save progress

data/cloth3d_io.py

@@ -236,7 +236,7 @@ def read_posed_garment_frame(folder, id, garment, frame, map=None, mask=None, sh
         verts[mask, :] = verts[mask, :] - np.squeeze(shape_offsets)[mask, :]
 
     if deorient:
-        R = scipy.spatial.transform.Rotation.from_euler(pose[:3]).inv()
+        R = scipy.spatial.transform.Rotation.from_euler('xyz', pose[:3]).inv()
         # R = scipy.spatial.transform.Rotation.from_euler([0,0,-zrot])
         verts = R.apply(verts)
 
@@ -244,13 +244,12 @@ def read_posed_garment_frame(folder, id, garment, frame, map=None, mask=None, sh
 
 
 # can i cache map?
-def read_posed_garment(folder, id, garment, map=None, mask=None, shape=None, shapedirs=None, poses=None, unshape=False, deorient=False):
+# NOT WORKING WELL
-    pc16_path = os.path.join(folder, id, garment + '.pc16')
+def read_posed_garment(folder, id, garment, mask=None, shape=None, shapedirs=None, poses=None, unshape=False, deorient=False):
-    verts = readPC2(pc16_path, True)
+    body_posed = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
+    offsets = readPC2(os.path.join(folder, id, garment + '_offsets_dynamic.pc16'), True)['V']
+    verts = (body_posed + offsets) * mask[:, None]
 
-    if map is None:
-        map = sio.loadmat(os.path.join(folder, id, garment + '_map.mat'))['map'].reshape((-1))
-    verts = verts[:, map, :]
 
     if unshape:
         shape_offsets = np.tensordot(shape, shapedirs, axes=[[1], [2]])
@@ -258,7 +257,7 @@ def read_posed_garment(folder, id, garment, map=None, mask=None, shape=None, sha
 
     if deorient:
         for i in range(verts.shape[0]):
-            R = scipy.spatial.transform.Rotation.from_euler(poses[:3, i]).inv()
+            R = scipy.spatial.transform.Rotation.from_euler('xyz', poses[:3, i]).inv()
             # R = scipy.spatial.transform.Rotation.from_euler([0,0,-zrot])
             verts[i] = R.apply(verts[i])
 
@@ -304,6 +303,16 @@ def read_posed_body(folder, id, poses=None, deorient=False):
         
     return verts
 
+
+def read_curvatures(h5file, id, frames, neighbourhood=None):
+    frames_filtered = [frame for frame in frames if frame.split()[0] == id]
+    garment = frames_filtered[0].split()[2]
+    curvatures_all = []
+    for frame_nr in range(len(frames_filtered)):
+        curv = h5file.get_node(f'/{id}/{frame_nr}/{garment}/curvature_raw').read()
+        curvatures_all.append(curv)
+    return np.array(curvatures_all)
+
 # import os
 # import sys
 # import numpy as np

data/convert_to_uv.py

@@ -13,6 +13,7 @@ import numpy as np
 import scipy as sp
 import trimesh as tm
 import cmapy as cm
+import tables as tb
 import scipy.io as sio
 import scipy.interpolate as spi
 import matplotlib.pyplot as plt
@@ -22,7 +23,8 @@ import plotly.express as ex
 import plotly.graph_objects as go
 
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
-from data.cloth3d_io import read_garment_mask, read_garment_template, read_posed_garment_frame, loadInfo, read_posed_body_frame, read_posed_body, read_posed_garment
+from data.cloth3d_io import read_garment_mask, read_garment_template, read_posed_garment_frame, loadInfo, read_posed_body_frame, read_posed_body, read_posed_garment, read_curvatures
+from data.utils import save_to_h5file
 
 
 os.environ["CUDA_VISIBLE_DEVICES"] = "6"
@@ -119,6 +121,8 @@ def convert_to_uv_batch(res, m2uv, uv_map, verts, neighbours=15, mask=None, meth
     rbf_dest_current = np.nonzero(rbf_dest_current)
     rbf_dest_current = np.array(rbf_dest_current).T
 
