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data_aug.py
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data_aug.py
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import os
import random
import numpy as np
import cv2
from tqdm import tqdm
from glob import glob
from sklearn.model_selection import train_test_split
from utils import create_dir, shuffle
from data import load_data
from albumentations import (
PadIfNeeded,
HorizontalFlip,
VerticalFlip,
CenterCrop,
Crop,
Compose,
Transpose,
RandomRotate90,
ElasticTransform,
GridDistortion,
OpticalDistortion,
RandomSizedCrop,
OneOf,
CLAHE,
RandomBrightnessContrast,
RandomGamma,
HueSaturationValue,
RGBShift,
RandomBrightness,
RandomContrast,
MotionBlur,
MedianBlur,
GaussianBlur,
GaussNoise,
ChannelShuffle,
CoarseDropout
)
def augment_data(files, save_path, augment=True):
""" Performing data augmentation. """
# original_size = (960, 540) 640x360
size = (640, 360)
crop_size = (640, 360)
files_name = ["instrument_instances.png", "raw.png"]
for idx, path in tqdm(enumerate(files), total=len(files)):
f = os.listdir(path)
f.sort()
len_f = len(f)
## Reading Image and Mask
image = os.path.join(path, files_name[1])
mask = os.path.join(path, files_name[0])
image_name = "_".join([d for d in image.split("..")[-1].split("/")])
image_name = image_name.split("_ml_dataset_")[-1].split(".")[0]
mask_name = "_".join([d for d in mask.split("..")[-1].split("/")])
mask_name = mask_name.split("_ml_dataset_")[-1].split(".")[0]
x = cv2.imread(image, cv2.IMREAD_COLOR)
if len_f == 1:
y = np.zeros((540, 960, 3))
else:
y = cv2.imread(mask, cv2.IMREAD_COLOR) * 255.0
h, w, c = x.shape
if augment == True:
## Crop
x_min = 0
y_min = 0
x_max = x_min + size[0]
y_max = y_min + size[1]
aug = Crop(p=1, x_min=x_min, x_max=x_max, y_min=y_min, y_max=y_max)
augmented = aug(image=x, mask=y)
x1 = augmented['image']
y1 = augmented['mask']
## Random Rotate 90 degree
aug = RandomRotate90(p=1)
augmented = aug(image=x, mask=y)
x2 = augmented['image']
y2 = augmented['mask']
## ElasticTransform
aug = ElasticTransform(p=1, alpha=120, sigma=120 * 0.05, alpha_affine=120 * 0.03)
augmented = aug(image=x, mask=y)
x3 = augmented['image']
y3 = augmented['mask']
## Grid Distortion
aug = GridDistortion(p=1)
augmented = aug(image=x, mask=y)
x4 = augmented['image']
y4 = augmented['mask']
## Optical Distortion
aug = OpticalDistortion(p=1, distort_limit=2, shift_limit=0.5)
augmented = aug(image=x, mask=y)
x5 = augmented['image']
y5 = augmented['mask']
## Vertical Flip
aug = VerticalFlip(p=1)
augmented = aug(image=x, mask=y)
x6 = augmented['image']
y6 = augmented['mask']
## Horizontal Flip
aug = HorizontalFlip(p=1)
augmented = aug(image=x, mask=y)
x7 = augmented['image']
y7 = augmented['mask']
## Grayscale
x8 = cv2.cvtColor(x, cv2.COLOR_RGB2GRAY)
y8 = y
## Grayscale Vertical Flip
aug = VerticalFlip(p=1)
augmented = aug(image=x8, mask=y8)
x9 = augmented['image']
y9 = augmented['mask']
## Grayscale Horizontal Flip
aug = HorizontalFlip(p=1)
augmented = aug(image=x8, mask=y8)
x10 = augmented['image']
y10 = augmented['mask']
##
# aug = RandomBrightnessContrast(p=1)
# augmented = aug(image=x, mask=y)
# x11 = augmented['image']
# y11 = augmented['mask']
#
# aug = RandomGamma(p=1)
# augmented = aug(image=x, mask=y)
# x12 = augmented['image']
# y12 = augmented['mask']
#
# aug = HueSaturationValue(p=1)
# augmented = aug(image=x, mask=y)
# x13 = augmented['image']
# y13 = augmented['mask']
#
# aug = RGBShift(p=1)
# augmented = aug(image=x, mask=y)
# x14 = augmented['image']
# y14 = augmented['mask']
#
# aug = RandomBrightness(p=1)
# augmented = aug(image=x, mask=y)
# x15 = augmented['image']
# y15 = augmented['mask']
#
# aug = RandomContrast(p=1)
# augmented = aug(image=x, mask=y)
# x16 = augmented['image']
# y16 = augmented['mask']
#
# aug = ChannelShuffle(p=1)
# augmented = aug(image=x, mask=y)
# x17 = augmented['image']
# y17 = augmented['mask']
aug = CoarseDropout(p=1, max_holes=8, max_height=32, max_width=32)
augmented = aug(image=x, mask=y)
x18 = augmented['image']
y18 = augmented['mask']
# aug = GaussNoise(p=1)
# augmented = aug(image=x, mask=y)
# x19 = augmented['image']
# y19 = augmented['mask']
#
# aug = MotionBlur(p=1, blur_limit=7)
# augmented = aug(image=x, mask=y)
# x20 = augmented['image']
# y20 = augmented['mask']
#
# aug = MedianBlur(p=1, blur_limit=10)
# augmented = aug(image=x, mask=y)
# x21 = augmented['image']
# y21 = augmented['mask']
#
# aug = GaussianBlur(p=1, blur_limit=10)
# augmented = aug(image=x, mask=y)
# x22 = augmented['image']
# y22 = augmented['mask']
images = [
x, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10,
#x11, x12, x14, x15, x16, x17,
x18
# x19, x20, x21, x22
]
masks = [
y, y1, y2, y3, y4, y5, y6, y7, y8, y9, y10,
# y10, y11, y12, y14, y15, y16, y17,
y18
# y19, y20, y21, y22
]
else:
images = [x]
masks = [y]
idx = 0
for i, m in zip(images, masks):
i = cv2.resize(i, size)
m = cv2.resize(m, size)
tmp_image_name = f"{image_name}_{idx}.jpg"
tmp_mask_name = f"{mask_name}_{idx}.jpg"
image_path = os.path.join(save_path, "image/", tmp_image_name)
mask_path = os.path.join(save_path, "mask/", tmp_mask_name)
cv2.imwrite(image_path, i)
cv2.imwrite(mask_path, m)
idx += 1
def main():
np.random.seed(42)
path = "../../../../ml_dataset/Instrument/"
train_path, valid_path, test_path = load_data(path)
print("Train: ", len(train_path))
print("Valid: ", len(valid_path))
print("Test: ", len(test_path))
create_dir("../new_data/train/image/")
create_dir("../new_data/train/mask/")
create_dir("../new_data/valid/image/")
create_dir("../new_data/valid/mask/")
create_dir("../new_data/test/image/")
create_dir("../new_data/test/mask/")
augment_data(train_path, "../new_data/train/", augment=False)
augment_data(valid_path, "../new_data/valid/", augment=False)
augment_data(test_path, "../new_data/test/", augment=False)
if __name__ == "__main__":
main()