├── .gitignore ├── LICENSE ├── README.md ├── imgs ├── frankfurt_000000_000294_42.png ├── frankfurt_000000_000576_21.png ├── frankfurt_000000_000576_46.png ├── frankfurt_000000_001016_20.png ├── medical_00_5_11.png └── medical_00_5_20.png ├── models ├── README.md └── download_and_unpack.sh ├── readme ├── frankfurt_000000_000294_42.png ├── medical_00_5_20.png └── model.png ├── requirements.txt └── src ├── EvalNet.py ├── GGNNPolyModel.py ├── PolygonModel.py ├── __init__.py ├── demo_inference.sh ├── demo_polyrnn.ipynb ├── inference.py ├── poly_utils.py ├── utils.py └── vis_predictions.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Created by .ignore support plugin (hsz.mobi) 2 | ### Example user template template 3 | ### Example user template 4 | temp/ 5 | demo/output 6 | # IntelliJ project files 7 | .idea 8 | *.iml 9 | out 10 | gen### Python template 11 | # Byte-compiled / optimized / DLL files 12 | __pycache__/ 13 | *.py[cod] 14 | *$py.class 15 | 16 | # C extensions 17 | *.so 18 | 19 | # Distribution / packaging 20 | .Python 21 | build/ 22 | develop-eggs/ 23 | dist/ 24 | downloads/ 25 | eggs/ 26 | .eggs/ 27 | lib/ 28 | lib64/ 29 | parts/ 30 | sdist/ 31 | var/ 32 | wheels/ 33 | *.egg-info/ 34 | .installed.cfg 35 | *.egg 36 | MANIFEST 37 | 38 | # PyInstaller 39 | # Usually these files are written by a python script from a template 40 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 41 | *.manifest 42 | *.spec 43 | 44 | # Installer logs 45 | pip-log.txt 46 | pip-delete-this-directory.txt 47 | 48 | # Unit test / coverage reports 49 | htmlcov/ 50 | .tox/ 51 | .coverage 52 | .coverage.* 53 | .cache 54 | nosetests.xml 55 | coverage.xml 56 | *.cover 57 | .hypothesis/ 58 | 59 | # Translations 60 | *.mo 61 | *.pot 62 | 63 | # Django stuff: 64 | *.log 65 | .static_storage/ 66 | .media/ 67 | local_settings.py 68 | 69 | # Flask stuff: 70 | instance/ 71 | .webassets-cache 72 | 73 | # Scrapy stuff: 74 | .scrapy 75 | 76 | # Sphinx documentation 77 | docs/_build/ 78 | 79 | # PyBuilder 80 | target/ 81 | 82 | # Jupyter Notebook 83 | .ipynb_checkpoints 84 | 85 | # pyenv 86 | .python-version 87 | 88 | # celery beat schedule file 89 | celerybeat-schedule 90 | 91 | # SageMath parsed files 92 | *.sage.py 93 | 94 | # Environments 95 | .env 96 | .venv 97 | env/ 98 | venv/ 99 | ENV/ 100 | env.bak/ 101 | venv.bak/ 102 | 103 | # Spyder project settings 104 | .spyderproject 105 | .spyproject 106 | 107 | # Rope project settings 108 | .ropeproject 109 | 110 | # mkdocs documentation 111 | /site 112 | 113 | # mypy 114 | .mypy_cache/ 115 | 116 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PolygonRNN++ 2 | 3 | This is the official inference code for Polygon-RNN++ (CVPR-2018). For technical details, please refer to: 4 | 5 | **An official pytorch reimplementation with training/tool code is available [here](https://github.com/fidler-lab/polyrnn-pp-pytorch)** 6 | 7 | **Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++** 8 | [David Acuna](http://www.cs.toronto.edu/~davidj/)\*, [Huan Ling](http:///www.cs.toronto.edu/~linghuan/)\*, [Amlan Kar](http://www.cs.toronto.edu/~amlan/)\*, [Sanja Fidler](http://www.cs.toronto.edu/~fidler/) (\* denotes equal contribution) 9 | CVPR 2018 10 | **[[Paper](https://arxiv.org/abs/1803.09693)] [[Video](https://www.youtube.com/watch?v=evGqMnL4P3E)] [[Project