--- a
+++ b/tf_iou.py
@@ -0,0 +1,63 @@
+################################################################################################################################
+
+import numpy      as np
+import tensorflow as tf
+
+################################################################################################################################
+
+# This function is designed to work with Keras datagenerators as a model metric.
+
+# SOURCE1: https://github.com/Balupurohit23/IOU-for-bounding-box-regression-in-Keras/blob/master/iou_metric.py
+# SOURCE2: https://www.kaggle.com/vbookshelf/keras-iou-metric-implemented-without-tensor-drama
+
+# I made use of the two sources because they each offered something I needed. I essentially used the framework of
+# Source 2, but with the code of Source 1 because the format aligned with my data.
+
+# I also reassigned the 'astype' numpy code to fit with tensorflow as well as converting the keras functions to np.
+
+def tf_iou(y_true, y_pred):
+    # iou as metric for bounding box regression
+    # input must be as [x1, y1, x2, y2]
+    
+    results = []
+    
+    for i in range(0,y_true.shape[0]):
+        
+        y_true = tf.dtypes.cast(y_true, tf.float32)
+        y_pred = tf.dtypes.cast(y_pred, tf.float32)
+
+        # AOG = Area of Groundtruth box
+        AoG = np.abs(np.transpose(y_true)[2] - np.transpose(y_true)[0] + 1) * np.abs(np.transpose(y_true)[3] -\
+                                                                                     np.transpose(y_true)[1] + 1)
+
+        # AOP = Area of Predicted box
+        AoP = np.abs(np.transpose(y_pred)[2] - np.transpose(y_pred)[0] + 1) * np.abs(np.transpose(y_pred)[3] - \
+                                                                                     np.transpose(y_pred)[1] + 1)
+
+        # overlaps are the co-ordinates of intersection box
+        overlap_0 = np.maximum(np.transpose(y_true)[0], np.transpose(y_pred)[0])
+        overlap_1 = np.maximum(np.transpose(y_true)[1], np.transpose(y_pred)[1])
+        overlap_2 = np.minimum(np.transpose(y_true)[2], np.transpose(y_pred)[2])
+        overlap_3 = np.minimum(np.transpose(y_true)[3], np.transpose(y_pred)[3])
+
+        # intersection area
+        intersection = (overlap_2 - overlap_0 + 1) * (overlap_3 - overlap_1 + 1)
+
+        # area of union of both boxes
+        union = AoG + AoP - intersection
+
+        # iou calculation
+        iou = intersection / union
+
+        # bounding values of iou to (0,1)
+        epsilon = np.finfo(np.float32).eps
+        iou     = np.clip(iou, 0.0 + epsilon, 1.0 - epsilon)
+        iou     = tf.dtypes.cast(iou, tf.float32)
+        
+        # append the result to a list at the end of each loop
+        results.append(iou)
+    
+    # return the mean IoU score for the batch
+    return np.mean(results)
+
+################################################################################################################################