def extract(self, im, detection):
#import numpy as np
from keras.preprocessing import image
#from keras_vggface.vggface import VGGFace
from keras_vggface import utils
rect = pt.rect_proto2pv(detection.location).rescale(1.5)
tile = im.crop(rect)
tile = tile.resize((224, 224))
#tile.show(delay=1000)
img = tile.asOpenCV2()
img = img[:, :, ::-1] # Convert BGR to RGB
#mat_ = cv2.cvtColor(mat,cv2.COLOR_RGB2GRAY)
#mat = cv2.cvtColor(mat_,cv2.COLOR_GRAY2RGB)
#img = image.load_img('../image/ajb.jpg', target_size=(224, 224))
img = image.img_to_array(img)
img = np.expand_dims(img, axis=0)
img = utils.preprocess_input(img, version=1) # or version=2
#preds = model.predict(x)
#print('Predicted:', utils.decode_predictions(preds))
#mat = mat[:,:,::-1]
#mat.shape = (1,224,224,3)
# Needed in multithreaded applications
with self.graph.as_default():
tmp = self.recognizer.predict(img)
return pv.meanUnit(tmp.flatten())
def extract(self, img, face_records):
'''Extract a template that allows the face to be matched.'''
# Compute the 512D vector that describes the face in img identified by
#shape.
im = pv.Image(img[:, :, ::-1])
for face_record in face_records.face_records:
rect = pt.rect_proto2pv(face_record.detection.location)
x, y, w, h = rect.asTuple()
# Extract view
rect = pv.Rect()
cx, cy = x + 0.5 * w, y + 0.5 * h
tmp = 1.5 * max(w, h)
cw, ch = tmp, tmp
crop = pv.AffineFromRect(pv.CenteredRect(cx, cy, cw, ch),
(256, 256))
pvim = pv.Image(img[:, :, ::-1]) # convert rgb to bgr
pvim = crop(pvim)
view = pt.image_pv2proto(pvim)
face_record.view.CopyFrom(view)
tile = pvim.resize((224, 224))
tile = tile.resize((112, 112))
face_im = tile.asOpenCV2()
face_im = face_im[:, :, ::-1] # Convert BGR to RGB
features = self.fr_model.get_embedding(face_im)
face_descriptor = pv.meanUnit(features.flatten())
face_record.template.data.CopyFrom(
pt.vector_np2proto(face_descriptor))
def locate(self, img, face_records, options):
'''Locate facial features.'''
# Get the landmarks/parts for the face in box d.
for face_record in face_records.face_records:
rect = pt.rect_proto2pv(face_record.detection.location)
x, y, w, h = rect.asTuple()
l, t, r, b = [int(tmp) for tmp in [x, y, x + w, y + h]]
d = dlib.rectangle(l, t, r, b)
shape = self.shape_pred(img, d)
print('s', shape)
def extract(self,img,face_records):
'''Extract a template that allows the face to be matched.'''
# Compute the 128D vector that describes the face in img identified by
# shape. In general, if two face descriptor vectors have a Euclidean
# distance between them less than 0.6 then they are from the same
# person, otherwise they are from different people. Here we just print
# the vector to the screen.
# TODO: Make this an option
JITTER_COUNT = 5
for face_record in face_records.face_records:
rect = pt.rect_proto2pv(face_record.detection.location)
x,y,w,h = rect.asTuple()
# Extract view
rect = pv.Rect()
cx,cy = x+0.5*w,y+0.5*h
tmp = 1.5*max(w,h)
cw,ch = tmp,tmp
crop = pv.AffineFromRect(pv.CenteredRect(cx,cy,cw,ch),(256,256))
#print (x,y,w,h,cx,cy,cw,ch,crop)
pvim = pv.Image(img[:,:,::-1]) # convert rgb to bgr
pvim = crop(pvim)
view = pt.image_pv2proto(pvim)
face_record.view.CopyFrom(view)
# Extract landmarks
l,t,r,b = [int(tmp) for tmp in [x,y,x+w,y+h]]
d = dlib.rectangle(l,t,r,b)
shape = self.shape_pred(img, d)
#print('s',dir(shape))
#print(shape.parts()[0])
for i in range(len(shape.parts())):
loc = shape.parts()[i]
landmark = face_record.landmarks.add()
landmark.landmark_id = "point_%02d"%i
landmark.location.x = loc.x
landmark.location.y = loc.y
#print('fr',face_record)
#print('shape:',face_records.face_records[0].landmarks)
face_descriptor = self.face_rec.compute_face_descriptor(img, shape, JITTER_COUNT)
face_descriptor = np.array(face_descriptor)
vec = face_descriptor.flatten()
face_record.template.data.CopyFrom(pt.vector_np2proto(vec))
def extract(self, img, face_records):
'''Extract a template that allows the face to be matched.'''