Page](http://www.cs.toronto.edu/polyrnn/)] [[Demo](http://www.cs.toronto.edu/~amlan/demo/)] [[Training/Tool Code](https://github.com/fidler-lab/polyrnn-pp-pytorch)]** 11 | ![Model](readme/model.png) 12 | 13 | ### Usage 14 | 1. Clone the repository 15 | ``` 16 | git clone https://github.com/davidjesusacu/polyrnn && cd polyrnn 17 | ``` 18 | 2. Install dependencies 19 | (Note: Using a GPU (and tensorflow-gpu) is recommended. The model will run on a CPU, albeit slowly.) 20 | ``` 21 | virtualenv env 22 | source env/bin/activate 23 | pip install -r requirements.txt 24 | ``` 25 | 3. Download the pre-trained models and graphs (448 MB) 26 | (These models were trained on the Cityscapes Dataset) 27 | ``` 28 | ./models/download_and_unpack.sh 29 | ``` 30 | 4. Run demo\_inference.sh 31 | ``` 32 | ./src/demo_inference.sh 33 | ``` 34 | This should produce results in the output/ folder that look like 35 | ![ex2](readme/frankfurt_000000_000294_42.png) 36 | ![ex1](readme/medical_00_5_20.png) 37 | 38 | ### Walkthrough 39 | Checkout the ipython [notebook](src/demo_polyrnn.ipynb) that provides a simple walkthrough demonstrating how 40 | to run our model on sample input image crops 41 | 42 | If you use this code, please cite: 43 | 44 | @inproceedings{AcunaCVPR18, 45 | title={Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++}, 46 | author={David Acuna and Huan Ling and Amlan Kar and Sanja Fidler}, 47 | booktitle={CVPR}, 48 | year={2018} 49 | } 50 | 51 | @inproceedings{CastrejonCVPR17, 52 | title = {Annotating Object Instances with a Polygon-RNN}, 53 | author = {Lluis Castrejon and Kaustav Kundu and Raquel Urtasun and Sanja Fidler}, 54 | booktitle = {CVPR}, 55 | year = {2017} 56 | } 57 | -------------------------------------------------------------------------------- /imgs/frankfurt_000000_000294_42.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fidler-lab/polyrnn-pp/b0e55c5b4b3ae6b5e44409192158692c79757f70/imgs/frankfurt_000000_000294_42.png -------------------------------------------------------------------------------- /imgs/frankfurt_000000_000576_21.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fidler-lab/polyrnn-pp/b0e55c5b4b3ae6b5e44409192158692c79757f70/imgs/frankfurt_000000_000576_21.png -------------------------------------------------------------------------------- /imgs/frankfurt_000000_000576_46.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fidler-lab/polyrnn-pp/b0e55c5b4b3ae6b5e44409192158692c79757f70/imgs/frankfurt_000000_000576_46.png -------------------------------------------------------------------------------- /imgs/frankfurt_000000_001016_20.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fidler-lab/polyrnn-pp/b0e55c5b4b3ae6b5e44409192158692c79757f70/imgs/frankfurt_000000_001016_20.png -------------------------------------------------------------------------------- /imgs/medical_00_5_11.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fidler-lab/polyrnn-pp/b0e55c5b4b3ae6b5e44409192158692c79757f70/imgs/medical_00_5_11.png -------------------------------------------------------------------------------- /imgs/medical_00_5_20.