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process_image.py
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process_image.py
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import os
import random
import numpy as np
import cv2
from glob import glob
from scipy.ndimage.interpolation import rotate
from tqdm import tqdm
from sklearn.model_selection import train_test_split
def read_image(imagefile, grayscale=False):
if grayscale == True:
image = cv2.imread(imagefile)
#image = np.expand_dims(image, -1)
else:
image = cv2.imread(imagefile)
return image
def save_image(image, mask, path, binary=True):
image = np.array(image)
if binary == True:
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
cv2.imwrite(path[0], image)
cv2.imwrite(path[1], mask)
def concat_images(images, rows, cols):
_, h, w, _ = images.shape
images = images.reshape((rows, cols, h, w, 3))
images = images.transpose(0, 2, 1, 3, 4)
images = images.reshape((rows * h, cols * w, 3))
return images
def check_size(size):
if type(size) == int:
size = (size, size)
if type(size) != tuple:
raise TypeError('size is int or tuple')
return size
def subtract(image):
image = image / 255
return image
def resize(image, size):
size = check_size(size)
image = cv2.resize(image, size)
return image
def center_crop(image, mask, crop_size, size):
h, w, _ = image.shape
crop_size = check_size(crop_size)
top = (h - crop_size[0]) // 2
left = (w - crop_size[1]) // 2
bottom = top + crop_size[0]
right = left + crop_size[1]
image = image[top:bottom, left:right, :]
mask = mask[top:bottom, left:right, :]
image = resize(image, size)
mask = resize(mask, size)
return image, mask
def random_crop(image, mask, crop_size, size):
crop_size = check_size(crop_size)
h, w, _ = image.shape
top = np.random.randint(0, h - crop_size[0])
left = np.random.randint(0, w - crop_size[1])
bottom = top + crop_size[0]
right = left + crop_size[1]
image = image[top:bottom, left:right, :]
mask = mask[top:bottom, left:right, :]
image = resize(image, size)
mask = resize(mask, size)
return image, mask
def horizontal_flip(image, mask, size):
image = image[:, ::-1, :]
mask = mask[:, ::-1, :]
image = resize(image, size)
mask = resize(mask, size)
return image, mask
def vertical_flip(image, mask, size):
image = image[::-1, :, :]
mask = mask[::-1, :, :]
image = resize(image, size)
mask = resize(mask, size)
return image, mask
def scale_augmentation(image, mask, scale_range, crop_size, size):
scale_size = np.random.randint(*scale_range)
image = cv2.resize(image, (scale_size, scale_size))
mask = cv2.resize(mask, (scale_size, scale_size))
image, mask = random_crop(image, mask, crop_size, size)
return image, mask
def random_rotation(image, mask, size, angle_range=(0, 90)):
h1, w1, _ = image.shape
h2, w2, _ = mask.shape
angle = np.random.randint(*angle_range)
image = rotate(image, angle)
image = resize(image, (h1, w1))
mask = rotate(mask, angle)
mask = resize(mask, (h2, w2))
image = resize(image, size)
mask = resize(mask, size)
return image, mask
def cutout(image_origin, mask_origin, mask_size, mask_value='mean'):
image = np.copy(image_origin)
mask = np.copy(mask_origin)
if mask_value == 'mean':
mask_value = image.mean()
elif mask_value == 'random':
mask_value = np.random.randint(0, 256)
h, w, _ = image.shape
top = np.random.randint(0 - mask_size // 2, h - mask_size)
left = np.random.randint(0 - mask_size // 2, w - mask_size)
bottom = top + mask_size
right = left + mask_size
if top < 0:
top = 0
if left < 0:
left = 0
image[top:bottom, left:right, :].fill(mask_value)
mask[top:bottom, left:right, :].fill(0)
image = resize(image, size)
mask = resize(mask, size)
return image, mask
def brightness_augment(img, mask, factor=0.5):
hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV) #convert to hsv
hsv = np.array(hsv, dtype=np.float64)
hsv[:, :, 2] = hsv[:, :, 2] * (factor + np.random.uniform()) #scale channel V uniformly
hsv[:, :, 2][hsv[:, :, 2] > 255] = 255 #reset out of range values
rgb = cv2.cvtColor(np.array(hsv, dtype=np.uint8), cv2.COLOR_HSV2RGB)
image = resize(rgb, size)
mask = resize(mask, size)
return image, mask
def rgb_to_grayscale(img, mask):
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img = [img, img, img]
img = np.transpose(img, (1, 2, 0))
image = resize(img, size)
mask = resize(mask, size)
return image, mask
def create_dir(name):
try:
os.mkdir(name)
except:
pass
if __name__ == '__main__':
### Image Augmentation
size = (256, 256)
crop_size = (300, 300)
path = "../../../ml_dataset/"
dataset_name = "kvasir_segmentation_dataset"
full_path = os.path.join(path, dataset_name)
new_path = "new_data/"
create_dir(new_path)
new_full_path = os.path.join(new_path, dataset_name)
train_path = os.path.join(new_full_path, "train")
valid_path = os.path.join(new_full_path, "valid")
test_path = os.path.join(new_full_path, "test")
if not os.path.exists(new_full_path):
os.mkdir(new_full_path)
for path in [train_path, valid_path, test_path]:
os.mkdir(path)
os.mkdir(os.path.join(path, "images"))
os.mkdir(os.path.join(path, "masks"))
images = glob(os.path.join(full_path, "images/", "*"))
masks = glob(os.path.join(full_path, "masks/", "*"))
images.sort()
masks.sort()
len_ids = len(images)
train_size = int((80/100)*len_ids)
valid_size = int((10/100)*len_ids) ## Here 10 is the percent of images used for validation
test_size = int((10/100)*len_ids) ## Here 10 is the percent of images used for testing
train_images, test_images = train_test_split(images, test_size=test_size, random_state=42)
train_masks, test_masks = train_test_split(masks, test_size=test_size, random_state=42)
train_images, valid_images = train_test_split(train_images, test_size=test_size, random_state=42)
train_masks, valid_masks = train_test_split(train_masks, test_size=test_size, random_state=42)
print("Total Size: ", len_ids)
print("Training Size: ", train_size)
print("Validation Size: ", valid_size)
print("Testing Size: ", test_size)
## Validation images and masks
for idx, p in tqdm(enumerate(test_images), total=len(test_images)):
## Path
name = p.split("/")[-1].split(".")[0]
image_path = test_images[idx]
mask_path = test_masks[idx]
if os.path.exists(image_path) and os.path.exists(mask_path):
image = read_image(image_path)
mask = read_image(mask_path, grayscale=True)
new_image_path = os.path.join(new_full_path, "test", "images/")
new_mask_path = os.path.join(new_full_path, "test", "masks/")
image = resize(image, size)
mask = resize(mask, size)
img_path = new_image_path + str(name) + ".jpg"
mask_path = new_mask_path + str(name) + ".jpg"
tmp_path = [img_path, mask_path]
save_image(image, mask, tmp_path)
## Testing images and masks
for idx, p in tqdm(enumerate(valid_images), total=len(valid_images)):
## Path
name = p.split("/")[-1].split(".")[0]
image_path = valid_images[idx]
mask_path = valid_masks[idx]
if os.path.exists(image_path) and os.path.exists(mask_path):
image = read_image(image_path)
mask = read_image(mask_path, grayscale=True)
new_image_path = os.path.join(new_full_path, "valid", "images/")
new_mask_path = os.path.join(new_full_path, "valid", "masks/")
image = resize(image, size)
mask = resize(mask, size)
img_path = new_image_path + str(name) + ".jpg"
mask_path = new_mask_path + str(name) + ".jpg"
tmp_path = [img_path, mask_path]
save_image(image, mask, tmp_path)
## Training images and masks
for idx, p in tqdm(enumerate(train_images), total=len(train_images)):
## Path
name = p.split("/")[-1].split(".")[0]
image_path = train_images[idx]
mask_path = train_masks[idx]
if os.path.exists(image_path) and os.path.exists(image_path):
image = read_image(image_path)
mask = read_image(mask_path, grayscale=True)
## Augment
image1, mask1 = center_crop(image, mask, crop_size, size)
image2, mask2 = random_crop(image, mask, crop_size, size)
image3, mask3 = horizontal_flip(image, mask, size)
image4, mask4 = vertical_flip(image, mask, size)
image5, mask5 = scale_augmentation(image, mask, (512, 768), crop_size, size)
image6, mask6 = random_rotation(image, mask, size)
image7, mask7 = cutout(image, mask, 256)
## Extra Cropping
image8, mask8 = random_crop(image, mask, crop_size, size)
image9, mask9 = random_crop(image, mask, crop_size, size)
## Extra Scale Augmentation
image10, mask10 = scale_augmentation(image, mask, (540, 820), crop_size, size)
image11, mask11 = scale_augmentation(image, mask, (720, 1024), crop_size, size)
## Extra Rotation
image12, mask12 = random_rotation(image, mask, size)
image13, mask13 = random_rotation(image, mask, size)
## Brightness
image14, mask14 = brightness_augment(image, mask, factor=0.3)
image15, mask15 = brightness_augment(image, mask, factor=0.6)
image16, mask16 = brightness_augment(image, mask, factor=0.9)
## More Rotation
image17, mask17 = random_rotation(image, mask, size)
image18, mask18 = random_rotation(image, mask, size)
## More Random Crop
image19, mask19 = random_crop(image, mask, crop_size, size)
image20, mask20 = random_crop(image, mask, crop_size, size)
## More Cutout
image21, mask21 = cutout(image, mask, 256)
image22, mask22 = cutout(image, mask, 256)
## Grayscale
image23, mask23 = rgb_to_grayscale(image, mask)
image24, mask24 = rgb_to_grayscale(image1, mask1)
image25, mask25 = rgb_to_grayscale(image2, mask2)
image26, mask26 = rgb_to_grayscale(image3, mask3)
image27, mask27 = rgb_to_grayscale(image4, mask4)
image28, mask28 = rgb_to_grayscale(image5, mask5)
image29, mask29 = rgb_to_grayscale(image15, mask15)
image30, mask30 = rgb_to_grayscale(image16, mask16)
## Original image and mask
image = resize(image, size)
mask = resize(mask, size)
## All images and masks
all_images = [image, image1, image2, image3, image4, image5, image6, image7,
image8, image9, image10, image11, image12, image13, image14, image15, image16,
image17, image18, image19, image20, image21, image22,
image23,image24, image25, image26, image27, image28, image29, image30
]
all_masks = [mask, mask1, mask2, mask3, mask4, mask5, mask6, mask7, mask8,
mask9, mask10, mask11, mask12, mask13, mask14, mask15, mask16,
mask17, mask18, mask19, mask20, mask21, mask22,
mask23, mask24, mask25, mask26, mask27, mask28, mask29, mask30
]
## Save the images and masks
new_image_path = os.path.join(new_full_path, "train", "images/")
new_mask_path = os.path.join(new_full_path, "train", "masks/")
for j in range(len(all_images)):
img_path = new_image_path + str(name) + "_" + str(j) + ".jpg"
msk_path = new_mask_path + str(name) + "_" + str(j) + ".jpg"
img = all_images[j]
msk = all_masks[j]
path = [img_path, msk_path]
save_image(img, msk, path)