图像预处理&Python

图像预处理

加载图像

from PIL import Image
import matplotlib.pyplot as plt
img1 = Image.open('C:/Users/31035/Desktop/resize1.png')
plt.imshow(img1)
img1.size

另寻为

img = Image.open("scr1.jpg")
img.save("src1.bmp")
img.save("src1.tiff")
print(img.mode)
print(img.format)

图片转换模式

img_gray = img.convert("L")
# print('mode=',img_gray.mode)
plt.figure(figsize=(5,5))
plt.imshow(img_gray )
plt.show()

展示RGB三个通道

img2 = Image.open("src1.tiff")
imgr,imgg,imgb = img2.split()
# plt.figure(figsize=(10,10))

plt.subplot(221)
plt.imshow(imgr,cmap="gray")
plt.title("R")

plt.subplot(222)
plt.imshow(imgg,cmap="gray")
plt.title("G")

plt.subplot(223)
plt.imshow(imgb,cmap="gray")
plt.title("B")

imgrgb = Image.merge("RGB",[imgr,imgg,imgb])
plt.subplot(224)
plt.imshow(imgrgb)
plt.title("RGB")

plt.show()

图像变数组

import numpy as np
arr_img_gray = np.array(img_gray)#图像变数组
# np.array(img_gray)
print(arr_img_gray.shape)
print(arr_img_gray)

黑白颠倒

arr_img_new = 255 - arr_img_gray
plt.subplot(121)
plt.imshow(arr_img_gray,cmap="gray")

plt.subplot(122)
plt.imshow(arr_img_new,cmap="gray")

plt.show()

其他操作

img_small = img.resize((64,34))
plt.imshow(img_small)
plt.show()

翻转图片

img_flr= img.transpose(Image.FLIP_LEFT_RIGHT)
img_tp = img.transpose(Image.TRANSPOSE)

plt.subplot(121)
plt.imshow(img_flr)
plt.subplot(122)
plt.imshow(img_tp)

plt.show()

裁剪图片

img_cut = img.crop((0,0,300,300))

plt.subplot(121)
plt.imshow(img)
plt.subplot(122)
plt.imshow(img_cut)

plt.show()

minist数据集

加载

mnist = tf.keras.datasets.mnist
(train_x, train_y), (test_x, test_y) = mnist.load_data(r"C:\Users\31035\.keras\datasets\mnist.npz") 
print('training set:',len(train_x))
print('testing set:',len(test_x))
print(train_x.shape,train_x.dtype)
print(train_y.shape,train_y.dtype)

显示几张图

for i in range(4):
    num = np.random.randint(1,60000)
    plt.subplot(1,4,i+1)
    plt.axis("off")
    plt.imshow(train_x[num],cmap='gray')
    plt.title(train_y[num])
plt.show()