def set_detector(self, detector):
if self.detector is not None and self.detector.name() == detector:
return
if detector == FaceDetectorDlib.NAME:
self.detector = FaceDetectorDlib()
elif detector == 'detector_ssd_mobilenet_v2':
self.detector = FaceDetectorSSDMobilenetV2()
def set_detector(self, detector):
if self.detector is not None and self.detector.name() == detector:
return
logger.debug('set_detector old=%s new=%s' % (self.detector, detector))
if detector == FaceDetectorDlib.NAME:
self.detector = FaceDetectorDlib()
elif detector == 'detector_ssd_inception_v2':
self.detector = FaceDetectorSSDInceptionV2()
elif detector == 'detector_ssd_mobilenet_v2':
self.detector = FaceDetectorSSDMobilenetV2()
def get_detector(name='dlib'):
"""
:type name: str
:param name:
:return:
"""
if name == 'dlib':
return FaceDetectorDlib()
return None
def get_detector(name='ssd_mobilenet_v2'):
"""
:type name: str
:param name:
:return:
"""
if name == 'dlib':
return FaceDetectorDlib()
elif name == 'ssd_inception_v2':
return FaceDetectorSSDInceptionV2()
elif name == 'ssd_mobilenet_v2':
return FaceDetectorSSDMobilenetV2()
return None
class DeepFace:
def __init__(self):
self.detector = None
self.recognizer = None
def set_detector(self, detector):
if self.detector is not None and self.detector.name() == detector:
return
logger.debug('set_detector old=%s new=%s' % (self.detector, detector))
if detector == FaceDetectorDlib.NAME:
self.detector = FaceDetectorDlib()
elif detector == 'detector_ssd_inception_v2':
self.detector = FaceDetectorSSDInceptionV2()
elif detector == 'detector_ssd_mobilenet_v2':
self.detector = FaceDetectorSSDMobilenetV2()
def set_recognizer(self, recognizer):
if self.recognizer is not None and self.recognizer.name(
) == recognizer:
return
logger.debug('set_recognizer old=%s new=%s' %
(self.recognizer, recognizer))
if recognizer == FaceRecognizerVGG.NAME:
self.recognizer = FaceRecognizerVGG()
elif recognizer == FaceRecognizerResnet.NAME:
self.recognizer = FaceRecognizerResnet('./db.pkl')
def recognizer_test_run(self,
detector=FaceDetectorDlib.NAME,
recognizer=FaceRecognizerResnet.NAME,
image='./samples/ajb.jpg',
visualize=False):
self.set_detector(detector)
self.set_recognizer(recognizer)
if isinstance(image, str):
logger.debug('read image, path=%s' % image)
npimg = cv2.imread(image, cv2.IMREAD_COLOR)
elif isinstance(image, np.ndarray):
npimg = image
else:
logger.error('Argument image should be str or ndarray. image=%s' %
str(image))
sys.exit(-1)
if npimg is None:
logger.error('image can not be read, path=%s' % image)
sys.exit(-1)
if recognizer:
logger.debug('run face recognition+')
result = self.recognizer.detect([npimg[..., ::-1]])
logger.debug('run face recognition-')
return
def run_recognizer(self,
npimg,
faces,
recognizer=FaceRecognizerResnet.NAME):
self.set_recognizer(recognizer)
rois = []
for face in faces:
# roi = npimg[face.y:face.y+face.h, face.x:face.x+face.w, :]
roi = get_roi(npimg, face, roi_mode=recognizer)
if int(os.environ.get('DEBUG_SHOW', 0)) == 1:
cv2.imshow('roi', roi)
cv2.waitKey(0)
rois.append(roi)
face.face_roi = roi
if len(rois) > 0:
logger.debug('run face recognition+')
result = self.recognizer.detect(rois=rois, faces=faces)
logger.debug('run face recognition-')
for face_idx, face in enumerate(faces):
face.face_feature = result['feature'][face_idx]
logger.debug('candidates: %s' % str(result['name'][face_idx]))
if result['name'][face_idx]:
name, score = result['name'][face_idx][0]
face.face_name = name
face.face_score = score
return faces
def run(self,
target_path='../result/test.jpg',
detector='detector_ssd_mobilenet_v2',
