File size: 2,900 Bytes
2fe881c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import os
from PIL import Image
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
BRANDON_ORIGINAL_DATASET_DIR = "C:\Ryan\PP stuff\\try1\Classification Data-20240212T032009Z-001\Classification Data\\Brandon"
MANUEL_ORIGINAL_DATASET_DIR = "C:\\Ryan\\PP stuff\\try1\\Classification Data-20240212T032009Z-001\\Classification Data\\Manuel"
BASE_DIR = "C:\\Ryan\\PP stuff\\try1\\face_recog"
#create directories for train/validation/test sets
train_dir = os.path.join(BASE_DIR, 'train')
validation_dir = os.path.join(BASE_DIR, 'validation')
test_dir = os.path.join(BASE_DIR, 'test')
train_bran_dir = os.path.join(train_dir, 'brandon')
train_man_dir = os.path.join(train_dir, 'manuel')
validation_bran_dir = os.path.join(validation_dir, 'brandon')
validation_man_dir = os.path.join(validation_dir, 'manuel')
test_bran_dir = os.path.join(test_dir, 'brandon')
test_man_dir = os.path.join(test_dir, 'manuel')
def resize():
target_size = (300, 350)
input_dir = "C:\Ryan\PersonalProject\\FriendRecog\\bot\images"
output_dir = "C:\\Ryan\\PersonalProject\\FriendRecog\\bot\\resized_images"
try:
for filename in os.listdir(input_dir):
# Construct the full path to the image file
input_path = os.path.join(input_dir, filename)
# Open the image
with Image.open(input_path) as img:
# Resize the image
resized_img = img.resize(target_size)
# Construct the output path
output_path = os.path.join(output_dir, filename)
# Save the resized image
resized_img.save(output_path)
finally:
pass
def data_augmentation():
augmented_datagen = ImageDataGenerator(
rescale = 1. / 255,
rotation_range = 40,
width_shift_range = 0.2,
height_shift_range = 0.2,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True,
fill_mode = "nearest")
augmented_generator = augmented_datagen.flow_from_directory(train_dir, target_size = (300, 350),
batch_size = 20,
class_mode = 'sparse')
augmented_dir = os.path.join(BASE_DIR, "augmented")
augmented_all = os.path.join(augmented_dir, "all")
os.mkdir(augmented_dir)
os.mkdir(augmented_all)
for i, (images, labels) in enumerate(augmented_generator):
if i >= 5:
break
for j in range(len(images)):
augmented_image = image.array_to_img(images[j])
filename = f"{i * len(images) + j}.png"
augmented_image_path = os.path.join(augmented_all, filename)
augmented_image.save(augmented_image_path) |