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