def re_normalize(inp: np.ndarray,
low: int = 0,
high: int = 255
):
"""Normalize the data to a certain range. Default: [0-255]"""
inp_out = bytescale(inp, low=low, high=high)
return inp_out
def re_normalize(inp: np.ndarray, low: int = 0, high: int = 255):
inp_out = bytescale(inp, low=low, high=high)
return inp_out
print("Original Values: ", digits.target[n_samples // 2:(n_samples // 2) + 5])
plt.show()
#Install Pillow library
#from PIL
#from scipy.misc import imread , imresize, bytescale
from sklearn.externals._pilutil import imresize, imread, bytescale
img = imread("three.jpeg")
img = imresize(img, (8, 8))
classifier = svm.SVC(gamma=0.001)
classifier.fit(imageData[:], digits.target[:])
img = img.astype(digits.images.dtype)
img = bytescale(img, high=16.0, low=0)
print("img.shape : ", img.shape)
print("\n", img)
x_testData = []
for row in img:
for col in row:
x_testData.append(sum(col) / 3.0)
print("x_testData: \n", x_testData)
print("len(x_testData):", len(x_testData))
x_testData = [x_testData]
print("len(x_testData) : ", len(x_testData))
result = classifier.predict(x_testData)