python3读取图片并灰度化图片的四种方法(OpenCV、PIL.Image、TensorFlow方法)总结

import numpy as np
import cv2
import tensorflow as tf
from PIL import Image

 img = cv2.imread(imgfile, cv2.IMREAD_GRAYSCALE)
  print("cv2.imread(imgfile, cv2.IMREAD_GRAYSCALE)结果如下：")
  print('大小：{}'.format(img.shape))
  print("类型：%s"%type(img))
  print(img)

 img = cv2.imread(imgfile)
  #print(img.shape)
  #print(img)
  gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #Y = 0.299R + 0.587G + 0.114B
  print("cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)结果如下：")
  print('大小:{}'.format(gray_img.shape))
  print("类型：%s" % type(gray_img))
  print(gray_img)

 img = np.array(Image.open(imgfile).convert('L'), 'f') #读取图片，灰度化，转换为数组，L = 0.299R + 0.587G + 0.114B。'f'为float类型
  print("Image方法的结果如下：")
  print('大小：{}'.format(img.shape))
  print("类型：%s" % type(img))
  print(img)

 with tf.Session() as sess:
    img = tf.read_file(imgfile) #读取图片，
    img_data = tf.image.decode_jpeg(img, channels=3) #解码
    #img_data = sess.run(tf.image.decode_jpeg(img, channels=3))
    img_data = sess.run(tf.image.rgb_to_grayscale(img_data)) #灰度化
    print('大小：{}'.format(img_data.shape))
    print("类型：%s" % type(img_data))
    print(img_data)