Add old one back again.

Author: newmana

stlearn/image_preprocessing/feature_extractor.py

@@ -5,12 +5,89 @@
 from anndata import AnnData
 from sklearn.decomposition import PCA
 from tqdm import tqdm
+import pandas as pd
 
 from .model_zoo import Model
 
 _CNN_BASE = Literal["resnet50", "vgg16", "inception_v3", "xception"]
 
 
+def old_extract_feature(
+    adata: AnnData,
+    cnn_base: _CNN_BASE = "resnet50",
+    n_components: int = 50,
+    verbose: bool = False,
+    copy: bool = False,
+    seeds: int = 1,
+) -> AnnData | None:
+    """\
+    Extract latent morphological features from H&E images using pre-trained
+    convolutional neural network base
+
+    Parameters
+    ----------
+    adata:
+        Annotated data matrix.
+    cnn_base:
+        Established convolutional neural network bases
+        choose one from ['resnet50', 'vgg16', 'inception_v3', 'xception']
+    n_components:
+        Number of principal components to compute for latent morphological features
+    verbose:
+        Verbose output
+    copy:
+        Return a copy instead of writing to adata.
+    seeds:
+        Fix random state
+    Returns
+    -------
+    Depending on `copy`, returns or updates `adata` with the following fields.
+    **X_morphology** : `adata.obsm` field
+        Dimension reduced latent morphological features.
+    """
+    feature_dfs = []
+    model = Model(cnn_base)
+
+    if "tile_path" not in adata.obs:
+        raise ValueError("Please run the function stlearn.pp.tiling")
+
+    def encode(tiles, model):
+        features = model.predict(tiles)
+        features = features.ravel()
+        return features
+
+    with tqdm(
+        total=len(adata),
+        desc="Extract feature",
+        bar_format="{l_bar}{bar} [ time left: {remaining} ]",
+    ) as pbar:
+        for spot, tile_path in adata.obs["tile_path"].items():
+            tile = Image.open(tile_path)
+            tile = np.asarray(tile, dtype="int32")
+            tile = tile.astype(np.float32)
+            tile = np.stack([tile])
+            if verbose:
+                print("extract feature for spot: {}".format(str(spot)))
+            features = encode(tile, model)
+            feature_dfs.append(pd.DataFrame(features, columns=[spot]))
+            pbar.update(1)
+
+    feature_df = pd.concat(feature_dfs, axis=1)
+
+    adata.obsm["X_tile_feature"] = feature_df.transpose().to_numpy()
+
+    from sklearn.decomposition import PCA
+
+    pca = PCA(n_components=n_components, random_state=seeds)
+    pca.fit(feature_df.transpose().to_numpy())
+
+    adata.obsm["X_morphology"] = pca.transform(feature_df.transpose().to_numpy())
+
+    print("The morphology feature is added to adata.obsm['X_morphology']!")
+
+    return adata if copy else None
+
+
 def extract_feature(
         adata: AnnData,
         cnn_base: _CNN_BASE = "resnet50",