60x Speedup on IUV2FBC Function. Greatly speeds up converting a 2D person image to a 3D model.

detectron/utils/densepose_methods.py

@@ -98,6 +98,69 @@ def barycentric_coordinates(self,P0, P1, P2, P):
         #
         return(1-(r+t),r,t)
 
+    def barycentric_coordinates_fast(self, P0, P1, P2, P):
+        # This is a merge of barycentric_coordinates_exists & barycentric_coordinates.
+        # Inputs are (n, 3) shaped.
+
+        u = P1 - P0   #u is (n,3)
+        v = P2 - P0   #v is (n,3)
+        w = P.T - P0    #w is (n,3)
+        #
+        vCrossW = np.cross(v, w) #result is (n,3)
+        vCrossU = np.cross(v, u) #result is (n,3)
+        A = np.einsum('nd,nd->n', vCrossW, vCrossU) # vector-wise dot product. Result shape is (n,)
+        #
+        uCrossW = np.cross(u, w)
+        uCrossV = - vCrossU
+        B = np.einsum('nd,nd->n', uCrossW, uCrossV)
+        #
+        sq_denoms = np.einsum('nd,nd->n', uCrossV, uCrossV) #result shape is  (n,)
+        sq_rs = np.einsum('nd,nd->n', vCrossW, vCrossW)
+        sq_ts = np.einsum('nd,nd->n', uCrossW, uCrossW)
+        rs = np.sqrt(sq_rs / sq_denoms)  #result shape is  (n,)
+        ts = np.sqrt(sq_ts / sq_denoms)
+        #
+        results = [None] * P0.shape[0]
+        for i in range(len(results)):
+            if not (A[i] < 0 or B[i] < 0):
+                if ((rs[i] <= 1) and (ts[i] <= 1) and (rs[i] + ts[i] <= 1)):
+                    results[i] = (1 - (rs[i] + ts[i]) , rs[i], ts[i])
+        return results
+
+    def IUV2FBC_fast( self, I_point , U_point, V_point):
+        P = np.array([ U_point , V_point , 0 ])
+
+        FaceIndicesNow  = np.where( self.FaceIndices == I_point )
+
+        FacesNow = self.FacesDensePose[FaceIndicesNow]
+        #
+        P_0 = np.vstack( (self.U_norm[FacesNow][:,0], self.V_norm[FacesNow][:,0], np.zeros(self.U_norm[FacesNow][:,0].shape))).transpose()
+        P_1 = np.vstack( (self.U_norm[FacesNow][:,1], self.V_norm[FacesNow][:,1], np.zeros(self.U_norm[FacesNow][:,1].shape))).transpose()
+        P_2 = np.vstack( (self.U_norm[FacesNow][:,2], self.V_norm[FacesNow][:,2], np.zeros(self.U_norm[FacesNow][:,2].shape))).transpose()
+        #
+        bcs = self.barycentric_coordinates_fast(P_0, P_1, P_2, P)
+        for i, bc in enumerate(bcs):
+            if bc is not None:
+                bc1,bc2,bc3 = bc
+                return(FaceIndicesNow[0][i],bc1,bc2,bc3)
+        #
+        # If the found UV is not inside any faces, select the vertex that is closest!
+        #
+        D1 = scipy.spatial.distance.cdist( np.array( [U_point,V_point])[np.newaxis,:] , P_0[:,0:2]).squeeze()
+        D2 = scipy.spatial.distance.cdist( np.array( [U_point,V_point])[np.newaxis,:] , P_1[:,0:2]).squeeze()
+        D3 = scipy.spatial.distance.cdist( np.array( [U_point,V_point])[np.newaxis,:] , P_2[:,0:2]).squeeze()
+        #
+        minD1 = D1.min()
+        minD2 = D2.min()
+        minD3 = D3.min()
+        #
+        if((minD1< minD2) & (minD1< minD3)):
+            return(  FaceIndicesNow[0][np.argmin(D1)] , 1.,0.,0. )
+        elif((minD2< minD1) & (minD2< minD3)):
+            return(  FaceIndicesNow[0][np.argmin(D2)] , 0.,1.,0. )
+        else:
+            return(  FaceIndicesNow[0][np.argmin(D3)] , 0.,0.,1. )
+
     def IUV2FBC( self, I_point , U_point, V_point):
         P = [ U_point , V_point , 0 ]
         FaceIndicesNow  = np.where( self.FaceIndices == I_point )