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Start the Redis server:
Make sure the dependencies for the face recognition service are installed in your virtual environment:
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pip install social-interaction-cloud[face-recognition] |
Use the following command to start the face recognition service, and pass the model files (WIP: find sourcethe cascade classifier file used in this example can be found here: haarcascade_frontalface_default.xml, and the resnet50 model file can be found here resnet50_ft_weight.pt):
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run-face-recognition --model resnet50_ft_weight.pt --cascadefile haarcascade_frontalface_default.xml |
Create a new file with the code below or use https://bitbucket.org/socialroboticshub/framework/src/master/sic_framework/tests/ demo_desktop_camera_facerecognition.py from GitHub.
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| title | Imports and callbacks |
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|
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| import queue
import cv2
from sic_framework.core.message_python2 import BoundingBoxesMessage
from sic_framework.core.message_python2 import CompressedImageMessage
from sic_framework.core.utils_cv2 import draw_on_image
from sic_framework.devices.desktop.desktop_camera import DesktopCamera
from sic_framework.services.face_recognition_dnn.face_recognition_service import DNNFaceRecognition
imgs_buffer = queue.Queue()
def on_image(image_message: CompressedImageMessage):
try:
imgs_buffer.get_nowait() # remove previous message if its still there
except queue.Empty:
pass
imgs_buffer.put(image_message.image)
faces_buffer = queue.Queue()
def on_faces(message: BoundingBoxesMessage):
try:
faces_buffer.get_nowait() # remove previous message if its still there
except queue.Empty:
pass
faces_buffer.put(message.bboxes)
# code continues below |
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