#Enter the path of your image data folder
image_data_folder_path = "/data/animals/"

# initialize the data and labels as an empty list
#we will reshape the image data and append it in the list-data
#we will encode the image labels and append it in the list-labels
data = []
labels = []

# grab the image paths and randomly shuffle them
imagePaths = sorted(list(paths.list_images(image_data_folder_path)))

#total number images
total_number_of_images = len(imagePaths)
print("\n")
print("Total number of images----->",total_number_of_images)

#randomly shuffle all the image file name
random.shuffle(imagePaths)

# loop over the shuffled input images
for imagePath in imagePaths:

	#Read the image into a numpy array using opencv
	#all the read images are of different shapes
	image = cv2.imread(imagePath)

	#resize the image to be 32x32 pixels (ignoring aspect ratio)
	#After reshape size of all the images will become 32x32x3
	#Total number of pixels in every image = 32x32x3=3072
	image = cv2.resize(image, (32, 32))

	#flatten converts every 3D image (32x32x3) into 1D numpy array of shape (3072,)
	#(3072,) is the shape of the flatten image
	#(3072,) shape means 3072 columns and 1 row
	image_flatten = image.flatten()

	#Append each image data 1D array to the data list
	data.append(image_flatten)

	# extract the class label from the image path and update the
	label = imagePath.split(os.path.sep)[-2]

	#Append each image label to the labels list
	labels.append(label)
# scale the raw pixel intensities to the range [0, 1]
#convert the data and label list to numpy array
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)