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fidler-lab/polyrnn-pp/b0e55c5b4b3ae6b5e44409192158692c79757f70/imgs/medical_00_5_20.png -------------------------------------------------------------------------------- /models/README.md: -------------------------------------------------------------------------------- 1 | # PolygonRNN++ 2 | 3 | ### Models 4 | * ```models/poly/``` Checkpoint and meta graph for PolygonRNN++ 5 | * ```models/evalnet/``` Checkpoint and meta graph for EvalNet 6 | * ```models/ggnn/``` Checkpoint and meta graph for GGNN 7 | 8 | ### Install 9 | ```bash 10 | ./download_and_unpack.sh 11 | ``` -------------------------------------------------------------------------------- /models/download_and_unpack.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | export FILENAME=checkpoints_cityscapes.tar.gz 4 | export URL=http://www.cs.toronto.edu/polyrnn/models/$FILENAME 5 | 6 | wget $URL 7 | tar -xvf $FILENAME ./models/ 8 | -------------------------------------------------------------------------------- /readme/frankfurt_000000_000294_42.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fidler-lab/polyrnn-pp/b0e55c5b4b3ae6b5e44409192158692c79757f70/readme/frankfurt_000000_000294_42.png -------------------------------------------------------------------------------- /readme/medical_00_5_20.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fidler-lab/polyrnn-pp/b0e55c5b4b3ae6b5e44409192158692c79757f70/readme/medical_00_5_20.png -------------------------------------------------------------------------------- /readme/model.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fidler-lab/polyrnn-pp/b0e55c5b4b3ae6b5e44409192158692c79757f70/readme/model.png -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | backports.functools-lru-cache==1.5 2 | backports.weakref==1.0.post1 3 | bleach==1.5.0 4 | cycler==0.10.0 5 | decorator==4.2.1 6 | funcsigs==1.0.2 7 | html5lib==0.9999999 8 | kiwisolver==1.0.1 9 | Markdown==2.6.11 10 | matplotlib==2.2.2 11 | mock==2.0.0 12 | networkx==2.1 13 | numpy==1.14.2 14 | opencv-python==3.4.0.12 15 | pbr==4.0.0 16 | Pillow==5.0.0 17 | protobuf==3.5.2.post1 18 | pyparsing==2.2.0 19 | python-dateutil==2.7.2 20 | pytz==2018.3 21 | PyWavelets==0.5.2 22 | scikit-image==0.13.1 23 | scipy==1.0.1 24 | six==1.11.0 25 | subprocess32==3.2.7 26 | tensorflow-gpu==1.3.0 27 | tensorflow-tensorboard==0.1.8 28 | tqdm==4.19.9 29 | Werkzeug==0.14.1 30 | -------------------------------------------------------------------------------- /src/EvalNet.py: -------------------------------------------------------------------------------- 1 | import tensorflow.contrib.layers as layers 2 | from collections import namedtuple 3 | import tensorflow as tf 4 | import numpy as np 5 | from tensorflow.contrib import slim 6 | import poly_utils as polyutils 7 | 8 | Inputs = namedtuple("Inputs", 9 | ["cnn_feats", "pred_polys", "predicted_mask", "ious", "hidd1", "hidd2", "cells_1", "cells_2", 10 | "pred_mask_imgs"]) 11 | 12 | 13 | class EvalNet(object): 14 | def __init__(self, batch_size, max_poly_len=71): 15 | self.seq_len = max_poly_len 16 | self.batch_size = batch_size 17 | self._ph = self._define_phs() 18 | self.cost = None 19 | self.predicted_ious = None 20 | # - 21 | self.name = "EvalNet" 22 | self.is_training = False 23 | self._zero_batch = np.zeros([self.batch_size, 1]) 24 | self._first_pass = True 25 | 26 | def _define_phs(self): 