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Update README.md
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@ -165,7 +165,7 @@ metrics = ["cosine", "euclidean", "euclidean_l2"]
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result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", distance_metric = metrics[1])
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#face recognition
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dfs = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", distance_metric = metrics[2])
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dfs = DeepFace.find(img_path = "img1.jpg", db_path = "C:/my_db", distance_metric = metrics[2])
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```
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**Facial Attribute Analysis** - [`Demo`](https://youtu.be/GT2UeN85BdA)
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@ -244,7 +244,7 @@ You can find out more about RetinaFace on this [repo](https://github.com/serengi
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You can run deepface for real time videos as well. Stream function will access your webcam and apply both face recognition and facial attribute analysis. The function starts to analyze a frame if it can focus a face sequentially 5 frames. Then, it shows results 5 seconds.
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```python
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DeepFace.stream(db_path = "C:/User/Sefik/Desktop/database")
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DeepFace.stream(db_path = "C:/database")
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```
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<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-3.jpg" width="90%" height="90%"></p>
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@ -275,7 +275,7 @@ face_objs = DeepFace.extract_faces(img_path="dataset/img1.jpg", anti_spoofing =
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assert all(face_obj["is_real"] is True for face_obj in face_objs)
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# anti spoofing test in real time analysis
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DeepFace.stream(db_path = "C:/User/Sefik/Desktop/database", anti_spoofing = True)
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DeepFace.stream(db_path = "C:/database", anti_spoofing = True)
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```
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**API** - [`Demo`](https://youtu.be/HeKCQ6U9XmI), [`Docker Demo`](https://youtu.be/9Tk9lRQareA)
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