separate data and visualization

app.py

@@ -1,127 +1,141 @@
 data_path = "./data/"
 
 import pandas as pd
+import datasets
 
 # load the csv into motion_capture_data
-
 import streamlit as st
-st.title("CyberOrigin Data Visualization")
-dataset_option = st.sidebar.selectbox(
-    'Select a dataset:',
-    ['Fold_towels', 'Pipette', 'Take_the_item', 'Twist_the_tube']
-)
-motion_capture_path = data_path+dataset_option +"/motionCaptureData.csv"
-video_path = data_path+dataset_option+"/video.mp4"
-motion_capture_data = pd.read_csv(motion_capture_path)
-# create a streamlit app that displays the motion capture data
-# and the video data
-st.video(video_path)
+dataset_names = ['Fold_towels', 'Pipette', 'Take_the_item', 'Twist_the_tube']
 
-body_part_names = ['Left Shoulder',
- 'Right Upper Arm',
- 'Left Lower Leg',
- 'Spine1',
- 'Right Upper Leg',
- 'Spine3',
- 'Right Lower Arm',
- 'Left Foot',
- 'Right Lower Leg',
- 'Right Shoulder',
- 'Left Hand',
- 'Left Upper Leg',
- 'Right Foot',
- 'Spine',
- 'Spine2',
- 'Left Lower Arm',
- 'Left Toe',
- 'Neck',
- 'Right Hand',
- 'Right Toe',
- 'Head',
- 'Left Upper Arm',
- 'Hips',]
+def load_data():
+    print("Loading data")
+    # load the motion capture data
+    all_datasets = {}
+    for name in dataset_names:
+        print("Loading dataset: ", name)
+        all_datasets[name] = pd.DataFrame(datasets.load_dataset("cyberorigin/"+name)['train'])
+    return all_datasets
 
-motion_capture_x = motion_capture_data[[body_part_name+"_x" for body_part_name in body_part_names]]
-motion_capture_y = motion_capture_data[[body_part_name+"_y" for body_part_name in body_part_names]]
-motion_capture_z = motion_capture_data[[body_part_name+"_z" for body_part_name in body_part_names]]
+@st.fragment
+def visualize(data):
+    dataset_option = st.selectbox(
+        'Select a dataset:',
+        dataset_names
+    )
+    # create a streamlit app that displays the motion capture data
+    # and the video data
+    st.video("https://huggingface.co/datasets/cyberorigin/"+dataset_option+"/resolve/main/Video/video.mp4")
+    motion_capture_data = data[dataset_option]
+    body_part_names = ['Left Shoulder',
+    'Right Upper Arm',
+    'Left Lower Leg',
+    'Spine1',
+    'Right Upper Leg',
+    'Spine3',
+    'Right Lower Arm',
+    'Left Foot',
+    'Right Lower Leg',
+    'Right Shoulder',
+    'Left Hand',
+    'Left Upper Leg',
+    'Right Foot',
+    'Spine',
+    'Spine2',
+    'Left Lower Arm',
+    'Left Toe',
+    'Neck',
+    'Right Hand',
+    'Right Toe',
+    'Head',
+    'Left Upper Arm',
+    'Hips',]
 
-import plotly.graph_objects as go
-import numpy as np
+    motion_capture_x = motion_capture_data[[body_part_name+"_x" for body_part_name in body_part_names]]
+    motion_capture_y = motion_capture_data[[body_part_name+"_y" for body_part_name in body_part_names]]
+    motion_capture_z = motion_capture_data[[body_part_name+"_z" for body_part_name in body_part_names]]
 
-# Sample Data Preparation
-data = []
-times = motion_capture_data["timestamp"]
-frames = [go.Frame(
-    data=[
-        go.Scatter3d(
-            x=motion_capture_x.iloc[k],
-            y=motion_capture_y.iloc[k],
-            z=motion_capture_z.iloc[k],
-            mode='markers',
-            marker=dict(size=5, color='blue')
-        )
-    ],
-    name=str(k)
-) for k in range(len(times))]
+    import plotly.graph_objects as go
+    import numpy as np
+
+    # Sample Data Preparation
+    data = []
+    times = motion_capture_data["timestamp"]
+    frames = [go.Frame(
+        data=[
+            go.Scatter3d(
+                x=motion_capture_x.iloc[k],
+                y=motion_capture_y.iloc[k],
+                z=motion_capture_z.iloc[k],
+                mode='markers',
+                marker=dict(size=5, color='blue')
+            )
+        ],
+        name=str(k)
+    ) for k in range(len(times))]
 
