github/deepface
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Sefik Ilkin Serengil 2022-08-20 13:40:45 +01:00 committed by GitHub
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@ -104,15 +104,19 @@ models = [
#face verification
result = DeepFace.verify(img1_path = "img1.jpg",
img2_path = "img2.jpg",
model_name = models[1])
model_name = models[1]
)
#face recognition
df = DeepFace.find(img_path = "img1.jpg",
db_path = "C:/workspace/my_db",
model_name = models[1])
model_name = models[1]
)
#embeddings
embedding = DeepFace.represent(img_path = "img.jpg", model_name = models[1])
embedding = DeepFace.represent(img_path = "img.jpg",
model_name = models[1]
)
```
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/model-portfolio-v8.jpg" width="95%" height="95%"></p>
@ -143,12 +147,14 @@ metrics = ["cosine", "euclidean", "euclidean_l2"]
#face verification
result = DeepFace.verify(img1_path = "img1.jpg",
img2_path = "img2.jpg",
distance_metric = metrics[1])
distance_metric = metrics[1]
)
#face recognition
df = DeepFace.find(img_path = "img1.jpg",
db_path = "C:/workspace/my_db",
distance_metric = metrics[1])
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.
@ -159,7 +165,8 @@ Deepface also comes with a strong facial attribute analysis module including [`a
```python
obj = DeepFace.analyze(img_path = "img4.jpg",
actions = ['age', 'gender', 'race', 'emotion'])
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>
@ -188,25 +195,30 @@ backends = [
#face verification
obj = DeepFace.verify(img1_path = "img1.jpg",
img2_path = "img2.jpg",
detector_backend = backends[4])
detector_backend = backends[4]
)
#face recognition
df = DeepFace.find(img_path = "img.jpg",
db_path = "my_db",
detector_backend = backends[4])
detector_backend = backends[4]
)
#embeddings
embedding = DeepFace.represent(img_path = "img.jpg",
detector_backend = backends[4])
detector_backend = backends[4]
)
#facial analysis
demography = DeepFace.analyze(img_path = "img4.jpg",
detector_backend = backends[4])
detector_backend = backends[4]
)
#face detection and alignment
face = DeepFace.detectFace(img_path = "img.jpg",
target_size = (224, 224),
detector_backend = backends[4])
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.