import gradio as gr
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.cv.image_utils import draw_face_detection_no_lm_result
from modelscope.preprocessors.image import LoadImage
from PIL import Image
import cv2
import numpy as np
###########################################
# gradio demo app 推断入口
###########################################
# gradio app demo 算法运行函数
def inference(input_file):
mog_face_detection_func = pipeline(Tasks.face_detection, 'damo/cv_resnet101_face-detection_cvpr22papermogface')
src_img_path = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/mog_face_detection.jpg'
raw_result = mog_face_detection_func(src_img_path)
print('face detection output: {}.'.format(raw_result))
# load image from url as rgb order
src_img = LoadImage.convert_to_ndarray(src_img_path)
# save src image as bgr order to local
src_img = cv2.cvtColor(np.asarray(src_img), cv2.COLOR_RGB2BGR)
cv2.imwrite('src_img.jpg', src_img)
# draw dst image from local src image as bgr order
dst_img = draw_face_detection_no_lm_result('src_img.jpg', raw_result)
# convert to rgb order
dst_img = cv2.cvtColor(np.asarray(dst_img), cv2.COLOR_BGR2RGB)
return dst_img
# gradio app 环境参数
css_style = "#fixed_size_img {height: 240px;} " \
"#overview {margin: auto;max-width: 600px; max-height: 400px;}"
title = "AI人脸检测应用"
###########################################
# gradio demo app
###########################################
with gr.Blocks(title=title, css=css_style) as demo:
gr.HTML('''
style="
display: inline-flex;
align-items: center;
gap: 0.8rem;
font-size: 1.75rem;
"
>
AI人脸检测应用
''')
with gr.Row():
img_input = gr.Image(type="pil", elem_id="fixed_size_img")
img_output = gr.Image(type="pil", elem_id="fixed_size_img")
with gr.Row():
btn_submit = gr.Button(value="一键生成", elem_id="blue_btn")
examples =跟单网gendan5.com [['https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/mog_face_detection.jpg']]
examples = gr.Examples(examples=examples, inputs=img_input, outputs=img_output, label="点击如下示例试玩", run_on_click=True)
btn_submit.click(inference, inputs=[img_input], outputs=img_output)
# btn_clear清除画布
if __name__ == "__main__":
demo.launch(share=True)