def show(args):
with fc.util.Camera() as cam:
# load recognizer, archive and start camera and window
detector = fc.FaceDetector()
recognizer = fc.FaceRecognizer()
dataset = pickle.load(args["EMBED_FILE"])
with fc.util.ImageWindow("camera") as window:
while True:
# load image from webcam
frame = cam.image()
if frame is None:
print("end of streaming")
break
# find faces, and who they are
faces, boxes = detector.crop_aligned_faces(frame, (112, 112),
with_boxes=True)
if len(faces) != 0:
found_names = recognizer.assign_names(dataset, faces)
found_names = [
"{} ({:.2f} %)".format(name, conf * 100)
for name, conf in found_names
]
window.show(frame, box_faces=(boxes, found_names))
else:
window.show(frame)
# check for end
if cv2.waitKey(1) & 0xFF == ord("q"):
break
def _load_from_video(video_path):
detector = fc.FaceDetector()
faces_to_return = []
cap = cv2.VideoCapture(video_path)
frame_rate = cap.get(5)
ret, frame = cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
i = 0
while ret is True:
if i % frame_rate == 0:
faces = detector.crop_aligned_faces(frame, resize=(112, 112))
if len(faces) == 1:
faces_to_return.append(faces[0])
ret, frame = cap.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
else:
ret, frame = cap.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
else:
ret, frame = cap.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
i += 1
cap.release()
return faces_to_return
def load_faces(folder: str) -> Dict[str, ndarray]:
"""
Search for faces in files of type jpg, png and mp4 inside `folder` path. Images or
frames of videos with none or more than one face are skipped.
To each recognized face is assigned a name equal to the name of the file until
'-' character. For instance a file named 'andreaconti-1.jpg' will led to
'andreaconti' label. If a video is found, frames are sampled at 1s distance.
Parameters
----------
folder : str
path of the folder in which search for faces
Returns
-------
array_like, array_like
tuple containing a list of faces and the list of matching labels
"""
detector = fc.FaceDetector()
result: Dict[str, ndarray] = {}
for f_name in os.listdir(folder):
# assigned name
name = os.path.basename(f_name)
name = os.path.splitext(name)[0]
name = name.split("-")[0]
# load face from image
if f_name.lower().endswith((".jpg", ".png")):
img = cv2.cvtColor(cv2.imread(os.path.join(folder, f_name)),
cv2.COLOR_BGR2RGB)
faces = detector.crop_aligned_faces(img, resize=(112, 112))
if len(faces) == 0:
print(f"[WARNING] face not found in {f_name}, skipped.")
continue
if len(faces) > 1:
print(f"[WARNING] too many faces found in {f_name}, skipped.")
continue
if name not in result.keys():
result[name] = []
face = faces[0]
result[name].append(face)
# load face from video
elif f_name.lower().endswith(".mp4"):
faces = _load_from_video(os.path.join(folder, f_name))
if name not in result.keys():
result[name] = []
face = faces[0]
result[name].extend(faces)
for k in result.keys():
result[k] = np.stack(result[k])
return result
def recognize(args):
detector = fc.FaceDetector()
embedder = fc.FaceEmbedder()
recognizer = pickle.load(args["EMBED_FILE"])
img = cv2.imread(args["IMG"])
cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
faces = detector.crop_aligned_faces(img, resize=(112, 112))
faces_embedded = embedder.embed_faces(faces)
names = recognizer.assign_names(faces_embedded)
for name in names:
print(f"found: {name}")
def test_face_embedding():
# load img
img = cv2.imread(file_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# find faces
face_detector = fc.FaceDetector()
faces = face_detector.crop_aligned_faces(img, resize=(112, 112))
assert faces.shape[0] == 1
# embed
embedder = fc.FaceRecognizer()
faces_emb = embedder.embed_faces(faces)
assert faces_emb.shape == (1, 128)
def watch(self, dataset: Dict[str, ndarray]):
detector = fc.FaceDetector()
recognizer = fc.FaceRecognizer()
with Camera() as cam:
while True:
frame = cam.image()
if frame is None:
return
# find faces, and who they are
faces = detector.crop_aligned_faces(frame, (112, 112))
if len(faces) != 0:
found_names = recognizer.assign_names(dataset, faces)
# call registered functions
for name, _ in found_names:
if name in self._handlers:
for fn in self._handlers[name]:
fn()
"""
Test of simple face detection
"""
from face_recognition_cam import __version__
import face_recognition_cam as fc
import cv2
import os
def test_version():
assert __version__ == '0.1.0'
file_path = os.path.join(os.path.dirname(__file__), 'test_data', 'sample.jpg')
face_detector = fc.FaceDetector()
def test_face_detection():
# load img
img = cv2.imread(file_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# find faces
boxes = face_detector.find_face_boxes(img)
assert boxes.shape[0] == 1
def test_face_embedding():