Skip to main content

Packaged version of the Yolov5 facial landmark detector

Project description

pip install y5facegg

Use from Python

Usage
import cv2
from y5facegg import Y5FACE

# set model params
model_path = "y5facegg/weights/yolov5s-face.pt"
device = "cuda:0" # or "cpu"

# init yolov5 model
model = Y5FACE(model_path, device)

# load an image
image_path = 'https://github.com/ultralytics/yolov5/blob/master/data/images/bus.jpg'

# perform inference
bgr_image = cv2.imread(image_path)
res_img = model.predict(bgr_image)
cv2.imwrite('result.jpg', res_img)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

y5facegg-0.0.2-py36.py37.py38-none-any.whl (93.5 kB view details)

Uploaded Python 3.6 Python 3.7 Python 3.8

File details

Details for the file y5facegg-0.0.2-py36.py37.py38-none-any.whl.

File metadata

  • Download URL: y5facegg-0.0.2-py36.py37.py38-none-any.whl
  • Upload date:
  • Size: 93.5 kB
  • Tags: Python 3.6, Python 3.7, Python 3.8
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.8.2 requests/2.24.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.6.13

File hashes

Hashes for y5facegg-0.0.2-py36.py37.py38-none-any.whl
Algorithm Hash digest
SHA256 aa15b6f4e502801f446155a5b5fa4c72479ab00a648868e8e7f075a7001d2e50
MD5 c3f9ef40ca44b3aab60568c5239a8ddf
BLAKE2b-256 0028eb1c0f262f8e734ee0bde4bc6255c2bd98508b7266c71e8407676f0d02ea

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page