27 | 28 | cnn_feats = tf.placeholder(tf.float32, shape=[self.batch_size, 28, 28, 128], name="cnn_feats") 29 | pred_mask_imgs = tf.placeholder(tf.float32, shape=[self.batch_size, 28, 28, 2], name="pred_mask_imgs") 30 | 31 | # -- 32 | pred_polys = tf.placeholder(tf.float32, shape=[self.batch_size, self.seq_len, 2], 33 | name="pred_polys") 34 | predicted_mask = tf.placeholder(tf.float32, shape=[self.batch_size, self.seq_len], 35 | name="predicted_mask") 36 | # --- 37 | h1 = tf.placeholder(tf.float32, shape=[self.batch_size, self.seq_len, 28, 28, 64], name="hidden1") 38 | cells_1 = tf.placeholder(tf.float32, shape=[self.batch_size, 1, 28, 28, 64], 39 | name="cell_state_hidden1") 40 | h2 = tf.placeholder(tf.float32, shape=[self.batch_size, self.seq_len, 28, 28, 16], name="hidden2") 41 | cells_2 = tf.placeholder(tf.float32, shape=[self.batch_size, 1, 28, 28, 16], 42 | name="cell_state_hidden2") 43 | 44 | ious = tf.placeholder(tf.float32, shape=[self.batch_size, 1], name="ious") 45 | return Inputs(cnn_feats, pred_polys, predicted_mask, ious, h1, h2, cells_1, cells_2, pred_mask_imgs) 46 | 47 | def training(self): 48 | raise NotImplementedError() 49 | 50 | def draw_mask(self, img_h, img_w, pred_poly, pred_mask): 51 | batch_size = pred_poly.shape[0] 52 | 53 | pred_poly_lens = np.sum(pred_mask, axis=1) 54 | 55 | assert pred_poly_lens.shape[0] == batch_size == self.batch_size, '%s,%s,%s' % ( 56 | str(pred_poly_lens.shape[0]), str(batch_size), str(self.batch_size)) 57 | 58 | masks_imgs = [] 59 | for i in range(batch_size): 60 | # Cleaning the polys 61 | p_poly = pred_poly[i][:pred_poly_lens[i], :] 62 | 63 | # Printing the mask 64 | # if self.draw_perimeter is False: 65 | try: 66 | mask1 = np.zeros((img_h, img_w)) 67 | mask1 = polyutils.draw_poly(mask1, p_poly.astype(np.int)) 68 | mask1 = np.reshape(mask1, [img_h, img_w, 1]) 69 | # else: 70 | mask = polyutils.polygon_perimeter(p_poly.astype(np.int), img_side=28) 71 | mask = np.reshape(mask, [img_h, img_w, 1]) 72 | except: 73 | import ipdb; 74 | ipdb.set_trace() 75 | 76 | mask = np.concatenate((mask, mask1), axis=2) 77 | 78 | masks_imgs.append(mask) 79 | masks_imgs = np.array(masks_imgs, dtype=np.float32) 80 | return np.reshape(masks_imgs, [self.batch_size, img_h, img_w, 2]) 81 | 82 | def _feed_dict(self, train_batch, is_training=True): 83 | 84 | pred_polys = train_batch['raw_polys'] * np.expand_dims(train_batch['masks'], axis=2) # (seq,batch,2) 85 | pred_polys = np.transpose(pred_polys, [1, 0, 2]) # (batch,seq,2) 86 | 87 | pred_mask = np.transpose(train_batch['masks'], [1, 0]) # (batch_size,seq_len) 88 | cnn_feats = train_batch['cnn_feats'] # (batch_size, 28, 28, 128) 89 | 90 | cells_1 = np.stack([np.split(train_batch['hiddens_list'][-1][0], 2, axis=3)[0]], axis=1) 91 | 92 | cells_2 = np.stack([np.split(train_batch['hiddens_list'][-1][1], 2, axis=3)[0]], axis=1) 93 | 94 | pred_mask_imgs = self.draw_mask(28, 28, pred_polys, pred_mask) 95 | 96 | if is_training: 97 | raise NotImplementedError() 98 | 99 | r = { 100 | self._ph.cells_1: cells_1, 101 | self._ph.cells_2: cells_2, 102 | self._ph.pred_mask_imgs: pred_mask_imgs, 103 | self._ph.cnn_feats: cnn_feats, 104 | self._ph.predicted_mask: pred_mask, 105 | self._ph.pred_polys: pred_polys, 106 | self._ph.ious: self._zero_batch 107 | } 108 | 