DaisyKit Python

https://pypi.org/project/daisykit/

Deploy AI Systems Yourself (DAISY) Kit. DaisyKit Python is the wrapper of DaisyKit SDK, an AI framework focusing on the ease of deployment. This package only supports Ubuntu Linux - Python 3 now. We will add support for other platforms and models in the future.

Install and run example

Install dependencies. Below commands are for Ubuntu. You can try other installation methods based on your OS.

sudo apt install pybind11-dev # Pybind11 - For Python/C++ Wrapper
sudo apt install libopencv-dev # For OpenCV
sudo apt install libvulkan-dev # Optional - For GPU support

Install DaisyKit

pip3 install --upgrade pip # Ensure pip is updated
pip3 install daisykit

Face Detection with mask recognition:

import cv2
import json
from daisykit.utils import get_asset_file
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)

    # 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()

Background Matting:

import cv2
import json
from daisykit.utils import get_asset_file
from daisykit import BackgroundMattingFlow

config = {
    "background_matting_model": {
        "model": get_asset_file("models/background_matting/erd/erdnet.param"),
        "weights": get_asset_file("models/background_matting/erd/erdnet.bin")
    }
}

# Load background
default_bg_file = get_asset_file("images/background.jpg")
background = cv2.imread(default_bg_file)
background = cv2.cvtColor(background, cv2.COLOR_BGR2RGB)

background_matting_flow = BackgroundMattingFlow(json.dumps(config), background)

# 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)

    mask = background_matting_flow.Process(frame)
    background_matting_flow.DrawResult(frame, mask)

    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)

    # 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()

Human Pose Detection:

import cv2
import json
from daisykit.utils import get_asset_file
from daisykit import HumanPoseMoveNetFlow

config = {
  "person_detection_model": {
    "model": get_asset_file("models/human_detection/ssd_mobilenetv2.param"),
    "weights": get_asset_file("models/human_detection/ssd_mobilenetv2.bin")
  },
  "human_pose_model": {
    "model": get_asset_file("models/human_pose_detection/movenet/lightning.param"),
    "weights": get_asset_file("models/human_pose_detection/movenet/lightning.bin"),
    "input_width": 192,
    "input_height": 192
  }
}

human_pose_flow = HumanPoseMoveNetFlow(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)

    poses = human_pose_flow.Process(frame)
    human_pose_flow.DrawResult(frame, poses)

    print(poses)

    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)

    # 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()

Barcode Detection:

import cv2
import json
from daisykit.utils import get_asset_file
from daisykit import BarcodeScannerFlow

config = {
  "try_harder": True,
  "try_rotate": True
}

barcode_scanner_flow = BarcodeScannerFlow(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)

    result = barcode_scanner_flow.Process(frame, draw=True)

    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)

    # 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()

Build Python package

Build environment: Ubuntu.

sudo apt install ninja-build
python3 -m pip install --user --upgrade twine

Build package:

python3 setup.py sdist

or

bash scripts/build_python.sh

Upload to Pypi (for DaisyKit authors only)

twine upload dist/*

TODO

• Multiplatform build.