def train(tagged):
"""
Trains an SVM classifier based on the training data passed.
Mostly based on http://dlib.net/svm_binary_classifier.py.html.
:param tagged: list of TaggedFace to train on
:return: dlib.svm
"""
x = dlib.vectors() # will carry the facial encodings
y = dlib.array() # will carry the gender label
print("Preparing dataset...")
total = len(tagged)
for i, t in enumerate(tagged):
print(f"\rEncoding {t.path} ({i + 1}/{total})...", end="")
faces = encode(t.img)
x.append(dlib.vector(faces[0]))
y.append(t.tag)
img = t.img
for _ in range(5):
faces = encode(img)
if not faces:
break
x.append(dlib.vector(faces[0]))
y.append(t.tag)
img = cv2.resize(img, None, fx=0.7, fy=0.7)
print("Training SVM...")
trainer = dlib.svm_c_trainer_radial_basis()
#trainer.be_verbose()
trainer.set_c(10)
model = trainer.train(x, y)
with open(PATH_SVMFILE, "wb") as filehandle:
pickle.dump(model, filehandle)
return None
def read_and_find(path):
"""
Reads an image from a path, and locates and encodes any face on it.
:param path: path to an image
:return: tuple of (cv2/np.ndarray, list of encodings, list of locations)
or empty tuple if no face was found
"""
print(f"Reading {path}...")
img = cv2.imread(path)
if img is None:
return tuple()
locations = detect(img)
if not (locations):
return tuple()
if C_LOAD_ATLAS: # we don't have to do the encodings in this case
return img, [], locations, path
return img, encode(img, locations=locations), locations, path
from phantom.faces import compare, encode, detect
known_faces = {
"Bruno": "c:/test/phantom/tests/img4.jpg",
"Luciano": "c:/test/phantom/tests/luciano2.jpg",
"Fer": "c:/test/phantom/tests/fer.jpg",
"Ana": "c:/test/phantom/tests/ana.jpg",
"Santi": "c:/test/phantom/tests/santi.jpg",
}
for name, impath in known_faces.items():
img = cv2.imread(impath)
if img is None:
known_faces[name] = None
continue
known_faces[name] = encode(img)
video = cv2.VideoCapture(0)
while True:
check, frame = video.read()
faces = detect(frame, upsample=0)
if faces:
encodings = encode(frame, locations=faces)
for i, e in enumerate(encodings):
for k, v in known_faces.items():
if compare(e, v) <= 0.6:
left, top, right, bottom = faces[i]
x = int (left * 0.5 + right * 0.5)
y = top