109 | return r 110 | 111 | def do_train(self, sess, train_batch, cost_op, backpass_op, train_writer, log, batch_idx): 112 | """ 113 | Perform a training iteration.l 114 | """ 115 | raise NotImplementedError() 116 | 117 | def build_graph(self): 118 | self._build_model() 119 | return self.predicted_ious 120 | 121 | def _myForwardPass(self): 122 | cnn_feats = self._ph.cnn_feats 123 | pred_polys = self._ph.pred_polys 124 | pred_mask_imgs = self._ph.pred_mask_imgs 125 | last_cell_state_1 = self._ph.cells_1[:, -1, :, :, :] 126 | last_cell_state_2 = self._ph.cells_2[:, -1, :, :, :] 127 | weight_decay = 0.00001 128 | 129 | predicted_history = tf.zeros(shape=(self.batch_size, 28, 28, 1)) 130 | 131 | # Drawing the canvas 132 | for i in range(self.seq_len): 133 | pred_polys_t = pred_polys[:, i] # batch x 134 | indices = tf.concat( 135 | [tf.reshape(tf.range(0, self.batch_size), (self.batch_size, 1)), tf.cast(pred_polys_t, tf.int32)], 136 | axis=1) 137 | updates = tf.ones(shape=self.batch_size) 138 | pred_polys_t = tf.scatter_nd(indices, updates, shape=(self.batch_size, 28, 28)) 139 | predicted_history = predicted_history + tf.expand_dims(pred_polys_t, axis=-1) 140 | 141 | xt = tf.concat([cnn_feats, predicted_history, pred_mask_imgs, last_cell_state_1, last_cell_state_2], 142 | axis=3) 143 | 144 | with slim.arg_scope([slim.conv2d], kernel_size=[3, 3], stride=1, 145 | weights_regularizer=slim.l2_regularizer(weight_decay), 146 | activation_fn=tf.nn.relu, 147 | normalizer_fn=slim.batch_norm, 148 | normalizer_params={"is_training": self.is_training, "decay": 0.99, "center": True, 149 | "scale": True}, 150 | weights_initializer=layers.variance_scaling_initializer( 151 | factor=2.0, mode='FAN_IN', 152 | uniform=False) 153 | ): 154 | self._conv1 = slim.conv2d(xt, scope="conv1", num_outputs=16) 155 | self._conv2 = slim.conv2d(self._conv1, scope="conv2", num_outputs=1) 156 | 157 | output = layers.fully_connected(slim.flatten(self._conv2), 1, weights_regularizer=layers.l2_regularizer(1e-5), 158 | scope="FC") 159 | return output 160 | 161 | def _build_model(self): 162 | prediction = self._myForwardPass() 163 | 164 | self.predicted_ious = prediction 165 | return self.predicted_ious 166 | 167 | def do_test(self, sess, instance, *_): 168 | output = sess.run( 169 | self.predicted_ious, 170 | feed_dict=self._feed_dict(instance, is_training=False) 171 | ) 172 | 173 | return output 174 | -------------------------------------------------------------------------------- /src/GGNNPolyModel.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import utils 3 | import numpy as np 4 | 5 | 6 | class GGNNPolygonModel(object): 7 | """Class to load GGNNPolygonModel and run inference.""" 8 | 9 | # Tensors names to gather from the graph 10 | 11 | # Input 12 | IMG = 'imgs:0' 13 | FEATURE_INDEX = "feature_index:0" 14 | ADJ = 'adjcent:0' 15 | POLY = 'polys:0' 16 | MASK = 'masks:0' 17 | 18 | # Outputs 19 | OUTPUT_POLYS_TENSOR_NAME = 'ggnn_out_poly:0' 20 | OUTPUT_MASKS_TENSOR_NAME = 'ggnn_out_masks:0' 21 | 22 | def __init__(self, meta_graph_path, graph=None): 23 | """Creates and loads PolygonModel. """ 24 | if graph is None: 25 | self.graph = tf.Graph() 26 | else: 27 | self.graph = graph 28 | 29 | self.saver = None 30 | self.eval_pred_fn = None 31 | self._restore_graph(meta_graph_path) 