File size: 4,262 Bytes
5d333f5 82b445a ca30ad2 82b445a ca30ad2 82b445a 5d333f5 ca30ad2 5d333f5 |
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 |
from mtcnn.mtcnn import MTCNN
from utils import *
face_detector = MTCNN()
# Description
# title = r"""
#
#align="center">IDM-VTON + Outfit Anyone in the Wild
# """
# description = r"""
# This demo combines <b>IDM-VTON </b> and <b>Outfit Anyone in the Wild </b>
# 1. Human pose detection and reconstruction using large human model from Outfit Anyone in the Wild.
# 2. Use IDM-VTON for training-free try-on.
# 3. Applying the refine network from Outfit Anyone in the Wild.
# """
css = """
.gradio-container {width: 85% !important}
"""
def onClick(cloth_image, pose_image, category,
caption, request: gr.Request):
if pose_image is None:
yield None, f"no user image found !"
return None, "no user image found !"
elif cloth_image is None:
yield None, f"no cloth image found !"
return None, "no cloth image found !"
try:
faces = face_detector.detect_faces(pose_image[:,:,::-1])
if len(faces)==0:
print(client_ip, 'faces num is 0! ', flush=True)
yield None, "Fatal Error !!! No face detected in pose image !!! "
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:
yield None, "Fatal Error !!! Headshot is not allowed in pose image!!!"
return None, "Fatal Error !!! Headshot is not allowed in pose image!!!"
uploads = upload_imgs(ApiUrl, UploadToken, cloth_image, pose_image)
if uploads is None:
yield None, "fail to upload"
return None, "fail to upload"
infId = publicFastSwap(ApiUrl, OpenId, ApiKey, uploads, category, caption)
if not infId:
yield None, "fail to public you task"
return None, "fail to public you task"
max_try = 30
wait_s = 3
yield None, "start to process, please wait..."
for i in range(max_try):
time.sleep(wait_s)
taskStatus = getTaskRes(ApiUrl, infId)
if taskStatus is None: continue
status = taskStatus['status']
if status in ['FAILED', 'CANCELLED', 'TIMED_OUT', ]:
yield None, f"task failed, query {i}, status {status}"
return None, f"task failed, query {i}, status {status}"
elif status in ['IN_QUEUE', 'IN_PROGRESS', 'IN_QUEUE', ]:
pass
yield None, f"task is on processing, query {i}, status {status}, please do not exit !!!"
elif status=='COMPLETED':
out = taskStatus['output']['job_results']['output1']
yield out, f"task is COMPLETED"
return out, f"{i} task COMPLETED"
yield None, "fail to query task.."
return None, "fail to query task.."
except Exception as e:
print(e)
return None, "fail to create task"
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")
with gr.Column():
with gr.Column():
pose_image = gr.Image(value=None, type="numpy", label="user photo")
with gr.Column():
with gr.Column():
category = gr.Dropdown(value="upper_cloth", choices=["upper_cloth",
"lower_cloth", "full_body", "dresses"], interactive=True)
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")
res_image = gr.Image(label="result image", value=None, type="filepath")
run_button.click(fn=onClick, inputs=[cloth_image, pose_image,
category, caption, ],
outputs=[res_image, info_text, ])
if __name__ == "__main__":
demo.queue(max_size=50)
demo.launch(server_name='0.0.0.0')
|