From 8be8dd6ed5cdaaa262d842cae2ac30c7ea59dae5 Mon Sep 17 00:00:00 2001
From: byungheon-jeong <byungheon.jeong@gmail.com>
Date: Thu, 5 Mar 2020 12:25:09 -0800
Subject: [PATCH] Create imageLoader.py
For loading HPWREN image database for Keras models
---
imageLoader.py | 70 ++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 70 insertions(+)
create mode 100644 imageLoader.py
diff --git a/imageLoader.py b/imageLoader.py
new file mode 100644
index 0000000..be1f9d9
--- /dev/null
+++ b/imageLoader.py
@@ -0,0 +1,70 @@
+#(128,128)
+
+def load_dataset(datasetPath, image_dimensions):
+ # grab the paths to all images in our dataset directory, then
+ # initialize our lists of images
+ imagePaths = os.listdir(datasetPath)
+ trainXList = []
+ testXList = []
+ testX = np.array([])
+ trainY = np.array([])
+ trainY = np.array([])
+ testY = np.array([])
+
+ testI = 0
+
+ # loop over the image paths
+ for directories in imagePaths:
+ tempF= []
+ tempNF = []
+
+ for element in os.listdir(datasetPath + "/"+ directories):
+ if re.search(".jpg", element):
+ image = cv2.imread(datasetPath + "/"+ directories + "/" + element)
+ image = cv2.resize(image, image_dimensions)
+ if "+" in element:
+ tempF.append(image)
+ else:
+ tempNF.append(image)
+
+ tempF = np.array(tempF, dtype="float32")
+ tempNF = np.array(tempNF, dtype="float32")
+
+ fireLabels = np.ones((tempF.shape[0],))
+ nonFireLabels = np.zeros((tempNF.shape[0],))
+ data = np.vstack([tempF, tempNF])
+ labels = np.hstack([fireLabels, nonFireLabels])
+ labels = to_categorical(labels, num_classes=2)
+
+ print(labels)
+
+ data /= 255
+
+ (t_trainX, t_testX, t_trainY, t_testY) = train_test_split(data, labels,
+ test_size=0.2, random_state=42)
+
+ trainXList.append(t_trainX)
+ testXList.append(t_testX)
+ print(t_trainY.shape, trainY.shape)
+
+ if trainY.size == 0:
+ trainY = t_trainY
+ testY = t_testY
+ else:
+ trainY = np.append(trainY, t_trainY, axis = 0)
+ testY = np.append(testY, t_testY, axis = 0)
+
+
+ trainX = np.vstack(trainXList)
+ testX = np.vstack(testXList)
+# trainY = np.hstack(trainYList)
+# testY = np.hstack(testYList)
+
+ labels = np.append(trainY, testY)
+ labels = to_categorical(labels, num_classes=2)
+ classTotals = labels.sum(axis=0)
+ classWeight = classTotals.max() / classTotals
+
+ print(trainX.shape, testX.shape, trainY.shape, testY.shape)
+
+ return trainX, testX, trainY, testY, classWeight
\ No newline at end of file
--
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