modified hnet loss

lanenet_model/lanenet_hnet_loss.py

@@ -34,13 +34,13 @@ def hnet_loss(gt_pts, transformation_coeffcient, name):
         Y = tf.transpose(pts_projects[1, :])
         X = tf.transpose(pts_projects[0, :])
         Y_One = tf.add(tf.subtract(Y, Y), tf.constant(1.0, tf.float32))
-        Y_stack = tf.stack([tf.pow(Y, 2), Y, Y_One], axis=1)
+        Y_stack = tf.stack([tf.pow(Y, 3), tf.pow(Y, 2), Y, Y_One], axis=1)
         w = tf.matmul(tf.matmul(tf.matrix_inverse(tf.matmul(tf.transpose(Y_stack), Y_stack)),
                                 tf.transpose(Y_stack)),
                       tf.expand_dims(X, -1))
         # 利用二阶多项式参数求解拟合位置并反算到原始投影空间计算损失
         x_preds = tf.matmul(Y_stack, w)
-        preds = tf.transpose(tf.stack([tf.squeeze(x_preds, -1), Y, pts_projects[2, :]], axis=1))
+        preds = tf.transpose(tf.stack([tf.squeeze(x_preds, -1), Y, Y_One], axis=1))
         x_transformation_back = tf.matmul(tf.matrix_inverse(H), preds)
 
         loss = tf.reduce_mean(tf.pow(gt_pts[0, :] - x_transformation_back[0, :], 2))
@@ -71,25 +71,41 @@ def hnet_transformation(gt_pts, transformation_coeffcient, name):
         Y = tf.transpose(pts_projects[1, :])
         X = tf.transpose(pts_projects[0, :])
         Y_One = tf.add(tf.subtract(Y, Y), tf.constant(1.0, tf.float32))
-        Y_stack = tf.stack([tf.pow(Y, 2), Y, Y_One], axis=1)
+        Y_stack = tf.stack([tf.pow(Y, 3), tf.pow(Y, 2), Y, Y_One], axis=1)
         w = tf.matmul(tf.matmul(tf.matrix_inverse(tf.matmul(tf.transpose(Y_stack), Y_stack)),
                                 tf.transpose(Y_stack)),
                       tf.expand_dims(X, -1))
 
         # 利用二阶多项式参数求解拟合位置
         x_preds = tf.matmul(Y_stack, w)
-        preds = tf.transpose(tf.stack([tf.squeeze(x_preds, -1), Y, pts_projects[2, :]], axis=1))
+        preds = tf.transpose(tf.stack([tf.squeeze(x_preds, -1), Y, Y_One], axis=1))
         preds_fit = tf.stack([tf.squeeze(x_preds, -1), Y], axis=1)
         x_transformation_back = tf.matmul(tf.matrix_inverse(H), preds)
 
-    return preds_fit
+    return x_transformation_back
 
 
 if __name__ == '__main__':
-    gt_labels = tf.constant([[[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [3.0, 3.0, 3.0]],
-                             [[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [3.0, 3.0, 3.0]]],
-                            dtype=tf.float32, shape=[2, 3, 3])
-    transformation_coffecient = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], dtype=tf.float32, shape=[6])
+    gt_labels = tf.constant([[[1.0, 1.0, 1.0], [2.0, 2.0, 1.0], [3.0, 3.0, 1.0]],
+                             [[1.0, 1.0, 1.0], [2.0, 2.0, 1.0], [3.0, 3.0, 1.0]]],
+                            dtype=tf.float32, shape=[6, 3])
+    transformation_coffecient = tf.constant([[0.58348501, -0.79861236, 2.30343866,
+                                              -0.09976104, -1.22268307, 2.43086767]],
+                                            dtype=tf.float32, shape=[6])
+
+    # import numpy as np
+    # c_val = [0.58348501, -0.79861236, 2.30343866,
+    #          -0.09976104, -1.22268307, 2.43086767]
+    # R = np.zeros([3, 3], np.float32)
+    # R[0, 0] = c_val[0]
+    # R[0, 1] = c_val[1]
+    # R[0, 2] = c_val[2]
+    # R[1, 1] = c_val[3]
+    # R[1, 2] = c_val[4]
+    # R[2, 1] = c_val[5]
+    # R[2, 2] = 1
+    #
+    # print(np.mat(R).I)
 
     _loss = hnet_loss(gt_labels, transformation_coffecient, 'loss')