def process_facedetection(model_detector, model_poseestimator,
model_ageestimator, model_genderestimator,
cam_resolution, cam_index): #model_emotionestimator
# Initialize the camera
camera = cam_init(cam_index, cam_resolution[0], cam_resolution[1])
# try:
# Initialize face detection
face_detector = FaceDetector(model=model_detector,
path=INPUT_DIR_MODEL_DETECTION,
minfacesize=120)
# Initialize face pose/age/gender estimation
face_pose_estimator = FacePoseEstimator(model=model_poseestimator,
path=INPUT_DIR_MODEL_ESTIMATION)
face_age_estimator = FaceAgeEstimator(model=model_ageestimator,
path=INPUT_DIR_MODEL_ESTIMATION)
face_gender_estimator = FaceGenderEstimator(
model=model_genderestimator, path=INPUT_DIR_MODEL_ESTIMATION)
# face_emotion_estimator = FaceEmotionEstimator(model=model_emotionestimator, path=INPUT_DIR_MODEL_ESTIMATION)
# except:
# print("Warning, check if models and trained dataset models exists!")
# (age, gender, emotion) = (None, None, None)
while (True):
# Capture frame from webcam
ret, frame = camera.read()
if frame is None:
print("Error, check if camera is connected!")
break
# Detect and identify faces in the frame
faces = face_detector.detect(frame)
for (index, face) in enumerate(faces):
(x, y, w, h) = face
# Detect age, gender, emotion
face_image = frame[y:y + h, h:h + w]
age = face_age_estimator.estimate(frame, face_image)
gender = face_gender_estimator.estimate(frame, face_image)
# emotion = face_emotion_estimator.estimate(frame, face_image)
# lable_face(frame, face, )
# Detect and draw face pose locations
shape = face_pose_estimator.detect(frame, face)
# face_pose_estimator.add_overlay(frame, shape)
# Display age, gender, emotion
if True: # Added condition to easily disable text
cv2.putText(frame, "Age: {}".format(age), (x, y - 45),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
cv2.putText(frame, "Gender: {}".format(gender), (x, y - 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 255),
1)
# cv2.putText(frame, "Emotion: {}".format(emotion),
# (x, y-15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)
# Display the resulting frame
cv2.imshow(WINDOW_NAME, frame)
# Check for user actions
keyPressed = cv2.waitKey(1) & 0xFF
if keyPressed == 27: # ESC
break
elif keyPressed == 13: # Enter
cv2.imwrite(
WINDOW_NAME + "_" +
datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + ".jpg",
frame)
# Release the camera
camera.release()
cv2.destroyAllWindows()
def process_facedetection():
cam_index = 0
cam_resolution = RESOLUTION_QVGA
model_detector = FaceDetectorModels.HAARCASCADE
# model_detector=FaceDetectorModels.DLIBHOG
# model_detector=FaceDetectorModels.DLIBCNN
# model_detector=FaceDetectorModels.SSDRESNET
# model_detector=FaceDetectorModels.MTCNN
# model_detector=FaceDetectorModels.FACENET
model_poseestimator = FacePoseEstimatorModels.DEFAULT
model_ageestimator = FaceAgeEstimatorModels.DEFAULT
model_genderestimator = FaceGenderEstimatorModels.DEFAULT
model_emotionestimator = FaceEmotionEstimatorModels.DEFAULT
# Initialize the camera
camera = cam_init(cam_index, cam_resolution[0], cam_resolution[1])
try:
# Initialize face detection
face_detector = FaceDetector(
model=model_detector,
path=INPUT_DIR_MODEL_DETECTION) #, optimize=True)
# Initialize face pose/age/gender estimation
face_pose_estimator = FacePoseEstimator(
model=model_poseestimator, path=INPUT_DIR_MODEL_ESTIMATION)
face_age_estimator = FaceAgeEstimator(model=model_ageestimator,
path=INPUT_DIR_MODEL_ESTIMATION)
face_gender_estimator = FaceGenderEstimator(
model=model_genderestimator, path=INPUT_DIR_MODEL_ESTIMATION)
face_emotion_estimator = FaceEmotionEstimator(
model=model_emotionestimator, path=INPUT_DIR_MODEL_ESTIMATION)
except:
print("Warning, check if models and trained dataset models exists!")
