Merge pull request #106 from Kohulan/development

feat: new input format as numpy array representing the image

DECIMER/config.py

@@ -11,6 +11,7 @@
 from PIL import Image
 from PIL import ImageEnhance
 from pillow_heif import register_heif_opener
+from typing import Union
 
 import DECIMER.Efficient_Net_encoder as Efficient_Net_encoder
 import DECIMER.Transformer_decoder as Transformer_decoder
@@ -95,26 +96,68 @@ def HEIF_to_pillow(image_path: str):
     return heif_file
 
 
-def remove_transparent(image_path: str):
+def remove_transparent(image: Union[str, np.ndarray]) -> Image.Image:
     """
-    Removes the transparent layer from a PNG image with an alpha channel
-    Args: image_path (str): path of input image
-    Returns: PIL.Image
+    Removes the transparent layer from a PNG image with an alpha channel.
+
+    Args:
+        image (Union[str, np.ndarray]): Path of the input image or a numpy array representing the image.
+
+    Returns:
+        PIL.Image.Image: The image with transparency removed.
     """
-    try:
-        png = Image.open(image_path).convert("RGBA")
-    except Exception as e:
-        if type(e).__name__ == "UnidentifiedImageError":
-            png = HEIF_to_pillow(image_path)
-        else:
-            print(e)
-            raise Exception
+    def process_image(png: Image.Image) -> Image.Image:
+        """
+        Helper function to remove transparency from a single image.
+
+        Args:
+            png (PIL.Image.Image): The input PIL image with transparency.
+
+        Returns:
+            PIL.Image.Image: The image with transparency removed.
+        """
+        background = Image.new("RGBA", png.size, (255, 255, 255))
+        alpha_composite = Image.alpha_composite(background, png)
+        return alpha_composite
 
-    background = Image.new("RGBA", png.size, (255, 255, 255))
+    def handle_image_path(image_path: str) -> Image.Image:
+        """
+        Helper function to handle image paths.
+
+        Args:
+            image_path (str): The path to the input image.
+
+        Returns:
+            PIL.Image.Image: The image with transparency removed.
+        """
+        try:
+            png = Image.open(image_path).convert("RGBA")
+        except Exception as e:
+            if type(e).__name__ == "UnidentifiedImageError":
+                png = HEIF_to_pillow(image_path)
+            else:
+                print(e)
+                raise Exception
+        return process_image(png)
+
+    def handle_numpy_array(array: np.ndarray) -> Image.Image:
+        """
+        Helper function to handle a numpy array.
+
+        Args:
+            array (np.ndarray): The numpy array representing the image.
+
+        Returns:
+            PIL.Image.Image: The image with transparency removed.
+        """
+        png = Image.fromarray(array).convert("RGBA")
+        return process_image(png)
 
-    alpha_composite = Image.alpha_composite(background, png)
+    # Check if input is a numpy array
+    if isinstance(image, np.ndarray):
+        return handle_numpy_array(array=image)
 
-    return alpha_composite
+    return handle_image_path(image_path=image)
 
 
 def get_bnw_image(image):
@@ -185,12 +228,12 @@ def increase_brightness(image):
     return image
 
 
-def decode_image(image_path: str):
+def decode_image(image_path: Union[str, np.ndarray]):
     """Loads an image and preprocesses the input image in several steps to get
     the image ready for DECIMER input.
 
     Args:
-        image_path (str): path of input image
+        image_path (Union[str, np.ndarray]): path of input image or numpy array representing the image.
 
     Returns:
         Processed image
@@ -237,7 +280,7 @@ def initialize_encoder_config(
             backbone_fn (method): Calls Efficient-Net V2 as backbone for encoder
             image_shape (int): Shape of the input image
             do_permute (bool, optional): . Defaults to False.
-            pretrained_weights (keras weights, optional): Use pretrainined efficient net weights or not. Defaults to None.
+            pretrained_weights (keras weights, optional): Use pretrained efficient net weights or not. Defaults to None.
         """
         self.encoder_config = dict(
             image_embedding_dim=image_embedding_dim,

DECIMER/decimer.py

@@ -5,6 +5,7 @@
 from typing import List
 from typing import Tuple
 
+import numpy as np
 import pystow
 import tensorflow as tf
 
@@ -122,19 +123,19 @@ def detokenize_output_add_confidence(
 
 
 def predict_SMILES(
-    image_path: str, confidence: bool = False, hand_drawn: bool = False
+        image_input: [str, np.ndarray], confidence: bool = False, hand_drawn: bool = False
 ) -> str:
     """Predicts SMILES representation of a molecule depicted in the given image.
 
     Args:
-        image_path (str): Path of chemical structure depiction image
-        confidence (bool): Flag to indicate whether to return confidence values along with SMILES prediction
-        hand_drawn (bool): Flag to indicate whether the molecule in the image is hand-drawn
+        image_input (str or np.ndarray): Path of chemical structure depiction image or a numpy array representing the image.
+        confidence (bool): Flag to indicate whether to return confidence values along with SMILES prediction.
+        hand_drawn (bool): Flag to indicate whether the molecule in the image is hand-drawn.
 
     Returns:
-        str: SMILES representation of the molecule in the input image, optionally with confidence values
+        str: SMILES representation of the molecule in the input image, optionally with confidence values.
     """
-    chemical_structure = config.decode_image(image_path)
+    chemical_structure = config.decode_image(image_input)
 
     model = DECIMER_Hand_drawn if hand_drawn else DECIMER_V2
     predicted_tokens, confidence_values = model(tf.constant(chemical_structure))