initial test for cape conversion

cape_convert.py

@@ -0,0 +1,178 @@
+import os
+import numpy as np
+import plotly.express as ex
+import plotly.graph_objects as go
+from simpleicp import PointCloud, SimpleICP
+from scipy.spatial.distance import cdist
+from custom_utils.custom_utils import _maskFaces
+
+
+
+os.environ["CUDA_VISIBLE_DEVICES"] = '2'
+import tensorflow as tf
+
+gpus = tf.config.experimental.list_physical_devices('GPU')
+if gpus:
+    try:
+        # Currently, memory growth needs to be the same across GPUs
+        for gpu in gpus:
+            tf.config.experimental.set_memory_growth(gpu, True)
+        logical_gpus = tf.config.experimental.list_logical_devices('GPU')
+        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
+    except RuntimeError as e:
+        # Memory growth must be set before GPUs have been initialized
+        print(e)
+
+from custom_utils.smpl import SMPL
+
+
+folder = '/home/workspace/data/dataset/cape_release'
+
+def get_frames():
+    # sequences = np.load()
+    test = np.load(os.path.join(folder, 'sequences/03383/shortlong_ATUsquat/shortlong_ATUsquat.000105.npz'))
+    faces = np.load(os.path.join(folder, 'misc/smpl_tris.npy'))
+    min_body = np.load(os.path.join(folder, 'minimal_body_shape/03383/03383_minimal.npy')) 
+    offsets_cano = test['v_cano'] - min_body
+    pose = test['pose']
+    mask = np.abs(offsets_cano.sum(axis=-1)) < 1e-2
+
+    smpl = SMPL()
+    smpl.run_garment()
+
+    # verts, faces = _maskFaces(test['v_posed'], faces, mask)
+
+    x = test['v_cano']
+    y = min_body
+    fig = go.Figure(data=[
+            go.Mesh3d(
+                # 8 vertices of a cube
+                x=x[:,0],
+                y=x[:,1],
+                z=x[:,2],
+                # i, j and k give the vertices of triangles
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2]
+            ),
+            go.Mesh3d(
+                # 8 vertices of a cube
+                x=y[:,0],
+                y=y[:,1],
+                z=y[:,2],
+                # i, j and k give the vertices of triangles
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2]
+            ),
+        ])
+
+    fig.update_layout(scene_aspectmode='manual',
+                    scene_aspectratio=dict(x=1, y=1, z=1))
+    fig.update_layout(
+        scene = dict(
+            xaxis = dict(nticks=4, range=[-1.5,1.5],),
+            yaxis = dict(nticks=4, range=[-1.5,1.5],),
+            zaxis = dict(nticks=4, range=[-1.5,1.5],),)
+            )
+
+    fig.show()
+
+
+    print()
+
+folder_processed = '/home/workspace/data/dataset/cape_processed/datasets/dataset_male_4clotypes'
+
+def get_frames_processed():
+    # sequences = np.load()dataset/cape_processed/datasets/dataset_male_4clotypes
+    disp = np.load(os.path.join(folder_processed, 'train/train_disp.npy'))
+    faces = np.load(os.path.join(folder, 'misc/smpl_tris.npy'))
+    min_body = np.load(os.path.join(folder, 'minimal_body_shape/03383/03383_minimal.npy')) 
+
+
+    x = verts
+    fig = go.Figure(data=[
+            go.Mesh3d(
+                # 8 vertices of a cube
+                x=x[:,0],
+                y=x[:,1],
+                z=x[:,2],
+                # i, j and k give the vertices of triangles
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2]
+            ),
+        ])
+
+    fig.update_layout(scene_aspectmode='manual',
+                    scene_aspectratio=dict(x=1, y=1, z=1))
+    fig.update_layout(
+        scene = dict(
+            xaxis = dict(nticks=4, range=[-1,1],),
+                        yaxis = dict(nticks=4, range=[-1,1],),
+                        zaxis = dict(nticks=4, range=[-1,1],),)
+                        )
+
+    fig.show()
+
+
+    print()
+
+
+folder_pop = '/home/workspace/data/dataset/cape_2/ps/scratch/ps_shared/qma/abps_data/CAPE_packed_for_POP_release/00096_shortlong/train'
+
+def get_frames_pop():
+    # sequences = np.load()dataset/cape_2/ps/scratch/ps_shared/qma/abps_data/CAPE_packed_for_POP_release/00096_shortlong/train/shortlong_ATUsquat.000009.npz
+    test = np.load(os.path.join(folder_pop, 'shortlong_ATUsquat.000009.npz'))
+    faces = np.load(os.path.join(folder, 'misc/smpl_tris.npy'))
+    min_body = np.load(os.path.join(folder, 'minimal_body_shape/03383/03383_minimal.npy')) 
+    offsets_cano = test['v_cano'] - min_body
+    mask = np.abs(offsets_cano.sum(axis=-1)) < 1e-2
+
+    # verts, faces = _maskFaces(test['v_posed'], faces, mask)
+
+    x = test['v_cano']
+    y = min_body
+    fig = go.Figure(data=[
+            go.Mesh3d(
+                # 8 vertices of a cube
+                x=x[:,0],
+                y=x[:,1],
+                z=x[:,2],
+                # i, j and k give the vertices of triangles
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2]
+            ),
+            go.Mesh3d(
+                # 8 vertices of a cube
+                x=y[:,0],
+                y=y[:,1],
+                z=y[:,2],
+                # i, j and k give the vertices of triangles
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2]
+            ),
+        ])
+
+    fig.update_layout(scene_aspectmode='manual',
+                    scene_aspectratio=dict(x=1, y=1, z=1))
+    fig.update_layout(
+        scene = dict(
+            xaxis = dict(nticks=4, range=[-1,1],),
+                        yaxis = dict(nticks=4, range=[-1,1],),
+                        zaxis = dict(nticks=4, range=[-1,1],),)
+                        )
+
+    fig.show()
+
+
+    print()
+
+
+
+if __name__ == '__main__':
+    train_split = get_frames()
+
+    print()