initial commit with Jupyter Notebooks, python functions folders and images

Author: CodeSigma91

EDA.ipynb

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+################################################################################################################################
+
+import numpy as np
+
+################################################################################################################################
+
+# Straightforward IoU calculation that is not concerned with tensorflow formatting.
+
+def calculate_iou(y_true, y_pred):
+    # input must be as [x1, y1, x2, y2]
+    
+    y_true = y_true.astype(np.float32)
+    y_pred = y_pred.astype(np.float32)
+
+    # AOG = Area of Groundtruth box
+    AoG = np.abs(y_true[2] - y_true[0]) * np.abs(y_true[3] - y_true[1])
+
+    # AOP = Area of Predicted box
+    AoP = np.abs(y_pred[2] - y_pred[0]) * np.abs(y_pred[3] - y_pred[1])
+
+    # overlaps are the co-ordinates of intersection box
+    overlap_0 = np.maximum(y_true[0], y_pred[0])
+    overlap_1 = np.maximum(y_true[1], y_pred[1])
+    overlap_2 = np.minimum(y_true[2], y_pred[2])
+    overlap_3 = np.minimum(y_true[3], y_pred[3])
+
+    # intersection area
+    intersection = np.abs(overlap_2 - overlap_0) * np.abs(overlap_3 - overlap_1)
+
+    # area of union of both boxes
+    union = AoG + AoP - intersection
+
+    # iou calculation
+    iou = intersection / union
+
+    # return the mean IoU score for the batch
+    return iou
+
+################################################################################################################################