import numpy as np
import mediapipe as mp
import supervision as sv
from PIL import Image

model = mp.solutions.face_mesh.FaceMesh()

edge_annotator = sv.EdgeAnnotator(color=sv.Color.BLACK, thickness=2)

image = Image.open(<PATH_TO_IMAGE>).convert('RGB')
results = model.process(np.array(image))
key_points = sv.KeyPoints.from_mediapipe(results, resolution_wh=image.size)

annotated_image = edge_annotator.annotate(scene=image, key_points=key_points)

import roboflow
from roboflow import Roboflow
import supervision as sv

roboflow.login()
rf = Roboflow()

project = rf.workspace(<WORKSPACE_ID>).project(<PROJECT_ID>)
dataset = project.version(<PROJECT_VERSION>).download("coco")

ds_train = sv.DetectionDataset.from_coco(
    images_directory_path=f"{dataset.location}/train",
    annotations_path=f"{dataset.location}/train/_annotations.coco.json",
)

path, image, annotation = ds_train[0]
    # loads image on demand

for path, image, annotation in ds_train:
    # loads image on demand