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| *.pyc | ||
| *~ | ||
| *.so | ||
| *.cpp | ||
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| # folders | ||
| saved_sessions | ||
| utils/external/build |
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| # Lifting from the Deep | ||
| Denis Tome', Chris Russell, Lourdes Agapito | ||
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| [Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image](https://arxiv.org/abs/1701.00295), CVPR 2017 | ||
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| This project is licensed under the terms of the GNU GPLv3 license. By using the software, you are agreeing to the terms of the license agreement ([link](https://github.com/DenisTome/Lifting-from-the-Deep-release/blob/master/LICENSE)). | ||
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|  | ||
| ## Dependencies | ||
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| - [Tensorflow](https://www.tensorflow.org/) | ||
| - [Cython](http://cython.readthedocs.io/en/latest/src/quickstart/install.html) | ||
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| ## Models | ||
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| The architecture extends the one proposed in [Convolutional Pose Machines (CPM)](https://github.com/shihenw/convolutional-pose-machines-release). | ||
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| For this demo, CPM's caffe-models trained on the MPI datasets ([link](https://github.com/shihenw/convolutional-pose-machines-release/tree/master/model)) are used for **2D pose estimation**, whereas for **3D pose estimation** our probabilistic 3D pose model is trained on the [Human3.6M dataset](http://vision.imar.ro/human3.6m/description.php). | ||
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| ## Testing | ||
| - First, run `setup.sh` to retreive the trained models and to install the external utilities. | ||
| - Run `demo.py` to evaluate the test image. | ||
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| ## Additional material | ||
| - Project [webpage](http://visual.cs.ucl.ac.uk/pubs/liftingFromTheDeep/) | ||
| - Some [videos](https://youtu.be/tKfkGttx0qs). | ||
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| ## Citation | ||
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| @article{tome2017lifting, | ||
| title={Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image}, | ||
| author={Tome, Denis and Russell, Chris and Agapito, Lourdes}, | ||
| journal={arXiv preprint arXiv:1701.00295}, | ||
| year={2017} | ||
| } |
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| import tensorflow as tf | ||
| import tensorflow.contrib.layers as layers | ||
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| def inference_person(image): | ||
| with tf.variable_scope('PersonNet'): | ||
