Create imageLoader.py

For loading HPWREN image database for Keras models

imageLoader.py

+#(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
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