start using pytables

data/convert_to_uv.py

@@ -326,6 +326,7 @@ def get_shapedirs():
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument("-f", "--folder", help = "end of range", default='/home/workspace/data/dataset/test_skirt_ready_2')
+    parser.add_argument("-x", "--extras", help = "end of range", default='/home/workspace/data/dataset/test_skirt_ready_2_extras')
     parser.add_argument("-s", "--start", help = "start of range", default=0)
     parser.add_argument("-e", "--end", help = "end of range", default=30000)
     parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
@@ -335,6 +336,9 @@ if __name__ == '__main__':
     # smpl = SMPL('FeatureLines/superSMPL')
 
     folder = args.folder 
+    extras_folder = args.extras
+
+    os.makedirs(extras_folder, exist_ok=True)
 
     print("skirt" if args.skirt == 1 else "not skirt")
 
@@ -387,7 +391,8 @@ if __name__ == '__main__':
     shapedirs = get_shapedirs()
 
     use_no_orient = True
-    data_to_convert = ['mesh_posed']
+    no_shape = False
+    data_to_convert = ['mesh_posed', 'mesh_unposed', 'mesh_static', 'mesh_curvature', 'body_posed']
 
     for id in tqdm.tqdm(ids):
 
@@ -395,12 +400,27 @@ if __name__ == '__main__':
         garments = info['outfit']
         assert info['poses'].shape[1] <= 300
 
+        garment = garments[0]
+
         mask = read_garment_mask(folder, id, garment)
-        verts_static = read_garment_template(folder, id, garment, mask)
+
-        body = read_posed_body(folder, id, info['poses'], use_no_orient)
+        if 'mesh_posed' in data_to_convert:
-        gt = read_posed_garment(folder, id, garment, mask=mask, shape=info['shape'])
+            mesh_posed = read_posed_garment(folder, id, garment, mask=mask, shape=info['shape'], poses=info['poses'], use_no_orient=use_no_orient, no_shape=no_shape)
+        if 'mesh_unposed' in data_to_convert:
+            mesh_unposed = read_unposed_garment(folder, id, garment, mask=mask, shape=info['shape'], no_shape=no_shape)
+        if 'mesh_static' in data_to_convert:
+            mesh_static = read_garment_template(folder, id, garment, mask)
+        if 'mesh_curvature' in data_to_convert:
+            mesh_curvature = compute_curvature(mesh_posed, F)
+        if 'body_posed' in data_to_convert:
+            body_posed = read_posed_body(folder, id, info['poses'], use_no_orient)
+
+
+        # verts_static = read_garment_template(folder, id, garment, mask)
+        # body = read_posed_body(folder, id, info['poses'], use_no_orient)
+        # gt = read_posed_garment(folder, id, garment, mask=mask, shape=info['shape'], ...)
         # gt = readPC2(os.path.join(folder, id, garment + '_registered.pc16'),  True)['V']
-        curvature = np.stack([np.load(os.path.join(folder, f'mesh_curvature_{neighbourhood}_2',  f'{id}_{i}.npz'))['mesh_curvature'] for i in range(frames_len)])
+        # curvature = np.stack([np.load(os.path.join(folder, f'mesh_curvature_{neighbourhood}_2',  f'{id}_{i}.npz'))['mesh_curvature'] for i in range(frames_len)])
         # mesh_unposed = [np.load(os.path.join(folder, id, 'mesh_unposed',  id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
         # mesh_unposed = np.stack([mesh_unposed[i]['mesh_unposed'] for i in range(gt.shape[0])])
         # mesh_unposed_no_shape = [np.load(os.path.join(folder, id, 'mesh_unposed_no_shape',  id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
@@ -422,26 +442,37 @@ if __name__ == '__main__':
 
         start = time.perf_counter()
 
