plot

aaa_helpers.txt

@@ -1,8 +1,3 @@
-nohup python cvae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/review/abl_all_10p_curv_10_r2.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_10_10p_r2" > nohup/review/curv_1_orig_curv_10_10p_r2.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_25_10p_r2" > nohup/review/curv_1_orig_curv_25_10p_r2.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_100_10p_r2" > nohup/review/curv_1_orig_curv_100_10p_r2.out 2>&1 &
-
 one final convolutional layer for smoothing, on the final coordinates
 
 skirts!!!!

configs/config_abl_overfit_jumpsuit_2.py

@@ -19,7 +19,7 @@ config['only_curvatures'] =           True
 ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
 assert ablation_count == 1 or ablation_count == 0
 
-config['gpu'] = '5'
+config['gpu'] = '3'
 config['name'] = 'abl_overfit_jumpsuit_2'
 config['notes'] = 'remove other conditions from input as well'
 

convert_to_uv_local_single.py

@@ -384,9 +384,9 @@ def test(verts, faces, values):
 
 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/local-data/valid')
     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=1500)
     parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
     parser.add_argument("-sk", "--skirt", help = "end of range", default=1)
     args = parser.parse_args()
@@ -408,7 +408,7 @@ if __name__ == '__main__':
     m2uv = np.load('custom_utils/m2uv' + ("_skirt" if args.skirt == 1 else "") + '.npy', allow_pickle=True).item()
     m2uv = m2uv_array(m2uv)
     uv_mask = np.load('custom_utils/mask_256x512' + ("_skirt" if args.skirt == 1 else "") + '.npy')
-    curvature_label_type = '6_25 '
+    curvature_label_type = '6_25'
 
     uv_res = 256
     uv_mask = (cv2.resize(uv_mask * 255.0, (uv_res, uv_res * 2)) / 255).astype(bool)[:, :, np.newaxis]
@@ -436,23 +436,6 @@ if __name__ == '__main__':
 
     # ids = os.listdir(os.path.join(local_folder, 'mesh_static'))[int(args.start):int(args.end)]
     # ids = [id.split('.')[0] for id in ids]
-
-    is_skirt = True if args.skirt == 1 else False
-    skirt_modifier = '_skirt' if is_skirt else ''
-
-    folder = args.folder
-    frames_file = 'frames' + skirt_modifier + '_10p.txt'
-    neighbourhood = 10
-
-    # 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)]
-    # 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} -neigh {neighbourhood}')
-
     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)]
@@ -469,16 +452,16 @@ if __name__ == '__main__':
         # plt.show()
         # pass
 
-        frames_len = glob.glob(os.path.join(local_folder, 'pose_shape', id + '_*'))
+        frames = glob.glob(os.path.join(local_folder, 'pose_shape', id + '_*'))
-        frames_len = len(frames_len)
+        frames = len(frames)
-        assert frames_len <= 300
+        assert frames <= 300
 
         # offsets = sio.loadmat(os.path.join(folder, id, garment + '_offsets_static.mat'))['label'][:,:3]
         verts_static = np.load(os.path.join(local_folder, 'mesh_static', id + '.npz'))['mesh_static']
         mask = np.load(os.path.join(local_folder, 'mesh_mask', id + '.npy')).astype(bool)
         # body = readPC2(os.path.join(folder, id, 'Body_posed.pc16'),  True)['V']
         # gt = readPC2(os.path.join(folder, id, garment + '_registered.pc16'),  True)['V']
-        curvature = np.stack([np.load(os.path.join(local_folder, f'mesh_curvature_{neighbourhood}_2',  f'{id}_{i}.npz'))['mesh_curvature'] for i in range(frames_len)])
+        # curvature = np.load(os.path.join(folder, id, 'curvature_label_' + curvature_label_type + '.npy'))
         # 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])]
@@ -487,8 +470,8 @@ if __name__ == '__main__':
         # body_no_orient = np.stack([body_no_orient[i]['mesh_posed_body_no_orient'] for i in range(gt.shape[0])])
         # mesh_unposed_no_shape_one_hot = [np.load(os.path.join(local_folder, 'mesh_unposed_no_shape_one_hot',  id + '_' + str(i) + '.npz')) for i in range(frames)]
         # mesh_unposed_no_shape_one_hot = [mesh_unposed_no_shape_one_hot[i]['mesh_unposed_no_shape'] for i in range(frames)]
-        # mesh_posed_no_orient = [np.load(os.path.join(local_folder, 'mesh_posed_no_orient',  id + '_' + str(i) + '.npz')) for i in range(frames)]
+        mesh_posed_no_orient = [np.load(os.path.join(local_folder, 'mesh_posed_no_orient',  id + '_' + str(i) + '.npz')) for i in range(frames)]
-        # mesh_posed_no_orient = [mesh_posed_no_orient[i]['mesh_posed_no_orient'] for i in range(frames)]
+        mesh_posed_no_orient = [mesh_posed_no_orient[i]['mesh_posed_no_orient'] for i in range(frames)]
 
