sift线特征提取代码_车道线检测LaneNet

LanNetSegmentation branch完成语义分割,即判断出像素属于车道or背景Embedding branch完成像素的向量表示,用于后续聚类,以完成实例分割H-Net

Segmentation branch

解决样本分布不均衡

车道线像素远小于背景像素.loss函数的设计对不同像素赋给不同权重,降低背景权重.

该分支的输出为(w,h,2)．

Embedding branch

loss的设计思路为使得属于同一条车道线的像素距离尽量小,属于不同车道线的像素距离尽可能大.即Discriminative loss.

该分支的输出为(w,h,n)．n为表示像素的向量的维度.

实例分割

在Segmentation branch完成语义分割,Embedding branch完成像素的向量表示后,做聚类,完成实例分割.

H-net

透视变换

to do

车道线拟合

LaneNet的输出是每条车道线的像素集合，还需要根据这些像素点回归出一条车道线。传统的做法是将图片投影到鸟瞰图中，然后使用二次或三次多项式进行拟合。在这种方法中，转换矩阵H只被计算一次，所有的图片使用的是相同的转换矩阵，这会导致坡度变化下的误差。

为了解决这个问题，论文训练了一个可以预测变换矩阵H的神经网络HNet，网络的输入是图片，输出是转置矩阵H。之前移植过Opencv逆透视变换矩阵的源码，里面转换矩阵需要8个参数，这儿只给了6个参数的自由度，一开始有些疑惑，后来仔细阅读paper，发现作者已经给出了解释，是为了对转换矩阵在水平方向上的变换进行约束。

代码分析

binary_seg_image, instance_seg_image = sess.run(            [binary_seg_ret, instance_seg_ret],            feed_dict={input_tensor: [image]}        )

输入(１,256,512,3)输出binary_seg_image:(1, 256, 512) instance_seg_image:(1, 256, 512, 4)

完成像素级别的分类和向量表示

class LaneNet的inference分为两步．

第一步提取分割的特征,包括了用于语义分割的特征和用以实例分割的特征.

class LaneNet(cnn_basenet.CNNBaseModel):    def inference(self, input_tensor, name):        """        :param input_tensor:        :param name:        :return:        """        with tf.variable_scope(name_or_scope=name, reuse=self._reuse):            # first extract image features            extract_feats_result = self._frontend.build_model(                input_tensor=input_tensor,                name='{:s}_frontend'.format(self._net_flag),                reuse=self._reuse            )            #得到一个字典,包含了用于语义分割的feature map和用于实例分割的feature map．            #binary_segment_logits　(1,256,512,2) 2是类别数目.即车道/背景．            #instance_segment_logits　(1,256,512,64) 用以后面再做卷积为每个像素生成一个向量表示            print('features:',extract_feats_result)            # second apply backend process            binary_seg_prediction, instance_seg_prediction = self._backend.inference(                binary_seg_logits=extract_feats_result['binary_segment_logits']['data'],                instance_seg_logits=extract_feats_result['instance_segment_logits']['data'],                name='{:s}_backend'.format(self._net_flag),                reuse=self._reuse            )            if not self._reuse:                self._reuse = True        return binary_seg_prediction, instance_seg_prediction

第一步得到的features如下:

features : OrderedDict([('encode_stage_1_share', {'data': , 'shape': [1, 256, 512, 64]}), ('encode_stage_2_share', {'data': , 'shape': [1, 128, 256, 128]}), ('encode_stage_3_share', {'data': , 'shape': [1, 64, 128, 256]}), ('encode_stage_4_share', {'data': , 'shape': [1, 32, 64, 512]}), ('encode_stage_5_binary', {'data': , 'shape': [1, 16, 32, 512]}), ('encode_stage_5_instance', {'data': , 'shape': [1, 16, 32, 512]}), ('binary_segment_logits', {'data': , 'shape': [1, 256, 512, 2]}), ('instance_segment_logits', {'data': , 'shape': [1, 256, 512, 64]})])

特征提取完毕,做后处理

class LaneNetBackEnd(cnn_basenet.CNNBaseModel):        def inference(self, binary_seg_logits, instance_seg_logits, name, reuse):            """            :param binary_seg_logits:            :param instance_seg_logits:            :param name:            :param reuse:            :return:            """            with tf.variable_scope(name_or_scope=name, reuse=reuse):                with tf.variable_scope(name_or_scope='binary_seg'):                    binary_seg_score = tf.nn.softmax(logits=binary_seg_logits)                    binary_seg_prediction = tf.argmax(binary_seg_score, axis=-1)                with tf.variable_scope(name_or_scope='instance_seg'):                    pix_bn = self.layerbn(                        inputdata=instance_seg_logits, is_training=self._is_training, name='pix_bn')                    pix_relu = self.relu(inputdata=pix_bn, name='pix_relu')                    instance_seg_prediction = self.conv2d(                        inputdata=pix_relu,                        out_channel=CFG.TRAIN.EMBEDDING_FEATS_DIMS,                        kernel_size=1,                        use_bias=False,                        name='pix_embedding_conv'                    )            return binary_seg_prediction, instance_seg_prediction

