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from mtcnn.mtcnn import MTCNN
from utils import *
cloth_examples = get_cloth_examples()
pose_examples = get_pose_examples()
face_detector = MTCNN()
# Description
title = r"""
<h1 align="center">IDM-VTON + Outfit Anyone in the Wild </h1>
"""
description = r"""
This demo combines <a href='https://github.com/yisol/IDM-VTON' target='_blank'><b>IDM-VTON </b></a> and <a href='https://github.com/selfitcamera/Outfit-Anyone-in-the-Wild' target='_blank'><b>Outfit Anyone in the Wild </b></a>
1. Human body detection and reconstruction using large human models from Outfit Anyone in the Wild.
2. Use IDM-VTON for single-picture clothing change without training.
3. Fix discordant parts of your image using the refine network from Outfit Anyone in the Wild.
This demo is for learning purposes only.<br>
<b>IDM-VTON + Outfit Anyone in the Wild test results on </b> <a href='https://heybeautify.online/ClothData/Publics/Shows/shows/IDM_OutfitAnyone/IDM_OutfitAnyone.html' target='_blank'><b>mix01</b></a>.<br>
<b>Outfit Anyone in the Wild test results on </b> <a href='https://heybeautify.online/ClothData/Publics/Shows/shows/man_v2/man_v2.html' target='_blank'><b>man01</b></a>.<br>
<b>Outfit Anyone in the Wild test results on </b> <a href='https://heybeautify.online/ClothData/Publics/Shows/shows/cider/cider_0403.html' target='_blank'><b>woman01</b></a>.<br>
"""
css = """
.gradio-container {width: 85% !important}
"""
mk_guide = "If image does not display successfully after button clicked in your browser(mostly Mac+Chrome), try [this demo](https://openxlab.org.cn/apps/detail/jiangxiaoguo/OutfitAnyone-in-the-Wild) please"
def onUpload():
return ""
def onClick(cloth_image, cloth_id, pose_image, pose_id, category,
denoise_steps, caption, request: gr.Request):
if pose_image is None:
return None, "no pose image found !", ""
if isinstance(cloth_id, dict):
cloth_id = cloth_id['label']
if isinstance(pose_id, dict):
pose_id = pose_id['label']
if len(pose_id)>0:
res = get_result_example(cloth_id, pose_id)
assert os.path.exists(res), res
return res, "Done! Use the pre-run results directly, the cloth size does not take effect ", ""
else:
try:
client_ip = request.client.host
x_forwarded_for = dict(request.headers).get('x-forwarded-for')
if x_forwarded_for:
client_ip = x_forwarded_for
faces = face_detector.detect_faces(pose_image[:,:,::-1])
if len(faces)==0:
print(client_ip, 'faces num is 0! ', flush=True)
return None, "Fatal Error !!! No face detected in pose image !!! ", ""
else:
x, y, w, h = faces[0]["box"]
H, W = pose_image.shape[:2]
max_face_ratio = 1/3.3
if w/W>max_face_ratio or h/H>max_face_ratio:
return None, "Fatal Error !!! Headshot is not allowed in pose image!!!", ""
if not check_warp(client_ip):
return None, "Failed !!! Our server is under maintenance, please try again tomorrow", ""
infId = upload_imgs(ApiUrl, OpenId, ApiKey, client_ip, cloth_image, pose_image)
if infId==0:
return None, "fail to upload", ""
elif infId==2:
return None, "There is a running task already, please wait and check the history tab. Please remember to give us a star on github, thx~", ""
elif infId==3:
return None, "can not creat task, you have exhausted free trial quota", ""
isPub = publicFastSwap(ApiUrl, OpenId, ApiKey, infId, category,
caption, denoise_steps)
if not isPub:
return None, "fail to public you task", ""
info = "task has been created successfully, you can refresh the page 1~3 mins latter, and check the following history tab"
info = info+"任务创建成功,请1-3分钟后刷新这个页面,历史结果会显示在下面的标签页"
return None, info, ""
except Exception as e:
print(e)
return None, "fail to create task", ""
def onLoad(request: gr.Request):
client_ip = request.client.host
x_forwarded_for = dict(request.headers).get('x-forwarded-for')
if x_forwarded_for:
client_ip = x_forwarded_for
his_datas = [None for _ in range(10)]
info = ''
try:
infs = getAllFastInfs(ApiUrl, OpenId, ApiKey, client_ip)
print(client_ip, 'history infs: ', len(infs))
cnt = 0
finish_n, fail_n, queue_n = 0, 0, 0
for i, inf in enumerate(infs):
if inf['state']==2:
if cnt>4: continue
pose, res = inf['pose'], inf['res']
his_datas[cnt*2] = f"<img src=\"{pose}\" >"
his_datas[cnt*2+1] = f"<img src=\"{res}\" >"
finish_n += 1
cnt += 1
elif inf['state'] in [-1, -2, 0]:
fail_n += 1
elif inf['state'] in [1]:
queue_n += 1
info = f"{client_ip}, you have {finish_n} successed tasks, {queue_n} running tasks, {fail_n} failed tasks."