+    out = []
+
     for i in range(batch):
         print('processing frame: ' + str(i))
 
@@ -139,7 +143,9 @@ def convert_to_uv_batch(res, m2uv, uv_map, verts, neighbours=15, mask=None, meth
         if garment_uv_mask is not None:
             result *= garment_uv_mask[:,:,np.newaxis]
         
-        np.savez_compressed(os.path.join(folder, 'uv_posed_no_orient', id + '_' + str(i) + '.npz'), uv_posed_no_orient=result)
+        out.append(result)
+    
+    return out
 
 
 def convert_to_uv_batch_label(res, m2uv, uv_map, values, neighbours=15, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=None):
@@ -323,25 +329,16 @@ def get_shapedirs():
         shapedirs.append(male['shapedirs'][to_keep, :])
     return 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("-f", "--folder", help = "end of range", default='/home/workspace/data/dataset/cloth3d/train')
-    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("-e", "--end", help = "end of range", default=1000)
     parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
-    parser.add_argument("-sk", "--skirt", help = "end of range", default=1)
+    parser.add_argument("-sk", "--skirt", help = "end of range", default=0)
     args = parser.parse_args()
 
-    # 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")
-
     F = np.load("custom_utils/smpl_faces" + ("_skirt" if args.skirt == 1 else "") + ".npy")
     uv_map = np.load('custom_utils/stretched_uv_map_' + ("skirt" if args.skirt == 1 else "3") + '.npy')
     m2uv = np.load('custom_utils/m2uv' + ("_skirt" if args.skirt == 1 else "") + '.npy', allow_pickle=True).item()
@@ -369,49 +366,64 @@ if __name__ == '__main__':
     rbf_dest[uv_map_discreet_unique[:, 0], uv_map_discreet_unique[:, 1]] = 0
     rbf_dest[np.invert(uv_mask[:,:,0])] = 0
 
-    is_skirt = True if args.skirt == 1 else False
-    skirt_modifier = '_skirt' if is_skirt else ''
 
+    #########################################################################################
+
+    use_no_orient = False
+    no_shape = False
+    is_skirt = True if args.skirt == 1 else False
     folder = args.folder
+    neighbourhood = 25
     frames_file = 'frames_all.txt'
+    data_to_convert = ['mesh_posed', 'mesh_static', 'mesh_curvature', 'body_posed'] # 'mesh_unposed',
+
+    print("skirt" if args.skirt == 1 else "not skirt")
+    print(f'params: -f {args.folder} -s {args.start} -e {args.end} -n {args.name} -skirt {args.skirt}')
+
+    skirt_modifier = '_skirt' if is_skirt else ''
+    orient_mod = '_no_orient' if use_no_orient else ''
+    shape_mod = '_no_shape' if no_shape else ''
 
     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)]
+    frames = lines
-    ids = [frame.split('_')[0] for frame in frames]
+    ids = [frame.split()[0] for frame in frames]
-
-    print(f'params: -f {args.folder} -s {args.start} -e {args.end} -n {args.name} -skirt {args.skirt}')
-
-    # ids = os.listdir(os.path.join(local_folder, 'pose_shape'))
-    # ids = [id.split('_')[0] for id in ids]
     ids = sorted(list(set(ids)))[int(args.start):int(args.end)]
 
     shapedirs = get_shapedirs()
 
-    use_no_orient = True
+    root = '/'.join(folder.split('/')[:-1])
-    no_shape = False
+    type = folder.split('/')[-1]
-    data_to_convert = ['mesh_posed', 'mesh_unposed', 'mesh_static', 'mesh_curvature', 'body_posed']
+    h5file = tb.open_file(os.path.join(root, f'{type}_extras.h5'), mode='a')
+
 
     for id in tqdm.tqdm(ids):
 
+
         info = loadInfo(os.path.join(folder, id, 'info.mat'))
-        garments = info['outfit']
+        garments = list(info['outfit'].keys())
         assert info['poses'].shape[1] <= 300
 
