investigate duplicate vertices

data/investigate.py

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+import os
+import sys
+import random
+import numpy as np
+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, read_curvatures, read_curvature_frame, writeOBJ
+from custom_utils.custom_utils import _maskFaces
+
+
+def get_frames(folder, frames_file):
+    frames = []
+    with open(os.path.join(folder, frames_file), 'r') as f:
+        for line in f:
+            frames.append(line.strip())
+    return frames
+
+
+def plot(mesh, faces):
+    fig = go.Figure()
+
+    fig.add_trace(go.Mesh3d(
+                x=mesh[:,0],
+                y=mesh[:,1],
+                z=mesh[:,2],
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2]
+            ),)
+    
+    fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
+                    )
+    helper = dict(xaxis = dict(nticks=4, range=[-1,1],),
+                yaxis = dict(nticks=4, range=[-1,1],),
+                zaxis = dict(nticks=4, range=[-1,1],),)
+    fig.update_layout(scene = helper,
+                        )
+
+    fig.show()
+
+
+def build_lookup_table(repeated):
+    # iterate through the list and create a dict from each sub array in the list where each dict has one element of the sub array as key and the other elements as value as a list and repeat for each element
+
+    lookup = {}
+    for i in range(len(repeated)):
+        for j in range(len(repeated[i])):
+            if len(repeated[i]) == 1:
+                continue
+            lookup[repeated[i][j]] = [repeated[i][k] for k in range(len(repeated[i]))] # if k != j
+    return lookup
+
+def mask_faces(all_faces, mask, repeated, verts):
+    # if any of the vertices of a face is masked, mask the face
+    lookup = build_lookup_table(repeated)
+    faces = []
+    for face in all_faces:
+        # if any two or all three vertices of the face have the same coordinates then continue
+        if np.allclose(verts[face[0]], verts[face[1]]) or np.allclose(verts[face[0]], verts[face[2]]) or np.allclose(verts[face[1]], verts[face[2]]):
+            continue
+
+        if mask[face[0]] and mask[face[1]] and mask[face[2]]:
+            faces.append(face)
+        else:
+            # create three arrays of vertices from the lookup table for each vertex of the face
+            # but only for those vertices that are in the lookup table, otherwise createa a single element array with the vertex
+            v1 = lookup[face[0]] if face[0] in lookup else [face[0]]
+            v2 = lookup[face[1]] if face[1] in lookup else [face[1]]
+            v3 = lookup[face[2]] if face[2] in lookup else [face[2]]
+            # try each combination of vertices from the three arrays and check if the face is masked
+            found_replacement = False
+            for i in range(len(v1)):
+                if not found_replacement:
+                    for j in range(len(v2)):
+                        if not found_replacement:
+                            for k in range(len(v3)):
+                                if not found_replacement:
+                                    if mask[v1[i]] and mask[v2[j]] and mask[v3[k]]:
+                                        # if the euclidean distance between any two of the vertices of the face is less than 0.05 then append the face
+                                        # if np.linalg.norm(verts[v1[i]] - verts[v2[j]]) < 0.05 and np.linalg.norm(verts[v1[i]] - verts[v3[k]]) < 0.05 and np.linalg.norm(verts[v2[j]] - verts[v3[k]]) < 0.05:
+                                        faces.append(np.array([v1[i], v2[j], v3[k]], dtype=np.int32))
+                                        assert np.all(verts[face] == verts[faces[-1]])
+                                        found_replacement = True
+                                        break
+                # for j in range(len(v2)):
+                #     for k in range(len(v3)):
+                #         if mask[v1[i]] and mask[v2[j]] and mask[v3[k]]:
+                #             faces.append([v1[i], v2[j], v3[k]])
+                #             break
+
+    faces = np.array(faces)
+    V_ind = np.zeros(mask.shape, dtype=np.int32)
+    V_ind[mask] = np.arange(mask.sum())
+    F_new = np.take(V_ind, faces.reshape(-1), axis=0).reshape((-1, 3))
+    return F_new
+
+
+def check(folder, frame):
+    id, frame_nr, garment = frame.split()
+    frame_nr = int(frame_nr)
+    info = loadInfo(os.path.join(folder, id, 'info.mat'))
+    mask = read_garment_mask(folder, id, garment)
+    mesh_posed = read_posed_garment_frame(folder, id, garment, frame_nr, mask=mask, shape=info['shape'], pose=info['poses'][:,frame_nr], deorient=use_no_orient, unshape=no_shape)
+
+    v = mesh_posed[mask,:]
+    is_skirt = (garment == 'Dress' or garment == 'Skirt')
+    skirt_mod = '_skirt' if is_skirt else ''
+    F = np.load(f'custom_utils/smpl_faces{skirt_mod}.npy').astype(np.int32)
+    _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
+
+    # plot(v, faces)
+
+    unq, idx, count = np.unique(mesh_posed, axis=0, return_counts=True, return_index=True)
+    repeated_groups = unq[count > 1]
+
+    mask_2 = np.zeros((14475), dtype=bool)
+    mask_2[idx] = True
+    v_2 = mesh_posed[mask_2,:]
+    _, faces_2 = _maskFaces(np.zeros((14475,3)), F, mask_2)
+    # plot(v_2, faces_2)
+
+    # writeOBJ('temp/investigate.obj', v, faces)
+
+    repeated = []
+    for repeated_group in repeated_groups:
+        repeated_idx = np.argwhere(np.all(mesh_posed == repeated_group, axis=1))
+        repeated.append(repeated_idx.ravel())
+
+    faces_3 = mask_faces(F, mask_2, repeated, mesh_posed)
+    writeOBJ('temp/investigate_3.obj', v_2, faces_3)
+    plot(v_2, faces_3)
+
+
+    pass
+    
+
+
+if __name__ == "__main__":
+    folder = '/home/workspace/data/dataset/cloth3d/train'
+    frames = get_frames(folder, 'frames_all.txt')
+    root = '/'.join(folder.split('/')[:-1])
+    type = folder.split('/')[-1]
+
+    use_no_orient = False
+    no_shape = False
+    is_skirt = True
+
+    skirt_modifier = '_skirt' if is_skirt else ''
+    orient_mod = '_no_orient' if use_no_orient else ''
+    shape_mod = '_no_shape' if no_shape else ''
+
+
+    while True:
+        frame = random.choice(frames)
+        frame = '05936 0 Dress'
+
+        check(folder, frame)
+
+        pass