32 | self.max_poly_len = 142 33 | 34 | def _restore_graph(self, meta_graph_path): 35 | with self.graph.as_default(): 36 | self.saver = tf.train.import_meta_graph(meta_graph_path, clear_devices=True) 37 | 38 | def _prediction(self): 39 | return { 40 | 'polys': self.graph.get_tensor_by_name(self.OUTPUT_POLYS_TENSOR_NAME), 41 | 'masks': self.graph.get_tensor_by_name(self.OUTPUT_MASKS_TENSOR_NAME) 42 | } 43 | 44 | def register_eval_fn(self, eval_pred_fn): 45 | self.eval_pred_fn = eval_pred_fn 46 | 47 | def do_test(self, sess, input_images, feature_index, polys, masks): 48 | """ 49 | Return polygon 50 | """ 51 | 52 | assert input_images.shape[1:] == (224, 224, 3), 'image must be rgb 224x224 but is (%s)' % str( 53 | input_images.shape) 54 | 55 | adjcent_matrix = self.create_adjacency_matrix(polys, masks) 56 | 57 | pred_dict = sess.run( 58 | self._prediction(), 59 | feed_dict={ 60 | self.IMG: input_images, 61 | self.FEATURE_INDEX: feature_index, 62 | self.ADJ: adjcent_matrix, 63 | self.POLY: polys, 64 | self.MASK: masks 65 | } 66 | ) 67 | # 68 | polygons = pred_dict['polys'] 69 | masks = pred_dict['masks'] 70 | # 71 | polygons = self._postprocess_polygons(polygons, masks) 72 | pred_dict['polys'] = polygons 73 | 74 | return {'polys_ggnn': polygons} 75 | 76 | def create_adjacency_matrix(self, batch_poly, batch_mask): 77 | """ 78 | Create adjacency matrix for ggnn 79 | 80 | Args: 81 | polygons: T x N x 2 vertices in range [0, grid_side] 82 | masks: T x N x 1 masks 83 | 84 | Returns: 85 | adjacency_matrix: [Batch_size, self.max_poly_len, self.max_poly_len * 3 * 2] 86 | """ 87 | batch_size = len(batch_poly) 88 | n_nodes = self.max_poly_len 89 | n_edge_types = 3 90 | a = np.zeros([batch_size, n_nodes, n_nodes * n_edge_types * 2]) 91 | for batch in range(len(batch_poly)): 92 | mask = batch_mask[batch] 93 | index, = np.where(mask == 0) 94 | if len(index) > 0: 95 | index = index[0] 96 | if index > 2: 97 | for i in range(index): 98 | if i % 2 == 0: 99 | if i < index - 2: 100 | 101 | a[batch][i][(0) * n_nodes + i + 2] = 1 102 | a[batch][i + 2][(0 + n_edge_types) * n_nodes + i] = 1 103 | 104 | a[batch][i + 2][(0) * n_nodes + i] = 1 105 | a[batch][i][(0 + n_edge_types) * n_nodes + i + 2] = 1 106 | 107 | a[batch][i][(1) * n_nodes + i + 1] = 1 108 | a[batch][i + 1][(1 + n_edge_types) * n_nodes + i] = 1 109 | 110 | a[batch][i + 1][(2) * n_nodes + i] = 1 111 | a[batch][i][(2 + n_edge_types) * n_nodes + i + 1] = 1 112 | 113 | else: 114 | a[batch][i][(0) * n_nodes + 0] = 1 115 | a[batch][0][(0 + n_edge_types) * n_nodes + i] = 1 116 | 117 | a[batch][0][(0) * n_nodes + i] = 1 118 | a[batch][i][(0 + n_edge_types) * n_nodes + 0] = 1 119 | 120 | a[batch][i][(1) * n_nodes + i + 1] = 1 121 | a[batch][i + 1][(1 + n_edge_types) * n_nodes + i] = 1 122 | 123 | a[batch][i + 1][(2) * n_nodes + i] = 1 124 | a[batch][i][(2 + n_edge_types) * n_nodes + i + 1] = 1 125 | 126 | else: 127 | if i < index - 1: 128 | a[batch][i][(2) * n_nodes + i + 1] = 1 129 | a[batch][i + 1][(2 + n_edge_types) * n_nodes + i] = 1 130 | 131 | a[batch][i + 1][(1) * n_nodes + i] = 1 132 | a[batch][i][(1 + n_edge_types) * n_nodes + i + 1] = 1 133 | 134 | 135 | else: 136 | a[batch][i][(2) * n_nodes + 0] = 1 137 | a[batch][0][(2 + n_edge_types) * n_nodes + i] = 1 138 | 139 | a[batch][0][(1) * n_nodes + i] = 1 140 | a[batch][i][(1 + n_edge_types) * n_nodes + 0] = 1 141 | 142 | return a 143 | 144 | def _postprocess_polygons(self, polygons, masks, ): 145 | """ 146 | Post process polygons. 