Remove Multiple from default imports

README.md

@@ -60,7 +60,6 @@ import numpy as np
 from PIL import Image
 from abraia import detect
 
-
 model_uri = f"https://api.abraia.me/files/multiple/models/yolov8n.onnx"
 
 model = detect.load_model(model_uri)
@@ -83,23 +82,19 @@ import numpy as np
 from abraia.draw import load_image, save_image, render_results
 from abraia.faces import Recognition
 
-
 img = load_image('images/rolling-stones.jpg')
 out = img.copy()
 
 recognition = Recognition()
 results = recognition.represent_faces(img)
 
+index = []
 for src in ['mick-jagger.jpg', 'keith-richards.jpg', 'ronnie-wood.jpg', 'charlie-watts.jpg']:
     img = load_image(f"images/{src}")
     rslt = recognition.represent_faces(img)[0]
-    sims = [recognition.compute_similarity(rslt['embeddings'], result['embeddings']) for result in results]
-    idx = np.argmax(sims)
-    if sims[idx] > 0.45:
-        results[idx]['label'] = os.path.splitext(src)[0]
-        results[idx]['confidence'] = sims[idx]
-    print(src, sims[idx], sims)
+    index.append({'name': os.path.splitext(src)[0], 'embeddings': rslt['embeddings']})
 
+result = recognition.identify_faces(results, index)
 render_results(out, results)
 save_image('images/rolling-stones-identified.jpg', out)
 ```
@@ -255,7 +250,7 @@ The Multiple extension has being developed by [ABRAIA](https://abraia.me/about)
 For instance, you can directly load and save ENVI files, and their metadata.
 
 ```python
-from abraia import Multiple
+from abraia.multiple import Multiple
 
 multiple = Multiple()
 

abraia/__init__.py

@@ -4,6 +4,5 @@
 
 from . import config
 from .client import Abraia, APIError
-from .multiple import Multiple
 
-__all__ = ['config', 'Abraia', 'APIError', 'Multiple']
+__all__ = ['config', 'Abraia', 'APIError']

abraia/faces/__init__.py

@@ -5,11 +5,11 @@
 
 
 def euclidean_distance(feat1, feat2):
-    return np.sqrt(np.square(feat1 - feat2).sum())
+    return float(np.linalg.norm(feat1 - feat2))
 
 
 def cosine_similarity(feat1, feat2):
-    return np.dot(feat1, feat2) / (np.linalg.norm(feat1) * np.linalg.norm(feat2))
+    return float(np.dot(feat1, feat2) / (np.linalg.norm(feat1) * np.linalg.norm(feat2)))
 
 
 class Recognition:
@@ -31,8 +31,14 @@ def represent_faces(self, img, results=None):
             result['embeddings'] = self.arcface.calculate_embeddings(face)
         return results
 
-    def compute_similarity(self, feat1, feat2):
-        return float(cosine_similarity(feat1, feat2))
+    def identify_faces(self, results, index, threshold=0.45):
+        for result in results:
+            sims = [cosine_similarity(result['embeddings'], ind['embeddings']) for ind in index]
+            idx = np.argmax(sims)
+            if sims[idx] > threshold:
+                result['confidence'] = sims[idx]
+                result['label'] = index[idx]['name']
+        return results
 
 
 __all__ = [Recognition]

abraia/multiple.py

@@ -1,4 +1,3 @@
-import os
 import tifffile
 import numpy as np
 from PIL import Image

notebooks/hyperspectral-analysis.ipynb

@@ -41,7 +41,9 @@
         "import numpy as np\n",
         "import pandas as pd\n",
         "import matplotlib.pyplot as plt\n",
-        "from abraia import Multiple, hsi\n",
+        "\n",
+        "from abraia.multiple import Multiple\n",
+        "from abraia import hsi\n",
         "\n",
         "plt.rcParams['figure.figsize'] = [12, 6]\n",
         "plt.rcParams['figure.dpi'] = 100\n",

notebooks/hyperspectral-classification.ipynb

@@ -45,7 +45,9 @@
         "import numpy as np\n",
         "import pandas as pd\n",
         "import matplotlib.pyplot as plt\n",
-        "from abraia import Multiple, hsi\n",
+        "\n",
+        "from abraia.multiple import Multiple\n",
+        "from abraia import hsi\n",
         "\n",
         "plt.rcParams['figure.figsize'] = [12, 6]\n",
         "plt.rcParams['figure.dpi'] = 100\n",

tests/test_multiple.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from abraia import Multiple
+from abraia.multiple import Multiple
 
 multiple = Multiple()