[152, 156, 161, ..., 166, 165, 164],
       [118, 122, 128, ..., 165, 164, 163],
       [118, 120, 128, ..., 166, 165, 164],
       [149, 151, 158, ..., 166, 165, 164],
       [163, 166, 173, ..., 170, 169, 168]], dtype=uint8)

# 相当于%256
(img_cat+img_cat2)[:5,:,0]

array([[ 38,  46,  56, ...,  66,  64,  62],
       [226, 234, 246, ...,  64,  62,  60],
       [226, 230, 246, ...,  66,  64,  62],
       [ 32,  36,  50, ...,  66,  64,  62],
       [ 60,  66,  80, ...,  74,  72,  70]], dtype=uint8)

cv2.add(img_cat,img_cat2)[:5,:,0]

array([[255, 255, 255, ..., 255, 255, 255],
       [226, 234, 246, ..., 255, 255, 255],
       [226, 230, 246, ..., 255, 255, 255],
       [255, 255, 255, ..., 255, 255, 255],
       [255, 255, 255, ..., 255, 255, 255]], dtype=uint8)

图像融合

# 若直接相加，会报错
img_cat + img_dog

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-57-017f1ecf18a8> in <module>
      1 # 若直接相加，会报错
----> 2 img_cat + img_dog


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

img_cat.shape

(414, 500, 3)

img_dog.shape

(429, 499, 3)

img_dog = cv2.resize(img_dog,(500,414))

img_dog.shape

(414, 500, 3)

res = cv2.addWeighted(img_cat,0.4,img_dog,0.6,0)

plt.imshow(res)

<matplotlib.image.AxesImage at 0x22218af3c50>

# 尺寸变换resize
res = cv2.resize(img, (0, 0), fx=4, fy=4)
plt.imshow(res)

<matplotlib.image.AxesImage at 0x222189214e0>

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

<matplotlib.image.AxesImage at 0x22219d88e48>