def facestrain():
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
image_dir = os.path.join(BASE_DIR, "images")
print("[INFO] quantifying faces...")
imagePaths = list(paths.list_images(image_dir))
knownEncodings = []
knownNames = []
for (i, imagePath) in enumerate(imagePaths):
print("[INFO] processing image {}/{}".format(i + 1, len(imagePaths)))
name = imagePath.split(os.path.sep)[-2]
print(name)
stream = open(imagePath, "rb")
bytes = bytearray(stream.read())
numpyarray = numpy.asarray(bytes, dtype=numpy.uint8)
image = cv2.imdecode(numpyarray, cv2.IMREAD_UNCHANGED)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
face, confidence = facedetect.detect_face(image)
boxes = []
for idx, f in enumerate(face):
cv2.rectangle(image, (f[0], f[1]), (f[2], f[3]), (255, 0, 0), 2)
boxes = [(f[1], f[2], f[3], f[0])]
encodings = facedetect.face_encodings(rgb, boxes)
for encoding in encodings:
knownEncodings.append(encoding)
knownNames.append(name)
print("[INFO] serializing encodings...")
data = {"encodings": knownEncodings, "names": knownNames}
f = open("encodings.pickle", "wb")
f.write(pickle.dumps(data))
f.close()
def detect_face(frameCount):
global vs, outputFrame, lock, dat
data = pickle.loads(open('encodings.pickle', "rb").read())
total = 0
while True:
if dat == 'stop':
continue
frame = vs.read()
if total > frameCount:
face, confidence = facedetect.detect_face(frame)
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
boxes = []
names = []
for idx, f in enumerate(face):
box = (f[1], f[2], f[3], f[0])
boxes.append(box)
encodings = facedetect.face_encodings(rgb, boxes)
for encoding in encodings:
matches = facedetect.compare_faces(data["encodings"],
encoding,
tolerance=0.35)
name = "Unknown"
face_distances = facedetect.face_distance(
data["encodings"], encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = data["names"][best_match_index]
names.append(name)
for ((top, right, bottom, left), name) in zip(boxes, names):
cv2.rectangle(frame, (left - 20, top - 20),
(right + 20, bottom + 20), (255, 0, 0), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left - 20, bottom - 15),
(right + 20, bottom + 20), (255, 0, 0),
cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left - 20, bottom + 15), font, 1.0,
(255, 255, 255), 2)
print(frame)
total += 1
with lock:
outputFrame = frame.copy()
print("[INFO] quantifying faces...")
imagePaths = list(paths.list_images(image_dir))
knownEncodings = []
knownNames = []
for (i, imagePath) in enumerate(imagePaths):
print("[INFO] processing image {}/{}".format(i+1, len(imagePaths)))
name = imagePath.split(os.path.sep)[-2]
print(name)
stream = open(imagePath, "rb")
bytes = bytearray(stream.read())
numpyarray = numpy.asarray(bytes, dtype=numpy.uint8)
image = cv2.imdecode(numpyarray, cv2.IMREAD_UNCHANGED)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
face, confidence = facedetect.detect_face(image)
boxes = []
for idx, f in enumerate(face):
cv2.rectangle(image, (f[0], f[1]), (f[2], f[3]), (255, 0, 0), 2)
boxes = [(f[1], f[2], f[3], f[0])]
encodings = facedetect.face_encodings(rgb, boxes)
for encoding in encodings:
knownEncodings.append(encoding)
knownNames.append(name)
print("[INFO] serializing encodings...")
data = {"encodings": knownEncodings, "names": knownNames}
f = open("encodings.pickle", "wb")
f.write(pickle.dumps(data))
f.close()