def get_frame(self):
success, frame_orig = self.video.read()
# We are using Motion JPEG, but OpenCV defaults to capture raw images,
# so we must encode it into JPEG in order to correctly display the
# video stream.
# faces = face_cascade.detectMultiScale(image, 1.3, 5)
frame = cv2.resize(src=frame_orig, dsize=(0, 0), fx=0.5, fy=0.5)
embedding_dict = load_embeddings()
frame = frame[:, :, ::-1]
if frame.size > 0:
faces, rects = get_faces_live(img=frame,
pnet=pnet,
rnet=rnet,
onet=onet,
image_size=image_size)
# If there are human faces detected
if faces:
for i in range(len(faces)):
face_img = faces[i]
rect = rects[i]
# Scale coordinates of face locations by the resize ratio
rect = [coordinate * 2 for coordinate in rect]
face_embedding = forward_pass(
img=face_img,
session=facenet_persistent_session,
images_placeholder=images_placeholder,
embeddings=embeddings,
phase_train_placeholder=phase_train_placeholder,
image_size=image_size)
# Compare euclidean distance between this embedding and the embeddings in 'embeddings/'
identity = identify_face(embedding=face_embedding,
embedding_dict=embedding_dict)
cv2.rectangle(img=frame_orig,
pt1=(rect[0], rect[1]),
pt2=(rect[2], rect[3]),
color=(255, 215, 0),
thickness=2)
W = int(rect[2] - rect[0]) // 2
cv2.putText(img=frame_orig,
text=identity,
org=(rect[0] + W - (W // 2), rect[1] - 7),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.5,
color=(255, 215, 0),
thickness=1,
lineType=cv2.LINE_AA)
ret, jpeg = cv2.imencode('.jpg', frame_orig)
return jpeg.tobytes()
def predict_image(file):
# file = request.files['file']
# file = os.path.join(APP_ROOT, 'uploads/Abdulrahman Safh.png')
# Read image file as numpy array of RGB dimension
#img = io.imread(fname=file)
img = imread(name=file, mode='RGB')
# Detect and crop a 160 x 160 image containing a human face in the image file
faces, rects = get_faces_live(img=img, pnet=pnet, rnet=rnet,
onet=onet, image_size=image_size)
#global d
# If there are human faces detected
if faces:
embedding_dict = load_embeddings()
if embedding_dict:
people_found = []
for i in range(len(faces)):
face_img = faces[i]
rect = rects[i]
face_embedding = forward_pass(
img=face_img, session=facenet_persistent_session,
images_placeholder=images_placeholder, embeddings=embeddings,
phase_train_placeholder=phase_train_placeholder,
image_size=image_size
)
# Compare euclidean distance between this embedding and the embeddings in 'embeddings/'
identity = identify_face(
embedding=face_embedding, embedding_dict=embedding_dict)
people_found.append(identity)
cv2.rectangle(img, (rect[0], rect[1]), (rect[2], rect[3]), (0, 255, 0), 3)
W = int(rect[2] - rect[0]) // 2
H = int(rect[3] - rect[1]) // 2
cv2.putText(img, identity, (rect[0] + W - (W // 2), rect[1] - 7),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 1, cv2.LINE_AA)
# code for saving the output images
# cv2.imwrite("SavedImgesFull/file_%d.jpg" % d, img)
#d += 1
return people_found
else:
# return ["No Face"]
return None
# return render_template(
# 'predict_result.html',
# identity="No embedding files detected! Please upload image files for embedding!"
# )
else:
# return ["No Image"]
return None
def face_present(image_path):
img = cv2.imread(image_path, -1)
save_loc = 'saved_image/new.jpg'
face_present = False
faces, rects = get_faces_live(
img=img,
pnet=pnet,
rnet=rnet,
onet=onet,
image_size=image_size
)
for face_img in faces:
cv2.imwrite(save_loc, face_img)
face_present = True
cv2.imwrite('static/saved_images/bounded.jpg', face_img)
return face_present
def face_detect_live():
"""Detects faces in real-time via Web Camera."""
