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bd096d2 970f13d bd096d2 970f13d bd096d2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | # -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2023 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: mica@tue.mpg.de
import face_alignment
import numpy as np
from insightface.app import FaceAnalysis
from pixel3dmm import env_paths
from loguru import logger
from datasets.creation.util import get_bbox
class Detectors:
def __init__(self):
self.RETINAFACE = 'RETINAFACE'
self.FAN = 'FAN'
detectors = Detectors()
class LandmarksDetector:
def __init__(self, model='retinaface', device='cuda:0'):
model = model.upper()
self.predictor = model
if model == detectors.RETINAFACE:
self._face_detector = FaceAnalysis(name='antelopev2',
root=f'{env_paths.ANT_DIR}',
providers=['CUDAExecutionProvider'])
self._face_detector.prepare(ctx_id=0, det_size=(224, 224))
elif model == detectors.FAN:
self._face_detector = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, device=device)
else:
logger.error(f'[ERROR] Landmark predictor not supported {model}')
exit(-1)
logger.info(f'[DETECTOR] Selected {model} as landmark detector.')
def detect(self, img):
if self.predictor == detectors.RETINAFACE:
bboxes, kpss = self._face_detector.det_model.detect(img, max_num=0, metric='default')
return bboxes, kpss
if self.predictor == detectors.FAN:
lmks, scores, detected_faces = self._face_detector.get_landmarks_from_image(img, return_landmark_score=True, return_bboxes=True)
if detected_faces is None:
return np.empty(0), np.empty(0)
bboxes = np.stack(detected_faces)
# bboxes = get_bbox(img, np.stack(lmks))
# bboxes[:, 4] = detected_faces[:, 4]
# https://github.com/Rubikplayer/flame-fitting/blob/master/data/landmarks_51_annotated.png
lmk51 = np.stack(lmks)[:, 17:, :]
kpss = lmk51[:, [20, 27, 13, 43, 47], :] # left eye, right eye, nose, left mouth, right mouth
kpss[:, 0, :] = lmk51[:, [21, 24], :].mean(1) # center of eye
kpss[:, 1, :] = lmk51[:, [27, 29], :].mean(1)
return bboxes, kpss
return None, None
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