 
         # verts_dynamic = gt
@@ -508,11 +491,11 @@ if __name__ == '__main__':
         # 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)
         # 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_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)
-        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, 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_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_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)
-        # 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_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_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)
 
@@ -533,9 +516,9 @@ if __name__ == '__main__':
         # for i in range(uv_gt.shape[0]):
         #     np.savez_compressed(os.path.join(folder, id, 'gt_uv', garment + '_' + str(i) + '.npz'), uv_gt=uv_gt[i, :, :, :])
 
-        os.makedirs(os.path.join(folder, f'uv_curvature_{neighbourhood}_2'), exist_ok=True)
+        # os.makedirs(os.path.join(folder, id, 'curvature_uv'), exist_ok=True)
-        for i in range(uv_curvature.shape[0]):
+        # for i in range(uv_curvature.shape[0]):
-            np.savez_compressed(os.path.join(folder, f'uv_curvature_{neighbourhood}_2', id + '_' + str(i) + '.npz'), curvature_uv=uv_curvature[i, :, :])
+        #     np.savez_compressed(os.path.join(folder, id, 'curvature_uv', garment + '_' + str(i) + '.npz'), curvature_uv=uv_curvature[i, :, :])
 
         # os.makedirs(os.path.join(folder, id, 'uv_unposed'), exist_ok=True)
         # for i in range(uv_unposed.shape[0]):

curvature_analysis.py

@@ -1,5 +1,8 @@
 import os
 import sys
+os.environ["CUDA_VISIBLE_DEVICES"] = '4'
+
+
 import tqdm
 import argparse
 import random
@@ -16,9 +19,33 @@ from scipy.sparse import csr_matrix, coo_matrix
 from scipy.sparse.csgraph import floyd_warshall, dijkstra
 from plotly.subplots import make_subplots
 from custom_utils.custom_utils import _maskFaces
+from postprocessing import fix_collision
 
 from custom_utils.supersmpl_io import readPC2, readPC2Frame, writePC2, readOBJ
 from custom_utils.custom_utils import _maskFaces
+from auto_encoder_curv import dice_dense
+
+def postprocess(mesh, faces, mask=None, body=None, do_smoothing=True):
+    if body is not None:
+        mesh_fixed = fix_collision(mesh, body[np.newaxis,:,:], mask[np.newaxis,:])[0].numpy()[mask, :]
+    else:
+        mesh_fixed = mesh
+
+    if do_smoothing:
+        m = tm.Trimesh(mesh_fixed, 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(mesh_fixed, outliers, 2)
+        outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
+        faces_fixed = np.concatenate((faces, outliers_faces), axis=0)
+        m = tm.Trimesh(mesh_fixed, faces_fixed, process=False, validate=False)
+        tm.smoothing.filter_humphrey(m, iterations=1, alpha=0.6, beta=0.4)
+
+        mesh = m.vertices
+    else:
+        mesh = mesh_fixed
+
+    return mesh
 
 def export_to_ply(mesh, file):
     ply = tm.exchange.ply.export_ply(mesh, encoding='binary', vertex_normal=True, include_attributes=True)
@@ -67,13 +94,13 @@ def get_bins(values, nr_of_bins):
     return bins
 