| conv1_1 = layers.conv2d(image, 64, 3, 1, activation_fn=None, scope='conv1_1') | ||
| conv1_1 = tf.nn.relu(conv1_1) | ||
| conv1_2 = layers.conv2d(conv1_1, 64, 3, 1, activation_fn=None, scope='conv1_2') | ||
| conv1_2 = tf.nn.relu(conv1_2) | ||
| pool1_stage1 = layers.max_pool2d(conv1_2, 2, 2) | ||
| conv2_1 = layers.conv2d(pool1_stage1, 128, 3, 1, activation_fn=None, scope='conv2_1') | ||
| conv2_1 = tf.nn.relu(conv2_1) | ||
| conv2_2 = layers.conv2d(conv2_1, 128, 3, 1, activation_fn=None, scope='conv2_2') | ||
| conv2_2 = tf.nn.relu(conv2_2) | ||
| pool2_stage1 = layers.max_pool2d(conv2_2, 2, 2) | ||
| conv3_1 = layers.conv2d(pool2_stage1, 256, 3, 1, activation_fn=None, scope='conv3_1') | ||
| conv3_1 = tf.nn.relu(conv3_1) | ||
| conv3_2 = layers.conv2d(conv3_1, 256, 3, 1, activation_fn=None, scope='conv3_2') | ||
| conv3_2 = tf.nn.relu(conv3_2) | ||
| conv3_3 = layers.conv2d(conv3_2, 256, 3, 1, activation_fn=None, scope='conv3_3') | ||
| conv3_3 = tf.nn.relu(conv3_3) | ||
| conv3_4 = layers.conv2d(conv3_3, 256, 3, 1, activation_fn=None, scope='conv3_4') | ||
| conv3_4 = tf.nn.relu(conv3_4) | ||
| pool3_stage1 = layers.max_pool2d(conv3_4, 2, 2) | ||
| conv4_1 = layers.conv2d(pool3_stage1, 512, 3, 1, activation_fn=None, scope='conv4_1') | ||
| conv4_1 = tf.nn.relu(conv4_1) | ||
| conv4_2 = layers.conv2d(conv4_1, 512, 3, 1, activation_fn=None, scope='conv4_2') | ||
| conv4_2 = tf.nn.relu(conv4_2) | ||
| conv4_3 = layers.conv2d(conv4_2, 512, 3, 1, activation_fn=None, scope='conv4_3') | ||
| conv4_3 = tf.nn.relu(conv4_3) | ||
| conv4_4 = layers.conv2d(conv4_3, 512, 3, 1, activation_fn=None, scope='conv4_4') | ||
| conv4_4 = tf.nn.relu(conv4_4) | ||
| conv5_1 = layers.conv2d(conv4_4, 512, 3, 1, activation_fn=None, scope='conv5_1') | ||
| conv5_1 = tf.nn.relu(conv5_1) | ||
| conv5_2_CPM = layers.conv2d(conv5_1, 128, 3, 1, activation_fn=None, scope='conv5_2_CPM') | ||
| conv5_2_CPM = tf.nn.relu(conv5_2_CPM) | ||
| conv6_1_CPM = layers.conv2d(conv5_2_CPM, 512, 1, 1, activation_fn=None, scope='conv6_1_CPM') | ||
| conv6_1_CPM = tf.nn.relu(conv6_1_CPM) | ||
| conv6_2_CPM = layers.conv2d(conv6_1_CPM, 1, 1, 1, activation_fn=None, scope='conv6_2_CPM') | ||
| concat_stage2 = tf.concat(3, [conv6_2_CPM, conv5_2_CPM]) | ||
| Mconv1_stage2 = layers.conv2d(concat_stage2, 128, 7, 1, activation_fn=None, scope='Mconv1_stage2') | ||
| Mconv1_stage2 = tf.nn.relu(Mconv1_stage2) | ||
| Mconv2_stage2 = layers.conv2d(Mconv1_stage2, 128, 7, 1, activation_fn=None, scope='Mconv2_stage2') | ||
| Mconv2_stage2 = tf.nn.relu(Mconv2_stage2) | ||
| Mconv3_stage2 = layers.conv2d(Mconv2_stage2, 128, 7, 1, activation_fn=None, scope='Mconv3_stage2') | ||
| Mconv3_stage2 = tf.nn.relu(Mconv3_stage2) | ||
| Mconv4_stage2 = layers.conv2d(Mconv3_stage2, 128, 7, 1, activation_fn=None, scope='Mconv4_stage2') | ||
| Mconv4_stage2 = tf.nn.relu(Mconv4_stage2) | ||
| Mconv5_stage2 = layers.conv2d(Mconv4_stage2, 128, 7, 1, activation_fn=None, scope='Mconv5_stage2') | ||
