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import gradio as gr
# necessary packages
from facemesh import mp_face_mesh_fn
from facedetect import mp_face_detect_fn
from handposedetect import mp_hand_pose_detect_fn
from objectron3d import mp_objectron_fn
from posestimate import mp_pose_estimation_fn
from holistic import mp_holistic_fn
from selfiseg import mp_selfi_segment_fn
def run_mediapipe(image, soln_type, min_detection_confidence):
if soln_type == 'facemesh':
annotated_image = mp_face_mesh_fn(image, min_detect_conf=min_detection_confidence)
elif soln_type == 'facedetect':
annotated_image = mp_face_detect_fn(image, min_detect_conf=min_detection_confidence)
elif soln_type == 'handpose':
annotated_image = mp_hand_pose_detect_fn(image, min_detect_conf=min_detection_confidence)
elif soln_type == 'pose estimate':
annotated_image = mp_pose_estimation_fn(image, min_detect_conf=min_detection_confidence)
elif soln_type == 'holistic':
annotated_image = mp_holistic_fn(image, min_detect_conf=min_detection_confidence)
elif soln_type == 'objectron':
annotated_image = mp_objectron_fn(image, min_detect_conf=min_detection_confidence)
elif soln_type == 'selfie segment':
annotated_image = mp_selfi_segment_fn(image)
return annotated_image
def main():
solutions = [
'facedetect',
'facemesh',
'handpose',
'pose estimate',
'holistic',
'objectron',
'selfie segment'
]
sample_images = [
["examples/0.jpg", solutions[0], 0.5],
["examples/1.jpg", solutions[1], 0.5],
["examples/2.png", solutions[2], 0.5],
["examples/3.jpg", solutions[3], 0.5],
["examples/4.jpg", solutions[4], 0.5],
["examples/5.jpg", solutions[5], 0.3],
["examples/6.jpg", solutions[6], None],
]
iface = gr.Interface(
fn=run_mediapipe,
inputs=[
gr.inputs.Image(label="Input Image"),
gr.inputs.Radio(
solutions,
type='value',
default=solutions[0],
label='Solutions'
),
gr.inputs.Slider(
0,
1,
step=0.05,
default=0.5,
label='Minimum Detection Confidence'
)
],
outputs=gr.outputs.Image(label="MediaPipe"),
title="Google MediaPipe Demo",
description="Few solutions of <a href='https://mediapipe.dev/'>Google MediaPipe</a> are presented here. Note, to test `Objectron` model, make sure to ensure <a href='https://github.com/google/mediapipe/blob/master/mediapipe/python/solutions/objectron.py#L128-L133'>Model Type</a>. Currently it only supports `Shoe`, `Chair`, `Cup` and `Camera` object.",
examples=sample_images
)
iface.launch()
if __name__ == '__main__':
main()
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