#https://numpy.org/doc/stable/user/absolute_beginners.html
#https://docs.python.org/3/tutorial/introduction.html#lists
from sklearn.datasets import fetch_olivetti_faces
data, targets = fetch_olivetti_faces(return_X_y=True)
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_text
from sklearn import tree
from sklearn import preprocessing
from sklearn import utils

from matplotlib import image
from matplotlib import pyplot

# load image as pixel array
image = image.imread('face-64.png')
from PIL import Image
import numpy as np
img = Image.open('face-64.png').convert('L')
# summarize shape of the pixel array
imgarr = np.array(img) 

print(imgarr.shape)
imgarrf = imgarr #normalize

#generate list of numbers  0 to 255 , classification requires integers
classes_list = []
for i in range(0, 256):
    classes_list.append(i)

print(classes_list)

tophalflady =  imgarrf.flatten()[0: 2048]  #4096 / 2


print(len(data)) #400 rows
print(len(data[0])) #4096 columns = 64 x 64 = 4096
n_pixels = data.shape[1] # (400,4096) [1] = 4096
print(n_pixels) #should be 4096 flattened
#slice the list data and target using index : meens start
# Upper half of the faces from 0  numpy arrays [rom start to middle]
X_train = data[:, : (n_pixels + 1) // 2]
X_train = np.rint(X_train * 255) #round to int
# Lower half of the faces [for all from end to middle] from end to 
#classifier expects integer values

y_target = data[:, n_pixels // 2 :]
y_target = np.rint(y_target * 255) #as int
#lab_enc = preprocessing.LabelEncoder()
#encoded = lab_enc.fit_transform(y_target)

clf = tree.DecisionTreeClassifier(random_state=0, max_depth=24)
clf = clf.fit(X_train, y_target)
arrpredict= clf.predict([ tophalflady ])

# load and display an image with Matplotlib


print(tophalflady.shape)#2048,1
tophalflady = np.expand_dims(tophalflady, axis=0)
print(tophalflady.shape)#2048,1
print(arrpredict.shape) #1,2048 linear flatten array, reshape to 2d
print('yes')
#arrpredict = numpy.transpose(arrpredict)
print(arrpredict.shape) #1,2048 linear flatten array, reshape to 2d
print(tophalflady.shape)#2048,1

#merge array top and predicted
topbottom = np.concatenate((tophalflady, arrpredict), axis=0)

# display the array of pixels as an image
#The image is quantized to 256 grey levels and stored as unsigned 8-bit integers; 
# #the loader will convert these to floating point values on the interval [0, 1],
#  which are easier to work with for many algorithms.
from numpy import asarray
#pyplot.imshow(oneimg.reshape( (64,64))) #reshape to 64 by 64
pyplot.imshow( topbottom.reshape((64,64)) ) #reshape to 32  rows and 64 columns
pyplot.show()