fixed boundaries

data/registration_weights.py

@@ -30,7 +30,7 @@ from data.cloth3d_io import (
 from smpl.smpl_torch import SMPL
 # from custom_utils.custom_utils import _maskFaces
 
-device = 'cpu'
+device = 'cuda'
 smpl = SMPL(device, 'smpl')
 
 
@@ -555,12 +555,12 @@ def get_boundaries(folder, frame):
     registered_cloth = np.load('temp/matlab_match_registered.npy')
     registered_faces = np.load('temp/matlab_match_faces.npy')
     registered_weight_faces = np.load('temp/matlab_match_weights_faces.npy')
-    registred_weights = np.load('temp/matlab_match_weights_weights.npy')
+    registered_weights = np.load('temp/matlab_match_weights_weights.npy')
     _, faces, _, _ = readOBJ(os.path.join(folder, id, garment + '.obj'))
     faces = quads2tris(faces)
     template = np.load('temp/mapping_cloth.npy')
 
-    registered_cloth = (template[registered_weight_faces] * registred_weights[:,:,None]).sum(1)
+    registered_cloth = (template[registered_weight_faces] * registered_weights[:,:,None]).sum(1)
 
     registered_meshfix = MeshFix(registered_cloth, registered_faces.astype(np.int32))
     registered_holes = registered_meshfix.extract_holes()
@@ -586,46 +586,51 @@ def get_boundaries(folder, frame):
         registered_border.extend(ind.ravel())
         registered_border_mask[i] = True
 
-    np.save('temp/registered_border.npy', registered_border)
     np.save('temp/template_border.npy', template_border)
     
     lines = generate_edges(registered_cloth, registered_holes, registered_border)
     lines_new = reindex_lines(lines, registered_border_mask)
     bounds, bounds_edges_global, bounds_edges_local, labels = get_boundary_components(registered_border, lines_new)
 
+    # for k in range(labels.max()+1):
+    #     bounds_edges_reindexed = reindex_edges(bounds_edges_local[k].copy(), labels == k)
+    #     verts_order, edges_order = order(bounds[k], bounds_edges_reindexed)
+    #     traces = []
+    #     for i in range(bounds_edges_global[k].shape[0]):
+    #         traces.append(go.Scatter3d(
+    #             x=registered_cloth[bounds_edges_global[k][i],0], y=registered_cloth[bounds_edges_global[k][i],1], z=registered_cloth[bounds_edges_global[k][i],2],
+    #             mode='lines', line=dict(color='red', width=1)))
+    #     for i in range(15):
+    #         traces.append(go.Scatter3d(
+    #             x=registered_cloth[bounds_edges_global[k][edges_order][i],0], y=registered_cloth[bounds_edges_global[k][edges_order][i],1], z=registered_cloth[bounds_edges_global[k][edges_order][i],2],
+    #             mode='lines', line=dict(color='blue', width=1)))
+    #     fig = go.Figure(data=traces)
+    #     fig.update_layout(scene = dict(
+    #         xaxis = dict(range=[-1,1],),
+    #         yaxis = dict(range=[-1,1],),
+    #         zaxis = dict(range=[-1,1],),),)
+    #     fig.update_layout(scene_aspectmode='cube')
+    #     fig.show()
+
     for k in range(labels.max()+1):
         bounds_edges_reindexed = reindex_edges(bounds_edges_local[k].copy(), labels == k)
         verts_order, edges_order = order(bounds[k], bounds_edges_reindexed)
-        traces = []
+        bounds_edges_global[k] = bounds_edges_global[k][edges_order]
-        for i in range(bounds_edges_global[k].shape[0]):
+        bounds[k] = bounds[k][verts_order]
-            traces.append(go.Scatter3d(
+
-                x=registered_cloth[bounds_edges_global[k][i],0], y=registered_cloth[bounds_edges_global[k][i],1], z=registered_cloth[bounds_edges_global[k][i],2],
+    np.savez('temp/registered_border.npz', {f'bound_{i}': bounds[i] for i in range(len(bounds))})
-                mode='lines', line=dict(color='red', width=1)))
+    np.savez('temp/registered_border_edges.npz', {f'bound_edges_{i}': bounds_edges_global[i] for i in range(len(bounds_edges_global))})
-        for i in range(15):
+
-            traces.append(go.Scatter3d(
+
-                x=registered_cloth[bounds_edges_global[k][edges_order][i],0], y=registered_cloth[bounds_edges_global[k][edges_order][i],1], z=registered_cloth[bounds_edges_global[k][edges_order][i],2],
+    # for k in range(labels.max()+1):
-                mode='lines', line=dict(color='blue', width=1)))
+    #     for j in range(bounds[k].shape[0]):
-        fig = go.Figure(data=traces)
+    #         w_1 = registered_weights[bounds[k][j]]
-        fig.update_layout(scene = dict(
+    #         w_2 = registered_weights[bounds[k][(j+1)%bounds[k].shape[0]]]
-            xaxis = dict(range=[-1,1],),
+
-            yaxis = dict(range=[-1,1],),
+            # pass
-            zaxis = dict(range=[-1,1],),),)
-        fig.update_layout(scene_aspectmode='cube')
-        fig.show()
 
 
 