-
-
         uv_verts_static, garment_uv_mask, garment_uv_mask_border = convert_to_uv(uv_res, m2uv, uv_map, verts_static, neighbours=3, mask=mask, method='rbf', garment_uv_mask=None)
         uv_verts_static = uv_verts_static.astype(np.float32)
-        # uv_body_static = convert_to_uv(uv_res, m2uv, uv_map, body_static, neighbours=3, mask=None, method='rbf', garment_uv_mask=None).astype(np.float32)
+
-        # uv_body = convert_to_uv_batch(uv_res, m2uv, uv_map, body[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
+        if 'mesh_posed' in data_to_convert:
-        # uv_posed_body_no_orient = convert_to_uv_batch(uv_res, m2uv, uv_map, body_no_orient[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
+            uv_posed = convert_to_uv_batch(uv_res, m2uv, uv_map, gt[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
-        # uv_gt = convert_to_uv_batch(uv_res, m2uv, uv_map, gt[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
+        if 'mesh_unposed' in data_to_convert:
-        uv_curvature = convert_to_uv_batch_label(uv_res, m2uv, uv_map, curvature[:, :], neighbours=3, mask=mask, method='nearest', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
+            uv_unposed = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
-        # uv_unposed = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
+        if 'mesh_curvature' in data_to_convert:
-        # uv_unposed_no_shape = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
+            uv_curvature = convert_to_uv_batch_label(uv_res, m2uv, uv_map, curvature[:, :], neighbours=3, mask=mask, method='nearest', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
-        # uv_unposed_no_shape_one_hot = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape_one_hot, neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border, id=id)
+        if 'body_posed' in data_to_convert:
-        # uv_posed_no_orient = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_posed_no_orient, neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border, id=id)
+            uv_body_posed = convert_to_uv_batch(uv_res, m2uv, uv_map, body_posed[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
-        # uv_shaped_body = convert_to_uv(uv_res, m2uv, uv_map, shaped_body, neighbours=3, mask=None, method='rbf', garment_uv_mask=None).astype(np.float32)
+
         print(time.perf_counter() - start)
 
+
+        if 'mesh_static' in data_to_convert:
+            # np.save(os.path.join(folder, id, garment + '_offsets_static_uv.npy'), uv_verts_static)
+            # np.save(os.path.join(folder, id, 'mask_uv.npy'), garment_uv_mask)
+        if 'mesh_posed' in data_to_convert:
+            mesh_posed = read_posed_garment(folder, id, garment, mask=mask, shape=info['shape'], poses=info['poses'], use_no_orient=use_no_orient, no_shape=no_shape)
+        if 'mesh_unposed' in data_to_convert:
+            mesh_unposed = read_unposed_garment(folder, id, garment, mask=mask, shape=info['shape'], no_shape=no_shape)
+        if 'mesh_curvature' in data_to_convert:
+            mesh_curvature = compute_curvature(mesh_posed, F)
+        if 'body_posed' in data_to_convert:
+            # for i in range(uv_body_posed.shape[0]):
+            #     np.savez_compressed(os.path.join(folder, id, 'uv_body_posed', garment + '_' + str(i) + '.npz'), uv_body=uv_body[i, :, :, :])
+        
+
         # start = time.perf_counter()
-        # np.save(os.path.join(folder, id, garment + '_offsets_static_uv.npy'), uv_verts_static)
         # np.save(os.path.join(folder, id, 'body_static_uv.npy'), uv_body_static)
-        # np.save(os.path.join(folder, id, 'mask_uv.npy'), garment_uv_mask)
 
