Create get_hwf.py

datasets/hwf/get_hwf.py

@@ -0,0 +1,36 @@
+import json
+from PIL import Image
+from torchvision.transforms import transforms
+
+img_transform = transforms.Compose([
+                        transforms.ToTensor(),
+                        transforms.Normalize((0.5,), (1,))
+                    ])
+
+def get_data(file):
+    X = []
+    Y = []
+    img_dir = './data/Handwritten_Math_Symbols/'
+    with open(file) as f:
+        data = json.load(f)
+        for idx in range(len(data)):
+            imgs = []
+            for img_path in data[idx]['img_paths']:
+                img = Image.open(img_dir + img_path).convert('L')
+                img = img_transform(img)
+                imgs.append(img)
+            X.append(imgs)
+            Y.append(data[idx]['res'])
+    return X, Y
+
+def get_hwf():
+    train_X, train_Y = get_data('./data/expr_train.json')
+    test_X, test_Y = get_data('./data/expr_test.json')
+    
+    return train_X, train_Y, test_X, test_Y
+
+if __name__ == "__main__":
+    train_X, train_Y, test_X, test_Y = get_hwf()
+    print(len(train_X), len(test_X))
+    print(len(train_X[0]), train_X[0][0].shape, train_Y[0])
+