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Diffstat (limited to 'preprocess.py')
| -rw-r--r-- | preprocess.py | 224 |
1 files changed, 0 insertions, 224 deletions
diff --git a/preprocess.py b/preprocess.py deleted file mode 100644 index b7cfdb67..00000000 --- a/preprocess.py +++ /dev/null @@ -1,224 +0,0 @@ -""" -Homework 5 - CNNs -CS1430 - Computer Vision -Brown University -""" - -import os -import random -import numpy as np -from PIL import Image -import tensorflow as tf - -import hyperparameters as hp - -class Datasets(): - """ Class for containing the training and test sets as well as - other useful data-related information. Contains the functions - for preprocessing. - """ - - def __init__(self, data_path, task): - - self.data_path = data_path - self.task = task - - # Dictionaries for (label index) <--> (class name) - self.idx_to_class = {} - self.class_to_idx = {} - - # For storing list of classes - self.classes = [""] * hp.num_classes - - # Mean and std for standardization - self.mean = np.zeros((3,)) - self.std = np.zeros((3,)) - self.calc_mean_and_std() - - # Setup data generators - self.train_data = self.get_data( - os.path.join(self.data_path, "train/"), task == '3', True, True) - self.test_data = self.get_data( - os.path.join(self.data_path, "test/"), task == '3', False, False) - - def calc_mean_and_std(self): - """ Calculate mean and standard deviation of a sample of the - training dataset for standardization. - - Arguments: none - - Returns: none - """ - - # Get list of all images in training directory - file_list = [] - for root, _, files in os.walk(os.path.join(self.data_path, "train/")): - for name in files: - if name.endswith(".jpg"): - file_list.append(os.path.join(root, name)) - - # Shuffle filepaths - random.shuffle(file_list) - - # Take sample of file paths - file_list = file_list[:hp.preprocess_sample_size] - - # Allocate space in memory for images - data_sample = np.zeros( - (hp.preprocess_sample_size, hp.img_size, hp.img_size, 3)) - - # Import images - for i, file_path in enumerate(file_list): - img = Image.open(file_path) - img = img.resize((hp.img_size, hp.img_size)) - img = np.array(img, dtype=np.float32) - img /= 255. - - # Grayscale -> RGB - if len(img.shape) == 2: - img = np.stack([img, img, img], axis=-1) - - data_sample[i] = img - - # TODO: Calculate the pixel-wise mean and standard deviation - # of the images in data_sample and store them in - # self.mean and self.std respectively. - # ========================================================== - - self.mean = np.mean(data_sample, axis=(1,2,3), dtype=np.float32) - self.std = np.std(data_sample, axis=(1,2,3), dtype=np.float32) - # ========================================================== - - print("Dataset mean: [{0:.4f}, {1:.4f}, {2:.4f}]".format( - self.mean[0], self.mean[1], self.mean[2])) - - print("Dataset std: [{0:.4f}, {1:.4f}, {2:.4f}]".format( - self.std[0], self.std[1], self.std[2])) - - def standardize(self, img): - """ Function for applying standardization to an input image. - - Arguments: - img - numpy array of shape (image size, image size, 3) - - Returns: - img - numpy array of shape (image size, image size, 3) - """ - - # TODO: Standardize the input image. Use self.mean and self.std - # that were calculated in calc_mean_and_std() to perform - # the standardization. - # ============================================================= - img = (img - np.max(self.mean))/np.max(self.std) - # ============================================================= - - return img - - def preprocess_fn(self, img): - """ Preprocess function for ImageDataGenerator. """ - - if self.task == '3': - img = tf.keras.applications.vgg16.preprocess_input(img) - else: - img = img / 255. - img = self.standardize(img) - return img - - def custom_preprocess_fn(self, img): - """ Custom preprocess function for ImageDataGenerator. """ - - if self.task == '3': - img = tf.keras.applications.vgg16.preprocess_input(img) - else: - img = img / 255. - img = self.standardize(img) - - # EXTRA CREDIT: - # Write your own custom data augmentation procedure, creating - # an effect that cannot be achieved using the arguments of - # ImageDataGenerator. This can potentially boost your accuracy - # in the validation set. Note that this augmentation should - # only be applied to some input images, so make use of the - # 'random' module to make sure this happens. Also, make sure - # that ImageDataGenerator uses *this* function for preprocessing - # on augmented data. - - if random.random() < 0.3: - img = img + tf.random.uniform( - (hp.img_size, hp.img_size, 1), - minval=-0.1, - maxval=0.1) - - return img - - def get_data(self, path, is_vgg, shuffle, augment): - """ Returns an image data generator which can be iterated - through for images and corresponding class labels. - - Arguments: - path - Filepath of the data being imported, such as - "../data/train" or "../data/test" - is_vgg - Boolean value indicating whether VGG preprocessing - should be applied to the images. - shuffle - Boolean value indicating whether the data should - be randomly shuffled. - augment - Boolean value indicating whether the data should - be augmented or not. - - Returns: - An iterable image-batch generator - """ - - if augment: - # TODO: Use the arguments of ImageDataGenerator() - # to augment the data. Leave the - # preprocessing_function argument as is unless - # you have written your own custom preprocessing - # function (see custom_preprocess_fn()). - # - # Documentation for ImageDataGenerator: https://bit.ly/2wN2EmK - # - # ============================================================ - - # data_gen = - data_gen = tf.keras.preprocessing.image.ImageDataGenerator( - preprocessing_function=self.preprocess_fn, rotation_range=20, width_shift_range=0.2, - height_shift_range=0.2, horizontal_flip=True, validation_split=0.2, fill_mode="reflect") - - # ============================================================ - else: - # Don't modify this - data_gen = tf.keras.preprocessing.image.ImageDataGenerator( - preprocessing_function=self.preprocess_fn) - - # VGG must take images of size 224x224 - img_size = 224 if is_vgg else hp.img_size - - classes_for_flow = None - - # Make sure all data generators are aligned in label indices - if bool(self.idx_to_class): - classes_for_flow = self.classes - - # Form image data generator from directory structure - data_gen = data_gen.flow_from_directory( - path, - target_size=(img_size, img_size), - class_mode='sparse', - batch_size=hp.batch_size, - shuffle=shuffle, - classes=classes_for_flow) - - # Setup the dictionaries if not already done - if not bool(self.idx_to_class): - unordered_classes = [] - for dir_name in os.listdir(path): - if os.path.isdir(os.path.join(path, dir_name)): - unordered_classes.append(dir_name) - - for img_class in unordered_classes: - self.idx_to_class[data_gen.class_indices[img_class]] = img_class - self.class_to_idx[img_class] = int(data_gen.class_indices[img_class]) - self.classes[int(data_gen.class_indices[img_class])] = img_class - - return data_gen |