Hand Pose Estimation

Python: Hand Pose Detection

The hand pose detection flow comprises two models: a hand detection model based on YOLOX and a 3D hand pose detection model released by Google this November. Thanks to FeiGeChuanShu (opens in a new tab) for the effort in early model conversion.

This hand pose flow can be used in AR games, hand gesture control, and many cool DIY projects.

Source code:

import cv2
import json
from daisykit.utils import get_asset_file, to_py_type
from daisykit import HandPoseDetectorFlow
config = {
    "hand_detection_model": {
        "model": get_asset_file("models/hand_pose/yolox_hand_swish.param"),
        "weights": get_asset_file("models/hand_pose/yolox_hand_swish.bin"),
        "input_width": 256,
        "input_height": 256,
        "score_threshold": 0.45,
        "iou_threshold": 0.65,
        "use_gpu": False
    "hand_pose_model": {
        "model": get_asset_file("models/hand_pose/hand_lite-op.param"),
        "weights": get_asset_file("models/hand_pose/hand_lite-op.bin"),
        "input_size": 224,
        "use_gpu": False
flow = HandPoseDetectorFlow(json.dumps(config))
# Open video stream from webcam
vid = cv2.VideoCapture(0)
    # Capture the video frame
    ret, frame =
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    poses = flow.Process(frame)
    flow.DrawResult(frame, poses)
    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
    # Convert poses to Python list of dict
    poses = to_py_type(poses)
    # Display the result frame
    cv2.imshow('frame', frame)
    # Press 'q' to exit
    if cv2.waitKey(1) & 0xFF == ord('q'):

In the above source code, input_width and input_height of the hand_detection_model can be adjusted for speed/accuracy trade-off.