changed api structure and added object detection

api/src/util.py

@@ -0,0 +1,61 @@
+from PIL import Image
+import os
+from matplotlib import pyplot as plt
+import torch
+
+class TransformedSubset(torch.utils.data.Dataset):
+    def __init__(self, subset, transform=None):
+        self.subset = subset
+        self.transform = transform
+
+    def __getitem__(self, idx):
+        x, y = self.subset[idx]
+
+        if self.transform:
+            x = self.transform(x)
+
+        return x, y
+
+    def __len__(self):
+        return len(self.subset)
+    
+
+def visualizeData(dataset):
+    images, labels = next(iter(dataset))
+
+    for i in range(4):
+        img = images[i]
+
+        # Convert tensor shape from [C,H,W] -> [H,W,C]
+        img = img.permute(1, 2, 0)
+
+        plt.figure(figsize=(3,3))
+        plt.imshow(img)
+        plt.title(f"Label: {labels[i].item()}")
+        plt.axis("off")
+
+    plt.show()
+
+def visualizeImage(image):
+    image = image.permute(1, 2, 0)
+
+    plt.figure(figsize=(3,3))
+    plt.imshow(image)
+    plt.axis("off")
+
+    plt.show()
+
+def iou(boxA, boxB):
+    xA = max(boxA[0], boxB[0])
+    yA = max(boxA[1], boxB[1])
+    xB = min(boxA[2], boxB[2])
+    yB = min(boxA[3], boxB[3])
+
+    inter_area = max(0, xB - xA) * max(0, yB - yA)
+
+    boxA_area = (boxA[2]-boxA[0]) * (boxA[3]-boxA[1])
+    boxB_area = (boxB[2]-boxB[0]) * (boxB[3]-boxB[1])
+
+    union = boxA_area + boxB_area - inter_area
+
+    return inter_area / union if union > 0 else 0