(age, gender, emotion) = (None, None, None)
while (True):
# Capture frame from webcam
ret, frame = camera.read()
if frame is None:
print("Error, check if camera is connected!")
break
# Detect and identify faces in the frame
faces = face_detector.detect(frame)
for (index, face) in enumerate(faces):
(x, y, w, h) = face
# Detect age, gender, emotion
face_image = frame[y:y + h, h:h + w]
age = face_age_estimator.estimate(frame, face_image)
gender = face_gender_estimator.estimate(frame, face_image)
emotion = face_emotion_estimator.estimate(frame, face_image)
# Detect and draw face pose locations
shape = face_pose_estimator.detect(frame, face)
face_pose_estimator.add_overlay(frame, shape)
# Display age, gender, emotion
cv2.putText(frame, "Age: {}".format(age), (x, y - 45),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
cv2.putText(frame, "Gender: {}".format(gender), (x, y - 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
cv2.putText(frame, "Emotion: {}".format(emotion), (x, y - 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
# Display updated frame to web app
yield (b'--frame\r\nContent-Type: image/jpeg\r\n\r\n' +
cv2.imencode('.jpg', frame)[1].tobytes() + b'\r\n\r\n')
# Release the camera
camera.release()
cv2.destroyAllWindows()
def process_facedetection(cam_resolution,
out_resolution,
framecount,
model_detector=0):
from libfaceid.pose import FacePoseEstimatorModels, FacePoseEstimator
from libfaceid.age import FaceAgeEstimatorModels, FaceAgeEstimator
from libfaceid.gender import FaceGenderEstimatorModels, FaceGenderEstimator
from libfaceid.emotion import FaceEmotionEstimatorModels, FaceEmotionEstimator
model_poseestimator = FacePoseEstimatorModels.DEFAULT
model_ageestimator = FaceAgeEstimatorModels.DEFAULT
model_genderestimator = FaceGenderEstimatorModels.DEFAULT
model_emotionestimator = FaceEmotionEstimatorModels.DEFAULT
# Initialize the camera
cap = cam_init(cam_resolution[0], cam_resolution[1])
###############################################################################
# FACE DETECTION
###############################################################################
# Initialize face detection
face_detector = FaceDetector(
model=model_detector,
path=INPUT_DIR_MODEL_DETECTION) #, optimize=True)
###############################################################################
# FACE POSE/AGE/GENDER/EMOTION ESTIMATION
###############################################################################
# Initialize face pose/age/gender/emotion estimation
if model_poseestimator is not None:
face_pose_estimator = FacePoseEstimator(
model=model_poseestimator, path=INPUT_DIR_MODEL_ESTIMATION)
if model_ageestimator is not None:
face_age_estimator = FaceAgeEstimator(model=model_ageestimator,
path=INPUT_DIR_MODEL_ESTIMATION)
if model_genderestimator is not None:
face_gender_estimator = FaceGenderEstimator(
model=model_genderestimator, path=INPUT_DIR_MODEL_ESTIMATION)
if model_emotionestimator is not None:
face_emotion_estimator = FaceEmotionEstimator(
model=model_emotionestimator, path=INPUT_DIR_MODEL_ESTIMATION)
(age, gender, emotion) = (None, None, None)
# Initialize fps counter
fps_frames = 0
fps_start = time()
while (True):
# Capture frame-by-frame
ret, frame = cap.read()
if ret == 0:
break
# Resize to QVGA so that RPI we can have acceptable fps
if out_resolution is not None:
frame = cv2.resize(frame, out_resolution)
###############################################################################
# FACE DETECTION
###############################################################################
# Detect faces and set bounding boxes
faces = face_detector.detect(frame)
for (index, face) in enumerate(faces):
(x, y, w, h) = face
###############################################################################
# FACE AGE/GENDER/EMOTION ESTIMATION
###############################################################################
face_image = frame[y:y + h, h:h + w]
if model_ageestimator is not None:
age = face_age_estimator.estimate(frame, face_image)
if model_genderestimator is not None:
gender = face_gender_estimator.estimate(frame, face_image)
if model_emotionestimator is not None:
emotion = face_emotion_estimator.estimate(frame, face_image)
###############################################################################
# FACE POSE ESTIMATION
###############################################################################
# Detect and draw face pose locations
if model_poseestimator is not None:
shape = face_pose_estimator.detect(frame, face)
face_pose_estimator.add_overlay(frame, shape)
else:
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 255),
1)
# Display age, gender, emotion
if age is not None and gender is not None and emotion is not None:
cv2.putText(frame, "Age: {}".format(age), (x, y - 45),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
cv2.putText(frame, "Gender: {}".format(gender), (x, y - 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
cv2.putText(frame, "Emotion: {}".format(emotion), (x, y - 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
# Display the resulting frame
cv2.imshow(WINDOW_NAME, frame)
# Update frame count
fps_frames += 1
if (framecount != 0 and fps_frames >= framecount):
break
# Check for user actions
keyPressed = cv2.waitKey(1) & 0xFF
if keyPressed == 27:
break
# Set the fps
fps = fps_frames / (time() - fps_start)
# Release the camera
cam_release(cap)
return fps
def process_facerecognition_livenessdetection_poseagegenderemotion(
cam_resolution,
out_resolution,
framecount,
image=None,
model_detector=0,
model_recognizer=0):
from libfaceid.liveness import FaceLivenessDetectorModels, FaceLiveness
from libfaceid.pose import FacePoseEstimatorModels, FacePoseEstimator
from libfaceid.age import FaceAgeEstimatorModels, FaceAgeEstimator
from libfaceid.gender import FaceGenderEstimatorModels, FaceGenderEstimator
from libfaceid.emotion import FaceEmotionEstimatorModels, FaceEmotionEstimator
model_poseestimator = FacePoseEstimatorModels.DEFAULT
model_ageestimator = FaceAgeEstimatorModels.DEFAULT
model_genderestimator = FaceGenderEstimatorModels.DEFAULT
model_emotionestimator = FaceEmotionEstimatorModels.DEFAULT
# Initialize the camera
if image is not None:
cap = cv2.VideoCapture(image)
else:
cap = cam_init(cam_resolution[0], cam_resolution[1])
###############################################################################
# FACE DETECTION
###############################################################################
# Initialize face detection
face_detector = FaceDetector(
model=model_detector,
path=INPUT_DIR_MODEL_DETECTION) #, optimize=True)
###############################################################################
# FACE RECOGNITION
###############################################################################
# Initialize face recognizer
face_encoder = FaceEncoder(model=model_recognizer,
path=INPUT_DIR_MODEL_ENCODING,
path_training=INPUT_DIR_MODEL_TRAINING,
training=False)
###############################################################################
# EYE BLINKING DETECTOR
###############################################################################
# Initialize detector for blinking eyes
face_liveness = FaceLiveness(model=FaceLivenessDetectorModels.EYEBLINKING,
path=INPUT_DIR_MODEL_ESTIMATION)
face_liveness.initialize()
(eye_counter, total_eye_blinks) = (0, 0)
###############################################################################
# FACE POSE/AGE/GENDER/EMOTION ESTIMATION
###############################################################################
# Initialize pose/age/gender/emotion estimation
if model_poseestimator is not None:
face_pose_estimator = FacePoseEstimator(
model=model_poseestimator, path=INPUT_DIR_MODEL_ESTIMATION)
if model_ageestimator is not None:
face_age_estimator = FaceAgeEstimator(model=model_ageestimator,
path=INPUT_DIR_MODEL_ESTIMATION)
if model_genderestimator is not None:
face_gender_estimator = FaceGenderEstimator(
model=model_genderestimator, path=INPUT_DIR_MODEL_ESTIMATION)
if model_emotionestimator is not None:
face_emotion_estimator = FaceEmotionEstimator(
model=model_emotionestimator, path=INPUT_DIR_MODEL_ESTIMATION)
(age, gender, emotion) = (None, None, None)
# Initialize fps counter
fps_frames = 0
fps_start = time()
fps = 0
saveVideo = False
out = None
while (True):
# Capture frame-by-frame
ret, frame = cap.read()
if ret == 0:
print("Unexpected error! " + image)
break
###############################################################################
# FACE DETECTION and FACE RECOGNITION
###############################################################################
# Detect and recognize each face in the images
# Resize to QVGA so that RPI we can have acceptable fps
if out_resolution is not None:
#frame = imutils.resize(frame, width=out_resolution[0])
(h, w) = image.shape[:2]
frame = cv2.resize(
frame,