+        # put all this in a function and call for all garments
+
+        # also masking is not the same as just filling the empty values with neighbouring
+        # values, at least the plots are different
+
         garment = garments[0]
 
         mask = read_garment_mask(folder, id, garment)
 
         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)
+            mesh_posed_frame = read_posed_garment_frame(folder, id, garment, 0, mask=mask, shape=info['shape'], pose=info['poses'][:,0], deorient=use_no_orient, unshape=no_shape)
+            mesh_posed = read_posed_garment(folder, id, garment, mask=mask, shape=info['shape'], poses=info['poses'], deorient=use_no_orient, unshape=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)
+            mesh_unposed = read_unposed_garment(folder, id, garment, mask=mask, shape=info['shape'], unshape=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)
+            mesh_curvature = read_curvatures(h5file, id, frames, neighbourhood=None)
         if 'body_posed' in data_to_convert:
             body_posed = read_posed_body(folder, id, info['poses'], use_no_orient)
 
@@ -442,33 +454,35 @@ 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_static, uv_mask, garment_uv_mask_border = convert_to_uv(uv_res, m2uv, uv_map, mesh_static, neighbours=3, mask=mask, method='rbf', garment_uv_mask=None)
-        uv_verts_static = uv_verts_static.astype(np.float32)
+        uv_static = uv_static.astype(np.float32)
 
         if 'mesh_posed' in data_to_convert:
-            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_posed = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_posed[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
         if 'mesh_unposed' in data_to_convert:
-            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=uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
         if 'mesh_curvature' 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_curvature = convert_to_uv_batch_label(uv_res, m2uv, uv_map, mesh_curvature[:, :], neighbours=3, mask=mask, method='nearest', garment_uv_mask=uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
         if 'body_posed' in data_to_convert:
             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)
 
         print(time.perf_counter() - start)
 
-
+        # mesh_static
-        if 'mesh_static' in data_to_convert:
+        save_to_h5file(h5file, id, 0, garment, uv_static, 'uv_static', 'float32')
-            # np.save(os.path.join(folder, id, garment + '_offsets_static_uv.npy'), uv_verts_static)
+        save_to_h5file(h5file, id, 0, garment, uv_mask, 'uv_mask', 'float32')
-            # 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)
+            for i in range(uv_posed.shape[0]):
+                save_to_h5file(h5file, id, i, garment, uv_posed[i, :, :, :], 'uv_posed' + orient_mod + shape_mod, 'float32')
         if 'mesh_unposed' in data_to_convert:
-            mesh_unposed = read_unposed_garment(folder, id, garment, mask=mask, shape=info['shape'], no_shape=no_shape)
+            for i in range(uv_unposed.shape[0]):
+                save_to_h5file(h5file, id, i, garment, uv_unposed[i, :, :, :], 'uv_unposed' + orient_mod + shape_mod, 'float32')
         if 'mesh_curvature' in data_to_convert:
-            mesh_curvature = compute_curvature(mesh_posed, F)
+            for i in range(uv_curvature.shape[0]):
+                save_to_h5file(h5file, id, i, garment, uv_curvature[i, :, :, :], 'uv_curvature', 'uint8')
         if 'body_posed' in data_to_convert:
-            # for i in range(uv_body_posed.shape[0]):
+            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, :, :, :])
+                save_to_h5file(h5file, id, i, garment, uv_body_posed[i, :, :, :], 'uv_body_posed', 'float32')
         
 
         # start = time.perf_counter()

data/curvatures.py

@@ -114,14 +114,6 @@ class Cache:
 
             mask = read_garment_mask(self.folder, id, garment)
             
