def split(data, size=0.2, random_state=42):
#Version 2.0 : VIRTUAL IMAGE SET
if hasattr(data, "virtual"):
if data.virtual == True:
return split_virtual(data, size, random_state)
X_train, X_test, y_train, y_test = train_test_split(data.X,
data.y,
test_size=size,
random_state=42)
print("[X] Test set generation (size = ,", str(size), ",): Done")
print("--- Train set : ", X_train.shape[0], "samples")
print("--- Test set : ", X_test.shape[0], "samples")
print("\n")
train = ezset()
train.X = X_train
train.y = y_train
train.synsets = data.synsets
test = ezset()
test.X = X_test
test.y = y_test
test.synsets = data.synsets
return train, test
def split_virtual(data, size, random_state):
data.imagedg._validation_split = size
print("[Notice: ezutils.split_virtual(): Unused argument 'random_state']")
train = ezset()
train.virtual = True
train.params = data.params
train.imagedg = data.imagedg
train.generator = train.imagedg.flow_from_directory(
train.params["path"],
target_size=train.params["resize"],
batch_size=train.params["batch_size"],
color_mode=train.params["color_mode"],
class_mode=train.params["class_mode"],
shuffle=True,
subset="training")
#Create virtual entry from memory (no memory consumption)
train.X = np.zeros((train.generator.samples, ) +
train.generator.image_shape)
train.y = np.zeros((train.generator.samples, ) +
(train.generator.num_classes, ))
test = ezset()
test.virtual = True
test.params = data.params
test.imagedg = data.imagedg
test.generator = test.imagedg.flow_from_directory(
test.params["path"],
target_size=test.params["resize"],
batch_size=test.params["batch_size"],
color_mode=test.params["color_mode"],
class_mode=test.params["class_mode"],
shuffle=True,
subset="validation")
#Create virtual entry from memory (no memory consumption)
test.X = np.zeros((test.generator.samples, ) + test.generator.image_shape)
test.y = np.zeros((test.generator.samples, ) +
(test.generator.num_classes, ))
return train, test
import keras
import matplotlib.pyplot as plt
import cv2
import numpy as np
from skimage.filters import prewitt
from skimage.filters import threshold_otsu
import time
# [EZSET] -------------------------------------------------------------------
parameters = {
"name": "Blob Test",
"path": "C:\\Users\\daian\\Desktop\\DATA\\Blob_test\\",
"resize": (64, 64)
}
data = ezset(parameters)
print(data.X.mean())
# [EZMODEL] ----------------------------------------------------------------
ez = load_ezmodel(filename="blob")
print(ez.data_test.X.max())
print(ez.data_test.X.mean())
import sys
sys.exit()
ez.data_test = data
ez.transformerY = None
p = ez.predict()
from ezmodel.ezmodel import ezmodel
from ezmodel.ezutils import split
from ezmodel.ezblocks import *
import keras
# [EZSET] -------------------------------------------------------------------
parameters = {
"name": "Bacteria",
"path": "C:\\Users\\daian\\Desktop\\DATA\\Bacteria8bit\\",
"resize": (32, 32),
"scaling": 1. / 255,
"batch_size": 32,
"color_mode": "grayscale",
"class_mode": "categorical"
}
data = ezset(parameters, virtual=True)
#Split dataset into Train/Test subset
train, test = split(data, size=0.2)
# Show 1 images using generator
# x,y = train_gen.next()
# print(x)
# import matplotlib.pyplot as plt
# for i in range(0,1):
# image = np.squeeze(x[i])
# plt.imshow(image,cmap="gray")
# plt.show()
#
# sys.exit()