recognizer=FaceRecognizerResnet.NAME,
image='./samples/yj/yue_jue1.jpg',
target=None,
visualize=False):
self.set_detector(detector)
self.set_recognizer(recognizer)
if image is None:
return []
elif isinstance(image, str):
logger.debug('read image, path=%s' % image)
npimg = cv2.imread(image, cv2.IMREAD_COLOR)
elif isinstance(image, np.ndarray):
npimg = image
else:
logger.error('Argument image should be str or ndarray. image=%s' %
str(image))
sys.exit(-1)
if npimg is None:
logger.error('image can not be read, path=%s' % image)
sys.exit(-1)
logger.debug('run face detection+ %dx%d' %
(npimg.shape[1], npimg.shape[0]))
faces = self.detector.detect(npimg)
logger.debug('run face detection- %s' % len(faces))
if recognizer:
faces = self.run_recognizer(npimg, faces, recognizer)
img = draw_bboxs(np.copy(npimg), faces, target=target)
cv2.imwrite(target_path, img)
if visualize and visualize not in ['false', 'False']:
cv2.imshow('DeepFace', img)
#cv2.waitKey(0)
return faces
def save_and_run(self,
path,
image_folder_path,
target_folder,
target=None,
visualize=True):
"""
:param visualize:
:param path: samples/faces
:param image_path: samples/yue_jue1.jpg
:param image_folder_path: source folder
:return:
"""
self.save_features_path(path)
assert os.path.exists(image_folder_path)
if not os.path.exists(target_folder):
os.mkdir(target_folder)
assert os.path.exists(target_folder)
path, dirs, files = next(os.walk(image_folder_path))
num_frames = len(files)
img_array = []
for i in range(num_frames):
fname = f'{image_folder_path}/{i}.jpg'
target_path = target_folder + f"/{i}.jpg"
self.run(target_path, image=fname, target=target, visualize=False)
def save_features_path(self, path="./samples/yj/faces/"):
"""
:param path: folder contain images("./samples/faces/")
:return:
"""
name_paths = [(os.path.basename(img_path)[:-4], img_path)
for img_path in glob(os.path.join(path, "*.jpg"))]
features = {}
for name, path in tqdm(name_paths):
logger.debug("finding faces for %s:" % path)
faces = self.run(image=path, target=None)
features[name] = faces[0].face_feature
import pickle
with open('db.pkl', 'wb') as f:
pickle.dump(features, f, protocol=2)
class DeepFace:
def __init__(self):
self.detector = None
self.recognizer = None
def set_detector(self, detector):
if self.detector is not None and self.detector.name() == detector:
return
logger.debug('set_detector old=%s new=%s' % (self.detector, detector))
if detector == FaceDetectorDlib.NAME:
self.detector = FaceDetectorDlib()
elif detector == 'detector_ssd_inception_v2':
self.detector = FaceDetectorSSDInceptionV2()
elif detector == 'detector_ssd_mobilenet_v2':
self.detector = FaceDetectorSSDMobilenetV2()
def set_recognizer(self, recognizer):
if self.recognizer is not None and self.recognizer.name() == recognizer:
return
logger.debug('set_recognizer old=%s new=%s' % (self.recognizer, recognizer))
if recognizer == FaceRecognizerVGG.NAME:
self.recognizer = FaceRecognizerVGG()
elif recognizer == FaceRecognizerResnet.NAME:
self.recognizer = FaceRecognizerResnet()
def blackpink(self, visualize=True):
imgs = ['./samples/blackpink/blackpink%d.jpg' % (i + 1) for i in range(7)]
for img in imgs:
self.run(image=img, visualize=visualize)
def recognizer_test_run(self, detector=FaceDetectorDlib.NAME, recognizer=FaceRecognizerResnet.NAME, image='./samples/ajb.jpg', visualize=False):
self.set_detector(detector)
self.set_recognizer(recognizer)
if isinstance(image, str):
logger.debug('read image, path=%s' % image)
npimg = cv2.imread(image, cv2.IMREAD_COLOR)
elif isinstance(image, np.ndarray):
npimg = image
else:
logger.error('Argument image should be str or ndarray. image=%s' % str(image))
sys.exit(-1)
if npimg is None:
logger.error('image can not be read, path=%s' % image)
sys.exit(-1)
if recognizer:
logger.debug('run face recognition+')
result = self.recognizer.detect([npimg[...,::-1]])
logger.debug('run face recognition-')
return
def run_recognizer(self, npimg, faces, recognizer=FaceRecognizerResnet.NAME):
self.set_recognizer(recognizer)
rois = []
for face in faces:
# roi = npimg[face.y:face.y+face.h, face.x:face.x+face.w, :]
roi = get_roi(npimg, face, roi_mode=recognizer)
if int(os.environ.get('DEBUG_SHOW', 0)) == 1:
cv2.imshow('roi', roi)
cv2.waitKey(0)
rois.append(roi)
face.face_roi = roi
if len(rois) > 0:
logger.debug('run face recognition+')
result = self.recognizer.detect(rois=rois, faces=faces)
logger.debug('run face recognition-')
for face_idx, face in enumerate(faces):
face.face_feature = result['feature'][face_idx]
logger.debug('candidates: %s' % str(result['name'][face_idx]))
name, score = result['name'][face_idx][0]
# if score < self.recognizer.get_threshold():
# continue
face.face_name = name
face.face_score = score
return faces
def run(self, detector='detector_ssd_mobilenet_v2', recognizer=FaceRecognizerResnet.NAME, image='./samples/blackpink/blackpink1.jpg',
visualize=False):
self.set_detector(detector)
self.set_recognizer(recognizer)
if image is None:
return []
elif isinstance(image, str):
logger.debug('read image, path=%s' % image)
npimg = cv2.imread(image, cv2.IMREAD_COLOR)
elif isinstance(image, np.ndarray):
npimg = image
else:
logger.error('Argument image should be str or ndarray. image=%s' % str(image))
sys.exit(-1)
if npimg is None:
logger.error('image can not be read, path=%s' % image)
sys.exit(-1)
logger.debug('run face detection+ %dx%d' % (npimg.shape[1], npimg.shape[0]))
faces = self.detector.detect(npimg)
logger.debug('run face detection- %s' % len(faces))
if recognizer:
faces = self.run_recognizer(npimg, faces, recognizer)
img = draw_bboxs(np.copy(npimg), faces)
cv2.imwrite('result.jpg', img)
if visualize and visualize not in ['false', 'False']:
cv2.imshow('DeepFace', img)
cv2.waitKey(0)
return faces
def save_and_run(self, path, image, visualize=True):
"""
:param visualize:
:param path: samples/faces
:param image_path: samples/blackpink1.jpg
:return:
"""
self.save_features_path(path)
self.run(image=image, visualize=visualize)
def save_features_path(self, path="./samples/blackpink/faces/"):
"""
:param path: folder contain images("./samples/faces/")
:return:
"""
name_paths = [(os.path.basename(img_path)[:-4], img_path)
for img_path in glob(os.path.join(path, "*.jpg"))]
features = {}
for name, path in tqdm(name_paths):
logger.debug("finding faces for %s:" % path)
faces = self.run(image=path)
features[name] = faces[0].face_feature
import pickle
with open('db.pkl', 'wb') as f:
pickle.dump(features, f, protocol=2)
def test_lfw(self, set='test', model='ssdm_resnet', visualize=True):
if set is 'train':
pairfile = 'pairsDevTrain.txt'
else:
pairfile = 'pairsDevTest.txt'
lfw_path = DeepFaceConfs.get()['dataset']['lfw']
path = os.path.join(lfw_path, pairfile)
with open(path, 'r') as f:
lines = f.readlines()[1:]
pairs = []
for line in lines:
elms = line.split()
if len(elms) == 3:
pairs.append((elms[0], int(elms[1]), elms[0], int(elms[2])))
elif len(elms) == 4:
pairs.append((elms[0], int(elms[1]), elms[2], int(elms[3])))
else:
logger.warning('line should have 3 or 4 elements, line=%s' % line)
detec = FaceDetectorDlib.NAME
if model == 'baseline':
recog = FaceRecognizerVGG.NAME
just_name = 'vgg'
elif model == 'baseline_resnet':