-            if self.include_neighbours:
-                k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :self.neighbourhood]
-                self.cache[id].k_neighbors = k_neighbors
-                G = tm.graph.vertex_adjacency_graph(m)
-                for i in range(v.shape[0]):
-                    for k in range(25):
-                        assert nx.has_path(G, i, k_neighbors[i, k]), f'No path between {i} and {k_neighbors[i, k]}'
-
             _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
             v = read_posed_garment_frame(folder, id, garment, 0)[mask,:]
             m = tm.Trimesh(v, faces, process=False, validate=False)
@@ -132,6 +124,14 @@ class Cache:
             faces = np.concatenate((faces, outliers_faces), axis=0)
             m = tm.Trimesh(v, faces, process=False, validate=False)
 
+            if self.include_neighbours:
+                k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :self.neighbourhood]
+                self.cache[id].k_neighbors = k_neighbors
+                G = tm.graph.vertex_adjacency_graph(m)
+                for i in range(v.shape[0]):
+                    for k in range(25):
+                        assert nx.has_path(G, i, k_neighbors[i, k]), f'No path between {i} and {k_neighbors[i, k]}'
+
             self.cache[id].mask = mask
             self.cache[id].faces = faces
 
@@ -162,7 +162,6 @@ def calculate_dist(folder, neighbourhood, frames):
 
 
 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('/'.join(folder.split('/')[:-1]), 'backup', f'mesh_curvature_raw'), exist_ok=True)
@@ -176,9 +175,6 @@ def generate_curvatures(folder, neighbourhood, frames):
         frame_nr = int(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)
         mask = cache[id].mask
         faces = cache[id].faces
         v = read_posed_garment_frame(folder, id, garment, frame_nr)[mask,:]
@@ -194,7 +190,6 @@ def generate_curvatures(folder, neighbourhood, frames):
         out = np.zeros((14475), dtype=np.float32)
         out[mask] = curvature
 
-
         np.savez_compressed(os.path.join('/'.join(folder.split('/')[:-1]), 'backup', f'mesh_curvature_raw', f'{id}_{frame_nr}_{garment}.npz'), mesh_curvature_raw=out)
         group = h5file.get_node(f'/{id}/{frame_nr}/{garment}')
         if f'curvature_raw' in group:
@@ -204,7 +199,6 @@ def generate_curvatures(folder, neighbourhood, frames):
             carray = h5file.create_carray(group, "curvature_raw", atom=tb.Float32Atom(), shape=out.shape, filters=filters)
             carray[:] = out
 
-
     return None
 
 
@@ -283,23 +277,12 @@ def visualize_curvature_labels(folder, frames):
         verts = read_posed_garment_frame(folder, id, garment, frame_nr)[mask, :]
         curv = h5file.get_node(f'/{id}/{frame_nr}/{garment}/curvature_mesh_global').read()[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(curv.max()))])
         colors_binned = np.zeros((curv.shape[0], 3))
         for i in range(1, int(curv.max())+1):
             # print(np.count_nonzero(curv == i))
             colors_binned[curv == i, :] = colors[i-1]
-        # 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],

data/utils.py

@@ -1,4 +1,4 @@
-
+import tables as tb
 
 class Frame:
     def __init__(self, id, frame_nr, garment):
@@ -12,3 +12,23 @@ class Frame:
     def __eq__(self, other):
         return (self.id, self.frame_nr) == (other.id, other.frame_nr)
 
+
+def save_to_h5file(h5file, id, frame_nr, garment, data, name, type):
+    filters = tb.Filters(complevel=9, complib='blosc2')
+    group = h5file.get_node(f'/{id}/{frame_nr}/{garment}')
+
+    if type == 'float32':
+        atom = tb.Float32Atom()
+    elif type == 'uint8':
+        atom = tb.UInt8Atom()
+    elif type == 'bool':
+        atom = tb.BoolAtom()
+    else:
+        raise Exception('Unknown type')
+
+    if f'{name}' in group:
+        array = h5file.get_node(f'/{id}/{frame_nr}/{garment}/{name}')
+        array[:] = data
+    else:
+        carray = h5file.create_carray(group, name, atom=atom, shape=data.shape, filters=filters)
+        carray[:] = data