embedding_dict = load_embeddings()
if embedding_dict:
try:
# Start non-blocking multi-threaded OpenCV video stream
cap = WebcamVideoStream(src=0).start()
while True:
frame_orig = cap.read() # Read frame
# Resize frame to half its size for faster computation
frame = cv2.resize(src=frame_orig,
dsize=(0, 0),
fx=0.5,
fy=0.5)
# Convert the image from BGR color (which OpenCV uses) to RGB color
frame = frame[:, :, ::-1]
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if frame.size > 0:
faces, rects = get_faces_live(img=frame,
pnet=pnet,
rnet=rnet,
onet=onet,
image_size=image_size)
# If there are human faces detected
if faces:
for i in range(len(faces)):
face_img = faces[i]
rect = rects[i]
# Scale coordinates of face locations by the resize ratio
rect = [coordinate * 2 for coordinate in rect]
face_embedding = forward_pass(
img=face_img,
session=facenet_persistent_session,
images_placeholder=images_placeholder,
embeddings=embeddings,
phase_train_placeholder=phase_train_placeholder,
image_size=image_size)
# Compare euclidean distance between this embedding and the embeddings in 'embeddings/'
identity = identify_face(
embedding=face_embedding,
embedding_dict=embedding_dict)
cv2.rectangle(img=frame_orig,
pt1=(rect[0], rect[1]),
pt2=(rect[2], rect[3]),
color=(255, 215, 0),
thickness=2)
W = int(rect[2] - rect[0]) // 2
cv2.putText(img=frame_orig,
text=identity,
org=(rect[0] + W - (W // 2),
rect[1] - 7),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.5,
color=(255, 215, 0),
thickness=1,
lineType=cv2.LINE_AA)
cv2.imshow(winname='Video', mat=frame_orig)
# Keep showing camera stream even if no human faces are detected
cv2.imshow(winname='Video', mat=frame_orig)
else:
continue
cap.stop() # Stop multi-threaded Video Stream
cv2.destroyAllWindows()
return render_template(template_name_or_list='index.html')
except Exception as e:
print(e)
else:
return render_template(
template_name_or_list="warning.html",
status=
"No embedding files detected! Please upload image files for embedding!"
)
def face_detect_live():
"""Detects faces in real-time via Web Camera."""
embedding_dict = load_embeddings()
if embedding_dict:
try:
cap = cv2.VideoCapture(0)
while True:
cap.grab() # For use in multi-camera environments when the cameras do not have hardware synchronization
return_code, frame_orig = cap.read() # Read frame
# Resize frame to half its size for faster computation
frame = cv2.resize(frame_orig, (0, 0), fx=0.5, fy=0.5)
# Convert the image from BGR color (which OpenCV uses) to RGB color
frame = frame[:, :, ::-1]
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if frame.size > 0:
faces, rects = get_faces_live(img=frame, pnet=pnet, rnet=rnet, onet=onet, image_size=image_size)
# If there are human faces detected
if faces:
for i in range(len(faces)):
face_img = faces[i]
rect = rects[i]
# Scale coordinates of face locations by the resize ratio
rect = [coordinate * 2 for coordinate in rect]
face_embedding = forward_pass(
img=face_img,
session=facenet_persistent_session,
images_placeholder=images_placeholder,
embeddings=embeddings,
phase_train_placeholder=phase_train_placeholder,
image_size=image_size
)
# Compare euclidean distance between this embedding and the embeddings in 'embeddings/'
identity = identify_face(embedding=face_embedding, embedding_dict=embedding_dict)
cv2.rectangle(frame_orig, (rect[0], rect[1]), (rect[2], rect[3]), (255, 215, 0), 2)
W = int(rect[2] - rect[0]) // 2
H = int(rect[3] - rect[1]) // 2
cv2.putText(frame_orig, identity, (rect[0]+W-(W//2), rect[1]-7),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 215, 0), 1, cv2.LINE_AA)
cv2.imshow('Video', frame_orig)
# Keep showing camera stream even if no human faces are detected
cv2.imshow('Video', frame_orig)
else:
continue
cap.release()
cv2.destroyAllWindows()
return render_template('index.html')
except Exception as e:
print(e)
else:
return render_template(
"warning.html",
status="No embedding files detected! Please upload image files for embedding!"