(out_resolution[0], int(h * out_resolution[0] / float(w))))
###############################################################################
# FACE DETECTION
###############################################################################
faces = face_detector.detect(frame)
for (index, face) in enumerate(faces):
(x, y, w, h) = face
###############################################################################
# FACE AGE/GENDER/EMOTION ESTIMATION
###############################################################################
face_image = frame[y:y + h, h:h + w]
if model_ageestimator is not None:
age = face_age_estimator.estimate(frame, face_image)
if model_genderestimator is not None:
gender = face_gender_estimator.estimate(frame, face_image)
if model_emotionestimator is not None:
emotion = face_emotion_estimator.estimate(frame, face_image)
###############################################################################
# FACE RECOGNITION
###############################################################################
face_id, confidence = face_encoder.identify(frame, (x, y, w, h))
###############################################################################
# EYE BLINKING DETECTION
###############################################################################
total_eye_blinks, eye_counter = face_liveness.detect(
frame, (x, y, w, h), total_eye_blinks, eye_counter)
###############################################################################
# FACE POSE ESTIMATION
###############################################################################
# Detect and draw face pose locations
if model_poseestimator is not None:
shape = face_pose_estimator.detect(frame, face)
face_pose_estimator.add_overlay(frame, shape)
# Display name, age, gender, emotion
cv2.putText(frame, "Age: {}".format(age), (20, 60),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
cv2.putText(frame, "Gender: {}".format(gender), (20, 80),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
cv2.putText(frame, "Emotion: {}".format(emotion), (20, 100),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
cv2.putText(frame,
"Name: {} [{:.2f}%]".format(face_id, confidence),
(20, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255, 255, 255), 1, cv2.LINE_AA)
###############################################################################
# EYE BLINKING DETECTION
###############################################################################
cv2.putText(frame, "Blinks: {}".format(total_eye_blinks), (20, 40),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
# Update frame count
fps_frames += 1
if (framecount != 0 and fps_frames >= framecount):
break
if (fps_frames % 30 == 29):
fps = fps_frames / (time() - fps_start)
fps_frames = 0
fps_start = time()
cv2.putText(frame, "FPS {:.2f}".format(fps), (20, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
# Save the frame to a video
if saveVideo:
out.write(frame)
# Display the resulting frame
cv2.imshow(WINDOW_NAME, frame)
# Check for user actions
keyPressed = cv2.waitKey(1) & 0xFF
if keyPressed == 27: # ESC
break
elif keyPressed == 32: # Space
saveVideo, out = save_video(saveVideo, out, frame.shape[:2],
WINDOW_NAME + ".avi")
elif keyPressed == 13: # Enter
save_photo(
frame, WINDOW_NAME + "_" +
datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + ".jpg")
# Set the fps
time_diff = time() - fps_start
if time_diff:
fps = fps_frames / time_diff
if image is not None:
cv2.waitKey(3000)
if saveVideo == True:
out.release()
# Release the camera
cam_release(cap)
return fps
def process_facedetection(model_detector, model_poseestimator,
model_ageestimator, model_genderestimator,
model_emotionestimator, cam_resolution, cam_index):
# Initialize the camera
camera = cam_init(cam_index, cam_resolution[0], cam_resolution[1])
try:
# Initialize face detection
face_detector = FaceDetector(
model=model_detector,
path=INPUT_DIR_MODEL_DETECTION) #, optimize=True)
# Initialize face pose/age/gender estimation
face_pose_estimator = FacePoseEstimator(
model=model_poseestimator, path=INPUT_DIR_MODEL_ESTIMATION)
face_age_estimator = FaceAgeEstimator(model=model_ageestimator,
path=INPUT_DIR_MODEL_ESTIMATION)
face_gender_estimator = FaceGenderEstimator(
model=model_genderestimator, path=INPUT_DIR_MODEL_ESTIMATION)
face_emotion_estimator = FaceEmotionEstimator(
model=model_emotionestimator, path=INPUT_DIR_MODEL_ESTIMATION)
except:
print("Warning, check if models and trained dataset models exists!")
