Python: Face Detection

Face detector flow in Daisykit contains a face detection model based on YOLO Fastest (opens in a new tab) and a facial landmark detection model based on PFLD (opens in a new tab). In addition, to encourage makers to join hands in the fighting with COVID-19, we selected a face detection model that can recognize people wearing face masks or not.

Let’s see into the source code below to understand how to run this model with your webcam. First, we initialize the flow with a config dictionary. It contains information about the models used in the flow. get_asset_file() function will automatically download the models and weights files from https://github.com/nrl-ai/daisykit-assets (opens in a new tab), so you don’t have to care about downloading them manually. The downloading only happens the first time we use the models. After that, you can run this code offline. You also can download all files yourself and put the paths to file in instead of get_asset_file(<assets address>). If you don’t need facial landmark output, set with_landmark to False.

import cv2
import json
from daisykit.utils import get_asset_file, to_py_type
import daisykit
 
config = {
    "face_detection_model": {
        "model": get_asset_file("models/face_detection/yolo_fastest_with_mask/yolo-fastest-opt.param"),
        "weights": get_asset_file("models/face_detection/yolo_fastest_with_mask/yolo-fastest-opt.bin"),
        "input_width": 320,
        "input_height": 320,
        "score_threshold": 0.7,
        "iou_threshold": 0.5,
        "use_gpu": False
    },
    "with_landmark": True,
    "facial_landmark_model": {
        "model": get_asset_file("models/facial_landmark/pfld-sim.param"),
        "weights": get_asset_file("models/facial_landmark/pfld-sim.bin"),
        "input_width": 112,
        "input_height": 112,
        "use_gpu": False
    }
}
 
face_detector_flow = daisykit.FaceDetectorFlow(json.dumps(config))
 
# Open video stream from webcam
vid = cv2.VideoCapture(0)
 
while(True):
 
    # Capture the video frame
    ret, frame = vid.read()
 
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 
    faces = face_detector_flow.Process(frame)
    # for face in faces:
    #     print([face.x, face.y, face.w, face.h,
    #           face.confidence, face.wearing_mask_prob])
    face_detector_flow.DrawResult(frame, faces)
 
    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
 
    # Convert faces to Python list of dict
    faces = to_py_type(faces)
 
    # Display the resulting frame
    cv2.imshow('frame', frame)
 
    # The 'q' button is set as the
    # quitting button you may use any
    # desired button of your choice
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
 
# After the loop release the cap object
vid.release()
# Destroy all the windows
cv2.destroyAllWindows()

All flows in Daisykit are initialized by a configuration string. Therefore we use json.dumps() to convert the config dictionary to a string. For example, in face detector flow:

face_detector_flow = daisykit.FaceDetectorFlow(json.dumps(config))

Run the flow to get detected faces by:

faces = face_detector_flow.Process(frame)

You can use the DrawResult() method to visualize the result or write a drawing function yourself. This AI flow can be used in DIY projects such as smart COVID-19 camera (opens in a new tab) or Snap-chat-like camera decorators.

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