| Mconv5_stage2 = tf.nn.relu(Mconv5_stage2) | ||
| Mconv6_stage2 = layers.conv2d(Mconv5_stage2, 128, 1, 1, activation_fn=None, scope='Mconv6_stage2') | ||
| Mconv6_stage2 = tf.nn.relu(Mconv6_stage2) | ||
| Mconv7_stage2 = layers.conv2d(Mconv6_stage2, 1, 1, 1, activation_fn=None, scope='Mconv7_stage2') | ||
| concat_stage3 = tf.concat(3, [Mconv7_stage2, conv5_2_CPM]) | ||
| Mconv1_stage3 = layers.conv2d(concat_stage3, 128, 7, 1, activation_fn=None, scope='Mconv1_stage3') | ||
| Mconv1_stage3 = tf.nn.relu(Mconv1_stage3) | ||
| Mconv2_stage3 = layers.conv2d(Mconv1_stage3, 128, 7, 1, activation_fn=None, scope='Mconv2_stage3') | ||
| Mconv2_stage3 = tf.nn.relu(Mconv2_stage3) | ||
| Mconv3_stage3 = layers.conv2d(Mconv2_stage3, 128, 7, 1, activation_fn=None, scope='Mconv3_stage3') | ||
| Mconv3_stage3 = tf.nn.relu(Mconv3_stage3) | ||
| Mconv4_stage3 = layers.conv2d(Mconv3_stage3, 128, 7, 1, activation_fn=None, scope='Mconv4_stage3') | ||
| Mconv4_stage3 = tf.nn.relu(Mconv4_stage3) | ||
| Mconv5_stage3 = layers.conv2d(Mconv4_stage3, 128, 7, 1, activation_fn=None, scope='Mconv5_stage3') | ||
| Mconv5_stage3 = tf.nn.relu(Mconv5_stage3) | ||
| Mconv6_stage3 = layers.conv2d(Mconv5_stage3, 128, 1, 1, activation_fn=None, scope='Mconv6_stage3') | ||
| Mconv6_stage3 = tf.nn.relu(Mconv6_stage3) | ||
| Mconv7_stage3 = layers.conv2d(Mconv6_stage3, 1, 1, 1, activation_fn=None, scope='Mconv7_stage3') | ||
| concat_stage4 = tf.concat(3, [Mconv7_stage3, conv5_2_CPM]) | ||
| Mconv1_stage4 = layers.conv2d(concat_stage4, 128, 7, 1, activation_fn=None, scope='Mconv1_stage4') | ||
| Mconv1_stage4 = tf.nn.relu(Mconv1_stage4) | ||
| Mconv2_stage4 = layers.conv2d(Mconv1_stage4, 128, 7, 1, activation_fn=None, scope='Mconv2_stage4') | ||
| Mconv2_stage4 = tf.nn.relu(Mconv2_stage4) | ||
| Mconv3_stage4 = layers.conv2d(Mconv2_stage4, 128, 7, 1, activation_fn=None, scope='Mconv3_stage4') | ||
| Mconv3_stage4 = tf.nn.relu(Mconv3_stage4) | ||
| Mconv4_stage4 = layers.conv2d(Mconv3_stage4, 128, 7, 1, activation_fn=None, scope='Mconv4_stage4') | ||
| Mconv4_stage4 = tf.nn.relu(Mconv4_stage4) | ||
| Mconv5_stage4 = layers.conv2d(Mconv4_stage4, 128, 7, 1, activation_fn=None, scope='Mconv5_stage4') | ||
| Mconv5_stage4 = tf.nn.relu(Mconv5_stage4) | ||
| Mconv6_stage4 = layers.conv2d(Mconv5_stage4, 128, 1, 1, activation_fn=None, scope='Mconv6_stage4') | ||
| Mconv6_stage4 = tf.nn.relu(Mconv6_stage4) | ||
| Mconv7_stage4 = layers.conv2d(Mconv6_stage4, 1, 1, 1, activation_fn=None, scope='Mconv7_stage4') | ||
| return Mconv7_stage4 | ||
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| def inference_pose(image, center_map): | ||
| with tf.variable_scope('PoseNet'): | ||
| pool_center_lower = layers.avg_pool2d(center_map, 9, 8, padding='SAME') | ||
| conv1_1 = layers.conv2d(image, 64, 3, 1, activation_fn=None, scope='conv1_1') | ||
