Open source

model_optimization.py

@@ -0,0 +1,27 @@
+import numpy as np
+from tensorflow.keras.applications import MobileNetV2
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
+
+
+def load_optimized_model():
+    model = MobileNetV2(
+        weights="imagenet", include_top=False, input_shape=(224, 224, 3)
+    )
+    print("Loaded MobileNetV2 model for optimized face detection.")
+    return model
+
+
+def preprocess_image(image_path):
+    image = load_img(image_path, target_size=(224, 224))
+    image = img_to_array(image)
+    image = image / 255.0  # Normalize image
+    image = np.expand_dims(image, axis=0)
+    return image
+
+
+# Example usage
+if __name__ == "__main__":
+    model = load_optimized_model()
+    preprocessed_image = preprocess_image("path/to/sample_image.jpg")
+    features = model.predict(preprocessed_image)
+    print("Extracted features:", features)

test_face_detection.py

@@ -0,0 +1,55 @@
+import os
+import unittest
+
+from face_recognition_module import \
+    detect_faces  # Replace with the actual module name
+
+
+class TestFaceDetection(unittest.TestCase):
+    def test_invalid_image_path(self):
+        """Test case for an invalid image path."""
+        invalid_path = "non_existent_directory/non_existent_image.jpg"
+        with self.assertRaises(FileNotFoundError):
+            detect_faces(invalid_path)
+
+    def test_corrupted_image_file(self):
+        """Test case for a corrupted image file."""
+        # Create a corrupted image file for testing
+        corrupted_image_path = "test_images/corrupted_image.jpg"
+        with open(corrupted_image_path, "w") as f:
+            f.write("This is not a valid image content")
+
+        with self.assertRaises(ValueError):
+            detect_faces(corrupted_image_path)
+
+        # Clean up the corrupted image file after testing
+        os.remove(corrupted_image_path)
+
+    def test_unsupported_image_format(self):
+        """Test case for an unsupported image format."""
+        unsupported_image_path = "test_images/sample.txt"
+        # Create a text file to simulate an unsupported image format
+        with open(unsupported_image_path, "w") as f:
+            f.write("This is a text file, not an image.")
+
+        with self.assertRaises(ValueError):
+            detect_faces(unsupported_image_path)
+
+        # Clean up the text file after testing
+        os.remove(unsupported_image_path)
+
+    def test_empty_image_file(self):
+        """Test case for an empty image file."""
+        empty_image_path = "test_images/empty_image.jpg"
+        # Create an empty file
+        open(empty_image_path, "w").close()
+
+        with self.assertRaises(ValueError):
+            detect_faces(empty_image_path)
+
+        # Clean up the empty image file after testing
+        os.remove(empty_image_path)
+
+
+if __name__ == "__main__":
+    unittest.main()

test_face_recognition.py

@@ -0,0 +1,27 @@
+import unittest
+
+from face_recognition_module import detect_faces, generate_embeddings
+
+
+class TestFaceRecognition(unittest.TestCase):
+    def test_detect_faces(self):
+        # Test with a sample image
+        image_path = "test_images/sample.jpg"
+        faces = detect_faces(image_path)
+        self.assertGreater(
+            len(faces), 0, "No faces detected in the sample image.")
+
+    def test_generate_embeddings(self):
+        # Test with a dummy face data
+        face_data = "sample_face_data"
+        embedding = generate_embeddings(face_data)
+        self.assertIsNotNone(
+            embedding, "Embedding generation failed for the given face data."
+        )
+        self.assertEqual(
+            len(embedding), 128, "Embedding length should be 128 dimensions."
+        )  # Example dimension
+
+
+if __name__ == "__main__":
+    unittest.main()