 
-def generate_curvatures(folder, frame, bins, neighbourhoods=[25], nr_of_labels=[6]):
+def generate_curvatures(v, faces, mask, neighbourhood=25, nr_of_labels=6):
-    id = frame.split('_')[0]
+    # id = frame.split('_')[0]
-    frame_nr = int(frame.split('_')[1].split('.')[0])
+    # frame_nr = int(frame.split('_')[1].split('.')[0])
 
-    mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
+    # mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
-    _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
+    # _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
-    v = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
+    # 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)
 
@@ -90,39 +117,41 @@ def generate_curvatures(folder, frame, bins, neighbourhoods=[25], nr_of_labels=[
 
     cloud = PyntCloud.from_file(temp_file)
 
-    curvatures = []
+    k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
-    for neighbourhood, nr_labels in zip(neighbourhoods, nr_of_labels):
+    #k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
-        k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+    ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
-        #k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+    cloud.add_scalar_field("curvature", ev=ev)
-        ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
+    curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
-        cloud.add_scalar_field("curvature", ev=ev)
+    
-        curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
+    bins = np.load(f'custom_data/train_bins.npy') # get_bins(curvature, nr_of_labels)
-        
+    labels = np.linspace(0, bins.shape[0] - 2, bins.shape[0] - 1)
-        bins_local = bins[neighbourhood] if bins is not None else get_bins(curvature, nr_labels)
-        labels = np.linspace(0, bins_local.shape[0] - 2, bins_local.shape[0] - 1)
 
-        frame_labels = np.zeros((curvature.shape[0]), dtype=np.float32)
+    frame_labels = np.zeros((curvature.shape[0]), dtype=np.float32)
-        for j in range(bins_local.shape[0] - 2):
+    for j in range(bins.shape[0] - 2):
-            frame_labels[(curvature >= bins_local[j]) * (curvature < bins_local[j+1])] = labels[j]
+        frame_labels[(curvature >= bins[j]) * (curvature < bins[j+1])] = labels[j]
-        frame_labels[curvature >= bins_local[-2]] = labels[-1]
+    frame_labels[curvature >= bins[-2]] = labels[-1]
+       
+    return frame_labels
 
-        curvatures.append(frame_labels)
+import tensorflow as tf
-        
-    return curvatures
 
-
+def visualize_curvature_labels(folder, results_folder, frame):
-def visualize_curvature_labels(folder, frame, bins, neighbourhoods, nr_of_labels):
     id = frame.split('_')[0]
     frame_nr = frame.split('_')[1].split('.')[0]
 
     mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
     _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
     verts = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
-    
+    pred = np.load(os.path.join(results_folder, frame + '.npy'))
-    curvature_labels_all = generate_curvatures(folder, frame, bins, neighbourhoods, nr_of_labels)
+    pred = postprocess(pred, faces)
+
+    curvature_labels_gt = generate_curvatures(verts, faces, mask, 25)
+    curvature_labels_pred = generate_curvatures(pred, faces, mask, 25)
+    # dice = dice_dense(curvature_labels_pred[None,:], curvature_labels_gt[None,:])
+    dice = dice_dense(tf.one_hot(curvature_labels_pred, 7)[None,:], tf.one_hot(curvature_labels_gt, 7)[None,:]).numpy()
     colors_binned_all = []
 
-    for curvature_labels in curvature_labels_all:
+    for curvature_labels in [curvature_labels_gt, curvature_labels_pred]:
         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):
@@ -130,10 +159,20 @@ def visualize_curvature_labels(folder, frame, bins, neighbourhoods, nr_of_labels
             colors_binned[curvature_labels == i, :] = colors[i]
         colors_binned_all.append(colors_binned)
 