| conv1_1 = tf.nn.relu(conv1_1) | ||
| conv1_2 = layers.conv2d(conv1_1, 64, 3, 1, activation_fn=None, scope='conv1_2') | ||
| conv1_2 = tf.nn.relu(conv1_2) | ||
| pool1_stage1 = layers.max_pool2d(conv1_2, 2, 2) | ||
| conv2_1 = layers.conv2d(pool1_stage1, 128, 3, 1, activation_fn=None, scope='conv2_1') | ||
| conv2_1 = tf.nn.relu(conv2_1) | ||
| conv2_2 = layers.conv2d(conv2_1, 128, 3, 1, activation_fn=None, scope='conv2_2') | ||
| conv2_2 = tf.nn.relu(conv2_2) | ||
| pool2_stage1 = layers.max_pool2d(conv2_2, 2, 2) | ||
| conv3_1 = layers.conv2d(pool2_stage1, 256, 3, 1, activation_fn=None, scope='conv3_1') | ||
| conv3_1 = tf.nn.relu(conv3_1) | ||
| conv3_2 = layers.conv2d(conv3_1, 256, 3, 1, activation_fn=None, scope='conv3_2') | ||
| conv3_2 = tf.nn.relu(conv3_2) | ||
| conv3_3 = layers.conv2d(conv3_2, 256, 3, 1, activation_fn=None, scope='conv3_3') | ||
| conv3_3 = tf.nn.relu(conv3_3) | ||
| conv3_4 = layers.conv2d(conv3_3, 256, 3, 1, activation_fn=None, scope='conv3_4') | ||
| conv3_4 = tf.nn.relu(conv3_4) | ||
| pool3_stage1 = layers.max_pool2d(conv3_4, 2, 2) | ||
| conv4_1 = layers.conv2d(pool3_stage1, 512, 3, 1, activation_fn=None, scope='conv4_1') | ||
| conv4_1 = tf.nn.relu(conv4_1) | ||
| conv4_2 = layers.conv2d(conv4_1, 512, 3, 1, activation_fn=None, scope='conv4_2') | ||
| conv4_2 = tf.nn.relu(conv4_2) | ||
| conv4_3_CPM = layers.conv2d(conv4_2, 256, 3, 1, activation_fn=None, scope='conv4_3_CPM') | ||
| conv4_3_CPM = tf.nn.relu(conv4_3_CPM) | ||
| conv4_4_CPM = layers.conv2d(conv4_3_CPM, 256, 3, 1, activation_fn=None, scope='conv4_4_CPM') | ||
| conv4_4_CPM = tf.nn.relu(conv4_4_CPM) | ||
| conv4_5_CPM = layers.conv2d(conv4_4_CPM, 256, 3, 1, activation_fn=None, scope='conv4_5_CPM') | ||
| conv4_5_CPM = tf.nn.relu(conv4_5_CPM) | ||
| conv4_6_CPM = layers.conv2d(conv4_5_CPM, 256, 3, 1, activation_fn=None, scope='conv4_6_CPM') | ||
| conv4_6_CPM = tf.nn.relu(conv4_6_CPM) | ||
| conv4_7_CPM = layers.conv2d(conv4_6_CPM, 128, 3, 1, activation_fn=None, scope='conv4_7_CPM') | ||
| conv4_7_CPM = tf.nn.relu(conv4_7_CPM) | ||
| conv5_1_CPM = layers.conv2d(conv4_7_CPM, 512, 1, 1, activation_fn=None, scope='conv5_1_CPM') | ||
| conv5_1_CPM = tf.nn.relu(conv5_1_CPM) | ||
| conv5_2_CPM = layers.conv2d(conv5_1_CPM, 15, 1, 1, activation_fn=None, scope='conv5_2_CPM') | ||
| concat_stage2 = tf.concat(3, [conv5_2_CPM, conv4_7_CPM, pool_center_lower]) | ||
| Mconv1_stage2 = layers.conv2d(concat_stage2, 128, 7, 1, activation_fn=None, scope='Mconv1_stage2') | ||
| Mconv1_stage2 = tf.nn.relu(Mconv1_stage2) | ||
| Mconv2_stage2 = layers.conv2d(Mconv1_stage2, 128, 7, 1, activation_fn=None, scope='Mconv2_stage2') | ||
| Mconv2_stage2 = tf.nn.relu(Mconv2_stage2) | ||
| Mconv3_stage2 = layers.conv2d(Mconv2_stage2, 128, 7, 1, activation_fn=None, scope='Mconv3_stage2') | ||
| Mconv3_stage2 = tf.nn.relu(Mconv3_stage2) | ||
| Mconv4_stage2 = layers.conv2d(Mconv3_stage2, 128, 7, 1, activation_fn=None, scope='Mconv4_stage2') | ||