         # os.makedirs(os.path.join(folder, id, 'body_posed_uv'), exist_ok=True)
         # for i in range(uv_body.shape[0]):

data/curvatures.py

@@ -0,0 +1,310 @@
+import os
+import sys
+import tqdm
+import argparse
+import random
+import string
+import time
+import tables as tb
+import numpy as np
+import trimesh as tm
+import cmapy as cm
+import scipy as sp
+import scipy.io as sio
+import plotly.graph_objects as go
+from pyntcloud import PyntCloud
+from scipy.sparse import csr_matrix, coo_matrix
+from scipy.sparse.csgraph import floyd_warshall, dijkstra
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+
+from data.cloth3d_io import readPC2, readPC2Frame, writePC2, readOBJ
+from custom_utils.custom_utils import _maskFaces
+# from FeatureLines.superSMPL.smpl import SMPL
+
+def get_body():
+    smpl = SMPL('FeatureLines/superSMPL')
+    pose = np.zeros((1, 72), dtype=np.float32)
+    beta = np.zeros((1, 11), dtype=np.float32)
+    R = smpl.run_rodrigues(pose)
+    T, _, _ = smpl.run(R, beta, True)
+    return np.squeeze(T)
+
+
+def get_skirt_body():
+    body = readOBJ('FeatureLines/superSMPL_skirt.obj')
+
+    return body[0]
+
+
+def export_to_ply(mesh, file):
+    ply = tm.exchange.ply.export_ply(mesh, encoding='binary', vertex_normal=True, include_attributes=True)
+    output_file = open(file, "wb+")
+    output_file.write(ply)
+    output_file.close()
+
+
+
+
+def get_closest_n_points(verts, idx, n):
+    dist = sp.spatial.distance.cdist(verts[idx, :], verts)
+    dist[dist == 0] = 1e9
+    idx_sort = np.argsort(dist, axis=-1)
+    closest = idx_sort[:, :n]
+    return closest
+
+
+def histedges_equalN(x, nbin):
+    npt = len(x)
+    return np.interp(np.linspace(0, npt, nbin + 1),
+                     np.arange(npt),
+                     np.sort(x))
+
+
+
+def k_neighbours(k):
+    body = get_skirt_body()
+    adj = np.ones((14475, 14475)) * 9999 # ???
+    diagonal = np.linspace(0, 14474, 14475, dtype=np.int32)
+    m = tm.Trimesh(body, F, process=False, validate=False)
+    edges = m.edges_unique
+    lengths = m.edges_unique_length
+    adj[edges[:, 0], edges[:, 1]] = lengths
+    adj[edges[:, 1], edges[:, 0]] = lengths
+    adj[diagonal, diagonal] = 0
+
+    adj = csr_matrix(adj)
+    dist = dijkstra(adj, directed=False, unweighted=False, limit=0.5)
+
+    np.save('MeshUtils/dijkstra_skirt.npy', dist)
+
+    # dist = floydWarshall(adj, 14475)
+
+    # idx_sort = np.argsort(dist, axis=-1)
+    # closest = idx_sort[:, :k]
+
+    pass
+
+
+def calculate_dist(folder, neighbourhood, frames):
+    out = dict()
+    # frames = frames[int(args.start):int(args.end)]
+
+    for frame in tqdm.tqdm(frames):
+        id = frame.split('_')[0]
+        if id in out.keys():
+            continue
+
+        mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
+        k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+        out[id] = k_neighbors
+
+    return out
+
+def generate_curvatures(folder, neighbourhood, frames):
+
+    # ids = os.listdir(folder)[int(args.start):int(args.end)]
+    # frames = frames[int(args.start):int(args.end)]
+    # os.makedirs(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}'), exist_ok=True)
+
+    k_neighbors_cache = calculate_dist(folder, neighbourhood, frames)
+
+    for frame in tqdm.tqdm(frames):
+
+        id = frame.split('_')[0]
+        frame_nr = frame.split('_')[1]
+        garment = frame.split('_')[2]
+    
+        # bodies = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
+        # offsetss = readPC2(os.path.join(folder, id, garment + '_registered.pc16'), True)['V']
+        mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
+        _, faces = _maskFaces(np.zeros((14475,3)), F, mask) #np.load(os.path.join(folder, 'faces', f'{id}.npy'))
+        v = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
+        
+        m = tm.Trimesh(v, faces, process=False, validate=False)
+
+        outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
+        outliers = np.nonzero(outliers)[0]
+        outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
+        outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
+        faces = np.concatenate((faces, outliers_faces), axis=0)
+
+        verts = v
+
+        m = tm.Trimesh(verts, faces, process=False, validate=False)
+        export_to_ply(m, temp_file)
+
+        cloud = PyntCloud.from_file(temp_file)
+
+        k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+        ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
+        cloud.add_scalar_field("curvature", ev=ev)
+        curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
+        out = np.zeros((14475), dtype=np.float32)
+        out[mask] = curvature
+        
+        # np.savez_compressed(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}', f'{id}_{frame_nr}.npz'), mesh_curvature_raw=out)
+        group = h5file.get_node(f'/{id}/{frame_nr}/{garment}')
+        h5file.create_array(group, "curvature_raw", out)
+
+    return None
+
+
+def get_bins(values, nr_of_bins):
+    flattened = []
+    for c in values:
+        flattened.extend(c.flatten())
+    flattened = np.array(flattened)
+    bins = histedges_equalN(flattened, nr_of_bins)
+    return bins
+
+
+def generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood):
+
+    os.makedirs(os.path.join(folder, f'mesh_curvature_{neighbourhood}_2'), exist_ok=True)
+    labels = np.linspace(0, bins.shape[0] - 2, bins.shape[0] - 1)
+
+    for i, frame in enumerate(tqdm.tqdm(frames)):
+        # id = frame.split('_')[0]