recog = FaceRecognizerResnet.NAME
just_name = 'resnet'
elif model == 'ssdm_resnet':
recog = FaceRecognizerResnet.NAME
just_name = 'resnet'
detec = 'detector_ssd_mobilenet_v2'
else:
raise Exception('invalid model name=%s' % model)
logger.info('pair length=%d' % len(pairs))
test_result = [] # score, label(1=same)
for name1, idx1, name2, idx2 in tqdm(pairs):
img1_path = os.path.join(lfw_path, name1, '%s_%04d.jpg' % (name1, idx1))
img2_path = os.path.join(lfw_path, name2, '%s_%04d.jpg' % (name2, idx2))
img1 = cv2.imread(img1_path, cv2.IMREAD_COLOR)
img2 = cv2.imread(img2_path, cv2.IMREAD_COLOR)
if img1 is None:
logger.warning('image not read, path=%s' % img1_path)
if img2 is None:
logger.warning('image not read, path=%s' % img2_path)
result1 = self.run(image=img1, detector=detec, recognizer=recog, visualize=False)
result2 = self.run(image=img2, detector=detec, recognizer=recog, visualize=False)
if len(result1) == 0:
logger.warning('face not detected, name=%s(%d)! %s(%d)' % (name1, idx1, name2, idx2))
test_result.append((0.0, name1 == name2))
continue
if len(result2) == 0:
logger.warning('face not detected, name=%s(%d) %s(%d)!' % (name1, idx1, name2, idx2))
test_result.append((0.0, name1 == name2))
continue
feat1 = result1[0].face_feature
feat2 = result2[0].face_feature
similarity = feat_distance_cosine(feat1, feat2)
test_result.append((similarity, name1 == name2))
# calculate accuracy TODO
accuracy = sum([label == (score > DeepFaceConfs.get()['recognizer'][just_name]['score_th']) for score, label in test_result]) / float(len(test_result))
logger.info('accuracy=%.8f' % accuracy)
# ROC Curve, AUC
tps = []
fps = []
accuracy0 = []
accuracy1 = []
acc_th = []
for th in range(0, 100, 5):
th = th / 100.0
tp = 0
tn = 0
fp = 0
fn = 0
for score, label in test_result:
if score >= th and label == 1:
tp += 1
elif score >= th and label == 0:
fp += 1
elif score < th and label == 0:
tn += 1
elif score < th and label == 1:
fn += 1
tpr = tp / (tp + fn + 1e-12)
fpr = fp / (fp + tn + 1e-12)
tps.append(tpr)
fps.append(fpr)
accuracy0.append(tn / (tn + fp + 1e-12))
accuracy1.append(tp / (tp + fn + 1e-12))
acc_th.append(th)
fpr, tpr, thresh = roc_curve([x[1] for x in test_result], [x[0] for x in test_result])
fnr = 1 - tpr
eer = fnr[np.nanargmin(np.absolute((fnr - fpr)))]
logger.info('1-eer=%.4f' % (1.0 - eer))
with open('./etc/test_lfw.pkl', 'rb') as f:
results = pickle.load(f)
if visualize in [True, 'True', 'true', 1, '1']:
fig = plt.figure()
a = fig.add_subplot(1, 2, 1)
plt.title('Experiment on LFW')
plt.plot(fpr, tpr, label='%s(%.4f)' % (model, 1 - eer)) # TODO : label
for model_name in results:
if model_name == model:
continue
fpr_prev = results[model_name]['fpr']
tpr_prev = results[model_name]['tpr']
eer_prev = results[model_name]['eer']
plt.plot(fpr_prev, tpr_prev, label='%s(%.4f)' % (model_name, 1 - eer_prev))
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
a.legend()
a.set_title('Receiver operating characteristic')
a = fig.add_subplot(1, 2, 2)
plt.plot(accuracy0, acc_th, label='Accuracy_diff')
plt.plot(accuracy1, acc_th, label='Accuracy_same')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
a.legend()
a.set_title('%s : TP, TN' % model)
fig.savefig('./etc/roc.png', dpi=300)
plt.show()
plt.draw()
with open('./etc/test_lfw.pkl', 'wb') as f:
results[model] = {
'fpr': fpr,
'tpr': tpr,
'acc_th': acc_th,
'accuracy0': accuracy0,
'accuracy1': accuracy1,
'eer': eer
}
pickle.dump(results, f, pickle.HIGHEST_PROTOCOL)
return 1.0 - eer
def set_detector(self, detector):
if self.detector is not None and self.detector.name() == detector:
return