| Mconv4_stage2 = tf.nn.relu(Mconv4_stage2) | ||
| Mconv5_stage2 = layers.conv2d(Mconv4_stage2, 128, 7, 1, activation_fn=None, scope='Mconv5_stage2') | ||
| Mconv5_stage2 = tf.nn.relu(Mconv5_stage2) | ||
| Mconv6_stage2 = layers.conv2d(Mconv5_stage2, 128, 1, 1, activation_fn=None, scope='Mconv6_stage2') | ||
| Mconv6_stage2 = tf.nn.relu(Mconv6_stage2) | ||
| Mconv7_stage2 = layers.conv2d(Mconv6_stage2, 15, 1, 1, activation_fn=None, scope='Mconv7_stage2') | ||
| concat_stage3 = tf.concat(3, [Mconv7_stage2, conv4_7_CPM, pool_center_lower]) | ||
| Mconv1_stage3 = layers.conv2d(concat_stage3, 128, 7, 1, activation_fn=None, scope='Mconv1_stage3') | ||
| Mconv1_stage3 = tf.nn.relu(Mconv1_stage3) | ||
| Mconv2_stage3 = layers.conv2d(Mconv1_stage3, 128, 7, 1, activation_fn=None, scope='Mconv2_stage3') | ||
| Mconv2_stage3 = tf.nn.relu(Mconv2_stage3) | ||
| Mconv3_stage3 = layers.conv2d(Mconv2_stage3, 128, 7, 1, activation_fn=None, scope='Mconv3_stage3') | ||
| Mconv3_stage3 = tf.nn.relu(Mconv3_stage3) | ||
| Mconv4_stage3 = layers.conv2d(Mconv3_stage3, 128, 7, 1, activation_fn=None, scope='Mconv4_stage3') | ||
| Mconv4_stage3 = tf.nn.relu(Mconv4_stage3) | ||
| Mconv5_stage3 = layers.conv2d(Mconv4_stage3, 128, 7, 1, activation_fn=None, scope='Mconv5_stage3') | ||
| Mconv5_stage3 = tf.nn.relu(Mconv5_stage3) | ||
| Mconv6_stage3 = layers.conv2d(Mconv5_stage3, 128, 1, 1, activation_fn=None, scope='Mconv6_stage3') | ||
| Mconv6_stage3 = tf.nn.relu(Mconv6_stage3) | ||
| Mconv7_stage3 = layers.conv2d(Mconv6_stage3, 15, 1, 1, activation_fn=None, scope='Mconv7_stage3') | ||
| concat_stage4 = tf.concat(3, [Mconv7_stage3, conv4_7_CPM, pool_center_lower]) | ||
| Mconv1_stage4 = layers.conv2d(concat_stage4, 128, 7, 1, activation_fn=None, scope='Mconv1_stage4') | ||
| Mconv1_stage4 = tf.nn.relu(Mconv1_stage4) | ||
| Mconv2_stage4 = layers.conv2d(Mconv1_stage4, 128, 7, 1, activation_fn=None, scope='Mconv2_stage4') | ||
| Mconv2_stage4 = tf.nn.relu(Mconv2_stage4) | ||
| Mconv3_stage4 = layers.conv2d(Mconv2_stage4, 128, 7, 1, activation_fn=None, scope='Mconv3_stage4') | ||
| Mconv3_stage4 = tf.nn.relu(Mconv3_stage4) | ||
| Mconv4_stage4 = layers.conv2d(Mconv3_stage4, 128, 7, 1, activation_fn=None, scope='Mconv4_stage4') | ||
| Mconv4_stage4 = tf.nn.relu(Mconv4_stage4) | ||
| Mconv5_stage4 = layers.conv2d(Mconv4_stage4, 128, 7, 1, activation_fn=None, scope='Mconv5_stage4') | ||
| Mconv5_stage4 = tf.nn.relu(Mconv5_stage4) | ||
| Mconv6_stage4 = layers.conv2d(Mconv5_stage4, 128, 1, 1, activation_fn=None, scope='Mconv6_stage4') | ||
| Mconv6_stage4 = tf.nn.relu(Mconv6_stage4) | ||
| Mconv7_stage4 = layers.conv2d(Mconv6_stage4, 15, 1, 1, activation_fn=None, scope='Mconv7_stage4') | ||
| concat_stage5 = tf.concat(3, [Mconv7_stage4, conv4_7_CPM, pool_center_lower]) | ||
| Mconv1_stage5 = layers.conv2d(concat_stage5, 128, 7, 1, activation_fn=None, scope='Mconv1_stage5') | ||