147 | 148 | Args: 149 | polygons: T x N x 2 vertices in range [0, grid_side] 150 | masks: T x N x 1 masks 151 | 152 | Returns: 153 | processed_polygons: list of N polygons 154 | """ 155 | 156 | result = utils._mask_polys(polygons, masks) 157 | result1 = [utils._poly0g_to_poly01(p, 112) for p in result] 158 | return result1 159 | -------------------------------------------------------------------------------- /src/PolygonModel.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import numpy as np 3 | import utils 4 | from distutils.version import LooseVersion 5 | 6 | class PolygonModel(object): 7 | """Class to load PolygonModel and run inference.""" 8 | 9 | # Tensors names to gather from the graph 10 | 11 | # Input 12 | INPUT_IMGS_TENSOR_NAME = 'InputImgs:0' 13 | INPUT_FIRST_TOP_K = "TopKFirstPoint:0" 14 | 15 | # Outputs 16 | OUTPUT_POLYS_TENSOR_NAME = 'OutputPolys:0' 17 | OUTPUT_MASKS_TENSOR_NAME = 'OutputMasks:0' 18 | OUTPUT_CNN_FEATS_TENSOR_NAME = 'OutputCNNFeats:0' 19 | # -- 20 | OUTPUT_STATE1_TENSOR_NAME = 'OutputState1:0' 21 | OUTPUT_STATE2_TENSOR_NAME = 'OutputState2:0' 22 | 23 | def __init__(self, meta_graph_path, graph=None): 24 | """Creates and loads PolygonModel. """ 25 | 26 | #check whether a supported version of tensorflow is installed 27 | if ( 28 | (LooseVersion(tf.__version__) < LooseVersion('1.3.0')) 29 | or (LooseVersion(tf.__version__) > LooseVersion('1.3.1')) 30 | ): 31 | err_string = 'you are using tensorflow version ' + tf.__version__ + ' but only versions 1.3.0 to 1.3.1 are supported' 32 | raise NotImplementedError(err_string) 33 | 34 | if graph is None: 35 | self.graph = tf.Graph() 36 | else: 37 | self.graph = graph 38 | 39 | self.saver = None 40 | self.eval_pred_fn = None 41 | self._restore_graph(meta_graph_path) 42 | 43 | def _restore_graph(self, meta_graph_path): 44 | with self.graph.as_default(): 45 | self.saver = tf.train.import_meta_graph(meta_graph_path, clear_devices=True) 46 | 47 | def _prediction(self): 48 | return { 49 | 'polys': self.graph.get_tensor_by_name(self.OUTPUT_POLYS_TENSOR_NAME), 50 | 'masks': self.graph.get_tensor_by_name(self.OUTPUT_MASKS_TENSOR_NAME), 51 | 'state1': self.graph.get_tensor_by_name(self.OUTPUT_STATE1_TENSOR_NAME), 52 | 'state2': self.graph.get_tensor_by_name(self.OUTPUT_STATE2_TENSOR_NAME), 53 | 'cnn_feats': self.graph.get_tensor_by_name(self.OUTPUT_CNN_FEATS_TENSOR_NAME) 54 | } 55 | 56 | def register_eval_fn(self, eval_pred_fn): 57 | self.eval_pred_fn = eval_pred_fn 58 | 59 | def do_test(self, sess, input_images, first_top_k=0): 60 | """ 61 | Return polygon 62 | """ 63 | assert input_images.shape[1:] == (224, 224, 3), 'image must be rgb 224x224 (%s)' % str(input_images.shape) 64 | pred_dict = sess.run( 65 | self._prediction(), 66 | feed_dict={self.INPUT_IMGS_TENSOR_NAME: input_images, self.INPUT_FIRST_TOP_K: first_top_k} 67 | ) 68 | # 69 | polygons = pred_dict['polys'] 70 | pred_dict['raw_polys'] = polygons 71 | masks = pred_dict['masks'] 72 | # 73 | polygons = self._postprocess_polygons(polygons, masks) 74 | pred_dict['polys'] = polygons 75 | pred_dict['hiddens_list'] = [[pred_dict['state1'], pred_dict['state2']]] 76 | 77 | if self.eval_pred_fn is not None: 78 | scores = self.eval_pred_fn(pred_dict) 79 | else: 80 | scores = None 81 | 82 | return {'polys': polygons, 'scores': scores} 83 | 84 | def _postprocess_polygons(self, polygons, masks, ): 85 | """ 86 | Post process polygons. 