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@ -32,7 +32,7 @@ A modern [**face recognition pipeline**](https://sefiks.com/2020/05/01/a-gentle-
This function verifies face pairs as same person or different persons. It expects exact image paths as inputs. Passing numpy or based64 encoded images is also welcome. This function verifies face pairs as same person or different persons. It expects exact image paths as inputs. Passing numpy or based64 encoded images is also welcome.
```python ```python
result = DeepFace.verify("img1.jpg", "img2.jpg") result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")
``` ```
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-1.jpg" width="95%" height="95%"></p> <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-1.jpg" width="95%" height="95%"></p>
@ -53,7 +53,7 @@ Deepface is a **hybrid** face recognition package. It currently wraps many **sta
```python ```python
models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib"] models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib"]
result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = models[1]) result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", model_name = models[1])
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_name = models[1]) df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_name = models[1])
``` ```
@ -69,7 +69,7 @@ Similarity could be calculated by different metrics such as [Cosine Similarity](
```python ```python
metrics = ["cosine", "euclidean", "euclidean_l2"] metrics = ["cosine", "euclidean", "euclidean_l2"]
result = DeepFace.verify("img1.jpg", "img2.jpg", distance_metric = metrics[1]) result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", distance_metric = metrics[1])
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", distance_metric = metrics[1]) df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", distance_metric = metrics[1])
``` ```
@ -117,16 +117,16 @@ Face detection and alignment are early stages of a modern face recognition pipel
backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface'] backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface']
#face detection and alignment #face detection and alignment
detected_face = DeepFace.detectFace("img.jpg", detector_backend = backends[4]) detected_face = DeepFace.detectFace(img_path = "img.jpg", detector_backend = backends[4])
#face verification #face verification
obj = DeepFace.verify("img1.jpg", "img2.jpg", detector_backend = backends[4]) obj = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", detector_backend = backends[4])
#face recognition #face recognition
df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backends[4]) df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backends[4])
#facial analysis #facial analysis
demography = DeepFace.analyze("img4.jpg", detector_backend = backends[4]) demography = DeepFace.analyze(img_path = "img4.jpg", detector_backend = backends[4])
``` ```
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-detectors.png" width="90%" height="90%"></p> <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-detectors.png" width="90%" height="90%"></p>
@ -163,7 +163,7 @@ Face recognition, facial attribute analysis and vector representation functions
Face recognition models represent facial images as vector embeddings. The idea behind facial recognition is that vectors should be more similar for same person than different persons. The question is that where and how to store facial embeddings in a large scale system. Herein, deepface offers a represention function to find vector embeddings from facial images. Face recognition models represent facial images as vector embeddings. The idea behind facial recognition is that vectors should be more similar for same person than different persons. The question is that where and how to store facial embeddings in a large scale system. Herein, deepface offers a represention function to find vector embeddings from facial images.
```python ```python
embedding = DeepFace.represent("img.jpg", model_name = 'Facenet') embedding = DeepFace.represent(img_path = "img.jpg", model_name = 'Facenet')
``` ```
Tech stack is vast to store vector embeddings. To determine the right tool, you should consider your task such as face verification or face recognition, priority such as speed or confidence, and also data size. Tech stack is vast to store vector embeddings. To determine the right tool, you should consider your task such as face verification or face recognition, priority such as speed or confidence, and also data size.