Дипломная работа

Дипломная работа/app/workers/face_rec_worker.py

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+import face_recognition
+import numpy as np
+import cv2
+
+from workers.mongo_worker import MongoWorker
+
+class FaceRecognition():
+    def __init__(self):
+        self.mongo_worker = MongoWorker()
+        self.face_encodings_in_cache = self.mongo_worker.get_all_encodings()
+
+    def get_encodings(self, face_image):
+        return face_recognition.face_encodings(face_image)
+
+    def softmax(self, values):
+        exp_values = np.exp(values)
+        exp_values_sum = np.sum(exp_values)
+
+        return exp_values / exp_values_sum
+
+    def kl_divergence(self, face_encodings, face_to_compare):
+        if len(face_encodings) == 0:
+            return np.empty((0))
+
+        face_encodings = np.asarray(self.softmax(face_encodings))
+        face_to_compare = np.asarray(self.softmax(face_to_compare))
+
+        face_encodings = face_encodings / face_encodings.sum(axis=1, keepdims=True)
+        face_to_compare = face_to_compare / face_to_compare.sum()
+
+        epsilon = 1e-10
+        face_encodings = np.clip(face_encodings, epsilon, 1)
+        face_to_compare = np.clip(face_to_compare, epsilon, 1)
+
+        return np.sum(face_encodings * np.log(face_encodings / face_to_compare), axis=1)
+
+    def face_distance(self, face_encodings:list[float]):
+        result = {}
+        if self.mongo_worker.count_persons() > len(self.face_encodings_in_cache):
+            self.face_encodings_in_cache = self.mongo_worker.get_all_encodings()
+
+        for person_id in self.face_encodings_in_cache:
+            result.update({person_id: self.kl_divergence(face_encodings, self.face_encodings_in_cache[person_id])})
+
+        print(result.values())
+        try:
+            minimal = min(result.values())
+            print(minimal)
+            if minimal > 0.001:
+                return None
+            return [key for key, val in result.items() if val == minimal][0]
+        except:
+            best_in_frame = dict()
+            for key, elements in result.items():
+                best_in_frame.update({key:min(elements)})
+            minimal = min(best_in_frame.values())
+            print(minimal)
+            if minimal > 0.001:
+                return None
+            return [key for key, val in best_in_frame.items() if val == minimal][0]
+
+if __name__ == "__main__":
+    face_recognition_worker = FaceRecognition()
+    img = cv2.imread("obama.jpg")
+    print(face_recognition_worker.get_encodings(img))