-    k = 0
-    traces = [go.Scatter3d(
-            x=registered_cloth[bounds[k],0], y=registered_cloth[bounds[k],1], z=registered_cloth[bounds[k],2],
-            mode='markers', line=dict(color='red', width=1))]
-    fig = go.Figure(data=traces)
-    fig.update_layout(scene = dict(
-        xaxis = dict(range=[-1,1],),
-        yaxis = dict(range=[-1,1],),
-        zaxis = dict(range=[-1,1],),),)
-    fig.update_layout(scene_aspectmode='cube')
-    fig.show()
 
     # plot registered cloth and tempalte as scatterplots and add another two scatter plots for the two boundary sets
     fig = go.Figure(data=[
@@ -962,7 +967,11 @@ def flatten_boundary(folder, frame):
 
 
     template_border = np.load('temp/template_border.npy')
-    registered_border = np.load('temp/registered_border.npy')
+    registered_border = np.load('temp/registered_border.npz', allow_pickle=True)['arr_0'].item()
+    registered_borders = [registered_border[k] for k in registered_border.keys()]
+    registered_border_edges = np.load('temp/registered_border_edges.npz', allow_pickle=True)['arr_0'].item()
+    registered_border_edges = [registered_border_edges[k] for k in registered_border_edges.keys()]
+    registered_border = np.concatenate(registered_borders)
 
     template_border_tree = KDTree(template[template_border,:])
 
@@ -996,6 +1005,63 @@ def flatten_boundary(folder, frame):
         yaxis = dict(range=[-1,1],),
         zaxis = dict(range=[-1,1],),),)
     fig.update_layout(scene_aspectmode='cube')
+    # fig.show()
+
+    fig = go.Figure(data=[
+        go.Mesh3d(
+            x=registered_cloth[:,0], y=registered_cloth[:,1], z=registered_cloth[:,2],
+            i=registered_faces[:,0], j=registered_faces[:,1], k=registered_faces[:,2],
+            color='lightpink', opacity=1),
+    ])
+    fig.update_layout(scene = dict(
+        xaxis = dict(range=[-1,1],),
+        yaxis = dict(range=[-1,1],),
+        zaxis = dict(range=[-1,1],),),)
+    fig.update_layout(scene_aspectmode='cube')
+    # fig.show()
+
+    # save registered_cloth as obj
+    # writeOBJ('temp/test_registered.obj', registered_cloth, registered_faces.astype(np.int32))
+
+
+    for k in range(len(registered_borders)):
+        border = registered_borders[k]
+        edges = registered_border_edges[k]
+        for i in range(border.shape[0]):
+            w_1 = weights_weights[border[i],:]
+            w_2 = weights_weights[border[(i+1)%border.shape[0]],:]
+            f_1 = weights_faces[border[i],:]
+            f_2 = weights_faces[border[(i+1)%border.shape[0]],:]
+
+            if np.all(f_1 == f_2):
+                reference = registered_cloth[f_1[0],:]
+                dist_1 = np.linalg.norm(registered_cloth[border[i],:] - reference, axis=0)
+                dist_2 = np.linalg.norm(registered_cloth[border[(i+1)%border.shape[0]],:] - reference, axis=0)
+                if (dist_1 < dist_2 and w_1[0] < w_2[0]) or (dist_1 > dist_2 and w_1[0] > w_2[0]):
+                    weights_weights[border[(i+1)%border.shape[0]],:] = weights_weights[border[i],:]
+
+                reference = registered_cloth[f_1[1],:]
+                dist_1 = np.linalg.norm(registered_cloth[border[i],:] - reference, axis=0)
+                dist_2 = np.linalg.norm(registered_cloth[border[(i+1)%border.shape[0]],:] - reference, axis=0)
+                if (dist_1 < dist_2 and w_1[1] < w_2[1]) or (dist_1 > dist_2 and w_1[1] > w_2[1]):
+                    weights_weights[border[(i+1)%border.shape[0]],:] = weights_weights[border[i],:]
+                # pass
+            # registered_cloth
+
+            # pass
+
+    registered_cloth = (template[weights_faces] * weights_weights[:,:,None]).sum(1)
+    fig = go.Figure(data=[
+        go.Mesh3d(
+            x=registered_cloth[:,0], y=registered_cloth[:,1], z=registered_cloth[:,2],
+            i=registered_faces[:,0], j=registered_faces[:,1], k=registered_faces[:,2],
+            color='lightpink', opacity=1),
+    ])
+    fig.update_layout(scene = dict(
+        xaxis = dict(range=[-1,1],),
+        yaxis = dict(range=[-1,1],),
+        zaxis = dict(range=[-1,1],),),)
+    fig.update_layout(scene_aspectmode='cube')
     fig.show()
 
     pass
@@ -1036,8 +1102,8 @@ def fill_holes(folder, frame):
 
 
 if __name__ == "__main__":
-    # folder = '/data-net/code/cloth3d/train'
+    folder = '/data-net/code/cloth3d/train'
-    folder = r'D:\Work\code\cloth3d\train'
+    # folder = r'D:\Work\code\cloth3d\train'
     # frames = [f'00761_{i}' for i in range(300)]
     # folder = r'Z:\Work\Huni\Barcelona\Dataset\cloth3d\train'
     frames = get_frames(folder, 'frames_all.txt')
@@ -1063,9 +1129,9 @@ if __name__ == "__main__":
         
 
         # register(folder, frame)
-        get_boundaries(folder, frame)
+        # get_boundaries(folder, frame)
         # optimize_boundaries_2(folder, frame)
-        # flatten_boundary(folder, frame)
+        flatten_boundary(folder, frame)
         # fill_holes(folder, frame)
         break
         pass