def __init__(self, token, ip):
self.ip = ip
# TODO: Set this threshold to whatever you want
self.temperature_threshold_C = 38.5 # In degrees C
# Initialize the trueface SDK using hte lite model
# We will be using facial recognition to ensure we only send 1 notification per identity
# So that if a sick person stands infront of the camera, we only send 1 notification and not countless
options = tfsdk.ConfigurationOptions()
options.fr_model = tfsdk.FACIALRECOGNITIONMODEL.LITE
self.sdk = tfsdk.SDK(options)
is_valid = self.sdk.set_license(token)
if (is_valid == False):
print("Invalid License Provided")
quit()
self.faceprint = {}
# Set up email stuff
# For this demo, we will be using the gmail smtp server
self.port = 465 # For SSL
self.smtp_server = "smtp.gmail.com"
# TODO: Enter the sender gmail account
# "Allow less secure apps" must be turned ON https://myaccount.google.com/lesssecureapps
self.sender_email = "*****@*****.**"
# TODO: Enter the recipient address
self.receiver_email = "*****@*****.**"
# TODO: Enter your gmail account password (corresponding to the sender_email account)
self.password = "******"
def __init__(self, token, ip):
self.ip = ip
# TODO: Set this threshold to whatever you want
self.temperature_threshold_C = 38.5 # In degrees C
# Initialize the trueface SDK using hte lite model
# We will be using facial recognition to ensure we only send 1 notification per identity
# So that if a sick person stands infront of the camera, we only send 1 notification and not countless
options = tfsdk.ConfigurationOptions()
options.fr_model = tfsdk.FACIALRECOGNITIONMODEL.LITE
self.sdk = tfsdk.SDK(options)
is_valid = self.sdk.set_license(token)
if (is_valid == False):
print("Invalid License Provided")
quit()
self.faceprint = {}
# Set up text message stuff
# Your Account Sid and Auth Token from twilio.com/console
# DANGER! This is insecure. See http://twil.io/secure
account_sid = 'ACe275517637075b7fb777f44f7b549efc'
auth_token = 'your_auth_token'
self.client = Client(account_sid, auth_token)
self.from_number = '+15017122661'
self.to_number = '+15558675310'
def usage():
print("Usage: {} <collection folder name> <input video filename>".format(
sys.argv[0]))
if len(sys.argv) != 3:
usage()
sys.exit(1)
collection_folder = sys.argv[1]
filepath = sys.argv[2]
if filepath.isdigit():
filepath = int(filepath)
options = tfsdk.ConfigurationOptions()
# Can set configuration options here
# ex:
#options.smallest_face_height = -1
options.fd_mode = tfsdk.FACEDETECTIONMODE.VERSATILE
# options.fd_filter = tfsdk.FACEDETECTIONFILTER.BALANCED
options.fr_model = tfsdk.FACIALRECOGNITIONMODEL.FULL
options.models_path = '/home/seelaman/Workspace/trueface/trueface.base/tfsdk_python3.7.gpu/trueface_sdk'
options.dbms = tfsdk.DATABASEMANAGEMENTSYSTEM.SQLITE
# Use the accurate object detector
#options.obj_model = tfsdk.OBJECTDETECTIONMODEL.ACCURATE
sdk = tfsdk.SDK(options)
# TODO: replace the string with your license code.
def identifyTemplate(self, templateQueue, token):
# Initialize the trueface SDK
# Be sure to use the same SDK options as the camera
# Since we will be using the GET /fr-template-lite endpoint, we will initialize the SDK using the lite model
options = tfsdk.ConfigurationOptions()
options.fr_model = tfsdk.FACIALRECOGNITIONMODEL.LITE
options.dbms = tfsdk.DATABASEMANAGEMENTSYSTEM.NONE # We will NOT be writing template to disk for this demo.
options.smallest_face_height = -1 # https://reference.trueface.ai/cpp/dev/latest/py/general.html#tfsdk.ConfigurationOptions
sdk = tfsdk.SDK(options)
is_valid = sdk.set_license(token)
if (is_valid == False):
print("Invalid License Provided")
quit()
# At this point, we can either load an existing trueface database & collection, or create a new one and populate it with data
# For the sake of this demo, we will create a new collection (memory only, meaning data will not persist after app shuts down)
# If we are using a database backend, need to first call create_database_connection
# Since we are using DATABASEMANAGEMENTSYSTEM.NONE this is not necessary
# For more aobut 1 to N, please refer to: https://reference.trueface.ai/cpp/dev/latest/usage/identification.html
# Create a new collection
res = sdk.create_load_collection("my_collection")
if (res != tfsdk.ERRORCODE.NO_ERROR):
print("Unable to create collection")
quit()
# Some identities to populate into our collection
# TODO: Can add images of yourself here
image_identities = [
("../../cpp_sdk/images/armstrong/armstrong1.jpg", "Armstrong"),
("../../cpp_sdk/images/armstrong/armstrong2.jpg", "Armstrong"), # Can add the same identity more than once
("../../cpp_sdk/images/obama/obama1.jpg", "Obama")
]
# Generate templates, enroll in our collection
for path, identity in image_identities:
print("Processing image:", path, "with identity:", identity)
# Generate a template for each image
res = sdk.set_image(path)
if (res != tfsdk.ERRORCODE.NO_ERROR):
print("Unable to set image at path:", path)
continue
# Detect the largest face in the image
found, faceBoxAndLandmarks = sdk.detect_largest_face()
if found == False:
print("No face detected in image:", path)
continue
# We want to only enroll high quality images into the database / collection
# Therefore, ensure that the face height is at least 100px
faceHeight = faceBoxAndLandmarks.bottom_right.y - faceBoxAndLandmarks.top_left.y
print("Face height:", faceHeight, "pixels")
if faceHeight < 100:
print("The face is too small in the image for a high quality enrollment.")
