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@ -102,10 +102,14 @@ models = [
]
#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", model_name = models[1])
result = DeepFace.verify(img1_path = "img1.jpg",
img2_path = "img2.jpg",
model_name = models[1])
#face recognition
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])
#embeddings
embedding = DeepFace.represent(img_path = "img.jpg", model_name = models[1])
@ -137,10 +141,14 @@ Similarity could be calculated by different metrics such as [Cosine Similarity](
metrics = ["cosine", "euclidean", "euclidean_l2"]
#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", distance_metric = metrics[1])
result = DeepFace.verify(img1_path = "img1.jpg",
img2_path = "img2.jpg",
distance_metric = metrics[1])
#face recognition
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])
```
Euclidean L2 form [seems](https://youtu.be/i_MOwvhbLdI) to be more stable than cosine and regular Euclidean distance based on experiments.
@ -150,7 +158,8 @@ Euclidean L2 form [seems](https://youtu.be/i_MOwvhbLdI) to be more stable than c
Deepface also comes with a strong facial attribute analysis module including [`age`](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [`gender`](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [`facial expression`](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) (including angry, fear, neutral, sad, disgust, happy and surprise) and [`race`](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/) (including asian, white, middle eastern, indian, latino and black) predictions.
```python
obj = DeepFace.analyze(img_path = "img4.jpg", actions = ['age', 'gender', 'race', 'emotion'])
obj = DeepFace.analyze(img_path = "img4.jpg",
actions = ['age', 'gender', 'race', 'emotion'])
```
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-2.jpg" width="95%" height="95%"></p>
@ -167,22 +176,37 @@ Face detection and alignment are important early stages of a modern face recogni
All deepface functions accept an optional detector backend input argument. You can switch among those detectors with this argument. OpenCV is the default detector.
```python
backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface', 'mediapipe']
backends = [
'opencv',
'ssd',
'dlib',
'mtcnn',
'retinaface',
'mediapipe'
]
#face verification
obj = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", detector_backend = backends[4])
obj = DeepFace.verify(img1_path = "img1.jpg",
img2_path = "img2.jpg",
detector_backend = backends[4])
#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])
#embeddings
embedding = DeepFace.represent(img_path = "img.jpg", detector_backend = backends[4])
embedding = DeepFace.represent(img_path = "img.jpg",
detector_backend = backends[4])
#facial analysis
demography = DeepFace.analyze(img_path = "img4.jpg", detector_backend = backends[4])
demography = DeepFace.analyze(img_path = "img4.jpg",
detector_backend = backends[4])
#face detection and alignment
face = DeepFace.detectFace(img_path = "img.jpg", target_size = (224, 224), detector_backend = backends[4])
face = DeepFace.detectFace(img_path = "img.jpg",
target_size = (224, 224),
detector_backend = backends[4])
```
Face recognition models are actually CNN models and they expect standard sized inputs. So, resizing is required before representation. To avoid deformation, deepface adds black padding pixels according to the target size argument after detection and alignment.