Depth estimation by CNN (CNN SLAM # 3)
I would like to mention here the estimation of depth images using CNN in CNN SLAM.
In the paper, the task of segmenting is also solved by CNN, but here I would like to focus only on estimating the depth image. Either way, the basic configuration of your network is pretty much the same, so you don't have to worry too much about understanding CNN SLAM.
The answer is on github
God.
https://github.com/iro-cp/FCRN-DepthPrediction
If you clone this, you can estimate the depth!
recipe
$ git clone https://github.com/iro-cp/FCRN-DepthPrediction.git
$ cd FCRN-DepthPrediction/tensorflow
$ python predict.py <Trained model file> <RGB image you want to estimate>
I need tensorflow, so install it. I have Python 3.5.3 and tensorflow has 1.2.1.
The trained model file is linked to the README on the above github page. You don't need to train here because you can get the model by clicking the place where it says Tensorflow Model.
The image you want to estimate the depth is prepared here. For example, if you give the following image and let it estimate ...
You can get this kind of output.
I can't evaluate it because I don't know the true value, but it doesn't seem to be bad in shape.
What if you want to learn?
If you also want to learn, you need to put True in the trainable argument of the Network class in Network.py, calculate the difference from the correct image, and add new code for backpropagation.
I personally saw the writing style in fcrn.py for the first time, so I didn't know how to play with it. So, here's what I changed to a simpler way (for me):
network.py
'''
The setup function is a member function in the Network class
'''
def setup(self, trainable):
xs = self.inputs['data']
conv1 = self.conv(xs, 7, 7, 64, 2, 2, relu=False, name='conv1')
bn_conv1 = self.batch_normalization(conv1, relu=True, name='bn_conv1')
pool1 = self.max_pool(bn_conv1, 3, 3, 2, 2, name='pool1')
res2a_branch1 = self.conv(pool1, 1, 1, 256, 1, 1, biased=False, relu=False, name='res2a_branch1')
bn2a_branch1 = self.batch_normalization(res2a_branch1, name='bn2a_branch1')
res2a_branch2a = self.conv(pool1, 1, 1, 64, 1, 1, biased=False, relu=False, name='res2a_branch2a')
bn2a_branch2a = self.batch_normalization(res2a_branch2a, relu=True, name='bn2a_branch2a')
res2a_branch2b = self.conv(bn2a_branch2a, 3, 3, 64, 1, 1, biased=False, relu=False, name='res2a_branch2b')
bn2a_branch2b = self.batch_normalization(res2a_branch2b, relu=True, name='bn2a_branch2b')
res2a_branch2c = self.conv(bn2a_branch2b, 1, 1, 256, 1, 1, biased=False, relu=False, name='res2a_branch2c')
bn2a_branch2c = self.batch_normalization(res2a_branch2c, name='bn2a_branch2c')
res2a = self.add((bn2a_branch1, bn2a_branch2c), name='res2a')
res2a_relu = self.relu(res2a, name='res2a_relu')
res2b_branch2a = self.conv(res2a_relu, 1, 1, 64, 1, 1, biased=False, relu=False, name='res2b_branch2a')
bn2b_branch2a = self.batch_normalization(res2b_branch2a, relu=True, name='bn2b_branch2a')
res2b_branch2b = self.conv(bn2b_branch2a, 3, 3, 64, 1, 1, biased=False, relu=False, name='res2b_branch2b')
bn2b_branch2b = self.batch_normalization(res2b_branch2b, relu=True, name='bn2b_branch2b')
res2b_branch2c = self.conv(bn2b_branch2b, 1, 1, 256, 1, 1, biased=False, relu=False, name='res2b_branch2c')
bn2b_branch2c = self.batch_normalization(res2b_branch2c, name='bn2b_branch2c')
res2b = self.add((res2a_relu, bn2b_branch2c), name='res2b')
res2b_relu = self.relu(res2b, name='res2b_relu')
res2c_branch2a = self.conv(res2b_relu, 1, 1, 64, 1, 1, biased=False, relu=False, name='res2c_branch2a')
bn2c_branch2a = self.batch_normalization(res2c_branch2a, relu=True, name='bn2c_branch2a')
res2c_branch2b = self.conv(bn2c_branch2a, 3, 3, 64, 1, 1, biased=False, relu=False, name='res2c_branch2b')
