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narugo1992
commited on
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•
89047b5
1
Parent(s):
17d3ab1
dev(narugo): add censor point detection
Browse files
app.py
CHANGED
@@ -2,6 +2,7 @@ import os
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import gradio as gr
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from face import _FACE_MODELS, _DEFAULT_FACE_MODEL, _gr_detect_faces
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from head import _gr_detect_heads, _HEAD_MODELS, _DEFAULT_HEAD_MODEL
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from manbits import _MANBIT_MODELS, _DEFAULT_MANBIT_MODEL, _gr_detect_manbits
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@@ -18,7 +19,7 @@ if __name__ == '__main__':
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gr_face_infer_size = gr.Slider(480, 960, value=640, step=32, label='Max Infer Size')
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with gr.Row():
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gr_face_iou_threshold = gr.Slider(0.0, 1.0, 0.7, label='IOU Threshold')
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gr_face_score_threshold = gr.Slider(0.0, 1.0, 0.
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gr_face_submit = gr.Button(value='Submit', variant='primary')
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@@ -82,6 +83,30 @@ if __name__ == '__main__':
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outputs=[gr_person_output_image],
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)
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with gr.Tab('Manbits Detection'):
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with gr.Row():
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with gr.Column():
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import gradio as gr
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from censor import _CENSOR_MODELS, _DEFAULT_CENSOR_MODEL, _gr_detect_censors
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from face import _FACE_MODELS, _DEFAULT_FACE_MODEL, _gr_detect_faces
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from head import _gr_detect_heads, _HEAD_MODELS, _DEFAULT_HEAD_MODEL
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from manbits import _MANBIT_MODELS, _DEFAULT_MANBIT_MODEL, _gr_detect_manbits
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gr_face_infer_size = gr.Slider(480, 960, value=640, step=32, label='Max Infer Size')
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with gr.Row():
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gr_face_iou_threshold = gr.Slider(0.0, 1.0, 0.7, label='IOU Threshold')
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gr_face_score_threshold = gr.Slider(0.0, 1.0, 0.25, label='Score Threshold')
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gr_face_submit = gr.Button(value='Submit', variant='primary')
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outputs=[gr_person_output_image],
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)
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with gr.Tab('Censor Point Detection'):
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with gr.Row():
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with gr.Column():
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gr_censor_input_image = gr.Image(type='pil', label='Original Image')
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gr_censor_model = gr.Dropdown(_CENSOR_MODELS, value=_DEFAULT_CENSOR_MODEL, label='Model')
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gr_censor_infer_size = gr.Slider(480, 960, value=640, step=32, label='Max Infer Size')
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with gr.Row():
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gr_censor_iou_threshold = gr.Slider(0.0, 1.0, 0.5, label='IOU Threshold')
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gr_censor_score_threshold = gr.Slider(0.0, 1.0, 0.35, label='Score Threshold')
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gr_censor_submit = gr.Button(value='Submit', variant='primary')
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with gr.Column():
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gr_censor_output_image = gr.Image(type='pil', label="Labeled")
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gr_censor_submit.click(
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_gr_detect_censors,
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inputs=[
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gr_censor_input_image, gr_censor_model,
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gr_censor_infer_size, gr_censor_score_threshold, gr_censor_iou_threshold,
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],
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outputs=[gr_censor_output_image],
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)
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with gr.Tab('Manbits Detection'):
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with gr.Row():
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with gr.Column():
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censor.py
ADDED
@@ -0,0 +1,42 @@
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from functools import lru_cache
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from typing import List, Tuple
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from huggingface_hub import hf_hub_download
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from imgutils.data import ImageTyping, load_image, rgb_encode
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from onnx_ import _open_onnx_model
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from plot import detection_visualize
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from yolo_ import _image_preprocess, _data_postprocess
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_CENSOR_MODELS = [
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'censor_detect_v0.7_s',
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]
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_DEFAULT_CENSOR_MODEL = _CENSOR_MODELS[0]
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@lru_cache()
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def _open_censor_detect_model(model_name):
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return _open_onnx_model(hf_hub_download(
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f'deepghs/anime_censor_detection',
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f'{model_name}/model.onnx'
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))
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_LABELS = ['nipple_f', 'penis', 'pussy']
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def detect_censors(image: ImageTyping, model_name: str, max_infer_size=640,
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conf_threshold: float = 0.35, iou_threshold: float = 0.5) \
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-> List[Tuple[Tuple[int, int, int, int], str, float]]:
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image = load_image(image, mode='RGB')
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new_image, old_size, new_size = _image_preprocess(image, max_infer_size)
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data = rgb_encode(new_image)[None, ...]
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output, = _open_censor_detect_model(model_name).run(['output0'], {'images': data})
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return _data_postprocess(output[0], conf_threshold, iou_threshold, old_size, new_size, _LABELS)
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def _gr_detect_censors(image: ImageTyping, model_name: str, max_infer_size=640,
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conf_threshold: float = 0.35, iou_threshold: float = 0.5):
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ret = detect_censors(image, model_name, max_infer_size, conf_threshold, iou_threshold)
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return detection_visualize(image, ret, _LABELS)
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face.py
CHANGED
@@ -26,7 +26,7 @@ _DEFAULT_FACE_MODEL = _FACE_MODELS[0]
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def _open_face_detect_model(model_name):
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return _open_onnx_model(hf_hub_download(
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f'deepghs/anime_face_detection',
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f'{model_name}/model.onnx'
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))
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@@ -34,7 +34,7 @@ _LABELS = ['face']
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def detect_faces(image: ImageTyping, model_name: str, max_infer_size=640,
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conf_threshold: float = 0.
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-> List[Tuple[Tuple[int, int, int, int], str, float]]:
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image = load_image(image, mode='RGB')
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new_image, old_size, new_size = _image_preprocess(image, max_infer_size)
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@@ -45,6 +45,6 @@ def detect_faces(image: ImageTyping, model_name: str, max_infer_size=640,
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def _gr_detect_faces(image: ImageTyping, model_name: str, max_infer_size=640,
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conf_threshold: float = 0.
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ret = detect_faces(image, model_name, max_infer_size, conf_threshold, iou_threshold)
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return detection_visualize(image, ret, _LABELS)
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def _open_face_detect_model(model_name):
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return _open_onnx_model(hf_hub_download(
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f'deepghs/anime_face_detection',
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f'{model_name}/model.onnx',
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))
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def detect_faces(image: ImageTyping, model_name: str, max_infer_size=640,
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conf_threshold: float = 0.25, iou_threshold: float = 0.7) \
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-> List[Tuple[Tuple[int, int, int, int], str, float]]:
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image = load_image(image, mode='RGB')
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new_image, old_size, new_size = _image_preprocess(image, max_infer_size)
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def _gr_detect_faces(image: ImageTyping, model_name: str, max_infer_size=640,
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conf_threshold: float = 0.25, iou_threshold: float = 0.7):
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ret = detect_faces(image, model_name, max_infer_size, conf_threshold, iou_threshold)
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return detection_visualize(image, ret, _LABELS)
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