+    fig = make_subplots(rows=1, cols=3, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(3)] ])
 
-    fig = make_subplots(rows=1, cols=len(colors_binned_all)+1, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(len(colors_binned_all)+1)] ])
+    fig.add_trace(go.Mesh3d(
-
+                # 8 vertices of a cube
-    colors = np.ones((verts.shape[0],3))*np.array([255,200,200])[None,:]
+                x=-pred[:,0],
+                y=pred[:,2],
+                z=pred[:,1],
+                # i, j and k give the vertices of triangles
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2],
+                vertexcolor=np.ones((verts.shape[0],3))*np.array([255,200,200])[None,:]
+            ),
+            row=1, col=2)
 
     fig.add_trace(go.Mesh3d(
                 # 8 vertices of a cube
@@ -144,32 +183,28 @@ def visualize_curvature_labels(folder, frame, bins, neighbourhoods, nr_of_labels
                 i = faces[:,0],
                 j = faces[:,1],
                 k = faces[:,2],
-                vertexcolor=colors,
+                vertexcolor=colors_binned_all[0]
-                lighting={ 'ambient':0.9, 'diffuse':0.5, 'roughness':0.5 }
             ),
             row=1, col=1)
-
-    for i, colors_binned in enumerate(colors_binned_all):
-        fig.add_trace(go.Mesh3d(
-                    # 8 vertices of a cube
-                    x=verts[:,0],
-                    y=verts[:,1],
-                    z=verts[:,2],
-                    # i, j and k give the vertices of triangles
-                    i = faces[:,0],
-                    j = faces[:,1],
-                    k = faces[:,2],
-                    vertexcolor=colors_binned,
-                    # lighting={ 'ambient':0.8, 'diffuse':0.5, 'roughness':0.5 }
-                ),
-                row=1, col=i+2)
     
-
+    fig.add_trace(go.Mesh3d(
+                # 8 vertices of a cube
+                x=-pred[:,0],
+                y=pred[:,2],
+                z=pred[:,1],
+                # i, j and k give the vertices of triangles
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2],
+                vertexcolor=colors_binned_all[1],
+                text=f'dice: {dice}'
+            ),
+            row=1, col=3)
 
     fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
                     scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
                     scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
-                    scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
+                    # scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
                     # scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
                     )
     helper = dict(xaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
@@ -179,7 +214,7 @@ def visualize_curvature_labels(folder, frame, bins, neighbourhoods, nr_of_labels
     fig.update_layout(scene = helper,
                         scene2 = helper,
                         scene3 = helper,
-                        scene4 = helper,
+                        # scene4 = helper,
                         # scene5 = helper,
                         )
 
@@ -203,33 +238,46 @@ def visualize_curvature_labels(folder, frame, bins, neighbourhoods, nr_of_labels
     #                     zaxis = dict(nticks=4, range=[-1,1],),)
     #                     )
 
-    fig.show()
+    return fig, dice
 
-    print()
+import wandb
 
+def init_wandb():
+    run = wandb.init(project='plots', name='review_4', dir='/home/workspace/local-data/temp/')
+    artifact = wandb.Artifact('code', type='code')
+    artifact.add_file('curvature_analysis.py')
+    run.log_artifact(artifact)
 
 if __name__ == '__main__':
+    # init_wandb()
 
-    is_skirt = True
+    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 = '/home/workspace/local-data/valid_skirt'
+    folder = '/home/workspace/local-data/train'
-    bins = dict()
+    results_folder = '/home/workspace/data/results/post/abl_overfit_jumpsuit_2-'
-    bins[10] = np.load(f'custom_data/train_bins_skirt_10.npy')
-    bins[25] = np.load(f'custom_data/train_bins_skirt.npy')
-    bins[100] = np.load(f'custom_data/train_bins_skirt_100.npy')
 
-    bins = None
+    frames_file = 'frames_jumpsuit_30k.txt'
+    frames = []
+    with open(os.path.join(folder, frames_file), 'r') as f:
+        lines = f.readlines()
+        lines = [line.strip() for line in lines]
+    frames = lines
 
