Create utils.py

utils.py

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+# utils.py
+
+import torch
+from torchvision import transforms
+from PIL import Image
+
+# Character-to-Index Mapping (should be the same as used during training)
+amharic_chars = list(' ሀሁሂሃሄህሆለሉሊላሌልሎሐሑሒሓሔሕሖመሙሚማሜምሞሰሱሲሳስሶረሩሪራሬርሮሠሡሢሣሤሥሦሸሹሺሻሼሽሾቀቁቂቃቄቅቆበቡቢባቤብቦተቱቲታቴትቶቸቹቺቻቼችቾኀኃነኑኒናኔንኖኘኙኚኛኜኝኞአኡኢኣኤእኦከኩኪካኬክኮኸኹኺኻኼኽኾወዉዊዋዌውዎዐዑዒዓዔዕዖዘዙዚዛዜዝዞዠዡዢዣዤዥዦየዩዪያዬይዮደዱዲዳዴድዶጀጁጂጃጄጅጆገጉጊጋጌግጎጠጡጢጣጤጥጦጨጩጪጫጬጭጮጰጱጲጳጴጵጶጸጹጺጻጼጽጾፀፁፂፃፄፅፆፈፉፊፋፌፍፎፐፑፒፓፔፕፖቨቩቪቫቬቭቮ0123456789፥፣()-ሏሟሷሯሿቧቆቈቋቷቿኗኟዟዧዷጇጧጯጿፏኳኋኧቯጐጕጓ።')
+
+char_to_idx = {char: idx + 1 for idx, char in enumerate(amharic_chars)}  # Start indexing from 1
+char_to_idx['<UNK>'] = len(amharic_chars) + 1  # Unknown characters
+idx_to_char = {idx: char for char, idx in char_to_idx.items()}
+idx_to_char[0] = '<blank>'  # CTC blank token
+
+def preprocess_image(image: Image.Image) -> torch.Tensor:
+    """
+    Preprocess the input image for the model.
+    """
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                             std=[0.229, 0.224, 0.225]),
+    ])
+    return transform(image)
+
+def decode_predictions(preds: torch.Tensor) -> str:
+    """
+    Decode the model's predictions using Best Path Decoding.
+    """
+    preds = torch.argmax(preds, dim=2).transpose(0, 1)  # [batch_size, H*W]
+    decoded_texts = []
+    
+    for pred in preds:
+        pred = pred.cpu().numpy()
+        decoded = []
+        previous = 0  # Assuming blank index is 0
+        for p in pred:
+            if p != previous and p != 0:
+                decoded.append(idx_to_char.get(p, '<UNK>'))
+            previous = p
+        recognized_text = ''.join(decoded)
+        decoded_texts.append(recognized_text)
+    
+    return decoded_texts