Python中读取图片并转化为numpy.ndarray()数据的6种方式

方式：                               返回类型

OpenCV                                            np.ndarray
PIL                                                     PIL.JpegImagePlugin.JpegImageFile
keras.preprocessing.image               PIL.JpegImagePlugin.JpegImageFile
Skimage.io                                        np.ndarray
matplotlib.pyplot                                np.ndarray
matplotlib.image                                np.ndarray

 

            
              import numpy as np
import cv2
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from PIL import Image
import skimage.io as io
import matplotlib.pyplot as plt
import matplotlib.image as mpig


'''
方式：                        返回类型

OpenCV                        np.ndarray
PIL                           PIL.JpegImagePlugin.JpegImageFile
keras.preprocessing.image     PIL.JpegImagePlugin.JpegImageFile
Skimage.io                    np.ndarray
matplotlib.pyplot             np.ndarray
matplotlib.image              np.ndarray
'''

imagePath="E:/DataSet/test1/trainSet/bus/300.jpg"


'''
方式一：使用OpenCV
'''
img1=cv2.imread(imagePath)
print("img1:",img1.shape)
print("img1:",type(img1))
print("-"*10)


'''
方式二：使用PIL
'''
img2=Image.open(imagePath)
print("img2:",img2)
print("img2:",type(img2))
#转换成np.ndarray格式
img2=np.array(img2)
print("img2:",img2.shape)
print("img2:",type(img2))
print("-"*10)


'''
方式三：使用keras.preprocessing.image
'''
img3=load_img(imagePath)
print("img3:",img3)
print("img3:",type(img3))
#转换成np.ndarray格式,使用np.array(),或者使用keras里的img_to_array()
#使用np.array()
#img3=np.array(img2)
#使用keras里的img_to_array()
img3=img_to_array(img3)
print("img3:",img3.shape)
print("img3:",type(img3))
print("-"*10)


'''
方式四：使用Skimage.io
'''
img4=io.imread(imagePath)
print("img4:",img4.shape)
print("img4:",type(img4))
print("-"*10)


'''
方式五：使用matplotlib.pyplot
'''
img5=plt.imread(imagePath)
print("img5:",img5.shape)
print("img5:",type(img5))
print("-"*10)


'''
方式六：使用matplotlib.image
'''
img6=mpig.imread(imagePath)
print("img6:",img6.shape)
print("img6:",type(img6))
print("-"*10)
            
          

运行结果：

            
              Using TensorFlow backend.
img1: (256, 384, 3)
img1: 
              
                
----------
img2: 
                
                  
img2: 
                  
                    
img2: (256, 384, 3)
img2: 
                    
                      
----------
img3: 
                      
                        
img3: 
                        
                          
img3: (256, 384, 3)
img3: 
                          
                            
----------
img4: (256, 384, 3)
img4: 
                            
                              
----------
img5: (256, 384, 3)
img5: 
                              
                                
----------
img6: (256, 384, 3)
img6: 
                                
                                  
----------