After doing some processing on an audio or image array, it needs to be normalized within a range before it can be written back to a file. This can be done like so:
# Normalize audio channels to between -1.0 and +1.0
audio[:,0] = audio[:,0]/abs(audio[:,0]).max()
audio[:,1] = audio[:,1]/abs(audio[:,1]).max()
# Normalize image to between 0 and 255
image = image/(image.max()/255.0)
Is there a less verbose, convenience function way to do this? matplotlib.colors.Normalize() doesn't seem to be related.
audio /= np.max(np.abs(audio),axis=0)
image *= (255.0/image.max())
Using /= and *= allows you to eliminate an intermediate temporary array, thus saving some memory. Multiplication is less expensive than division, so
image *= 255.0/image.max() # Uses 1 division and image.size multiplications
is marginally faster than
image /= image.max()/255.0 # Uses 1+image.size divisions
Since we are using basic numpy methods here, I think this is about as efficient a solution in numpy as can be.
In-place operations do not change the dtype of the container array. Since the desired normalized values are floats, the audio and image arrays need to have floating-point point dtype before the in-place operations are performed.
If they are not already of floating-point dtype, you'll need to convert them using astype. For example,
image = image.astype('float64')
If the array contains both positive and negative data, I'd go with:
import numpy as np
a = np.random.rand(3,2)
# Normalised [0,1]
b = (a - np.min(a))/np.ptp(a)
# Normalised [0,255] as integer
c = 255*(a - np.min(a))/np.ptp(a).astype(int)
# Normalised [-1,1]
d = 2.*(a - np.min(a))/np.ptp(a)-1
also, worth mentioning even if it's not OP's question, standardization:
e = (a - np.mean(a)) / np.std(a)
You can also rescale using sklearn. The advantages are that you can adjust normalize the standard deviation, in addition to mean-centering the data, and that you can do this on either axis, by features, or by records.
from sklearn.preprocessing import scale
X = scale( X, axis=0, with_mean=True, with_std=True, copy=True )
The keyword arguments axis, with_mean, with_std are self explanatory, and are shown in their default state. The argument copy performs the operation in-place if it is set to False. Documentation here.
You can use the "i" (as in idiv, imul..) version, and it doesn't look half bad:
image /= (image.max()/255.0)
For the other case you can write a function to normalize an n-dimensional array by colums:
def normalize_columns(arr):
rows, cols = arr.shape
for col in xrange(cols):
arr[:,col] /= abs(arr[:,col]).max()
A simple solution is using the scalers offered by the sklearn.preprocessing library.
scaler = sk.MinMaxScaler(feature_range=(0, 250))
scaler = scaler.fit(X)
X_scaled = scaler.transform(X)
# Checking reconstruction
X_rec = scaler.inverse_transform(X_scaled)
The error X_rec-X will be zero. You can adjust the feature_range for your needs, or even use a standart scaler sk.StandardScaler()
You are trying to min-max scale the values of audio between -1 and +1 and image between 0 and 255.
Using sklearn.preprocessing.minmax_scale, should easily solve your problem.
e.g.:
audio_scaled = minmax_scale(audio, feature_range=(-1,1))
and
shape = image.shape
image_scaled = minmax_scale(image.ravel(), feature_range=(0,255)).reshape(shape)
note: Not to be confused with the operation that scales the norm (length) of a vector to a certain value (usually 1), which is also commonly referred to as normalization.
I tried following this, and got the error
TypeError: ufunc 'true_divide' output (typecode 'd') could not be coerced to provided output parameter (typecode 'l') according to the casting rule ''same_kind''
The numpy array I was trying to normalize was an integer array. It seems they deprecated type casting in versions > 1.10, and you have to use numpy.true_divide() to resolve that.
arr = np.array(img)
arr = np.true_divide(arr,[255.0],out=None)
img was an PIL.Image object.
来源：https://stackoverflow.com/questions/1735025/how-to-normalize-a-numpy-array-to-within-a-certain-range