Python种种图象库的图象读写体式格局的简朴引见（附代码）【Python教程】,Python

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现在主流的图象库有几下几种：

1. OpenCV 2. PIL(Pillow) 3. matplotlib.image 4. skimage 5. scipy.misc

结论：以上图片库中当属OpenCV最为壮大，成熟。

1.1 OpenCV 图象的读取与贮存

import cv2
#读取图象 直接是numpy矩阵花样
img = cv2.imread('horse.jpg',1) # 0示意读入灰色图片，1示意读入彩色图片
cv2.imshow('image',img) # 显现图象
print(img.shape)   # （height，width，channel）
print(img.size)    # 像素数目
print(img.dtype)   # 数据范例
print(img)         # 打印图象的numpy数组，3纬数组

#贮存图象
# 当前目次贮存
cv2.write(‘horse1.jpg',img)
# 自定义贮存
cv2.write(‘/path_name/’ + str(image_name) + '.jpg',img)
          
cv2.waitKey()

1.2OpenCV 图象灰化处置惩罚

import cv2
#要领一
img = cv2.imread('horse.jpg',0) # 0示意读入灰色图片,或许运用cv2.IMREAD_GRATSCALE 替换0
cv2.imshow('gray image',img)

#要领二
img = cv2.imread('horse.jpg')
gray_img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow('gray image',gray_img)

print(gray_img.shape) # (height, width)
print(gray_img.size)  # 像素数目
print(gray_img)       # 打印图象的numpy数组，2维
cv2.waitKey()

1.3 OpenCV 矩阵花样变更

Why？：OpenCV的矩阵花样 (height, width, channels) -->> 深度进修矩阵范例多是 (channels，height，width)

import cv2
import numpy as np
img = cv2.imread('horse.jpg',1)
cv2.imshow('image',img)
# 矩阵花样的变更
print(img.shape)
img = img.transpose(2,0,1) #变更函数 
print(img.shape)

# 矩阵扩大 (batch_size, channels, height, width) 展望单张图片的操纵
# 加一列作为图片的个数
img = np.expand_dims(img, axis=0) #运用numpy函数
print(img.shape)

# 练习阶段构建batchdata_lst = []
loop:
    img = cv2.imread('xxx.jpg')
    data_lst.append(img)
data_arr = np.array(data_lst)

1.4 OpenCV 图片归一化 (Data Normalization)

import cv2
# 为了削减盘算量，须要把像素值0-255转换到0-1之间
img = cv2.imread('horse.jpg')
img = img.astype('float') / 255.0 # 先转化数据范例为float
print(img.dtype)
print(img)

1.5 OpenCV BRG转换为RGB

import cv2
img = cv2.imread('horse.jpg')
img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB) # 转为RGB format
print(img)

1.6 OpenCV 接见像素点

import cv2
img = cv2.imread('horse.jpg')
gray_img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # 转为Gray image
print(img[4,4])        # 3 channels
print(gray_img[4,4])   # 1 channel

1.7 OpenCV 感兴趣地区剪切(ROI)

import cv2
img = cv2.imread('horse.jpg')
print(img.shape)
roi = img[0:437,0:400] # [y:height,x:width]

cv2.imshow('roi',roi)
cv2.waitKey()

2.1 PIL 图象读取与贮存

from PIL import Image
import numpy as np
#图象读取
img = Image.open('horse.jpg')
print(img.format) # 图片花样
print(img.size)   # (width,height)
print(img.mode)   # 图片通道范例

#将图象转化为矩阵花样
arr = np.array(img)
print(arr.shape)
print(arr.dtype)

#图象贮存
new_img = Image.fromarray(arr)
new_img.save('test.jpg')

img.show()

2.2 PIL 图象灰化处置惩罚

#图象灰化处置惩罚
gray = Image.open('horse.jpg').convert('L')
gray_arr = np.array(gray)
print(gray_arr.shape) # (height,width)
print(gray_arr.dtype)
print(gray_arr)
gray.show()

2.3 PIL 感兴趣地区剪切

# 感兴趣地区剪切
img = Image.open('horse.jpg')
roi = img.crop((0,0,200,200)) # (左上x，左上y，右下x，右下y)
roi.show()

2.4 通道操纵

# 通道处置惩罚
r,g,b = img.split() #星散
img = Image.merge("RGB",(b,g,r)) #兼并
img = img.copy() #复制

3.1 Matplotlib 读取和存储图片

import matplotlib.pyplot as plt
import numpy as np
# 图象读取为numpy数组花样
img = plt.imread('horse.jpg')

plt.axis('off') # 封闭刻度显现

print(img.shape) # (height, width, channel)
print(img.size) # 像素数目
print(img.dtype)

#贮存图片
plt.savefig('./name.jpg')

figure = plt.figure(figsize=(20,10)) # 调解显现图片的大小

plt.imshow(img)
plt.show()

3.2 Matplotlib 图片灰化处置惩罚

#图片灰化处置惩罚
# 平均值发
img_mean = img.mean(axis=2)
plt.imshow(img_mean,cmap='gray')
plt.show()

#最大值法
img_max = img.max(axis=-1)
plt.imshow(img_max,cmap='gray')
plt.show()

#RGB三原色法
gravity = np.array([0.299,0.587,0.114])
img_gravity = np.dot(img,gravity)
plt.imshow(img_gravity,cmap="gray")
plt.show()

4.1 skimage 读取和贮存图象

from skimage import io
#读取图象numpy数组花样
img = io.imread('horse.jpg')
print(img.shape)
print(img.dtype)
print(img.size)
#print(img)
io.imshow(img)

#贮存图象
io.imsave('test.jpg',img)

4.2 skimage 灰化处置惩罚

#图象灰化处置惩罚并归一化
img = io.imread('horse.jpg',as_gray=True)
print(img.shape)
print(img.dtype) # 数据范例位float
print(img.size)
print(img)
io.imshow(img)
io.show()

5.1 scipy.misc 读取和贮存图象

#在1.2.0 以后统一用imageio模块
import imageio
import matplotlib.pyplot as plt
#读取图片为numpy数组
img = imageio.imread('horse.jpg')
print(img.dtype)
print(img.size)  # 像素数目
print(img.shape) #(height, width, channels)
plt.imshow(img)
plt.show()
print(img)
#贮存图片
imageio.imsave('test.jpg',img)

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