def face_Encode(self):
name = self.textEdit.toPlainText()
title = self.textEdit_2.toPlainText()
encodingsBox = []
namesBox = []
titlesBox = []
cap = cv2.VideoCapture(0)
time.sleep(1)
while True:
ret, frame = cap.read()
if ret == True:
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
boxes = face_recognition.face_locations(rgb_frame, model="hog")
encodings = face_recognition.face_encodings(rgb_frame, boxes)
for encoding in encodings:
encodingsBox.append(encoding)
namesBox.append(name)
titlesBox.append(title)
cv2.imshow("Frame", frame)
if cv2.waitKey(1) == 27 & 0xff:
break
data = {
'encodings': encodingsBox,
'names': namesBox,
'titles': titlesBox
}
f = open("Models/encodings.pickle", "wb")
f.write(pickle.dumps(data))
f.close()
cap.release()
cv2.destroyAllWindows()
def get_vector(image_path):
# Load the jpg file into a numpy array
image = face_recognition.load_image_file(image_path)
face_locations = face_recognition.face_locations(image)
face_encodings = face_recognition.face_encodings(image, face_locations)
return face_encodings
def drawRectangle(image, known_encodings):
status = False
# Find all the faces and face encodings in the unknown image
face_locations = fr.face_locations(image)
known_face_names = ["Michael Reeves", "Michael Reeves"]
face_encodings = fr.face_encodings(image, face_locations)
# Convert the image to a PIL-format image so that we can draw on top of it with the Pillow library
# See http://pillow.readthedocs.io/ for more about PIL/Pillow
pil_image = Image.fromarray(image)
# Create a Pillow ImageDraw Draw instance to draw with
draw = ImageDraw.Draw(pil_image)
for (top, right, bottom,
left), face_encoding in zip(face_locations, face_encodings):
# See if the face is a match for the known face(s)
matches = fr.compare_faces(known_encodings, face_encoding)
name = "Unknown"
# If a match was found in known_face_encodings, just use the first one.
# if True in matches:
# first_match_index = matches.index(True)
# name = known_face_names[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances = fr.face_distance(known_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
# Draw a label with a name below the face
pad = 10
text_width, text_height = draw.textsize(name)
draw.rectangle(((left - pad, bottom - text_height - 10),
(right + pad, bottom + pad)),
fill=(255, 0, 0),
outline=(255, 0, 0))
draw.text((left + 6, bottom - text_height - 5),
name,
fill=(255, 255, 255, 255))
# Draw a box around the face using the Pillow module
draw.rectangle(
((left - pad, top - pad), (right + pad, bottom + pad)),
outline=(255, 0, 0),
width=3)
status = True
del draw
buffered = BytesIO()
pil_image.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue())
return [status, img_str]
def recognize_faces(self, msg):
# Grab a single frame of video
frame = image_to_numpy(msg)
if self.process_frame:
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Find all the faces and face encodings in the current frame of video
_face_locations = face_locations(rgb_small_frame)
_face_encodings = face_encodings(rgb_small_frame, _face_locations)
face_names = []
for face_encoding in _face_encodings:
# See if the face is a match for the known face(s)
matches = compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
# If a match was found in known_face_encodings, just use the first one.
if True in matches:
first_match_index = matches.index(True)
name = known_face_names[first_match_index]
face_names.append(name)
# Display the results
for (top, right, bottom, left), name in zip(_face_locations, face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
# Display the resulting image
cv2.imshow('Face recognition', frame)
cv2.waitKey(1)
self.process_frame = not self.process_frame
def match(clf, filename):
# Load the test image with unknown faces into a numpy array
test_image = face_recognition.load_image_file(filename)
# Find all the faces in the test image using the default HOG-based model
face_locations = face_recognition.face_locations(test_image)
no = len(face_locations)
print("\nNumber of faces detected: ", no)
# Predict all the faces in the test image using the trained classifier
firstname = None
print("\n(☞ﾟヮﾟ)☞ ☜(ﾟヮﾟ☜)\n")
print("Found:")
for i in range(no):
test_image_enc = face_recognition.face_encodings(test_image)[i]
name = clf.predict([test_image_enc])
probs = clf.predict_proba([test_image_enc])
print(*name)
firstname = (str(name[0]))
return (firstname, no)
def train():
# Training the SVC classifier
# The training data would be all the face encodings from all the known images and the labels are their names
encodings = []
names = []
# Training directory
train_dir = os.listdir("train_dir/")
# Loop through each person in the training directory
for person in train_dir:
pix = os.listdir("train_dir/" + person)
# pix = [item for item in pix if not item.startswith('.') and os.path.isfile(os.path.join(root, item))]
# Loop through each training image for the current person
for person_img in pix:
# Get the face encodings for the face in each image file
if person_img == ".DS_Store":
continue
face = face_recognition.load_image_file(
"train_dir/" + person + "/" + person_img
)
face_bounding_boxes = face_recognition.face_locations(face)
if len(face_bounding_boxes) == 1:
face_enc = face_recognition.face_encodings(face)[0]
# Add face encoding for current image with corresponding label (name) to the training data
encodings.append(face_enc)
names.append(person)
# Create and train the SVC classifier
clf = svm.SVC(gamma="scale", probability=True)
clf.fit(encodings, names)
dump(clf, 'clf.joblib')
return clf
def Face_Recognition_(self, frame):
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
rgb_small_frame = small_frame[:, :, ::-1]
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(
rgb_small_frame, face_locations)
face_names = []
face_titles = []
for face_encoding in face_encodings:
matches = face_recognition.compare_faces(knownFE, face_encoding)
name = "Unknown"
title = "Unknown"
if True in matches:
first_match_index = matches.index(True)
name = knownN[first_match_index]
title = knownT[first_match_index]
face_names.append(name)
face_titles.append(title)
for (top, right, bottom,
left), name, title in zip(face_locations, face_names,
face_titles):
top *= 4
right *= 4
bottom *= 4
left *= 4
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.rectangle(frame, (left, bottom - 35), (right, bottom),
(0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name + ": " + title, (left + 6, bottom - 6),
font, 1.0, (255, 255, 255), 1)
return frame, face_names, face_titles
from face_recognition.face_recognition import load_image_file, face_encodings, face_locations, compare_faces DIR = os.path.dirname(os.path.realpath(__file__)) # This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the # other example, but it includes some basic performance tweaks to make things run a lot faster: # 1. Process each video frame at 1/4 resolution (though still display it at full resolution) # 2. Only detect faces in every other frame of video. # PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam. # OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this # specific demo. If you have trouble installing it, try any of the other demos that don't require it instead. # Load a sample picture and learn how to recognize it. obama_image = load_image_file(os.path.join(DIR, "obama.jpg")) obama_face_encoding = face_encodings(obama_image)[0] # Load a second sample picture and learn how to recognize it. biden_image = load_image_file(os.path.join(DIR, "biden.jpg")) biden_face_encoding = face_encodings(biden_image)[0] # Load a second sample picture and learn how to recognize it. emilka_image = load_image_file(os.path.join(DIR, "emilka.jpg")) emilka_face_encoding = face_encodings(emilka_image)[0] # Load a second sample picture and learn how to recognize it. wagram_image = load_image_file(os.path.join(DIR, "wagram.jpg")) wagram_face_encoding = face_encodings(wagram_image)[0] # Create arrays of known face encodings and their names
name = users.get_name_from_id(user)
for imagePath in users.get_pictures_from_id(user):
# load the input image and convert it from BGR (OpenCV ordering)
# to dlib ordering (RGB)
image = cv2.imread(imagePath)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# detect the (x, y)-coordinates of the bounding boxes
# corresponding to each face in the input image
boxes = face_recognition.face_locations(rgb, \
model=args["detection_method"])
# compute the facial embedding for the face
encodings = face_recognition.face_encodings(rgb, boxes)
# loop over the encodings
for encoding in encodings:
# add each encoding + user(name) to our lists of known users and
# encodings
knownEncodings.append(encoding)
knownUsers.append(user)
# knownNames.append(name)
# dump the facial encodings + names to disk
print("[INFO] serializing encodings...")
data = {"encodings": knownEncodings, "users": knownUsers}
f = open(args["encodings"], "wb")
f.write(pickle.dumps(data))
f.close()