bohdansok
commented
3 years ago Just an example, how it works:
import face_recognition
from face_recognition import api as frapi
import numpy as np
import dlib
class Full_object_detection_masked(dlib.full_object_detection):
def part(self, idx:int):
if idx in range(2, 15) or idx in range(48, 68):
return (0, 0)
return super().part(idx)
def parts(self):
lst = dlib.points()
for idx in range(0, 2):
old_x = super().part(idx).x
old_y = super().part(idx).y
lst.insert(idx, dlib.point(old_x, old_y))
for idx in range(2, 15):
lst.insert(idx, dlib.point(0, 0))
for idx in range(15, 29):
old_x = super().part(idx).x
old_y = super().part(idx).y
lst.insert(idx, dlib.point(old_x, old_y))
for idx in range(29, 36):
lst.insert(idx, dlib.point(0, 0))
for idx in range(36, 48):
old_x = super().part(idx).x
old_y = super().part(idx).y
lst.insert(idx, dlib.point(old_x, old_y))
for idx in range(48, 68):
lst.insert(idx, dlib.point(0, 0))
return lst
def face_encodings_masked(face_image, known_face_locations=None, num_jitters=1, model="large"):
"""
Given an image, return the 128-dimension face encoding for each face in the image.
:param face_image: The image that contains one or more faces
:param known_face_locations: Optional - the bounding boxes of each face if you already know them.
:param num_jitters: How many times to re-sample the face when calculating encoding. Higher is more accurate, but slower (i.e. 100 is 100x slower)
:param model: Optional - which model to use. "large" (default) or "small" which only returns 5 points but is faster.
:return: A list of 128-dimensional face encodings (one for each face in the image)
"""
raw_landmarks = frapi._raw_face_landmarks(face_image,
known_face_locations,
model)
masked_raw_landmarks = []
for lm in raw_landmarks:
masked_raw_landmarks.append(Full_object_detection_masked(lm.rect, lm.parts()))
return [np.array(
frapi.face_encoder.compute_face_descriptor(
face_image,
raw_landmark_set,
num_jitters)) for raw_landmark_set in masked_raw_landmarks]
dlib-issue-bot
Hi,
I want to use my landmarks array to extract the face descriptor. How do I convert a np.ndarray (68,2) to the _dlib_pybind11.full_object_detection ?
I was trying to use dlib.get_face_chip in case to pass directly the aligned face but that also wants the lanmarks in the dlib format