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data.py
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data.py
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import os
import cv2
import numpy as np
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.utils import to_categorical,normalize
import matplotlib.pyplot as plt
import segmentation_models as sm
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
def read_images(path):
x = cv2.imread(path, cv2.IMREAD_COLOR)
x = x / 255.0
x = x.astype(np.float32)
x = normalize(x,axis=1)
return x
def read_masks(path):
x = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
x = x.astype(np.float32)
x = np.expand_dims(x, axis=-1)
return x
def read_train_flow(path_train):
path_img = os.path.join(path_train, 'images')
path_masks = os.path.join(path_train, 'masks')
names = os.listdir(path_img)
all_img = []
all_masks = []
for name in names:
img = read_images(os.path.join(path_img, name))
mask = read_masks(os.path.join(path_masks, name))
all_img.append(img)
all_masks.append(mask)
all_img = np.array(all_img)
all_masks = np.array(all_masks)
return all_img, all_masks
def read_val_flow(path_val, num_class=2):
path_img = os.path.join(path_val, 'images')
path_masks = os.path.join(path_val, 'masks')
names = os.listdir(path_img)
all_img = []
all_masks = []
for name in names:
img = read_images(os.path.join(path_img, name))
mask = read_masks(os.path.join(path_masks, name))
all_img.append(img)
all_masks.append(mask)
all_img = np.array(all_img)
all_masks = np.array(all_masks)
return all_img, all_masks
def preprocess_data(img, mask, num_class=2, backbone=''):
if backbone == '':
img = scaler.fit_transform(img.reshape(-1, img.shape[-1])).reshape(img.shape)
mask = to_categorical(mask, num_class)
mask = mask.reshape((mask.shape[0], mask.shape[1], mask.shape[2], num_class))
else:
preprocess_input = sm.get_preprocessing(backbone)
img = scaler.fit_transform(img.reshape(-1, img.shape[-1])).reshape(img.shape)
img = preprocess_input(img)
mask = to_categorical(mask, num_class)
mask = mask.reshape((mask.shape[0], mask.shape[1],mask.shape[2], num_class))
return (img, mask)
def my_image_mask_generator(image_generator, mask_generator, num_classes=2,backbone=''):
train_generator = zip(image_generator, mask_generator)
for (img, mask) in train_generator:
img, mask = preprocess_data(img, mask, num_class=num_classes,backbone=backbone)
yield (img, mask)
def make_generator_flow(imgs, masks, batch_size=2, seed=42, augment_dict_i={}, augment_dict_m={},backbone=''):
if augment_dict_i == {}:
img_gen = ImageDataGenerator()
mask_gen = ImageDataGenerator()
else:
img_gen = ImageDataGenerator(**augment_dict_i)
img_gen.fit(imgs, augment=True, seed=seed)
mask_gen = ImageDataGenerator(**augment_dict_m)
mask_gen.fit(masks, augment=True, seed=seed)
img_gener = img_gen.flow(imgs, batch_size=batch_size, seed=seed, shuffle=False)
mask_gen = mask_gen.flow(masks, batch_size=batch_size, seed=seed, shuffle=False)
my_generator = my_image_mask_generator(img_gener, mask_gen,backbone=backbone)
return my_generator
def make_generator_flow_dir(train_img_path, train_mask_path, num_class=2, augment_dict_i={}, augment_dict_m={},target_size=(),batch_size=2,seed=42,backbone=''): # target size have to be declared
if augment_dict_i == {}:
img_gen = ImageDataGenerator()
mask_gen = ImageDataGenerator()
else:
img_gen = ImageDataGenerator(**augment_dict_i)
img_gen.fit(imgs, augment=True, seed=seed)
mask_gen = ImageDataGenerator(**augment_dict_m)
mask_gen.fit(masks, augment=True, seed=seed)
image_generator = img_gen.flow_from_directory(
train_img_path,
target_size=target_size,
class_mode=None,
batch_size=batch_size,
seed=seed)
mask_generator = mask_gen.flow_from_directory(
train_mask_path,
target_size=target_size,
class_mode=None,
color_mode='grayscale',
batch_size=batch_size,
seed=seed)
my_generator = my_image_mask_generator(image_generator,mask_generator,backbone=backbone,num_classes=num_class)
return my_generator
def plot_gen(gen, batch_size=2):
x, y = gen.__next__()
for i in range(0, batch_size):
image = x[i]
mask = np.argmax(y[i],axis=2) # have to be after one hot encoding
plt.subplot(1, 2, 1)
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.imshow(mask, cmap='gray')
plt.show()