change app.py

app.py

@@ -6,28 +6,28 @@ import cv2
 import gradio as gr
 import torch
 import math
-import spaces
+# import spaces
 from huggingface_hub import hf_hub_download
 try:
     import mmpose
 except:
-    os.system('pip install /home/user/app/main/transformer_utils')
-hf_hub_download(repo_id="caizhongang/SMPLer-X", filename="smpler_x_h32.pth.tar", local_dir="/home/user/app/pretrained_models")
-os.system('cp -rf /home/user/app/assets/conversions.py /home/user/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torchgeometry/core/conversions.py')
-DEFAULT_MODEL='smpler_x_h32'
-OUT_FOLDER = '/home/user/app/demo_out'
+    os.system('pip install /Volumes/zzz/smplerx2/main/transformer_utils')
+# hf_hub_download(repo_id="caizhongang/SMPLer-X", filename="smpler_x_h32.pth.tar", local_dir="/home/user/app/pretrained_models")
+#os.system('cp -rf /home/user/app/assets/conversions.py /home/user/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torchgeometry/core/conversions.py')
+DEFAULT_MODEL='smpler_x_s32'
+OUT_FOLDER = '/home/ztx/Downloads/smplerx2/output'
 os.makedirs(OUT_FOLDER, exist_ok=True)
 num_gpus = 1 if torch.cuda.is_available() else -1
-print("!!!", torch.cuda.is_available())      
-print(torch.cuda.device_count())   
-print(torch.version.cuda)  
-index = torch.cuda.current_device()
-print(index)  
-print(torch.cuda.get_device_name(index))
+print("!!!", torch.cuda.is_available())
+print(torch.cuda.device_count())
+print(torch.version.cuda)
+#index = torch.cuda.current_device()
+#print(index)
+#print(torch.cuda.get_device_name(index))
 from main.inference import Inferer
 inferer = Inferer(DEFAULT_MODEL, num_gpus, OUT_FOLDER)
 
-@spaces.GPU(enable_queue=True, duration=300)
+# @spaces.GPU(enable_queue=True, duration=300)
 def infer(video_input, in_threshold=0.5, num_people="Single person", render_mesh=False):
     # from main.inference import Inferer
     # inferer = Inferer(DEFAULT_MODEL, num_gpus, OUT_FOLDER)
@@ -68,24 +68,24 @@ def infer(video_input, in_threshold=0.5, num_people="Single person", render_mesh
     yield img, video_path, save_mesh_file, save_smplx_file
 
 TITLE = '''<h1 align="center">SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation</h1>'''
-VIDEO = '''
-<center><iframe width="960" height="540" 
-src="https://www.youtube.com/embed/DepTqbPpVzY?si=qSeQuX-bgm_rON7E"title="SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen>
-</iframe>
-</center><br>'''
-DESCRIPTION = '''
-<b>Official Gradio demo</b> for <a href="https://caizhongang.com/projects/SMPLer-X/"><b>SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation</b></a>.<br>
-<p>
-Note: You can drop a video at the panel (or select one of the examples) 
-to obtain the 3D parametric reconstructions of the detected humans.
-</p>
-'''
+# VIDEO = '''
+# <center><iframe width="960" height="540"
+# src="https://www.youtube.com/embed/DepTqbPpVzY?si=qSeQuX-bgm_rON7E"title="SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen>
+# </iframe>
+# </center><br>'''
+# DESCRIPTION = '''
+# <b>Official Gradio demo</b> for <a href="https://caizhongang.com/projects/SMPLer-X/"><b>SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation</b></a>.<br>
+# <p>
+# Note: You can drop a video at the panel (or select one of the examples)
+# to obtain the 3D parametric reconstructions of the detected humans.
+# </p>
+# '''
 
 with gr.Blocks(title="SMPLer-X", css=".gradio-container") as demo:
 
     gr.Markdown(TITLE)
-    gr.HTML(VIDEO)
-    gr.Markdown(DESCRIPTION)
+    # gr.HTML(VIDEO)
+    # gr.Markdown(DESCRIPTION)
 
     with gr.Row():
         with gr.Column():
@@ -101,7 +101,7 @@ with gr.Blocks(title="SMPLer-X", css=".gradio-container") as demo:
                 scale=1,)
             gr.HTML("""<br/>""")
             mesh_as_vertices = gr.Checkbox(
-                label="Render as mesh", 
+                label="Render as mesh",
                 info="By default, the estimated SMPL-X parameters are rendered as vertices for faster visualization. Check this option if you want to visualize meshes instead.",
                 interactive=True,
                 scale=1,)
@@ -119,18 +119,18 @@ with gr.Blocks(title="SMPLer-X", css=".gradio-container") as demo:
     # example_images = gr.Examples([])
     send_button.click(fn=infer, inputs=[video_input, threshold, num_people, mesh_as_vertices], outputs=[processed_frames, video_output, meshes_output, smplx_output])
     # with gr.Row():
-    example_videos = gr.Examples([
-        ['/home/user/app/assets/01.mp4'], 
-        ['/home/user/app/assets/02.mp4'], 
-        ['/home/user/app/assets/03.mp4'],
-        ['/home/user/app/assets/04.mp4'],
-        ['/home/user/app/assets/05.mp4'], 
-        ['/home/user/app/assets/06.mp4'], 
-        ['/home/user/app/assets/07.mp4'],
-        ['/home/user/app/assets/08.mp4'],
-        ['/home/user/app/assets/09.mp4'],
-        ], 
-        inputs=[video_input, 0.5])
+    # example_videos = gr.Examples([
+    #     ['/home/user/app/assets/01.mp4'],
+    #     ['/home/user/app/assets/02.mp4'],
+    #     ['/home/user/app/assets/03.mp4'],
+    #     ['/home/user/app/assets/04.mp4'],
+    #     ['/home/user/app/assets/05.mp4'],
+    #     ['/home/user/app/assets/06.mp4'],
+    #     ['/home/user/app/assets/07.mp4'],
+    #     ['/home/user/app/assets/08.mp4'],
+    #     ['/home/user/app/assets/09.mp4'],
+    #     ],
+    #     inputs=[video_input, 0.5])
 
 #demo.queue()
 demo.queue().launch(debug=True)