Spaces:
Running
Running
File size: 8,272 Bytes
90b4364 89047b5 90b4364 f240626 522af51 3903f4f ebc32f0 90b4364 4818b14 90b4364 89047b5 90b4364 522af51 7e6465f 522af51 ebc32f0 4818b14 ebc32f0 f240626 89047b5 302fc24 89047b5 f240626 3903f4f 4818b14 3903f4f 90b4364 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 |
import os
import gradio as gr
from censor import _CENSOR_MODELS, _DEFAULT_CENSOR_MODEL, _gr_detect_censors
from face import _FACE_MODELS, _DEFAULT_FACE_MODEL, _gr_detect_faces
from hand import _gr_detect_hands, _HAND_MODELS, _DEFAULT_HAND_MODEL
from head import _gr_detect_heads, _HEAD_MODELS, _DEFAULT_HEAD_MODEL
from manbits import _MANBIT_MODELS, _DEFAULT_MANBIT_MODEL, _gr_detect_manbits
from person import _PERSON_MODELS, _DEFAULT_PERSON_MODEL, _gr_detect_person
if __name__ == '__main__':
with gr.Blocks() as demo:
with gr.Tabs():
with gr.Tab('Face Detection'):
with gr.Row():
with gr.Column():
gr_face_input_image = gr.Image(type='pil', label='Original Image')
gr_face_model = gr.Dropdown(_FACE_MODELS, value=_DEFAULT_FACE_MODEL, label='Model')
gr_face_infer_size = gr.Slider(480, 960, value=640, step=32, label='Max Infer Size')
with gr.Row():
gr_face_iou_threshold = gr.Slider(0.0, 1.0, 0.7, label='IOU Threshold')
gr_face_score_threshold = gr.Slider(0.0, 1.0, 0.25, label='Score Threshold')
gr_face_submit = gr.Button(value='Submit', variant='primary')
with gr.Column():
gr_face_output_image = gr.Image(type='pil', label="Labeled")
gr_face_submit.click(
_gr_detect_faces,
inputs=[
gr_face_input_image, gr_face_model,
gr_face_infer_size, gr_face_score_threshold, gr_face_iou_threshold,
],
outputs=[gr_face_output_image],
)
with gr.Tab('Head Detection'):
with gr.Row():
with gr.Column():
gr_head_input_image = gr.Image(type='pil', label='Original Image')
gr_head_model = gr.Dropdown(_HEAD_MODELS, value=_DEFAULT_HEAD_MODEL, label='Model')
gr_head_infer_size = gr.Slider(480, 960, value=640, step=32, label='Max Infer Size')
with gr.Row():
gr_head_iou_threshold = gr.Slider(0.0, 1.0, 0.7, label='IOU Threshold')
gr_head_score_threshold = gr.Slider(0.0, 1.0, 0.3, label='Score Threshold')
gr_head_submit = gr.Button(value='Submit', variant='primary')
with gr.Column():
gr_head_output_image = gr.Image(type='pil', label="Labeled")
gr_head_submit.click(
_gr_detect_heads,
inputs=[
gr_head_input_image, gr_head_model,
gr_head_infer_size, gr_head_score_threshold, gr_head_iou_threshold,
],
outputs=[gr_head_output_image],
)
with gr.Tab('Person Detection'):
with gr.Row():
with gr.Column():
gr_person_input_image = gr.Image(type='pil', label='Original Image')
gr_person_model = gr.Dropdown(_PERSON_MODELS, value=_DEFAULT_PERSON_MODEL, label='Model')
gr_person_infer_size = gr.Slider(480, 960, value=640, step=32, label='Max Infer Size')
with gr.Row():
gr_person_iou_threshold = gr.Slider(0.0, 1.0, 0.5, label='IOU Threshold')
gr_person_score_threshold = gr.Slider(0.0, 1.0, 0.3, label='Score Threshold')
gr_person_submit = gr.Button(value='Submit', variant='primary')
with gr.Column():
gr_person_output_image = gr.Image(type='pil', label="Labeled")
gr_person_submit.click(
_gr_detect_person,
inputs=[
gr_person_input_image, gr_person_model,
gr_person_infer_size, gr_person_score_threshold, gr_person_iou_threshold,
],
outputs=[gr_person_output_image],
)
with gr.Tab('Hand Detection'):
with gr.Row():
with gr.Column():
gr_hand_input_image = gr.Image(type='pil', label='Original Image')
gr_hand_model = gr.Dropdown(_HAND_MODELS, value=_DEFAULT_HAND_MODEL, label='Model')
gr_hand_infer_size = gr.Slider(480, 960, value=640, step=32, label='Max Infer Size')
with gr.Row():
gr_hand_iou_threshold = gr.Slider(0.0, 1.0, 0.7, label='IOU Threshold')
gr_hand_score_threshold = gr.Slider(0.0, 1.0, 0.35, label='Score Threshold')
gr_hand_submit = gr.Button(value='Submit', variant='primary')
with gr.Column():
gr_hand_output_image = gr.Image(type='pil', label="Labeled")
gr_hand_submit.click(
_gr_detect_hands,
inputs=[
gr_hand_input_image, gr_hand_model,
gr_hand_infer_size, gr_hand_score_threshold, gr_hand_iou_threshold,
],
outputs=[gr_hand_output_image],
)
with gr.Tab('Censor Point Detection'):
with gr.Row():
with gr.Column():
gr_censor_input_image = gr.Image(type='pil', label='Original Image')
gr_censor_model = gr.Dropdown(_CENSOR_MODELS, value=_DEFAULT_CENSOR_MODEL, label='Model')
gr_censor_infer_size = gr.Slider(480, 960, value=640, step=32, label='Max Infer Size')
with gr.Row():
gr_censor_iou_threshold = gr.Slider(0.0, 1.0, 0.5, label='IOU Threshold')
gr_censor_score_threshold = gr.Slider(0.0, 1.0, 0.25, label='Score Threshold')
gr_censor_submit = gr.Button(value='Submit', variant='primary')
with gr.Column():
gr_censor_output_image = gr.Image(type='pil', label="Labeled")
gr_censor_submit.click(
_gr_detect_censors,
inputs=[
gr_censor_input_image, gr_censor_model,
gr_censor_infer_size, gr_censor_score_threshold, gr_censor_iou_threshold,
],
outputs=[gr_censor_output_image],
)
with gr.Tab('Manbits Detection\n(Deprecated)'):
with gr.Row():
with gr.Column():
gr_manbit_input_image = gr.Image(type='pil', label='Original Image')
gr_manbit_model = gr.Dropdown(_MANBIT_MODELS, value=_DEFAULT_MANBIT_MODEL, label='Model')
gr_manbit_infer_size = gr.Slider(480, 960, value=640, step=32, label='Max Infer Size')
with gr.Row():
gr_manbit_iou_threshold = gr.Slider(0.0, 1.0, 0.7, label='IOU Threshold')
gr_manbit_score_threshold = gr.Slider(0.0, 1.0, 0.25, label='Score Threshold')
gr_manbit_submit = gr.Button(value='Submit', variant='primary')
with gr.Column():
gr_manbit_output_image = gr.Image(type='pil', label="Labeled")
gr_manbit_submit.click(
_gr_detect_manbits,
inputs=[
gr_manbit_input_image, gr_manbit_model,
gr_manbit_infer_size, gr_manbit_score_threshold, gr_manbit_iou_threshold,
],
outputs=[gr_manbit_output_image],
)
demo.queue(os.cpu_count()).launch()
|