对每个像素的分类,做softmax转成概率.再argmax求概率较大值的下标.对每个像素的向量表示,用1x1卷积核做卷积,得到channel维度=CFG.TRAIN.EMBEDDING_FEATS_DIMS(配置为4).即(1,256,512,64)卷积得到(1,256,512,4)的tensor．即每个像素用一个四维向量表示.

所以,整个LaneNet的inference返回的是两个tensor.一个shape为(1,256,512) 一个为(1,256,512,4).

后处理

class LaneNetPostProcessor(object):    def postprocess(self, binary_seg_result, instance_seg_result=None,                min_area_threshold=100, source_image=None,                data_source='tusimple'):

对binary_seg_result,先通过形态学操作将小的空洞去除.参考 https://www.cnblogs.com/sdu20112013/p/11672634.html

然后做聚类.

def _get_lane_embedding_feats(binary_seg_ret, instance_seg_ret):        """        get lane embedding features according the binary seg result        :param binary_seg_ret:        :param instance_seg_ret:        :return:        """        idx = np.where(binary_seg_ret == 255) #idx (b,h,w)        lane_embedding_feats = instance_seg_ret[idx]                # idx_scale = np.vstack((idx[0] / 256.0, idx[1] / 512.0)).transpose()        # lane_embedding_feats = np.hstack((lane_embedding_feats, idx_scale))        lane_coordinate = np.vstack((idx[1], idx[0])).transpose()        assert lane_embedding_feats.shape[0] == lane_coordinate.shape[0]        ret = {            'lane_embedding_feats': lane_embedding_feats,            'lane_coordinates': lane_coordinate        }        return ret

获取到坐标及对应坐标像素对应的向量表示.

np.where(condition)

只有条件 (condition)，没有x和y，则输出满足条件 (即非0) 元素的坐标 (等价于numpy.nonzero)。这里的坐标以tuple的形式给出，通常原数组有多少维，输出的tuple中就包含几个数组，分别对应符合条件元素的各维坐标。

测试结果

tensorflow-gpu 1.15.2

4张titan xp

(4, 256, 512) (4, 256, 512, 4)

I0302 17:04:31.276140 29376 test_lanenet.py:222] imgae inference cost time: 2.58794s

(32, 256, 512) (32, 256, 512, 4)

I0302 17:05:50.322593 29632 test_lanenet.py:222] imgae inference cost time: 4.31036s

类似于高吞吐量,高延迟.对单帧图片处理在1-2s,多幅图片同时处理,平均下来的处理速度在0.1s.

论文里的backbone为enet,在nvida 1080 ti上推理速度52fps.

对于这个问题的解释,作者的解释是

2.Origin paper use Enet as backbone net but I use vgg16 as backbone net so speed will not get as fast as that. 3.Gpu need a short time to warm up and you can adjust your batch size to test the speed again:)

一个是特征提取网络和论文里不一致,一个是gpu有一个短暂的warm up的时间.

我自己的测试结果是在extract image features耗时较多.换一个backbone可能会有改善.

def inference(self, input_tensor, name):        """        :param input_tensor:        :param name:        :return:        """        print("***************,input_tensor shape:",input_tensor.shape)        with tf.variable_scope(name_or_scope=name, reuse=self._reuse):            t_start = time.time()            # first extract image features            extract_feats_result = self._frontend.build_model(                input_tensor=input_tensor,                name='{:s}_frontend'.format(self._net_flag),                reuse=self._reuse            )            t_cost = time.time() - t_start            glog.info('extract image features cost time: {:.5f}s'.format(t_cost))            # second apply backend process            t_start = time.time()            binary_seg_prediction, instance_seg_prediction = self._backend.inference(                binary_seg_logits=extract_feats_result['binary_segment_logits']['data'],                instance_seg_logits=extract_feats_result['instance_segment_logits']['data'],                name='{:s}_backend'.format(self._net_flag),                reuse=self._reuse            )            t_cost = time.time() - t_start            glog.info('backend process cost time: {:.5f}s'.format(t_cost))            if not self._reuse:                self._reuse = True        return binary_seg_prediction, instance_seg_prediction