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DataPrepare.py
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DataPrepare.py
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import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
import os,sys,time
import sklearn as sk
from IPython import display
import matplotlib.pyplot as plt
#from sklearn.preprocessing import train_test_split
from sklearn.datasets import load_files
from tensorflow_core._api.v2.compat.v1.random.experimental import Generator
from numba import njit,jit,vectorize
device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
raise SystemError('GPU device not found')
print('Found GPU at: {}'.format(device_name))
"""
root = "D:\\projects\\BlenderAutoAnimator"
#TrainData = load_files("D:\\projects\\BlenderAutoAnimator\\Train")
TrainData = "D:\\projects\\BlenderAutoAnimator\\TrainData2"
filenames = []
#x = [[[[]for i in range(4)] for i in range(67)] ]
x = []
y = []
X1 = np.array([],dtype=np.int64).reshape(0,48,48,48)
X2 = []
z = []
def preprocess1(data):
global X1
data = tf.reshape(data,[300,268,1])
data = tf.image.resize(data,[48,2304])
data = tf.reshape(data,[1,48,48,48]) # Reshaping the data
#X2.append(data)
X1 = np.append(X1,data,axis = 0)
return X1
for target in os.listdir(TrainData):
print(target)
for f in os.listdir(os.path.join(TrainData+"\\"+target)):
o_data = (np.loadtxt(TrainData+"\\"+target+"\\"+f))
o_data = o_data.reshape(int(o_data.shape[0]/67),67,4) # Turn the image to (300, 67, 4)
initial = o_data.shape[0]
if(initial<=300):
data = o_data
additionals = int((300-initial)%initial) # If image.shape[1] is less than 300 then add the extra data starting from frame 1 (10's digit)
for i in range(int((300-initial)/initial)):
data = np.vstack((data,o_data)) # additional daata (100's digit)
data = np.vstack((data,o_data[:additionals])) # Create a bunch of data by stacking it one on the other
# Special steps for resizing 3D data
preprocess1(data)"""
root = "D:\\projects\\BlenderAutoAnimator"