# Compute the 512D vector that describes the face in img identified by
#shape.
#print(type(img),img.shape)
img = img[:, :, ::
-1] #convert from rgb to bgr. There is BGRtoRGB conversion in get_embedding
for face_record in face_records.face_records:
#print(face_record)
if face_record.detection.score != -1:
landmarks = np.zeros((5, 2), dtype=np.float)
for i in range(0, len(face_record.landmarks)):
vals = face_record.landmarks[i]
landmarks[i, 0] = vals.location.x
landmarks[i, 1] = vals.location.y
_img = self.preprocess.norm_crop(img, landmark=landmarks)
#print(_img.shape)
embedding = self.fr_model.get_embedding(_img).flatten()
embedding_norm = np.linalg.norm(embedding)
normed_embedding = embedding / embedding_norm
#print(normed_embedding.shape)
# Extract view
x, y, w, h = pt.rect_proto2pv(
face_record.detection.location).asTuple()
cx, cy = x + 0.5 * w, y + 0.5 * h
tmp = 1.5 * max(w, h)
cw, ch = tmp, tmp
crop = pv.AffineFromRect(pv.CenteredRect(cx, cy, cw, ch),
(256, 256))
#print (x,y,w,h,cx,cy,cw,ch,crop)
pvim = pv.Image(img[:, :, ::-1]) # convert rgb to bgr
pvim = crop(pvim)
view = pt.image_pv2proto(pvim)
face_record.view.CopyFrom(view)
else:
normed_embedding = np.zeros(512, dtype=float)
face_record.template.data.CopyFrom(
pt.vector_np2proto(normed_embedding))
def processDetections(each):
im, results, options = each
if results.done():
recs = results.result().face_records
i = 0
for face in recs:
global FACE_COUNT
FACE_COUNT += 1
# Filter faces based on min size
size = min(face.detection.location.width,
face.detection.location.height)
if size < options.min_size:
continue
# Filter faces based on attributes
if not processAttributeFilter(face, options):
continue
# Process Detections
if options.detections_csv is not None:
global DETECTIONS_CSV
global DETECTIONS_FILE
import csv
if DETECTIONS_CSV == None:
DETECTIONS_FILE = open(options.detections_csv, 'w')
DETECTIONS_CSV = csv.writer(DETECTIONS_FILE)
DETECTIONS_CSV.writerow([
'source', 'frame', 'detect_id', 'type', 'score', 'x',
'y', 'w', 'h'
])
DETECTIONS_CSV.writerow([
face.source, face.frame, i, face.detection.detection_class,
face.detection.score, face.detection.location.x,
face.detection.location.y, face.detection.location.width,
face.detection.location.height
]),
DETECTIONS_FILE.flush()
# Process Detections
if options.attributes_csv is not None:
global ATTRIBUTES_CSV
global ATTRIBUTES_FILE
import csv
if ATTRIBUTES_CSV == None:
ATTRIBUTES_FILE = open(options.attributes_csv, 'w')
ATTRIBUTES_CSV = csv.writer(ATTRIBUTES_FILE)
ATTRIBUTES_CSV.writerow([
'source', 'frame', 'detect_id', 'type', 'score', 'x',
'y', 'w', 'h', 'attribute', 'value'
])
attributes = list(face.attributes)
attributes.sort(key=lambda x: x.key)
for attribute in attributes:
key = attribute.key
value = attribute.fvalue
ATTRIBUTES_CSV.writerow([
face.source,
face.frame,
i,
face.detection.detection_class,
face.detection.score,
face.detection.location.x,
face.detection.location.y,
face.detection.location.width,
face.detection.location.height,
key,
value,
]),
ATTRIBUTES_FILE.flush()
# Save Images with Detections
# Save Detected Faces
face_out_dir = ""
if options.face_log:
