Problem with saving pickle object into arrays from images in python - arrays

Question

I have the following class for loading and converting my images into train and test arrays for a deep learning model in Tensorflow 2.
The images are in three folders, named 'Car', 'Cat' and 'Man', which are within the Train and Test main folders. Each image is of 300 x 400 pixels.
import os
import pickle
import cv2
import numpy as np
os.getcwd()
out: 'C:\\Users\\me\\Jupiter_Notebooks\\Dataset_Thermal\\SeekThermal'
path_train = "../SeekThermal/Train"
path_test = "../SeekThermal/Test"
class MasterImage(object):
def __init__(self,PATH='', IMAGE_SIZE = 50):
self.PATH = PATH
self.IMAGE_SIZE = IMAGE_SIZE
self.image_data = []
self.x_data = []
self.y_data = []
self.CATEGORIES = []
# This will get List of categories
self.list_categories = []
def get_categories(self):
for path in os.listdir(self.PATH):
if '.DS_Store' in path:
pass
else:
self.list_categories.append(path)
print("Found Categories ",self.list_categories,'\n')
return self.list_categories
def process_image(self):
try:
"""
Return Numpy array of image
:return: X_Data, Y_Data
"""
self.CATEGORIES = self.get_categories()
for categories in self.CATEGORIES: # Iterate over categories
train_folder_path = os.path.join(self.PATH, categories) # Folder Path
class_index = self.CATEGORIES.index(categories) # this will get index for classification
for img in os.listdir(train_folder_path): # This will iterate in the Folder
new_path = os.path.join(train_folder_path, img) # image Path
try: # if any image is corrupted
image_data_temp = cv2.imread(new_path) # Read Image as numbers
image_temp_resize = cv2.resize(image_data_temp,(self.IMAGE_SIZE,self.IMAGE_SIZE))
self.image_data.append([image_temp_resize,class_index])
random.shuffle(self.image_data)
except:
pass
data = np.asanyarray(self.image_data) # or, data = np.asanyarray(self.image_data,dtype=object)
# Iterate over the Data
for x in data:
self.x_data.append(x[0]) # Get the X_Data
self.y_data.append(x[1]) # get the label
X_Data = np.asarray(self.x_data) / (255.0) # Normalize Data
Y_Data = np.asarray(self.y_data)
# reshape x_Data
X_Data = X_Data.reshape(-1, self.IMAGE_SIZE, self.IMAGE_SIZE, 3)
return X_Data, Y_Data
except:
print("Failed to run Function Process Image ")
def pickle_image(self):
"""
:return: None Creates a Pickle Object of DataSet
"""
# Call the Function and Get the Data
X_Data,Y_Data = self.process_image()
# Write the Entire Data into a Pickle File
pickle_out = open('X_Data','wb')
pickle.dump(X_Data, pickle_out)
pickle_out.close()
# Write the Y Label Data
pickle_out = open('Y_Data', 'wb')
pickle.dump(Y_Data, pickle_out)
pickle_out.close()
print("Pickled Image Successfully ")
return X_Data,Y_Data
def load_dataset(self):
try:
# Read the Data from Pickle Object
X_Temp = open('..\SeekThermal\X_Data','rb')
X_Data = pickle.load(X_Temp)
Y_Temp = open('..\SeekThermal\Y_Data','rb')
Y_Data = pickle.load(Y_Temp)
print('Reading Dataset from Pickle Object')
return X_Data,Y_Data
except:
print('Could not Found Pickle File ')
print('Loading File and Dataset ..........')
X_Data,Y_Data = self.pickle_image()
return X_Data,Y_Data
I dont understand what the problem is with the pickle file, because just last week I able to create these arrays successfully with the same code??
Is there an easier way to load images in Tensorflow rather than through the custom defined class?
a = MasterImage(PATH = path_train,IMAGE_SIZE = 224)
a.process_image()
out:
it produces an array with a warning.
VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
data = np.asanyarray(self.image_data)
a.pickle_image()
out:
TypeError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_1692\507657192.py in <cell line: 1>()
----> 1 a.pickle_image()
~\AppData\Local\Temp\ipykernel_1692\1410849712.py in pickle_image(self)
71 """
72 # Call the Function and Get the Data
---> 73 X_Data,Y_Data = self.process_image()
74
75 # Write the Entire Data into a Pickle File
TypeError: cannot unpack non-iterable NoneType object
a.load_dataset()
out:
Could not Found Pickle File
Loading File and Dataset ..........
Found Categories ['Car', 'Cat', 'Man', 'Car', 'Cat', 'Man']
Pickled Image Successfully
I'm running Python 3.8.8 via anaconda on Windows 10. Thank you for any advice.