bn2c_branch2b = self.batch_normalization(res2c_branch2b, relu=True, name='bn2c_branch2b')
res2c_branch2c = self.conv(bn2c_branch2b, 1, 1, 256, 1, 1, biased=False, relu=False, name='res2c_branch2c')
bn2c_branch2c = self.batch_normalization(res2c_branch2c, name='bn2c_branch2c')
res2c = self.add((res2b_relu, bn2c_branch2c), name='res2c')
res2c_relu = self.relu(res2c, name='res2c_relu')
res3a_branch1 = self.conv(res2c_relu, 1, 1, 512, 2, 2, biased=False, relu=False, name='res3a_branch1')
bn3a_branch1 = self.batch_normalization(res3a_branch1, name='bn3a_branch1')
res3a_branch2a = self.conv(res2c_relu, 1, 1, 128, 2, 2, biased=False, relu=False, name='res3a_branch2a')
bn3a_branch2a = self.batch_normalization(res3a_branch2a, relu=True, name='bn3a_branch2a')
res3a_branch2b = self.conv(bn3a_branch2a, 3, 3, 128, 1, 1, biased=False, relu=False, name='res3a_branch2b')
bn3a_branch2b = self.batch_normalization(res3a_branch2b, relu=True, name='bn3a_branch2b')
res3a_branch2c = self.conv(bn3a_branch2b, 1, 1, 512, 1, 1, biased=False, relu=False, name='res3a_branch2c')
bn3a_branch2c = self.batch_normalization(res3a_branch2c, name='bn3a_branch2c')
res3a = self.add((bn3a_branch1, bn3a_branch2c), name='res3a')
res3a_relu = self.relu(res3a, name='res3a_relu')
res3b_branch2a = self.conv(res3a_relu, 1, 1, 128, 1, 1, biased=False, relu=False, name='res3b_branch2a')
bn3b_branch2a = self.batch_normalization(res3b_branch2a, relu=True, name='bn3b_branch2a')
res3b_branch2b = self.conv(bn3b_branch2a, 3, 3, 128, 1, 1, biased=False, relu=False, name='res3b_branch2b')
bn3b_branch2b = self.batch_normalization(res3b_branch2b, relu=True, name='bn3b_branch2b')
res3b_branch2c = self.conv(bn3b_branch2b, 1, 1, 512, 1, 1, biased=False, relu=False, name='res3b_branch2c')
bn3b_branch2c = self.batch_normalization(res3b_branch2c, name='bn3b_branch2c')
res3b = self.add((res3a_relu, bn3b_branch2c), name='res3b')
res3b_relu = self.relu(res3b, name='res3b_relu')
res3c_branch2a = self.conv(res3b_relu, 1, 1, 128, 1, 1, biased=False, relu=False, name='res3c_branch2a')
bn3c_branch2a = self.batch_normalization(res3c_branch2a, relu=True, name='bn3c_branch2a')
res3c_branch2b = self.conv(bn3c_branch2a, 3, 3, 128, 1, 1, biased=False, relu=False, name='res3c_branch2b')
bn3c_branch2b = self.batch_normalization(res3c_branch2b, relu=True, name='bn3c_branch2b')
res3c_branch2c = self.conv(bn3c_branch2b, 1, 1, 512, 1, 1, biased=False, relu=False, name='res3c_branch2c')
bn3c_branch2c = self.batch_normalization(res3c_branch2c, name='bn3c_branch2c')
res3c = self.add((res3b_relu, bn3c_branch2c), name='res3c')
res3c_relu = self.relu(res3c, name='res3c_relu')
res3d_branch2a = self.conv(res3c_relu, 1, 1, 128, 1, 1, biased=False, relu=False, name='res3d_branch2a')
bn3d_branch2a = self.batch_normalization(res3d_branch2a, relu=True, name='bn3d_branch2a')
res3d_branch2b = self.conv(bn3d_branch2a, 3, 3, 128, 1, 1, biased=False, relu=False, name='res3d_branch2b')
bn3d_branch2b = self.batch_normalization(res3d_branch2b, relu=True, name='bn3d_branch2b')
res3d_branch2c = self.conv(bn3d_branch2b, 1, 1, 512, 1, 1, biased=False, relu=False, name='res3d_branch2c')
bn3d_branch2c = self.batch_normalization(res3d_branch2c, name='bn3d_branch2c')
res3d = self.add((res3c_relu, bn3d_branch2c), name='res3d')
res3d_relu = self.relu(res3d, name='res3d_relu')
res4a_branch1 = self.conv(res3d_relu, 1, 1, 1024, 2, 2, biased=False, relu=False, name='res4a_branch1')
bn4a_branch1 = self.batch_normalization(res4a_branch1, name='bn4a_branch1')
res4a_branch2a = self.conv(res3d_relu, 1, 1, 256, 2, 2, biased=False, relu=False, name='res4a_branch2a')
bn4a_branch2a = self.batch_normalization(res4a_branch2a, relu=True, name='bn4a_branch2a')