continue
# Get the aligned chip so we can compute the image quality
face = sdk.extract_aligned_face(faceBoxAndLandmarks)
# Compute the image quality score
res, quality = sdk.estimate_face_image_quality(face)
if (res != tfsdk.ERRORCODE.NO_ERROR):
print("There was an error computing the image quality")
continue
# Ensure the image quality is above a threshold (TODO: adjust this threshold based on your use case).
print("Face quality:", quality)
if quality < 0.8:
print("The image quality is too poor for enrollment")
continue
# As a final check, we can check the orientation of the head and ensure that it is facing forward
# To see the effect of yaw and pitch on match score, refer to: https://reference.trueface.ai/cpp/dev/latest/py/face.html#tfsdk.SDK.estimate_head_orientation
res, yaw, pitch, roll = sdk.estimate_head_orientation(faceBoxAndLandmarks)
if (res != tfsdk.ERRORCODE.NO_ERROR):
print("Unable to compute head orientation")
print("Head orientation, Yaw:", yaw * 180 / 3.14, ", Pitch:", pitch * 180 / 3.14, ", Roll:", roll * 180 / 3.14, "degrees")
# TODO: Can filter out images with extreme yaw and pitch here
# Now that we have confirmed the images are high quality, generate a template from that image
res, faceprint = sdk.get_face_feature_vector(face)
if (res != tfsdk.ERRORCODE.NO_ERROR):
print("There was an error generating the faceprint")
continue
# Enroll the feature vector into the collection
res, UUID = sdk.enroll_template(faceprint, identity)
if (res != tfsdk.ERRORCODE.NO_ERROR):
print("Unable to enroll feature vector")
continue
# TODO: Can store the UUID for later use
print("Success, enrolled template with UUID:", UUID)
print("--------------------------------------------")
while True:
# TODO: If the queue get's too backed up, can remove some items without processing
template = templateQueue.get()
errorcode, probe_faceprint = tfsdk.SDK.json_to_faceprint(template)
if (errorcode != tfsdk.ERRORCODE.NO_ERROR):
print("There was an error decoding the json to a faceprint")
continue
# Now run 1 to N identification
ret_code, found, candidate = sdk.identify_top_candidate(probe_faceprint)
if ret_code != tfsdk.ERRORCODE.NO_ERROR:
print("Something went wrong!")
elif found == True:
print("Found match with identity:", candidate.identity)
print("Match probability:", candidate.match_probability)
else:
print("Unable to find match")
print(" ")
def __init__(self, token, ip):
self.ip = ip
# Initialize the trueface SDK
# Be sure to use the same SDK options as the camera
# Since we will be using the GET /fr-template-lite endpoint, we will initialize the SDK using the lite model
options = tfsdk.ConfigurationOptions()
options.fr_model = tfsdk.FACIALRECOGNITIONMODEL.LITE
options.dbms = tfsdk.DATABASEMANAGEMENTSYSTEM.NONE # We will NOT be writing template to disk for this demo.
options.smallest_face_height = -1 # https://reference.trueface.ai/cpp/dev/latest/py/general.html#tfsdk.ConfigurationOptions
self.sdk = tfsdk.SDK(options)
is_valid = self.sdk.set_license(token)
if (is_valid == False):
print("Invalid License Provided")
quit()
# At this point, we can either load an existing trueface database & collection, or create a new one and populate it with data
# For the sake of this demo, we will create a new collection (memory only, meaning data will not persist after app shuts down)
# If we are using a database backend, need to first call create_database_connection
# Since we are using DATABASEMANAGEMENTSYSTEM.NONE this is not necessary
# For more aobut 1 to N, please refer to: https://reference.trueface.ai/cpp/dev/latest/usage/identification.html
# Create a new collection
res = self.sdk.create_load_collection("my_collection")
if (res != tfsdk.ERRORCODE.NO_ERROR):
print("Unable to create collection")
quit()
# Some identities to populate into our collection
# TODO: Can add images of yourself here
image_identities = [
("../../cpp_sdk/images/armstrong/armstrong1.jpg", "Armstrong"),
("../../cpp_sdk/images/armstrong/armstrong2.jpg",
"Armstrong"), # Can add the same identity more than once
("../../cpp_sdk/images/obama/obama1.jpg", "Obama")
]
# Generate templates, enroll in our collection
for path, identity in image_identities:
# Generate a template for each image
res = self.sdk.set_image(path)
if (res != tfsdk.ERRORCODE.NO_ERROR):
print("Unable to set image at path:", path)
quit()
# Extract the feature vector
res, v = self.sdk.get_largest_face_feature_vector()
if (res != tfsdk.ERRORCODE.NO_ERROR):
print("Unable to generate feature vector")
quit()
# Enroll the feature vector into the collection
res, UUID = self.sdk.enroll_template(v, identity)
if (res != tfsdk.ERRORCODE.NO_ERROR):
print("Unable to enroll feature vector")
quit()
# TODO: Can store the UUID for later use
print("Success, enrolled template with UUID:", UUID)