(age, gender, emotion) = (None, None, None)
while (True):
# Capture frame from webcam
ret, frame = camera.read()
if frame is None:
print("Error, check if camera is connected!")
break
# Detect and identify faces in the frame
faces = face_detector.detect(frame)
for (index, face) in enumerate(faces):
(x, y, w, h) = face
# Detect age, gender, emotion
face_image = frame[y:y + h, h:h + w]
age = face_age_estimator.estimate(frame, face_image)
gender = face_gender_estimator.estimate(frame, face_image)
emotion = face_emotion_estimator.estimate(frame, face_image)
# Detect and draw face pose locations
shape = face_pose_estimator.detect(frame, face)
face_pose_estimator.add_overlay(frame, shape)
# Display age, gender, emotion
cv2.putText(frame, "Age: {}".format(age), (x, y - 45),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
cv2.putText(frame, "Gender: {}".format(gender), (x, y - 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
cv2.putText(frame, "Emotion: {}".format(emotion), (x, y - 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1,
cv2.LINE_AA)
# Display the resulting frame
cv2.imshow(WINDOW_NAME, frame)
# Check for user actions
if cv2.waitKey(1) & 0xFF == 27: # ESC
break
# Release the camera
camera.release()
cv2.destroyAllWindows()
def process_facedetection():
cam_index = 0
cam_index = int(args.webcam)
# cam_index = 'tiv.mp4'
cam_resolution = RESOLUTION_VGA
# model_detector=FaceDetectorModels.HAARCASCADE
# model_detector=FaceDetectorModels.DLIBHOG
model_detector=FaceDetectorModels.DLIBCNN
# model_detector=FaceDetectorModels.SSDRESNET
# model_detector=FaceDetectorModels.MTCNN
# model_detector=FaceDetectorModels.FACENET
model_poseestimator=FacePoseEstimatorModels.DEFAULT
model_ageestimator=FaceAgeEstimatorModels.DEFAULT
model_genderestimator=FaceGenderEstimatorModels.DEFAULT
model_emotionestimator=FaceEmotionEstimatorModels.DEFAULT
# Initialize the camera
camera = cam_init(cam_index, cam_resolution[0], cam_resolution[1])
try:
# Initialize face detection
face_detector = FaceDetector(model=model_detector, path=INPUT_DIR_MODEL_DETECTION)#, optimize=True)
# Initialize face pose/age/gender estimation
face_pose_estimator = FacePoseEstimator(model=model_poseestimator, path=INPUT_DIR_MODEL_ESTIMATION)
face_age_estimator = FaceAgeEstimator(model=model_ageestimator, path=INPUT_DIR_MODEL_ESTIMATION)
face_gender_estimator = FaceGenderEstimator(model=model_genderestimator, path=INPUT_DIR_MODEL_ESTIMATION)
face_emotion_estimator = FaceEmotionEstimator(model=model_emotionestimator, path=INPUT_DIR_MODEL_ESTIMATION)
except:
print("Warning, check if models and trained dataset models exists!")
(age, gender, emotion) = (None, None, None)
df = pd.DataFrame(columns=['Gender'])
while (True):
# Capture frame from webcam
ret, frame = camera.read()
t1 = time.time() # current time
num_seconds = t1 - t0 # diff
if num_seconds > 30: # e.g. break after 30 seconds
break
if frame is None:
print("Error, check if camera is connected!")
break
# Detect and identify faces in the frame
faces = face_detector.detect(frame)
for (index, face) in enumerate(faces):
(x, y, w, h) = face
# Detect age, gender, emotion
face_image = frame[y:y+h, h:h+w]
age = face_age_estimator.estimate(frame, face_image)
gender = face_gender_estimator.estimate(frame, face_image)
emotion = face_emotion_estimator.estimate(frame, face_image)
# Detect and draw face pose locations
shape = face_pose_estimator.detect(frame, face)
face_pose_estimator.add_overlay(frame, shape)
# Display age, gender, emotion
cv2.putText(frame, "Age: {}".format(age),
(x, y-45), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)
cv2.putText(frame, "Gender: {}".format(gender),
(x, y-30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)
cv2.putText(frame, "Emotion: {}".format(emotion),
(x, y-15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)
df = df.append({'Gender':gender}, ignore_index=True)
#print(dfg)
# Display updated frame to web app
yield (b'--frame\r\nContent-Type: image/jpeg\r\n\r\n' + cv2.imencode('.jpg', frame)[1].tobytes() + b'\r\n\r\n')
df1 = df.groupby('Gender').size()
data = df1.values.tolist()
print(data)
# Release the camera
camera.release()
cv2.destroyAllWindows()
return data