+        # mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy'))
+        # print(i) 
+        curvature = curvatures[i]
+
+        frame_labels = np.zeros((curvature.shape[0]), dtype=np.float32)
+        for j in range(bins.shape[0] - 2):
+            frame_labels[(curvature >= bins[j]) * (curvature < bins[j+1])] = labels[j]
+        frame_labels[curvature >= bins[-2]] = labels[-1]
+
+        np.savez_compressed(os.path.join(folder, f'mesh_curvature_{neighbourhood}_2', f'{frame}.npz'), mesh_curvature=frame_labels)
+        # writePC2(os.path.join(garment_dir, "curvature_label_" + str(nr_of_labels) + "_" + str(neighbourhood) + ".pc16"), unmasked_label, True)
+
+
+def visualize_curvature_labels(folder):
+    ids = os.listdir(folder)#[:10]
+
+    for id in tqdm.tqdm(ids):
+        garment_dir = os.path.join(folder, id)
+
+        if not os.path.isdir(garment_dir):
+            continue
+
+        garments = [f.replace('.pc16','') for f in os.listdir(garment_dir) if f.find('_') == -1 and f != 'info.mat' and not f.endswith('.obj')]
+        # assert len (garments) == 1
+        if len (garments) != 1:
+            continue
+        garment = garments[0]
+    
+        frame = 10
+
+        bodies = readPC2Frame(os.path.join(folder, id, 'Body_posed.pc16'), frame, True)
+        offsetss = readPC2Frame(os.path.join(folder, id, garment + '_registered.pc16'), frame, True)
+        # curvature_labels = readPC2Frame(os.path.join(folder, id, 'curvature_label_6_25.pc16'), frame, True)[:, 0]
+        curvature_labels = np.load(os.path.join(folder, id, 'curvature_label_6_25.npy'), allow_pickle=True)[frame, :]
+        mask = np.squeeze(sio.loadmat(os.path.join(garment_dir, garment + '_mask.mat'))['mask'].astype(bool))
+        
+        verts = offsetss
+        verts, faces = _maskFaces(verts, F, mask)
+        # m = tm.Trimesh(verts, faces, process=False, validate=False)
+
+        # outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
+        # outliers = np.nonzero(outliers)[0]
+        # outliers_n_neighbours = get_closest_n_points(verts, outliers, 2)
+        # outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
+        # faces = np.concatenate((faces, outliers_faces), axis=0)
+
+        colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, int(curvature_labels.max()+1))])
+        colors_binned = np.zeros((curvature_labels.shape[0], 3))
+        for i in range(int(curvature_labels.max())+1):
+            print(np.count_nonzero(curvature_labels == i))
+            colors_binned[curvature_labels == i, :] = colors[i]
+        colors_binned = colors_binned[mask, :]
+
+        # m = tm.Trimesh(verts, faces, process=False, validate=False, vertex_colors=colors_binned)
+        # m.show()
+        x = verts
+        meshes = [go.Mesh3d(
+                    x=x[:,0],
+                    y=x[:,2],
+                    z=x[:,1],
+                    i = faces[:,0],
+                    j = faces[:,1],
+                    k = faces[:,2],
+                    vertexcolor=colors_binned
+                    ),]
+
+        fig = go.Figure(data=meshes)
+        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()
+
+
+def read_raw_curvatures(folder, frames, neighbourhood):
+    curvatures_all = []
+    for frame in tqdm.tqdm(frames):
+        curv = np.load(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}', f'{frame}.npz'))['mesh_curvature_raw']
+        curvatures_all.append(curv)
+    
+    return curvatures_all
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-f", "--folder", help = "process the given folder", default=r'/home/workspace/data/dataset/cloth3d/train')
+    parser.add_argument("-s", "--start", help = "start of range", default=0)
+    parser.add_argument("-e", "--end", help = "end of range", default=10)
+    parser.add_argument("-n", "--name", help = "end of range", default="lhunor")
+    args = parser.parse_args()
+
+    is_skirt = False
+    skirt_modifier = '_skirt' if is_skirt else ''
+    F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
+    # k_neighbours(k=25)
+
+    folder = args.folder
+    frames_file = 'frames_all.txt'
+
+    nr_of_labels = 6
+    neighbourhood = 25
+
+    temp_file = '/home/workspace/local-data/temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
+    if not is_skirt:
+        dist = np.load('custom_data/dijkstra.npy')
+    else:
+        dist = np.load('custom_data/dijkstra_skirt.npy')
+
+    print('SKIRT' if is_skirt else 'NOT SKIRT')
+    print(f'params: -f {args.folder} -s {args.start} -e {args.end} -n {args.name} -skirt {is_skirt} -neigh {neighbourhood}')
+
+    # frames = sorted(os.listdir(os.path.join(folder, 'mesh_posed')))[int(args.start):int(args.end)]
+    # frames = [frame.split('.')[0] for frame in frames]
+
+    frames = []
+    with open(os.path.join(folder, frames_file), 'r') as f:
+        lines = f.readlines()
+        lines = [line.strip() for line in lines]
+    frames = lines[int(args.start):int(args.end)]
+
+    root = '/'.join(folder.split('/')[:-1])
+    type = folder.split('/')[-1]
+    h5file = tb.open_file(os.path.join(root, f'{type}_extras.h5'), mode='a')
+
+
+    generate_curvatures(folder, neighbourhood, frames)
+    # curvatures = read_raw_curvatures(folder, frames, neighbourhood)
+    # bins = get_bins(curvatures, nr_of_labels)
+    # np.save(f'custom_data/train_bins_skirt_{neighbourhood}.npy', bins)
+    # bins = np.load(f'custom_data/train_bins_skirt_{neighbourhood}.npy')
+    # generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood)
+    # visualize_curvature_labels(folder)
+
+    h5file.close()
+
+    pass