)
def get_frame():
embedding_dict = load_embeddings()
if embedding_dict:
try:
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 500)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 500)
while True:
#get camera frame
ret, frame = cap.read() # Read frame
#print(frame)
# Resize frame to half its size for faster computation
frame = cv2.resize(src=frame, dsize=(0, 0), fx=0.8, fy=0.8)
# Convert the image from BGR color (which OpenCV uses) to RGB color
#frame = frame[:, :, ::-1]
#if cv2.waitKey(1) & 0xFF == ord('q'):
# break
#
if frame.size > 0:
faces, rects = get_faces_live(img=frame,
pnet=pnet,
rnet=rnet,
onet=onet,
image_size=image_size)
if faces:
for i in range(len(faces)):
face_img = faces[i]
rect = rects[i]
# Scale coordinates of face locations by the resize ratio
rect = [coordinate for coordinate in rect]
face_embedding = forward_pass(
img=face_img,
session=facenet_persistent_session,
images_placeholder=images_placeholder,
embeddings=embeddings,
phase_train_placeholder=phase_train_placeholder,
image_size=image_size)
# Compare euclidean distance between this embedding and the embeddings in 'embeddings/'
identity = identify_face(
embedding=face_embedding,
embedding_dict=embedding_dict)
cv2.rectangle(img=frame,
pt1=(rect[0], rect[1]),
pt2=(rect[2], rect[3]),
color=(0, 0, 255),
thickness=2)
W = int(rect[2] - rect[0]) // 2
cv2.putText(img=frame,
text=identity,
org=(rect[0] + W - (W // 2),
rect[1] - 7),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.5,
color=(0, 0, 255),
thickness=1,
lineType=cv2.LINE_AA)
ret, jpeg = cv2.imencode('.jpg', frame)
ret, jpeg = cv2.imencode('.jpg', frame)
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' +
jpeg.tobytes() + b'\r\n\r\n')
else:
continue
except Exception as e:
print(e)
def face_detect_live():
"""Detects faces in real-time via Web Camera."""
embedding_dict = load_embeddings()
if embedding_dict:
try:
cap = cv2.VideoCapture(0)
while True:
return_code, frame = cap.read() # RGB frame
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if frame.size > 0:
faces, rects = get_faces_live(img=frame,
pnet=pnet,
rnet=rnet,
onet=onet,
image_size=image_size)
# If there are human faces detected
if faces:
for i in range(len(faces)):
face_img = faces[i]
rect = rects[i]
face_embedding = forward_pass(
img=face_img,
session=facenet_persistent_session,
images_placeholder=images_placeholder,
embeddings=embeddings,
phase_train_placeholder=phase_train_placeholder,
image_size=image_size)
# Compare euclidean distance between this embedding and the embeddings in 'embeddings/'
identity = identify_face(
embedding=face_embedding,
embedding_dict=embedding_dict)
cv2.rectangle(frame, (rect[0], rect[1]),
(rect[2], rect[3]), (255, 215, 0), 2)
W = int(rect[2] - rect[0]) // 2
H = int(rect[3] - rect[1]) // 2
cv2.putText(frame, identity,
(rect[0] + W - (W // 2), rect[1] - 7),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255, 215, 0), 1, cv2.LINE_AA)
cv2.imshow('Video', frame)
# Keep showing camera stream even if no human faces are detected
cv2.imshow('Video', frame)
else:
continue
cap.release()
cv2.destroyAllWindows()
return render_template('index.html')
except Exception as e:
print(e)
else:
return "No embedding files detected! Please upload image files for embedding!"
def face_detect_live():
# Load text reading engine
#engine = pyttsx3.init()
spoken_face_names = []
greetings = [
'How do you do', 'Hello', 'Hi', 'Hai', 'Hey', 'How have you been',
'How are you', 'How is it going', 'Salam alikom ', 'Esh loonak ya',
'Ahlaaaan'
]
embedding_dict = load_embeddings()
if embedding_dict:
try:
cap = cv2.VideoCapture(0)
while True:
return_code, frame = cap.read() # RGB frame
if cv2.waitKey(1) & 0xFF == ord('q'):
break
faces, rects = get_faces_live(img=frame,
pnet=pnet,
rnet=rnet,
onet=onet,
image_size=image_size)
# If there are human faces detected
if faces:
for i in range(len(faces)):
face_img = faces[i]
rect = rects[i]
face_embedding = forward_pass(
img=face_img,
session=facenet_persistent_session,
images_placeholder=images_placeholder,
embeddings=embeddings,
phase_train_placeholder=phase_train_placeholder,
image_size=image_size)
# Compare euclidean distance between this embedding and the embeddings in 'embeddings/'
identity = identify_face(embedding=face_embedding,
embedding_dict=embedding_dict)
cv2.rectangle(frame, (rect[0], rect[1]),
(rect[2], rect[3]), (255, 215, 0), 2)
W = int(rect[2] - rect[0]) // 2
H = int(rect[3] - rect[1]) // 2
cv2.putText(frame, identity,
(rect[0] + W - (W // 2), rect[1] - 7),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255, 215, 0), 1, cv2.LINE_AA)
if identity == "Unknown":
continue
elif identity in spoken_face_names:
continue
else:
print(random.choice(greetings) + " " + identity)
#engine.say(random.choice(greetings) + name)
#engine.runAndWait()
spoken_face_names.append(identity)
continue
cv2.imshow('Video', frame)
else:
continue
cap.release()
cv2.destroyAllWindows()
return render_template('index.html')
except Exception as e:
print(e)
else:
return "No loaded faces detected! Please upload image files for embedding!"