File size: 2,229 Bytes
64ea7b2 | 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 | from aix import Dataset
import aix.constants as C
import os
import numpy as np
import cv2
def load_image(path):
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
array = np.reshape(img, (img.shape[0], img.shape[1], -1)) / 255
return array
def temp_numpy_masks(training_fold):
image_rows = C.IMG_WIDTH
image_cols = C.IMG_HEIGHT
X_train = np.ndarray((len(training_fold[0]), image_rows, image_cols, 1), dtype = np.float64)
y_train = np.ndarray((len(training_fold[1]), image_rows, image_cols, 1), dtype = np.float64)
X_test = np.ndarray((len(training_fold[2]), image_rows, image_cols, 1), dtype = np.float64)
y_test = np.ndarray((len(training_fold[3]), image_rows, image_cols, 1), dtype = np.float64)
for i, obj in enumerate(training_fold[0]):
img = load_image(obj)
img = cv2.resize(img, (image_cols, image_rows), interpolation = cv2.INTER_AREA)
img = np.expand_dims(img, axis = 2)
X_train[i] = img
for i, obj in enumerate(training_fold[1]):
mask = load_image(obj)
mask = cv2.resize(mask, (image_cols, image_rows), interpolation = cv2.INTER_AREA)
mask = np.expand_dims(mask, axis = 2)
y_train[i] = mask
for i, obj in enumerate(training_fold[2]):
img = load_image(obj)
img = cv2.resize(img, (image_cols, image_rows), interpolation = cv2.INTER_AREA)
img = np.expand_dims(img, axis = 2)
X_test[i] = img
for i, obj in enumerate(training_fold[3]):
mask = load_image(obj)
mask = cv2.resize(mask, (image_cols, image_rows), interpolation = cv2.INTER_AREA)
mask = np.expand_dims(mask, axis = 2)
y_test[i] = mask
return X_train, y_train, X_test, y_test
def load_training_batch(name = 'average_model', oocytes_list = [], images_path = '', annotations_path = ''):
dataset = Dataset(name, oocytes_list, images_path, annotations_path)
gen = dataset.train_test_iterator()
j = 0
folds = []
for x_train, x_test, y_train, y_test in gen:
j += 1
print(j)
folds.append(temp_numpy_masks([x_train, y_train, x_test, y_test]))
# save temporary numpies??
# DO MODEL stuff in here
return folds
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