-
-    nr_of_labels = 6
-    neighbourhood = 50
     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')
 
-    visualize_curvature_labels(folder, '02572_1', bins, neighbourhoods=[25, 25, 25], nr_of_labels=[3,6,12])
+    i = 0
+    while True:
+        frame = np.random.choice(frames)
+        # frame = '03265_193'
+        frame = '03102_49'
+        fig, dice = visualize_curvature_labels(folder, results_folder, frame)
+        wandb.log({f'{frame}-dice:{dice}': fig}, step=i)
+        i += 1
+        print(frame)

curvature_label_parallel.py

@@ -4,17 +4,15 @@ import os
 
 if __name__ == '__main__':
 
-    # folder = '/home/workspace/data/dataset/test_skirt_ready_2'
+    folder = '/home/workspace/local-data/cape/train/male'
-    # folder = '/home/workspace/data/dataset/local-cache/train_skirt'
+    out_folder = '/home/workspace/local-data/cape_processed/train/male'
-    folder = '/home/workspace/local-data/valid_skirt'
+    ids = os.listdir(folder)
-    ids = os.listdir(folder + '/mesh_posed')
 
     count = 0
-    step = 10000
+    step = 1
 
-    while count < 20*step:
+    while count < len(ids):
-    # while count < 10000:
         # os.system('nohup python MeshUtils/curvature_labels.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'curv_train_' + str(count) + '_' + str(count+step) + ' > curv_train_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
-        os.system('nohup python curvature_labels.py -f ' + folder + ' -s ' + str(0) + ' -e ' + str(0 + step) + ' -n ' + 'speed_test_' + str(count) + '_' + str(count+step) + ' > nohup/speed/speed_test_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
+        os.system('nohup python curvature_labels_cape.py -if ' + folder + ' -of ' + out_folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'curv_gen_2_cape_train_male_' + str(count) + '_' + str(count+step) + ' > nohup/curv_gen_2_cape_train_male_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('python Data/convert_to_uv.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + '')
         count += step

curvature_labels.py

@@ -4,7 +4,6 @@ import tqdm
 import argparse
 import random
 import string
-import time
 import numpy as np
 import trimesh as tm
 import cmapy as cm
@@ -41,8 +40,6 @@ def export_to_ply(mesh, file):
     output_file.close()
 
 
-
-
 def get_closest_n_points(verts, idx, n):
     dist = sp.spatial.distance.cdist(verts[idx, :], verts)
     dist[dist == 0] = 1e9
@@ -83,65 +80,67 @@ def k_neighbours(k):
     pass
 
 
-def calculate_dist(folder, neighbourhood, frames):
+def generate_curvatures(folder, neighbourhood):
-    out = dict()
-    # frames = frames[int(args.start):int(args.end)]
 
-    for frame in tqdm.tqdm(frames):
+    ids = os.listdir(folder)[int(args.start):int(args.end)]
-        id = frame.split('_')[0]
+
-        if id in out.keys():
+    curvatures_all = []
+
+    for id in tqdm.tqdm(ids):
+        garment_dir = os.path.join(folder, id)
+
+        if not os.path.isdir(garment_dir):
             continue
 
-        mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
+        # if os.path.exists(garment_dir + '/curvature_25_raw.npy'):
-        k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+        #     continue
-        out[id] = k_neighbors
 
-    return out
+        curvatures_garment = []
 
-def generate_curvatures(folder, neighbourhood, frames):
+        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
-    # ids = os.listdir(folder)[int(args.start):int(args.end)]
+        if len (garments) != 1:
-    # frames = frames[int(args.start):int(args.end)]
+            continue
-    os.makedirs(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}'), exist_ok=True)
+        garment = garments[0]
-
-    k_neighbors_cache = calculate_dist(folder, neighbourhood, frames)
-
-    # for frame in tqdm.tqdm(frames):
-
-    #     id = frame.split('_')[0]
-    #     frame_nr = frame.split('_')[1].split('.')[0]
     
