Spaces:
Running
Running
File size: 4,083 Bytes
6ec8547 a5c1f85 6ec8547 a5c1f85 6ec8547 a5c1f85 6ec8547 a5c1f85 6ec8547 a5c1f85 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
#!/usr/bin/env python
from __future__ import annotations
import os
import pathlib
import shlex
import subprocess
import tarfile
if os.environ.get('SYSTEM') == 'spaces':
subprocess.call(shlex.split('pip uninstall -y opencv-python'))
subprocess.call(shlex.split('pip uninstall -y opencv-python-headless'))
subprocess.call(
shlex.split('pip install opencv-python-headless==4.5.5.64'))
import gradio as gr
import huggingface_hub
import mediapipe as mp
import numpy as np
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_face_mesh = mp.solutions.face_mesh
TITLE = 'MediaPipe Face Mesh'
DESCRIPTION = 'https://google.github.io/mediapipe/'
HF_TOKEN = os.getenv('HF_TOKEN')
def load_sample_images() -> list[pathlib.Path]:
image_dir = pathlib.Path('images')
if not image_dir.exists():
image_dir.mkdir()
dataset_repo = 'hysts/input-images'
filenames = ['001.tar', '005.tar']
for name in filenames:
path = huggingface_hub.hf_hub_download(dataset_repo,
name,
repo_type='dataset',
use_auth_token=HF_TOKEN)
with tarfile.open(path) as f:
f.extractall(image_dir.as_posix())
return sorted(image_dir.rglob('*.jpg'))
def run(
image: np.ndarray,
max_num_faces: int,
min_detection_confidence: float,
show_tesselation: bool,
show_contours: bool,
show_irises: bool,
) -> np.ndarray:
with mp_face_mesh.FaceMesh(
static_image_mode=True,
max_num_faces=max_num_faces,
refine_landmarks=True,
min_detection_confidence=min_detection_confidence) as face_mesh:
results = face_mesh.process(image)
res = image[:, :, ::-1].copy()
if results.multi_face_landmarks is not None:
for face_landmarks in results.multi_face_landmarks:
if show_tesselation:
mp_drawing.draw_landmarks(
image=res,
landmark_list=face_landmarks,
connections=mp_face_mesh.FACEMESH_TESSELATION,
landmark_drawing_spec=None,
connection_drawing_spec=mp_drawing_styles.
get_default_face_mesh_tesselation_style())
if show_contours:
mp_drawing.draw_landmarks(
image=res,
landmark_list=face_landmarks,
connections=mp_face_mesh.FACEMESH_CONTOURS,
landmark_drawing_spec=None,
connection_drawing_spec=mp_drawing_styles.
get_default_face_mesh_contours_style())
if show_irises:
mp_drawing.draw_landmarks(
image=res,
landmark_list=face_landmarks,
connections=mp_face_mesh.FACEMESH_IRISES,
landmark_drawing_spec=None,
connection_drawing_spec=mp_drawing_styles.
get_default_face_mesh_iris_connections_style())
return res[:, :, ::-1]
image_paths = load_sample_images()
examples = [[path.as_posix(), 5, 0.5, True, True, True]
for path in image_paths]
gr.Interface(
fn=run,
inputs=[
gr.Image(label='Input', type='numpy'),
gr.Slider(label='Max Number of Faces',
minimum=0,
maximum=10,
step=1,
value=5),
gr.Slider(label='Minimum Detection Confidence',
minimum=0,
maximum=1,
step=0.05,
value=0.5),
gr.Checkbox(label='Show Tesselation', value=True),
gr.Checkbox(label='Show Contours', value=True),
gr.Checkbox(label='Show Irises', value=True),
],
outputs=gr.Image(label='Output', type='numpy'),
examples=examples,
title=TITLE,
description=DESCRIPTION,
).launch(show_api=False)
|