if fail_n>0:
info = info+" Please upload a half/full-body human image, not just a clothing image!!!"
if queue_n>0:
position = inf['position']
info = info+" Wait for 3~10 mins and refresh this page, successed results will display in the history tab at the bottom. "
info = info+f" your task position in queue is {position}. "
info = info+f" 任务正在排队,队列位置 {position}. "
time.sleep(3)
except Exception as e:
print(e)
his_datas = his_datas + [info]
return his_datas
with gr.Blocks(css=css) as demo:
# description
gr.Markdown(title)
gr.Markdown(description)
with gr.Row():
with gr.Column():
with gr.Column():
cloth_image = gr.Image(value=None, type="numpy", label="")
cloth_id = gr.Label(value=cloth_examples[0][0], label="Clothing Image", visible=False)
example = gr.Examples(inputs=[cloth_id, cloth_image],
examples_per_page=3,
examples = cloth_examples)
with gr.Column():
with gr.Column():
pose_image = gr.Image(value=None, type="numpy", label="")
pose_id = gr.Label(value=pose_examples[0][0], label="Pose Image", visible=False)
example_pose = gr.Examples(inputs=[pose_id, pose_image],
examples_per_page=3,
examples=pose_examples)
with gr.Column():
with gr.Column():
category = gr.Dropdown(value="upper_cloth", choices=["upper_cloth",
"lower_cloth", "full_body", "dresses"], interactive=True)
denoise_steps = gr.Slider(20, 30, value=20, interactive=True, label="denoise_steps")
caption = gr.Textbox(value="", interactive=True, label='cloth caption')
info_text = gr.Textbox(value="", interactive=False, label='runtime information')
run_button = gr.Button(value="Run")
init_res = get_result_example(cloth_examples[0][0], pose_examples[0][0])
res_image = gr.Image(label="result image", value=None, type="filepath")
MK01 = gr.Markdown()
with gr.Tab('history'):
with gr.Row():
MK02 = gr.Markdown()
with gr.Row():
his_pose_image1 = gr.HTML()
his_res_image1 = gr.HTML()
with gr.Row():
his_pose_image2 = gr.HTML()
his_res_image2 = gr.HTML()
with gr.Row():
his_pose_image3 = gr.HTML()
his_res_image3 = gr.HTML()
with gr.Row():
his_pose_image4 = gr.HTML()
his_res_image4 = gr.HTML()
with gr.Row():
his_pose_image5 = gr.HTML()
his_res_image5 = gr.HTML()
run_button.click(fn=onClick, inputs=[cloth_image, cloth_id, pose_image,
pose_id, category, denoise_steps, caption, ],
outputs=[res_image, info_text, MK01])
pose_image.upload(fn=onUpload, inputs=[], outputs=[pose_id],)
cloth_image.upload(fn=onUpload, inputs=[], outputs=[cloth_id],)
demo.load(onLoad, inputs=[], outputs=[his_pose_image1, his_res_image1,
his_pose_image2, his_res_image2, his_pose_image3, his_res_image3,
his_pose_image4, his_res_image4, his_pose_image5, his_res_image5,
MK02])
if __name__ == "__main__":
demo.queue(max_size=50)
# demo.launch(server_name='0.0.0.0', server_port=225)
demo.launch(server_name='0.0.0.0')
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