| Mconv1_stage5 = tf.nn.relu(Mconv1_stage5) | ||
| Mconv2_stage5 = layers.conv2d(Mconv1_stage5, 128, 7, 1, activation_fn=None, scope='Mconv2_stage5') | ||
| Mconv2_stage5 = tf.nn.relu(Mconv2_stage5) | ||
| Mconv3_stage5 = layers.conv2d(Mconv2_stage5, 128, 7, 1, activation_fn=None, scope='Mconv3_stage5') | ||
| Mconv3_stage5 = tf.nn.relu(Mconv3_stage5) | ||
| Mconv4_stage5 = layers.conv2d(Mconv3_stage5, 128, 7, 1, activation_fn=None, scope='Mconv4_stage5') | ||
| Mconv4_stage5 = tf.nn.relu(Mconv4_stage5) | ||
| Mconv5_stage5 = layers.conv2d(Mconv4_stage5, 128, 7, 1, activation_fn=None, scope='Mconv5_stage5') | ||
| Mconv5_stage5 = tf.nn.relu(Mconv5_stage5) | ||
| Mconv6_stage5 = layers.conv2d(Mconv5_stage5, 128, 1, 1, activation_fn=None, scope='Mconv6_stage5') | ||
| Mconv6_stage5 = tf.nn.relu(Mconv6_stage5) | ||
| Mconv7_stage5 = layers.conv2d(Mconv6_stage5, 15, 1, 1, activation_fn=None, scope='Mconv7_stage5') | ||
| concat_stage6 = tf.concat(3, [Mconv7_stage5, conv4_7_CPM, pool_center_lower]) | ||
| Mconv1_stage6 = layers.conv2d(concat_stage6, 128, 7, 1, activation_fn=None, scope='Mconv1_stage6') | ||
| Mconv1_stage6 = tf.nn.relu(Mconv1_stage6) | ||
| Mconv2_stage6 = layers.conv2d(Mconv1_stage6, 128, 7, 1, activation_fn=None, scope='Mconv2_stage6') | ||
| Mconv2_stage6 = tf.nn.relu(Mconv2_stage6) | ||
| Mconv3_stage6 = layers.conv2d(Mconv2_stage6, 128, 7, 1, activation_fn=None, scope='Mconv3_stage6') | ||
| Mconv3_stage6 = tf.nn.relu(Mconv3_stage6) | ||
| Mconv4_stage6 = layers.conv2d(Mconv3_stage6, 128, 7, 1, activation_fn=None, scope='Mconv4_stage6') | ||
| Mconv4_stage6 = tf.nn.relu(Mconv4_stage6) | ||
| Mconv5_stage6 = layers.conv2d(Mconv4_stage6, 128, 7, 1, activation_fn=None, scope='Mconv5_stage6') | ||
| Mconv5_stage6 = tf.nn.relu(Mconv5_stage6) | ||
| Mconv6_stage6 = layers.conv2d(Mconv5_stage6, 128, 1, 1, activation_fn=None, scope='Mconv6_stage6') | ||
| Mconv6_stage6 = tf.nn.relu(Mconv6_stage6) | ||
| Mconv7_stage6 = layers.conv2d(Mconv6_stage6, 15, 1, 1, activation_fn=None, scope='Mconv7_stage6') | ||
| return Mconv7_stage6 |
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| # -*- coding: utf-8 -*- | ||
| """ | ||
| Created on Dec 20 17:39 2016 | ||
| @author: Denis Tome' | ||
| """ | ||
| import numpy as np | ||
| import cv2 | ||
| import utils.config as config | ||
| import tensorflow as tf | ||
| import cpm | ||
| from utils.draw import * | ||
| from prob_model import Prob3dPose | ||
| import utils.process as ut | ||
|
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| fname = 'images/test_image.png' | ||
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| image = cv2.cvtColor(cv2.imread(fname), cv2.COLOR_BGR2RGB) | ||
| scale = config.INPUT_SIZE/(image.shape[0] * 1.0) | ||
| image = cv2.resize(image, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC) | ||