if not os.path.exists(options.face_log):
os.makedirs(options.face_log, exist_ok=True)
#print(face.detection.location)
rect = pt.rect_proto2pv(face.detection.location)
rect = rect.rescale(1.5)
affine = pv.AffineFromRect(rect, (128, 128))
try:
pvim = pv.Image(im[:, :, ::-1])
view = affine(pvim)
#print('Face',rect)
#print(view)
base_name, ext = os.path.splitext(
os.path.basename(face.source))
out_path = os.path.join(
options.face_log,
os.path.basename(base_name) + '_face_%03d' %
(face.detection.detection_id, ) + ext)
view.save(out_path)
print('Saving face:', out_path)
except:
print("WARNING: Image not processed correctly:",
face.source)
out_path = os.path.join(
options.face_log,
os.path.basename(base_name) + '_orig' + ext)
if not os.path.lexists(out_path):
os.symlink(os.path.abspath(face.source), out_path)
#print(options.face_log)
#pass
i += 1
return False
return True
def extract(self, img, face_records):
'''Extract a template that allows the face to be matched.'''
# Compute the 128D vector that describes the face in img identified by
# shape. In general, if two face descriptor vectors have a Euclidean
# distance between them less than 0.6 then they are from the same
# person, otherwise they are from different people. Here we just print
# the vector to the screen.
im = pv.Image(img[:, :, ::-1])
for face_record in face_records.face_records:
rect = pt.rect_proto2pv(face_record.detection.location)
x, y, w, h = rect.asTuple()
# Extract view
rect = pv.Rect()
cx, cy = x + 0.5 * w, y + 0.5 * h
tmp = 1.5 * max(w, h)
cw, ch = tmp, tmp
crop = pv.AffineFromRect(pv.CenteredRect(cx, cy, cw, ch),
(256, 256))
pvim = pv.Image(img[:, :, ::-1]) # convert rgb to bgr
pvim = crop(pvim)
view = pt.image_pv2proto(pvim)
face_record.view.CopyFrom(view)
# Extract landmarks
l, t, r, b = [int(tmp) for tmp in [x, y, x + w, y + h]]
d = dlib.rectangle(l, t, r, b)
shape = self.shape_pred(img, d)
for i in range(len(shape.parts())):
loc = shape.parts()[i]
landmark = face_record.landmarks.add()
landmark.landmark_id = "point_%02d" % i
landmark.location.x = loc.x
landmark.location.y = loc.y
# Get detection rectangle and crop the face
#rect = pt.rect_proto2pv(face_record.detection.location).rescale(1.5)
#tile = im.crop(rect)
tile = pvim.resize((224, 224))
#tile.show(delay=1000)
face_im = tile.asOpenCV2()
face_im = face_im[:, :, ::-1] # Convert BGR to RGB
#mat_ = cv2.cvtColor(mat,cv2.COLOR_RGB2GRAY)
#mat = cv2.cvtColor(mat_,cv2.COLOR_GRAY2RGB)
#img = image.load_img('../image/ajb.jpg', target_size=(224, 224))
from keras_vggface import utils
from keras.preprocessing import image
face_im = image.img_to_array(face_im)
face_im = np.expand_dims(face_im, axis=0)
face_im = utils.preprocess_input(face_im,
version=2) # or version=2
# Needed in multithreaded applications
with self.graph.as_default():
tmp = self.recognizer.predict(face_im)
face_descriptor = pv.meanUnit(tmp.flatten())
#print('shape:',face_records.face_records[0].landmarks)
#face_descriptor = self.face_rec.compute_face_descriptor(img, shape, JITTER_COUNT)
#face_descriptor = np.array(face_descriptor)
#vec = face_descriptor.flatten()
face_record.template.data.CopyFrom(
pt.vector_np2proto(face_descriptor))