Code 1
(.env) [boris@fedora35server PILLOW]$ cat imageShow.py
# load and display an image with Matplotlib
from matplotlib import image
from matplotlib import pyplot
# load image as pixel array
image = image.imread('coala.jpeg')
# summarize shape of the pixel array
print(image.dtype)
print(image.shape)
# display the array of pixels as an image
pyplot.imshow(image)
pyplot.show()
Code 2
(.env) [boris@fedora35server PILLOW]$ cat imageToNDArray.py
from PIL import Image
from numpy import savetxt
from numpy import savez_compressed
from numpy import asarray
# load the image
image = Image.open('coala.jpeg')
# convert image to numpy array
data = asarray(image)
print(type(data))
print(data)
# unload 3D array to files via reshape
savetxt("data.txt", data.reshape((3,-1)), fmt="%s", header=str(data.shape))
savez_compressed("data.npz", data.reshape((3,-1)), fmt="%s", header=str(data.shape))
print("Both Unloads data done")
# summarize shape
print(data.shape)
# create Pillow image
image2 = Image.fromarray(data)
print(type(image2))
# summarize image details
print(image2.mode)
print(image2.size)
(.env) [boris@fedora35server PILLOW]$ python imageToNDArray.py
<class 'numpy.ndarray'>
[[[ 25 26 12]
[ 25 26 12]
[ 25 26 12]
...
[113 106 52]
[113 106 52]
[114 107 53]]
[[ 25 26 12]
[ 25 26 12]
[ 25 26 12]
...
[113 106 52]
[113 106 52]
[114 107 53]]
[[ 25 26 12]
[ 25 26 12]
[ 25 26 12]
...
[112 105 51]
[113 106 52]
[113 106 52]]
...
[[139 118 71]
[136 115 68]
[130 109 62]
...
[ 67 91 91]
[ 68 89 90]
[ 66 88 86]]
[[150 128 81]
[143 121 74]
[134 111 67]
...
[ 73 93 91]
[ 69 89 87]
[ 65 85 83]]
[[151 127 81]
[144 120 74]
[135 111 67]
...
[ 74 93 91]
[ 70 89 85]
[ 66 85 81]]]
(450, 800, 3)
<class 'PIL.Image.Image'>
RGB
(800, 450)
Можно получить CSV сразу через 2-ух цветовую гамму
(.env) [boris@fedora35server PILLOW]$ cat imageToCSV.py
from PIL import Image
import numpy as np
import sys
import os
import csv
#Useful function
def createFileList(myDir, format='.jpeg'):
fileList = []
print(myDir)
for root, dirs, files in os.walk(myDir, topdown=False):
for name in files:
if name.endswith(format):
fullName = os.path.join(root, name)
fileList.append(fullName)
return fileList
# load the original image
myFileList = createFileList('./')
for file in myFileList:
print(file)
img_file = Image.open(file)
# img_file.show()
# get original image parameters...
width, height = img_file.size
format = img_file.format
mode = img_file.mode
# Make image Greyscale
img_grey = img_file.convert('L')
img_grey.save('result.png')
img_grey.show()
# Save Greyscale values
value = np.asarray(img_grey.getdata(), dtype=int).reshape((img_grey.size[1], img_grey.size[0]))
value = value.flatten()
print(value)
with open("img_pixels.csv", 'a') as f:
writer = csv.writer(f)
writer.writerow(value)
(.env) [boris@fedora35server PILLOW]$ python imageToCSV.py
./
./coala.jpeg
[24 24 24 ... 87 83 79]
(.env) [boris@fedora35server PILLOW]$ ll
total 4680
-rw-r--r--. 1 boris boris 44769 Apr 18 10:42 coala.jpeg
-rw-rw-r--. 1 boris boris 318 Apr 18 10:49 imageShow.py
-rw-rw-r--. 1 boris boris 1071 Apr 18 13:44 imageToCSV.py
-rw-rw-r--. 1 boris boris 691 Apr 18 13:33 imageToNDArray.py
-rw-rw-r--. 1 boris boris 2514008 Apr 18 13:52 img_pixels.csv
-rw-rw-r--. 1 boris boris 144709 Apr 18 13:52 result.png
Преобразование изображения с помощью Keras API
(.env) [boris@fedora35server PILLOW]$ cat imageConvTF.py
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import array_to_img
from tensorflow.keras.preprocessing.image import save_img
# load the image
img = load_img('coala.jpeg')
print("Orignal:" ,type(img))
print(type(img))
print(img.format)
print(img.mode)
print(img.size)
img.show()
# convert to numpy array
img_array = img_to_array(img)
print("NumPy array info:")
print(type(img_array))
print("type:",img_array.dtype)
print("shape:",img_array.shape)
print(img_array)
# convert back to image
img_pil = array_to_img(img_array)
print("converting NumPy array:",type(img_pil))
References
