fix minor issue when changing to python 3.9

app.py

@@ -1,134 +1,134 @@
-import os
-import sys
-import os.path as osp
-from pathlib import Path
-import cv2
-import gradio as gr
-import torch
-import math
-import spaces
-
-try:
-    import mmpose
-except:
-    os.system('pip install /home/user/app/main/transformer_utils')
-
-os.system('cp -rf /home/user/app/assets/conversions.py /home/user/.pyenv/versions/3.8.18/lib/python3.8/site-packages/torchgeometry/core/conversions.py')
-DEFAULT_MODEL='smpler_x_h32'
-OUT_FOLDER = '/home/user/app/demo_out'
-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))
-from main.inference import Inferer
-inferer = Inferer(DEFAULT_MODEL, num_gpus, OUT_FOLDER)
-
-@spaces.GPU(enable_queue=True)
-def infer(video_input, in_threshold=0.5, num_people="Single person", render_mesh=False):
-    os.system(f'rm -rf {OUT_FOLDER}/*')
-    multi_person = False if (num_people == "Single person") else True
-    cap = cv2.VideoCapture(video_input)
-    fps = math.ceil(cap.get(5))
-    width = int(cap.get(3))
-    height = int(cap.get(4))
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    video_path = osp.join(OUT_FOLDER, f'out.m4v')
-    final_video_path = osp.join(OUT_FOLDER, f'out.mp4')
-    video_output = cv2.VideoWriter(video_path, fourcc, fps, (width, height))
-    success = 1
-    frame = 0
-    while success:
-        success, original_img = cap.read()
-        if not success:
-            break
-        frame += 1
-        img, mesh_paths, smplx_paths = inferer.infer(original_img, in_threshold, frame, multi_person, not(render_mesh))
-        video_output.write(img)
-        yield img, None, None, None
-    cap.release()
-    video_output.release()
-    cv2.destroyAllWindows()
-    os.system(f'ffmpeg -i {video_path} -c copy {final_video_path}')
-
-    #Compress mesh and smplx files
-    save_path_mesh = os.path.join(OUT_FOLDER, 'mesh')
-    save_mesh_file = os.path.join(OUT_FOLDER, 'mesh.zip')
-    os.makedirs(save_path_mesh, exist_ok= True)
-    save_path_smplx = os.path.join(OUT_FOLDER, 'smplx')
-    save_smplx_file = os.path.join(OUT_FOLDER, 'smplx.zip')
-    os.makedirs(save_path_smplx, exist_ok= True)
-    os.system(f'zip -r {save_mesh_file} {save_path_mesh}')
-    os.system(f'zip -r {save_smplx_file} {save_path_smplx}')
-    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>
-'''
-
-with gr.Blocks(title="SMPLer-X", css=".gradio-container") as demo:
-
-    gr.Markdown(TITLE)
-    gr.HTML(VIDEO)
-    gr.Markdown(DESCRIPTION)
-
-    with gr.Row():
-        with gr.Column():
-            video_input = gr.Video(label="Input video", elem_classes="video")
-            threshold = gr.Slider(0, 1.0, value=0.5, label='BBox detection threshold')
-        with gr.Column(scale=2):
-            num_people = gr.Radio(
-                choices=["Single person", "Multiple people"],
-                value="Single person",
-                label="Number of people",
-                info="Choose how many people are there in the video. Choose 'single person' for faster inference.",
-                interactive=True,
-                scale=1,)
-            gr.HTML("""<br/>""")
-            mesh_as_vertices = gr.Checkbox(
-                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,)
-
-            send_button = gr.Button("Infer")
-    gr.HTML("""<br/>""")
-
-    with gr.Row():
-        with gr.Column():
-            processed_frames = gr.Image(label="Last processed frame")
-            video_output = gr.Video(elem_classes="video")
-        with gr.Column():
-            meshes_output = gr.File(label="3D meshes")
-            smplx_output = gr.File(label= "SMPL-X models")
-    # 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])
-
-#demo.queue()