data/preprocessing.py

@@ -3,11 +3,16 @@ import sys
 
 import math
 import shutil
+import warnings
+import tables as tb
 import numpy as np
 import scipy.io as sio
 
 from tqdm import tqdm
 
+from tables import NaturalNameWarning
+warnings.filterwarnings('ignore', category=NaturalNameWarning)
+
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
 from data.cloth3d_io import readPC2, loadInfo
 from data.utils import Frame
@@ -102,6 +107,31 @@ def genereated_registered_and_mask(folder, is_skirt = False):
         np.save(os.path.join(garment_dir, garment + '_mask.npy'), mask)
 
 
+def create_extras_file(folder):
+    # create a new pytables file
+    root = '/'.join(folder.split('/')[:-1])
+    type = folder.split('/')[-1]
+    h5file = tb.open_file(os.path.join(root, f'{type}_extras.h5'), mode='w', title='Extras')
+
+    frames = []
+    with open(os.path.join(folder, 'frames_all.txt'), 'r') as f:
+        lines = f.readlines()
+        lines = [line.strip() for line in lines]
+    frames = lines
+
+    for frame in tqdm(frames):
+        # add a new empty group to the root group for each frame
+        id, frame_nr, garment = frame.split()
+        if not id in h5file.root:
+            h5file.create_group('/', id)
+        if not frame_nr in h5file.root[id]:
+            h5file.create_group(f'/{id}', frame_nr)
+        h5file.create_group(f'/{id}/{frame_nr}', f'{garment}')
+
+
+    h5file.close()
+
+
 
 if __name__ == '__main__':
 
@@ -109,4 +139,5 @@ if __name__ == '__main__':
 
     # clean_dataset(folder)
     # generate_frames_file(folder, 'frames_all.txt')
-    split_data(folder, 0.9)
+    # split_data(folder, 0.9)
+    create_extras_file(folder)