github/deepface
1
1
Fork 0
mirror of https://github.com/serengil/deepface.git synced 2025-06-06 19:45:21 +00:00
Code Issues Packages Projects Releases Wiki Activity

dlib pseudo-batched forward

Browse Source
This commit is contained in:
galthran-wq 2025-02-11 17:21:01 +00:00
parent 3a9385fad8
commit a4a579e5eb
1 changed files with 23 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

42
deepface/models/facial_recognition/Dlib.py
View File

@ -1,5 +1,5 @@
# built-in dependencies
from typing import List
from typing import List, Union
# 3rd party dependencies
import numpy as np
@ -26,35 +26,39 @@ class DlibClient(FacialRecognition):
self.input_shape = (150, 150)
self.output_shape = 128
def forward(self, img: np.ndarray) -> List[float]:
def forward(self, img: np.ndarray) -> Union[List[float], List[List[float]]]:
"""
Find embeddings with Dlib model.
This model necessitates the override of the forward method
because it is not a keras model.
Args:
img (np.ndarray): pre-loaded image in BGR
img (np.ndarray): pre-loaded image(s) in BGR
Returns
embeddings (list): multi-dimensional vector
embeddings (list of lists or list of floats): multi-dimensional vectors
"""
# return self.model.predict(img)[0].tolist()
# Handle single image case
if len(img.shape) == 3:
img = np.expand_dims(img, axis=0)
# extract_faces returns 4 dimensional images
if len(img.shape) == 4:
img = img[0]
embeddings = []
for single_img in img:
# bgr to rgb
single_img = single_img[:, :, ::-1] # bgr to rgb
# bgr to rgb
img = img[:, :, ::-1] # bgr to rgb
# img is in scale of [0, 1] but expected [0, 255]
if single_img.max() <= 1:
single_img = single_img * 255
# img is in scale of [0, 1] but expected [0, 255]
if img.max() <= 1:
img = img * 255
single_img = single_img.astype(np.uint8)
img = img.astype(np.uint8)
img_representation = self.model.model.compute_face_descriptor(img)
img_representation = np.array(img_representation)
img_representation = np.expand_dims(img_representation, axis=0)
return img_representation[0].tolist()
img_representation = self.model.model.compute_face_descriptor(single_img)
img_representation = np.array(img_representation)
embeddings.append(img_representation.tolist())
if len(embeddings) == 1:
return embeddings[0]
else:
return embeddings
class DlibResNet: