Merge pull request #1226 from L0FX/dev-postgresql

Create the data folder for the healthcare model zoo

examples/healthcare/data/malaria.py

@@ -0,0 +1,122 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+try:
+    import pickle
+except ImportError:
+    import cPickle as pickle
+
+import numpy as np
+import os
+import sys
+from PIL import Image
+
+
+# need to save to specific local directories
+def load_train_data(dir_path="/tmp/malaria", resize_size=(128, 128)):
+    dir_path = check_dataset_exist(dirpath=dir_path)
+    path_train_label_1 = os.path.join(dir_path, "training_set/Parasitized")
+    path_train_label_0 = os.path.join(dir_path, "training_set/Uninfected")
+    train_label_1 = load_image_path(os.listdir(path_train_label_1))
+    train_label_0 = load_image_path(os.listdir(path_train_label_0))
+    labels = []
+    Images = np.empty((len(train_label_1) + len(train_label_0),
+                       3, resize_size[0], resize_size[1]), dtype=np.uint8)
+    for i in range(len(train_label_0)):
+        image_path = os.path.join(path_train_label_0, train_label_0[i])
+        temp_image = np.array(Image.open(image_path).resize(
+            resize_size).convert("RGB")).transpose(2, 0, 1)
+        Images[i] = temp_image
+        labels.append(0)
+    for i in range(len(train_label_1)):
+        image_path = os.path.join(path_train_label_1, train_label_1[i])
+        temp_image = np.array(Image.open(image_path).resize(
+            resize_size).convert("RGB")).transpose(2, 0, 1)
+        Images[i + len(train_label_0)] = temp_image
+        labels.append(1)
+
+    Images = np.array(Images, dtype=np.float32)
+    labels = np.array(labels, dtype=np.int32)
+    return Images, labels
+
+
+# need to save to specific local directories
+def load_test_data(dir_path='/tmp/malaria', resize_size=(128, 128)):
+    dir_path = check_dataset_exist(dirpath=dir_path)
+    path_test_label_1 = os.path.join(dir_path, "testing_set/Parasitized")
+    path_test_label_0 = os.path.join(dir_path, "testing_set/Uninfected")
+    test_label_1 = load_image_path(os.listdir(path_test_label_1))
+    test_label_0 = load_image_path(os.listdir(path_test_label_0))
+    labels = []
+    Images = np.empty((len(test_label_1) + len(test_label_0),
+                       3, resize_size[0], resize_size[1]), dtype=np.uint8)
+    for i in range(len(test_label_0)):
+        image_path = os.path.join(path_test_label_0, test_label_0[i])
+        temp_image = np.array(Image.open(image_path).resize(
+            resize_size).convert("RGB")).transpose(2, 0, 1)
+        Images[i] = temp_image
+        labels.append(0)
+    for i in range(len(test_label_1)):
+        image_path = os.path.join(path_test_label_1, test_label_1[i])
+        temp_image = np.array(Image.open(image_path).resize(
+            resize_size).convert("RGB")).transpose(2, 0, 1)
+        Images[i + len(test_label_0)] = temp_image
+        labels.append(1)
+
+    Images = np.array(Images, dtype=np.float32)
+    labels = np.array(labels, dtype=np.int32)
+    return Images, labels
+
+
+def load_image_path(list):
+    new_list = []
+    for image_path in list:
+        if (image_path.endswith(".png") or image_path.endswith(".jpg")):
+            new_list.append(image_path)
+    return new_list
+
+
+def check_dataset_exist(dirpath):
+    if not os.path.exists(dirpath):
+        print(
+            'Please download the malaria dataset first'
+        )
+        sys.exit(0)
+    return dirpath
+
+
+def normalize(train_x, val_x):
+    mean = [0.5339, 0.4180, 0.4460]  # mean for malaria dataset
+    std = [0.3329, 0.2637, 0.2761]  # std for malaria dataset
+    train_x /= 255
+    val_x /= 255
+    for ch in range(0, 2):
+        train_x[:, ch, :, :] -= mean[ch]
+        train_x[:, ch, :, :] /= std[ch]
+        val_x[:, ch, :, :] -= mean[ch]
+        val_x[:, ch, :, :] /= std[ch]
+    return train_x, val_x
+
+
+def load(dir_path):
+    train_x, train_y = load_train_data(dir_path=dir_path)
+    val_x, val_y = load_test_data(dir_path=dir_path)
+    train_x, val_x = normalize(train_x, val_x)
+    train_y = train_y.flatten()
+    val_y = val_y.flatten()
+    return train_x, train_y, val_x, val_y