def __init__(self):
super().__init__()
self._detection_model = FaceAnalysis(
det_name=self.DETECTION_MODEL_NAME, rec_name=None, ga_name=None)
self._calculation_model = model_zoo.get_model(
self.CALCULATION_MODEL_NAME)
self._CTX_ID_CPU = -1
self._NMS = 0.4
self._detection_model.prepare(ctx_id=self._CTX_ID_CPU, nms=self._NMS)
self._calculation_model.prepare(ctx_id=self._CTX_ID_CPU)
self.det_prob_threshold = 0.8
def __init__(self, options):
'''
Constructor
'''
#import insightface
from insightface.app import FaceAnalysis
os.environ['MXNET_CUDNN_AUTOTUNE_DEFAULT'] = '0'
# kwargs = {'root':os.path.join(options.storage_dir,'models')}
#load Retina face model
if options.gpuid == -1:
ctx_id = -1
else:
ctx_id = int(options.gpuid)
#self.app = FaceAnalysis(allowed_modules=['detection','recognition'])
self.app = FaceAnalysis()
self.app.prepare(ctx_id=0, det_size=(640, 640))
print("InsightFaces Models Loaded.")
import cv2
import numpy as np
import os
import insightface
from insightface.app import FaceAnalysis
from insightface.data import get_image as ins_get_image
if __name__ == '__main__':
app = FaceAnalysis(allowed_modules=['detection', 'landmark_2d_106'])
app.prepare(ctx_id=0, det_size=(640, 640))
img = ins_get_image('t1')
faces = app.get(img)
#assert len(faces)==6
tim = img.copy()
color = (200, 160, 75)
for face in faces:
lmk = face.landmark_2d_106
lmk = np.round(lmk).astype(np.int)
for i in range(lmk.shape[0]):
p = tuple(lmk[i])
cv2.circle(tim, p, 1, color, 1, cv2.LINE_AA)
cv2.imwrite('./test_out.jpg', tim)
# pip install onnxruntime
import argparse
import cv2
import sys
import numpy as np
import insightface
from insightface.app import FaceAnalysis
assert insightface.__version__>='0.2'
parser = argparse.ArgumentParser(description='insightface test')
# general
parser.add_argument('--ctx', default=0, type=int, help='ctx id, <0 means using cpu')
args = parser.parse_args()
app = FaceAnalysis(name='antelope')
app.prepare(ctx_id=args.ctx, det_size=(640,640))
img = cv2.imread('katya1.jpg')
faces = app.get(img)
assert len(faces)==1 #6
rimg = app.draw_on(img, faces)
cv2.imwrite("./katya1_out.jpg", rimg)
print(len(faces))
for face in faces:
print(face.bbox)
print(face.kps)
print(face.embedding.shape)
def __init__(self) -> None:
self.app = FaceAnalysis()
# ctx id, <0 means using cpu
self.app.prepare(ctx_id=0, det_size=(640, 640))
import argparse
import cv2
import sys
import numpy as np
import insightface
from insightface.app import FaceAnalysis
from insightface.data import get_image as ins_get_image
parser = argparse.ArgumentParser(description='insightface gender-age test')
# general
parser.add_argument('--ctx', default=0, type=int, help='ctx id, <0 means using cpu')
args = parser.parse_args()
app = FaceAnalysis(allowed_modules=['detection', 'genderage'])
app.prepare(ctx_id=args.ctx, det_size=(640,640))
img = ins_get_image('t1')
faces = app.get(img)
assert len(faces)==6
for face in faces:
print(face.bbox)
print(face.sex, face.age)
import sys
import glob
import torch
import pickle
import os
import numpy as np
import cv2
import os.path as osp
import insightface
from insightface.app import FaceAnalysis
from insightface.utils import face_align
app = FaceAnalysis()
app.prepare(ctx_id=0, det_size=(224, 224))
output_size = 384
input_dir = '/root/codebase/FaceSynthetics'
output_dir = 'data/synthetics'
if not osp.exists(output_dir):
os.makedirs(output_dir)
X = []
Y = []
for i in range(0, 100000):
if i % 1000 == 0:
print('loading', i)
x = "%06d.png" % i
img_path = osp.join(input_dir, x)
img = cv2.imread(img_path)