-    #     # bodies = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
+        bodies = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
-    #     # offsetss = readPC2(os.path.join(folder, id, garment + '_registered.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 = np.squeeze(sio.loadmat(os.path.join(garment_dir, garment + '_mask.mat'))['mask'].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)
+        _, faces = _maskFaces(bodies[0, :, :], F, mask)
+        v = bodies[0, mask, :] + offsetss[0, mask, :]
+        m = tm.Trimesh(v, faces, process=False, validate=False)
 
-    #     outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
+        outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
-    #     outliers = np.nonzero(outliers)[0]
+        outliers = np.nonzero(outliers)[0]
-    #     outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
+        outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
-    #     outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
+        outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
-    #     faces = np.concatenate((faces, outliers_faces), axis=0)
+        faces = np.concatenate((faces, outliers_faces), axis=0)
 
-    #     verts = v
+        step = 0
+        for body, offsets in zip(bodies, offsetss):
+            verts = body + offsets
+            verts = verts[mask, :]
 
-    #     m = tm.Trimesh(verts, faces, process=False, validate=False)
+            m = tm.Trimesh(verts, faces, process=False, validate=False)
-    #     export_to_ply(m, temp_file)
+            export_to_ply(m, temp_file)
 
-    #     cloud = PyntCloud.from_file(temp_file)
+            cloud = PyntCloud.from_file(temp_file)
+            # cloud.plot(mesh=True)
 
-    #     k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+            k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
-    #     ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
+            ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
-    #     cloud.add_scalar_field("curvature", ev=ev)
+            cloud.add_scalar_field("curvature", ev=ev)
-    #     curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
+            curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
-    #     out = np.zeros((14475), dtype=np.float32)
+            curvatures_garment.append(curvature)
-    #     out[mask] = curvature
-        
-    #     np.savez_compressed(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}', f'{id}_{frame_nr}.npz'), mesh_curvature_raw=out)
 
-    return None
+            print(step)
+            step += 1
+
+        # curvatures_all.append(np.array(curvatures_garment))
+        np.save(garment_dir + '/curvature_25_raw.npy', np.array(curvatures_garment))
+
+    return curvatures_all
 
 
 def get_bins(values, nr_of_bins):
@@ -153,23 +152,41 @@ def get_bins(values, nr_of_bins):
     return bins
 
 
-def generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood):
+def generate_curvature_labels(folder, bins, curvatures):
 
-    os.makedirs(os.path.join(folder, f'mesh_curvature_{neighbourhood}_2'), exist_ok=True)
+    ids = os.listdir(folder)#[:10]
     labels = np.linspace(0, bins.shape[0] - 2, bins.shape[0] - 1)
 
-    for i, frame in enumerate(tqdm.tqdm(frames)):
+    i = 0
-        # id = frame.split('_')[0]
+    for id in tqdm.tqdm(ids):
-        # mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy'))
+        garment_dir = os.path.join(folder, id)
-        # print(i) 
+
+        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]
+        mask = np.squeeze(sio.loadmat(os.path.join(garment_dir, garment + '_mask.mat'))['mask'].astype(bool))
+
         curvature = curvatures[i]
+        i += 1
 
-        frame_labels = np.zeros((curvature.shape[0]), dtype=np.float32)
+        curvature_label = np.zeros((curvature.shape[0], curvature.shape[1]))
-        for j in range(bins.shape[0] - 2):
+        frame = 0
-            frame_labels[(curvature >= bins[j]) * (curvature < bins[j+1])] = labels[j]
+        for frame in range(curvature.shape[0]):
-        frame_labels[curvature >= bins[-2]] = labels[-1]
+            frame_labels = np.zeros((curvature.shape[1]), dtype=np.float32)
+            for j in range(bins.shape[0] - 2):
+                frame_labels[(curvature[frame, :] >= bins[j]) * (curvature[frame, :] < bins[j+1])] = labels[j]
+            frame_labels[curvature[frame, :] >= bins[-2]] = labels[-1]
+            curvature_label[frame, :] = frame_labels
+            frame += 1
 