res4a_branch2b = self.conv(bn4a_branch2a, 3, 3, 256, 1, 1, biased=False, relu=False, name='res4a_branch2b')
bn4a_branch2b = self.batch_normalization(res4a_branch2b, relu=True, name='bn4a_branch2b')
res4a_branch2c = self.conv(bn4a_branch2b, 1, 1, 1024, 1, 1, biased=False, relu=False, name='res4a_branch2c')
bn4a_branch2c = self.batch_normalization(res4a_branch2c, name='bn4a_branch2c')
res4a = self.add((bn4a_branch1, bn4a_branch2c), name='res4a')
res4a_relu = self.relu(res4a, name='res4a_relu')
res4b_branch2a = self.conv(res4a_relu, 1, 1, 256, 1, 1, biased=False, relu=False, name='res4b_branch2a')
bn4b_branch2a = self.batch_normalization(res4b_branch2a, relu=True, name='bn4b_branch2a')
res4b_branch2b = self.conv(bn4b_branch2a, 3, 3, 256, 1, 1, biased=False, relu=False, name='res4b_branch2b')
bn4b_branch2b = self.batch_normalization(res4b_branch2b, relu=True, name='bn4b_branch2b')
res4b_branch2c = self.conv(bn4b_branch2b, 1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b_branch2c')
bn4b_branch2c = self.batch_normalization(res4b_branch2c, name='bn4b_branch2c')
res4b = self.add((res4a_relu, bn4b_branch2c), name='res4b')
res4b_relu = self.relu(res4b, name='res4b_relu')
res4c_branch2a = self.conv(res4b_relu, 1, 1, 256, 1, 1, biased=False, relu=False, name='res4c_branch2a')
bn4c_branch2a = self.batch_normalization(res4c_branch2a, relu=True, name='bn4c_branch2a')
res4c_branch2b = self.conv(bn4c_branch2a, 3, 3, 256, 1, 1, biased=False, relu=False, name='res4c_branch2b')
bn4c_branch2b = self.batch_normalization(res4c_branch2b, relu=True, name='bn4c_branch2b')
res4c_branch2c = self.conv(bn4c_branch2b, 1, 1, 1024, 1, 1, biased=False, relu=False, name='res4c_branch2c')
bn4c_branch2c = self.batch_normalization(res4c_branch2c, name='bn4c_branch2c')
res4c = self.add((res4b_relu, bn4c_branch2c), name='res4c')
res4c_relu = self.relu(res4c, name='res4c_relu')
res4d_branch2a = self.conv(res4c_relu, 1, 1, 256, 1, 1, biased=False, relu=False, name='res4d_branch2a')
bn4d_branch2a = self.batch_normalization(res4d_branch2a, relu=True, name='bn4d_branch2a')
res4d_branch2b = self.conv(bn4d_branch2a, 3, 3, 256, 1, 1, biased=False, relu=False, name='res4d_branch2b')
bn4d_branch2b = self.batch_normalization(res4d_branch2b, relu=True, name='bn4d_branch2b')
res4d_branch2c = self.conv(bn4d_branch2b, 1, 1, 1024, 1, 1, biased=False, relu=False, name='res4d_branch2c')
bn4d_branch2c = self.batch_normalization(res4d_branch2c, name='bn4d_branch2c')
res4d = self.add((res4c_relu, bn4d_branch2c), name='res4d')
res4d_relu = self.relu(res4d, name='res4d_relu')
res4e_branch2a = self.conv(res4d_relu, 1, 1, 256, 1, 1, biased=False, relu=False, name='res4e_branch2a')
bn4e_branch2a = self.batch_normalization(res4e_branch2a, relu=True, name='bn4e_branch2a')
res4e_branch2b = self.conv(bn4e_branch2a, 3, 3, 256, 1, 1, biased=False, relu=False, name='res4e_branch2b')
bn4e_branch2b = self.batch_normalization(res4e_branch2b, relu=True, name='bn4e_branch2b')
res4e_branch2c = self.conv(bn4e_branch2b, 1, 1, 1024, 1, 1, biased=False, relu=False, name='res4e_branch2c')
bn4e_branch2c = self.batch_normalization(res4e_branch2c, name='bn4e_branch2c')
res4e = self.add((res4d_relu, bn4e_branch2c), name='res4e')
res4e_relu = self.relu(res4e, name='res4e_relu')
res4f_branch2a = self.conv(res4e_relu, 1, 1, 256, 1, 1, biased=False, relu=False, name='res4f_branch2a')
bn4f_branch2a = self.batch_normalization(res4f_branch2a, relu=True, name='bn4f_branch2a')
res4f_branch2b = self.conv(bn4f_branch2a, 3, 3, 256, 1, 1, biased=False, relu=False, name='res4f_branch2b')
bn4f_branch2b = self.batch_normalization(res4f_branch2b, relu=True, name='bn4f_branch2b')
res4f_branch2c = self.conv(bn4f_branch2b, 1, 1, 1024, 1, 1, biased=False, relu=False, name='res4f_branch2c')