logger.debug('set_detector old=%s new=%s' % (self.detector, detector))
if detector == FaceDetectorDlib.NAME:
self.detector = FaceDetectorDlib()
class DeepFace:
def __init__(self):
self.detector = None
self.recognizer = None
def set_detector(self, detector):
if self.detector is not None and self.detector.name() == detector:
return
if detector == FaceDetectorDlib.NAME:
self.detector = FaceDetectorDlib()
elif detector == 'detector_ssd_mobilenet_v2':
self.detector = FaceDetectorSSDMobilenetV2()
def set_recognizer(self, recognizer):
if self.recognizer is not None and self.recognizer.name(
) == recognizer:
return
if recognizer == FaceRecognizerVGG.NAME:
self.recognizer = FaceRecognizerVGG()
elif recognizer == FaceRecognizerResnet.NAME:
self.recognizer = FaceRecognizerResnet('redhot.pkl')
def blackpink(self, visualize=True):
imgs = [
'./samples/blackpink/blackpink%d.jpg' % (i + 1) for i in range(7)
]
for img in imgs:
self.run(image=img, visualize=visualize)
def recognizer_test_run(self,
detector=FaceDetectorDlib.NAME,
recognizer=FaceRecognizerVGG.NAME,
image='./samples/ajb.jpg',
visualize=False):
self.set_detector(detector)
self.set_recognizer(recognizer)
if isinstance(image, str):
npimg = cv2.imread(image, cv2.IMREAD_COLOR)
elif isinstance(image, np.ndarray):
npimg = image
else:
sys.exit(-1)
if npimg is None:
sys.exit(-1)
if recognizer:
result = self.recognizer.detect([npimg[..., ::-1]])
return
def run_recognizer(self,
npimg,
faces,
recognizer=FaceRecognizerResnet.NAME):
self.set_recognizer(recognizer)
rois = []
for face in faces:
# roi = npimg[face.y:face.y+face.h, face.x:face.x+face.w, :]
roi = get_roi(npimg, face, roi_mode=recognizer)
if int(os.environ.get('DEBUG_SHOW', 0)) == 1:
cv2.imshow('roi', roi)
cv2.waitKey(0)
rois.append(roi)
face.face_roi = roi
if len(rois) > 0:
result = self.recognizer.detect(rois=rois, faces=faces)
for face_idx, face in enumerate(faces):
face.face_feature = result['feature'][face_idx]
if result['name'][face_idx]:
name, score = result['name'][face_idx][0]
# if score < self.recognizer.get_threshold():
# continue
face.face_name = name
face.face_score = score
return faces
# detector_ssd_mobilenet_v2
def run(self,
detector='detector_ssd_mobilenet_v2',
recognizer=FaceRecognizerResnet.NAME,
image='./samples/redhot/redhot_1.jpg',
visualize=True):
self.set_detector(detector) # Inicializar modelo de deteccion
self.set_recognizer(recognizer) # Inicializar modelo de reconocimiento
npimg = cv2.imread(image, cv2.IMREAD_COLOR) # Leer imagen
if npimg is None: # Verificar lectura de imagen
sys.exit(-1)
t = time.time()
faces = self.detector.detect(npimg) # Correr detector de faces
#print("Detecta {} faces en {} segundos".format(len(faces), round(time.time()-t,2)))
if recognizer:
t = time.time()
faces = self.run_recognizer(
npimg, faces, recognizer) # Correr Reconocimiento de rostros
# print("Reconoce {} faces en {} segundos".format(len(faces), round(time.time()-t, 2)))
img = draw_bboxs(np.copy(npimg), faces) # Dibujar los bounding boxes
if visualize:
f = 2
cv2.imshow('DeepFace',
cv2.resize(img, (img.shape[1] * f,
img.shape[0] * f))) # Mostrar imagen
cv2.waitKey(0)
return faces
def save_features_path(self, path="./samples/blackpink/faces/"):
"""
:param path: folder contain images("./samples/faces/")
:return:
"""
name_paths = [(os.path.basename(img_path).split('_')[0], img_path)
for img_path in glob(os.path.join(path, "*.*"))]
features = {}
for name, path in tqdm(name_paths):
faces = self.run(image=path, visualize=False)
features[name] = faces[0].face_feature
import pickle
with open('redhot.pkl', 'wb') as f:
pickle.dump(features, f, protocol=2)