-        np.savez_compressed(os.path.join(folder, f'mesh_curvature_{neighbourhood}_2', f'{frame}.npz'), mesh_curvature=frame_labels)
+        unmasked_label = np.zeros((curvature.shape[0], 14475), dtype=np.float32)
+        unmasked_label[:, mask] = curvature_label
+        np.save(os.path.join(garment_dir, "curvature_label_" + str(nr_of_labels) + "_" + str(neighbourhood) + ".npy"), unmasked_label)
         # writePC2(os.path.join(garment_dir, "curvature_label_" + str(nr_of_labels) + "_" + str(neighbourhood) + ".pc16"), unmasked_label, True)
 
 
@@ -240,10 +257,24 @@ def visualize_curvature_labels(folder):
         print()
 
 
-def read_raw_curvatures(folder, frames, neighbourhood):
+def read_raw_curvatures(folder):
+
+    ids = os.listdir(folder)[int(args.start):int(args.end)]
+
     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']
+    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
+
+        curv = np.load(garment_dir + '/curvature_25_raw.npy')
         curvatures_all.append(curv)
     
     return curvatures_all
@@ -251,46 +282,28 @@ def read_raw_curvatures(folder, frames, neighbourhood):
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument("-f", "--folder", help = "process the given folder", default=r'/home/workspace/data/dataset/local-cache/train_skirt')
+    parser.add_argument("-f", "--folder", help = "process the given folder", default=r'/home/workspace/data/dataset/test')
     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=1500)
     parser.add_argument("-n", "--name", help = "end of range", default="lhunor")
     args = parser.parse_args()
 
-    is_skirt = True
+    F = np.load("FeatureLines/superSMPL/smpl_faces.npy")
-    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' + skirt_modifier + '_10p.txt'
-
     nr_of_labels = 6
-    neighbourhood = 100
+    neighbourhood = 25
-    temp_file = '/home/workspace/local-data/temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
+    temp_file = 'temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
-    if not is_skirt:
+    dist = np.load('MeshUtils/dijsktra.npy')
-        dist = np.load('custom_data/dijkstra.npy')
+    # dist = np.load('MeshUtils/dijkstra_skirt.npy')
-    else:
-        dist = np.load('custom_data/dijkstra_skirt.npy')
 
-    print('SKIRT' if is_skirt else 'NOT SKIRT')
+    generate_curvatures(folder, neighbourhood)
-    print(f'params: -f {args.folder} -s {args.start} -e {args.end} -n {args.name} -skirt {is_skirt} -neigh {neighbourhood}')
+    # curvatures = read_raw_curvatures(folder)
-
-    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)]
-
-    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)
+    # np.save('train_bins_skirt.npy', bins)
-    # bins = np.load(f'custom_data/train_bins_skirt_{neighbourhood}.npy')
+    # bins = np.load('train_bins.npy')
-    # generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood)
+    # generate_curvature_labels(folder, bins, curvatures)
     # visualize_curvature_labels(folder)
 
     pass

cvae.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_all_post import config
+from configs.config_abl_overfit_jumpsuit_2 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -41,11 +41,11 @@ from losses import collision_loss as collision_penalty
 from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
-from models.cvae_model import CVAENew as CVAE
+from models.cvae_model import CVAE
 
 
 def init_wandb():
-    run = wandb.init(project='ablation', name=config['name'], notes=config['notes'], dir='/wandb', tags=config['tags'])
+    run = wandb.init(project='ablation', name=config['name'], notes=config['notes'], dir='/wandb')
     artifact = wandb.Artifact('code', type='code')
     artifact.add_file('cvae.py')
     artifact.add_file('models/cvae_model.py')
@@ -58,9 +58,7 @@ def init_wandb():
     artifact.add_file('convnext/models/convnext_tf.py')
     config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.')]
     for config_file_name in config_file_names:
-        file_name = '/'.join(config_file_name.split('.')) + '.py'
+        artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
-        if os.path.exists(file_name):
-            artifact.add_file(file_name)
     run.log_artifact(artifact)
 
 uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_adaptive=False, is_smpl_orig=config['is_cape'])

evaluate_ae.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_all_post import config
+from configs.config_abl_overfit_jumpsuit_2 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -42,7 +42,7 @@ from losses import collision_loss as collision_penalty
 from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
-from models.cvae_model import CVAENew as CVAE
+from models.cvae_model import CVAE
 from custom_utils.supersmpl_io import writeOBJ
 # from unif_metric import chamfer_and_score
 
@@ -104,7 +104,7 @@ if __name__ == '__main__':
 
     iteration = ''
 
-    load_model(model, 'checkpoints/post/' + checkpoint_name, iteration=iteration)
+    load_model(model, 'checkpoints/' + checkpoint_name, iteration=iteration)
     # load_model(model, 'checkpoints/' + 'abl_all_120k')
 
     output_folder = '/home/workspace/data/results/post/' + checkpoint_name + '-' + iteration

fix_curv_uv_loc.py

@@ -1,23 +0,0 @@
-import os
-import shutil
-import tqdm
-
-if __name__ == '__main__':
-    folder = '/home/workspace/data/dataset/local-cache/train_skirt'
-    is_skirt = True 
-    skirt_modifier = '_skirt' if is_skirt else ''
-    frames_file = 'frames' + skirt_modifier + '_10p.txt'
-    neighbourhood = 10
-
-    frames = []
-    with open(os.path.join(folder, frames_file), 'r') as f:
-        lines = f.readlines()
-        lines = [line.strip() for line in lines]
-    frames = lines
-
-    os.makedirs(os.path.join(folder, f'uv_curvature_{neighbourhood}'), exist_ok=True)
-    for frame in tqdm.tqdm(frames):
-        id = frame.split('_')[0]
-        src = os.path.join(folder, f'{id}', 'curvature_uv_10', f'{frame}.npz')
-        dst = os.path.join(folder, f'uv_curvature_{neighbourhood}', f'{frame}.npz')
-        shutil.move(src, dst)

run_parallel.py

@@ -4,26 +4,24 @@ import os
 
 if __name__ == '__main__':
 
-    # folder = '/home/workspace/data/dataset/local-cache/train_skirt'
     folder = '/home/workspace/data/dataset/test_skirt_ready_2'
-    # folder = '/home/workspace/local-data/valid_skirt'
     # folder = '/home/workspace/local-data/cape_processed/train/male'
     # folder = '/home/workspace/local-data/valid_skirt'
     # folder = '/home/workspace/local-data/valid'
     # ids = os.listdir(folder)
-    # ids = os.listdir(folder + '/mesh_posed_no_orient')#[280000:]
+    ids = os.listdir(folder + '/mesh_posed_no_orient')#[280000:]
     skirt = 0
 
     count = 0
-    step = 20
+    step = 2000
 
     # !!!!!!! change skirt parameter in script
 
-    while count < 150:
+    while count < len(ids):
         # os.system('nohup python convert_to_uv_cape.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_2_cape_train_male_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_2_cape_train_male_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('nohup python convert_to_uv.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_unposed_onehot_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_train_unposed_onehot_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('nohup python convert_to_uv_local.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_local_unposed_onehot_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_train_local_unposed_onehot_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
-        os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_curv_2_10_test_skirt_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_curv_2_10_test_skirt_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
+        os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_posed_no_orient_test_skirt_ready_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_posed_no_orient_test_skirt_ready_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_skirt_local_unposed_onehot_3_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_train_skirt_local_unposed_onehot_3_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_valid_skirt_local_unposed_onehot_3_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_valid_skirt_local_unposed_onehot_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('nohup python downscale.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'downscale_valid_posed_no_orient_' + str(count) + '_' + str(count+step) + ' > nohup/downscale_valid_posed_no_orient_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')