-demo.queue().launch(debug=True)
+import os
+import sys
+import os.path as osp
+from pathlib import Path
+import cv2
+import gradio as gr
+import torch
+import math
+import spaces
+
+try:
+    import mmpose
+except:
+    os.system('pip install /home/user/app/main/transformer_utils')
+
+os.system('cp -rf /home/user/app/assets/conversions.py /home/user/.pyenv/versions/3.9.18/lib/python3.9/site-packages/torchgeometry/core/conversions.py')
+DEFAULT_MODEL='smpler_x_h32'
+OUT_FOLDER = '/home/user/app/demo_out'
+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))
+from main.inference import Inferer
+inferer = Inferer(DEFAULT_MODEL, num_gpus, OUT_FOLDER)
+
+@spaces.GPU(enable_queue=True)
+def infer(video_input, in_threshold=0.5, num_people="Single person", render_mesh=False):
+    os.system(f'rm -rf {OUT_FOLDER}/*')
+    multi_person = False if (num_people == "Single person") else True
+    cap = cv2.VideoCapture(video_input)
+    fps = math.ceil(cap.get(5))
+    width = int(cap.get(3))
+    height = int(cap.get(4))
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    video_path = osp.join(OUT_FOLDER, f'out.m4v')
+    final_video_path = osp.join(OUT_FOLDER, f'out.mp4')
+    video_output = cv2.VideoWriter(video_path, fourcc, fps, (width, height))
+    success = 1
+    frame = 0
+    while success:
+        success, original_img = cap.read()
+        if not success:
+            break
+        frame += 1
+        img, mesh_paths, smplx_paths = inferer.infer(original_img, in_threshold, frame, multi_person, not(render_mesh))
+        video_output.write(img)
+        yield img, None, None, None
+    cap.release()
+    video_output.release()
+    cv2.destroyAllWindows()
+    os.system(f'ffmpeg -i {video_path} -c copy {final_video_path}')
+
+    #Compress mesh and smplx files
+    save_path_mesh = os.path.join(OUT_FOLDER, 'mesh')
+    save_mesh_file = os.path.join(OUT_FOLDER, 'mesh.zip')
+    os.makedirs(save_path_mesh, exist_ok= True)
+    save_path_smplx = os.path.join(OUT_FOLDER, 'smplx')
+    save_smplx_file = os.path.join(OUT_FOLDER, 'smplx.zip')
+    os.makedirs(save_path_smplx, exist_ok= True)
+    os.system(f'zip -r {save_mesh_file} {save_path_mesh}')
+    os.system(f'zip -r {save_smplx_file} {save_path_smplx}')
+    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>
+'''
+
+with gr.Blocks(title="SMPLer-X", css=".gradio-container") as demo:
+
+    gr.Markdown(TITLE)
+    gr.HTML(VIDEO)
+    gr.Markdown(DESCRIPTION)
+
+    with gr.Row():
+        with gr.Column():
+            video_input = gr.Video(label="Input video", elem_classes="video")
+            threshold = gr.Slider(0, 1.0, value=0.5, label='BBox detection threshold')
+        with gr.Column(scale=2):
+            num_people = gr.Radio(
+                choices=["Single person", "Multiple people"],
+                value="Single person",
+                label="Number of people",
+                info="Choose how many people are there in the video. Choose 'single person' for faster inference.",
+                interactive=True,
+                scale=1,)
+            gr.HTML("""<br/>""")
+            mesh_as_vertices = gr.Checkbox(
+                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,)
+
+            send_button = gr.Button("Infer")
+    gr.HTML("""<br/>""")
+
+    with gr.Row():
+        with gr.Column():
+            processed_frames = gr.Image(label="Last processed frame")
+            video_output = gr.Video(elem_classes="video")
+        with gr.Column():
+            meshes_output = gr.File(label="3D meshes")
+            smplx_output = gr.File(label= "SMPL-X models")
+    # 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])
+
+#demo.queue()
+demo.queue().launch(debug=True)