bn4f_branch2c = self.batch_normalization(res4f_branch2c, name='bn4f_branch2c')
res4f = self.add((res4e_relu, bn4f_branch2c), name='res4f')
res4f_relu = self.relu(res4f, name='res4f_relu')
res5a_branch1 = self.conv(res4f_relu, 1, 1, 2048, 2, 2, biased=False, relu=False, name='res5a_branch1')
bn5a_branch1 = self.batch_normalization(res5a_branch1, name='bn5a_branch1')
res5a_branch2a = self.conv(res4f_relu, 1, 1, 512, 2, 2, biased=False, relu=False, name='res5a_branch2a')
bn5a_branch2a = self.batch_normalization(res5a_branch2a, relu=True, name='bn5a_branch2a')
res5a_branch2b = self.conv(bn5a_branch2a, 3, 3, 512, 1, 1, biased=False, relu=False, name='res5a_branch2b')
bn5a_branch2b = self.batch_normalization(res5a_branch2b, relu=True, name='bn5a_branch2b')
res5a_branch2c = self.conv(bn5a_branch2b, 1, 1, 2048, 1, 1, biased=False, relu=False, name='res5a_branch2c')
bn5a_branch2c = self.batch_normalization(res5a_branch2c, name='bn5a_branch2c')
res5a = self.add((bn5a_branch1, bn5a_branch2c), name='res5a')
res5a_relu = self.relu(res5a, name='res5a_relu')
res5b_branch2a = self.conv(res5a_relu, 1, 1, 512, 1, 1, biased=False, relu=False, name='res5b_branch2a')
bn5b_branch2a = self.batch_normalization(res5b_branch2a, relu=True, name='bn5b_branch2a')
res5b_branch2b = self.conv(bn5b_branch2a, 3, 3, 512, 1, 1, biased=False, relu=False, name='res5b_branch2b')
bn5b_branch2b = self.batch_normalization(res5b_branch2b, relu=True, name='bn5b_branch2b')
res5b_branch2c = self.conv(bn5b_branch2b, 1, 1, 2048, 1, 1, biased=False, relu=False, name='res5b_branch2c')
bn5b_branch2c = self.batch_normalization(res5b_branch2c, name='bn5b_branch2c')
res5b = self.add((res5a_relu, bn5b_branch2c), name='res5b')
res5b_relu = self.relu(res5b, name='res5b_relu')
res5c_branch2a = self.conv(res5b_relu, 1, 1, 512, 1, 1, biased=False, relu=False, name='res5c_branch2a')
bn5c_branch2a = self.batch_normalization(res5c_branch2a, relu=True, name='bn5c_branch2a')
res5c_branch2b = self.conv(bn5c_branch2a, 3, 3, 512, 1, 1, biased=False, relu=False, name='res5c_branch2b')
bn5c_branch2b = self.batch_normalization(res5c_branch2b, relu=True, name='bn5c_branch2b')
res5c_branch2c = self.conv(bn5c_branch2b, 1, 1, 2048, 1, 1, biased=False, relu=False, name='res5c_branch2c')
bn5c_branch2c = self.batch_normalization(res5c_branch2c, name='bn5c_branch2c')
res5c = self.add((res5b_relu, bn5c_branch2c), name='res5c')
res5c_relu = self.relu(res5c, name='res5c_relu')
layer1 = self.conv(res5c_relu, 1, 1, 1024, 1, 1, biased=True, relu=False, name='layer1')
layer1_BN = self.batch_normalization(layer1, relu=False, name='layer1_BN')
layer2 = self.up_project(layer1_BN, [3, 3, 1024, 512], id='2x', stride=1, BN=True)
layer3 = self.up_project(layer2, [3, 3, 512, 256], id='4x', stride=1, BN=True)
layer4 = self.up_project(layer3, [3, 3, 256, 128], id='8x', stride=1, BN=True)
layer5 = self.up_project(layer4, [3, 3, 128, 64], id='16x', stride=1, BN=True)
layer5_drop = self.dropout(layer5, name='drop', keep_prob=1.)
self.predict = self.conv(layer5_drop, 3, 3, 1, 1, 1, name='ConvPred')
if trainable:
ts = self.inputs['true']
differ = tf.subtract(x=self.predict, y=ts)
abs_differ = tf.abs(differ)
self.loss = tf.reduce_mean(abs_differ, name='loss')
self.train_step = tf.train.GradientDescentOptimizer(0.001).minimize(self.loss)
self.inputs is a dictionary type, and when trainable == True, there is a placeholder that stores the input image and a placeholder that stores the correct answer image. I am doing it.
We are still investigating, but it seems that the estimation accuracy differs depending on the loss function. I want to use the loss function that gives the most accuracy when performing additional learning.