| b_image = np.array(image[np.newaxis] / 255.0 - 0.5, dtype=np.float32) | ||
|
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| tf.reset_default_graph() | ||
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| with tf.variable_scope('CPM'): | ||
| # placeholders for person network | ||
| image_in = tf.placeholder(tf.float32, [1, config.INPUT_SIZE, image.shape[1], 3]) | ||
| heatmap_person = cpm.inference_person(image_in) | ||
| heatmap_person_large = tf.image.resize_images(heatmap_person, [config.INPUT_SIZE, image.shape[1]]) | ||
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| # placeholders for pose network | ||
| N = 16 | ||
| pose_image_in = tf.placeholder(tf.float32, [N, config.INPUT_SIZE, config.INPUT_SIZE, 3]) | ||
| pose_centermap_in = tf.placeholder(tf.float32, [N, config.INPUT_SIZE, config.INPUT_SIZE, 1]) | ||
| heatmap_pose = cpm.inference_pose(pose_image_in, pose_centermap_in) | ||
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| with tf.Session() as sess: | ||
| sess.run(tf.global_variables_initializer()) | ||
| saver = tf.train.Saver() | ||
| saver.restore(sess, 'saved_sessions/person_MPI/init') | ||
| hmap_person = sess.run(heatmap_person_large, {image_in: b_image}) | ||
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| hmap_person = np.squeeze(hmap_person) | ||
| centers = ut.detect_objects_heatmap(hmap_person) | ||
| b_pose_image, b_pose_cmap = ut.prepare_input_posenet(b_image[0], centers, [config.INPUT_SIZE, image.shape[1]], | ||
| [config.INPUT_SIZE, config.INPUT_SIZE]) | ||
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| # sess = tf.InteractiveSession() | ||
| with tf.Session() as sess: | ||
| saver = tf.train.Saver() | ||
| saver.restore(sess, 'saved_sessions/pose_MPI/init') | ||
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| feed_dict = { | ||
| pose_image_in: b_pose_image, | ||
| pose_centermap_in: b_pose_cmap | ||
| } | ||
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| _hmap_pose = sess.run(heatmap_pose, feed_dict) | ||
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| # Estimate 2D poses | ||
| parts, visible = ut.detect_parts_heatmaps(_hmap_pose, centers, [config.INPUT_SIZE, config.INPUT_SIZE]) | ||
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| # Estimate 3D poses | ||
| poseLifting = Prob3dPose() | ||
| pose2D, weights = Prob3dPose.transform_joints(parts, visible) | ||
| pose3D = poseLifting.compute_3d(pose2D, weights) | ||
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| # Show 2D poses | ||
| plt.figure() | ||
| draw_limbs(image, parts, visible) | ||
| plt.imshow(image) | ||
| plt.axis('off') | ||
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| # Show 3D poses | ||
| for single_3D in pose3D: | ||
| plot_pose(single_3D) | ||
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| plt.show() | ||
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