-# Create the initial scatter plot
-initial_scatter = go.Scatter3d(
-    x=motion_capture_x.iloc[0],
-    y=motion_capture_y.iloc[0],
-    z=motion_capture_z.iloc[0],
-    mode='markers',
-    marker=dict(size=5, color='blue')
-)
+    # Create the initial scatter plot
+    initial_scatter = go.Scatter3d(
+        x=motion_capture_x.iloc[0],
+        y=motion_capture_y.iloc[0],
+        z=motion_capture_z.iloc[0],
+        mode='markers',
+        marker=dict(size=5, color='blue')
+    )
 
-# Create the layout with slider
-layout = go.Layout(
-    title='Motion Capture Visualization',
-    updatemenus=[{
-        'buttons': [
-            {
-                'args': [None, {'frame': {'duration': 1, 'redraw': True}, 'fromcurrent': True}],
-                'label': 'Play',
-                'method': 'animate'
+    # Create the layout with slider
+    layout = go.Layout(
+        title='Motion Capture Visualization',
+        updatemenus=[{
+            'buttons': [
+                {
+                    'args': [None, {'frame': {'duration': 1, 'redraw': True}, 'fromcurrent': True}],
+                    'label': 'Play',
+                    'method': 'animate'
+                },
+                {
+                    'args': [[None], {'frame': {'duration': 0, 'redraw': True}, 'mode': 'immediate', 'transition': {'duration': 0}}],
+                    'label': 'Pause',
+                    'method': 'animate'
+                }
+            ],
+            'direction': 'left',
+            'pad': {'r': 10, 't': 87},
+            'showactive': True,
+            'type': 'buttons',
+            'x': 0.1,
+            'xanchor': 'right',
+            'y': 0,
+            'yanchor': 'top'
+        }],
+        sliders=[{
+            'active': 0,
+            'steps': [{
+                'label': str(k),
+                'method': 'animate',
+                'args': [
+                    [str(k)],
+                    {'mode': 'immediate', 'frame': {'duration': 300, 'redraw': True}, 'transition': {'duration': len(times)/30}}
+                ]
+            } for k in range(len(times))],
+            'currentvalue': {
+                'prefix': 'Time: ',
+                'visible': True,
+                'xanchor': 'right'
             },
-            {
-                'args': [[None], {'frame': {'duration': 0, 'redraw': True}, 'mode': 'immediate', 'transition': {'duration': 0}}],
-                'label': 'Pause',
-                'method': 'animate'
-            }
-        ],
-        'direction': 'left',
-        'pad': {'r': 10, 't': 87},
-        'showactive': True,
-        'type': 'buttons',
-        'x': 0.1,
-        'xanchor': 'right',
-        'y': 0,
-        'yanchor': 'top'
-    }],
-    sliders=[{
-        'active': 0,
-        'steps': [{
-            'label': str(k),
-            'method': 'animate',
-            'args': [
-                [str(k)],
-                {'mode': 'immediate', 'frame': {'duration': 300, 'redraw': True}, 'transition': {'duration': len(times)/30}}
-            ]
-        } for k in range(len(times))],
-        'currentvalue': {
-            'prefix': 'Time: ',
-            'visible': True,
-            'xanchor': 'right'
-        },
-        'pad': {'b': 10},
-        'len': 0.9,
-        'x': 0.1,
-        'y': 0,
-    }]
-)
+            'pad': {'b': 10},
+            'len': 0.9,
+            'x': 0.1,
+            'y': 0,
+        }]
+    )
+
+    # Create the figure
+    fig = go.Figure(data=[initial_scatter], frames=frames, layout=layout)
 
-# Create the figure
-fig = go.Figure(data=[initial_scatter], frames=frames, layout=layout)
+    # Display the figure in the streamlit app
+    st.plotly_chart(fig)
 
-# Display the figure in the streamlit app
-st.plotly_chart(fig)
+
+st.title("CyberOrigin Data Visualization")
+data = load_data()
+visualize(data)

data/Fold_towels/video.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e9560ac4ab1b31cc64a6d0fe94d219762bd8d8a2d9b84f81ea47cdc21f6f1d7e
-size 23094167

data/Pipette/motionCaptureData.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:80e4cd197478028843d89d59682054e09c40bf5410390cacebbd0180620b1d4d
-size 10682090

data/Pipette/video.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ccb1ef4d825f1bba9e914b496bf815c03b7366dc307133a661437b96b85592d0
-size 23941195

data/Take_the_item/video.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9c3d2e92f0ff2773933e91015753af6098e8fabd9a89cfb1b1ca33e2bfa52974
-size 6758822

data/Twist_the_tube/video.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5a00a3108ba51f32cbccddfe198f5dd64b45b9bb0a9eb56cf93782fa16a828e3
-size 13989984

requirements.txt

@@ -1,4 +1,5 @@
 streamlit
 pandas
 plotly
-numpy
+numpy
+datasets