87 | 88 | Args: 89 | polygons: T x N x 2 vertices in range [0, grid_side] 90 | masks: T x N x 1 masks 91 | 92 | Returns: 93 | processed_polygons: list of N polygons 94 | """ 95 | result = np.swapaxes(polygons, 0, 1) 96 | masks = np.swapaxes(masks, 0, 1) 97 | result = utils._mask_polys(result, masks) 98 | result = [utils._poly0g_to_poly01(p) for p in result] 99 | 100 | return result 101 | -------------------------------------------------------------------------------- /src/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fidler-lab/polyrnn-pp/b0e55c5b4b3ae6b5e44409192158692c79757f70/src/__init__.py -------------------------------------------------------------------------------- /src/demo_inference.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | python src/inference.py \ 4 | --PolyRNN_metagraph='models/poly/polygonplusplus.ckpt.meta' \ 5 | --PolyRNN_checkpoint='models/poly/polygonplusplus.ckpt' \ 6 | --EvalNet_checkpoint='models/evalnet/evalnet.ckpt' \ 7 | --InputFolder='imgs/' \ 8 | --GGNN_checkpoint='models/ggnn/ggnn.ckpt' \ 9 | --GGNN_metagraph='models/ggnn/ggnn.ckpt.meta' \ 10 | --OutputFolder='output/' \ 11 | --Use_ggnn=True 12 | 13 | python src/vis_predictions.py \ 14 | -pred_dir='output/' \ 15 | --show_ggnn 16 | -------------------------------------------------------------------------------- /src/inference.py: -------------------------------------------------------------------------------- 1 | import matplotlib 2 | matplotlib.use('Agg') 3 | 4 | import tensorflow as tf 5 | import glob 6 | import os 7 | import numpy as np 8 | from PolygonModel import PolygonModel 9 | from EvalNet import EvalNet 10 | from GGNNPolyModel import GGNNPolygonModel 11 | import utils 12 | import skimage.io as io 13 | import tqdm 14 | import json 15 | 16 | # 17 | tf.logging.set_verbosity(tf.logging.INFO) 18 | # -- 19 | flags = tf.flags 20 | FLAGS = flags.FLAGS 21 | # --- 22 | flags.DEFINE_string('PolyRNN_metagraph', '', 'PolygonRNN++ MetaGraph ') 23 | flags.DEFINE_string('PolyRNN_checkpoint', '', 'PolygonRNN++ checkpoint ') 24 | flags.DEFINE_string('EvalNet_checkpoint', '', 'Evaluator checkpoint ') 25 | flags.DEFINE_string('GGNN_metagraph', '', 'GGNN poly MetaGraph ') 26 | flags.DEFINE_string('GGNN_checkpoint', '', 'GGNN poly checkpoint ') 27 | flags.DEFINE_string('InputFolder', '../imgs/', 'Folder with input image crops') 28 | flags.DEFINE_string('OutputFolder', '../output/', 'OutputFolder') 29 | flags.DEFINE_boolean('Use_ggnn', False, 'Use GGNN to postprocess output') 30 | 31 | # 32 | 33 | _BATCH_SIZE = 1 34 | _FIRST_TOP_K = 5 35 | 36 | def save_to_json(crop_name, predictions_dict): 37 | output_dict = {'img_source': crop_name, 'polys': predictions_dict['polys'][0].tolist()} 38 | if 'polys_ggnn' in predictions_dict: 39 | output_dict['polys_ggnn'] = predictions_dict['polys_ggnn'][0].tolist() 40 | 41 | fname = os.path.basename(crop_name).split('.')[0] + '.json' 42 | 43 | fname = os.path.join(FLAGS.OutputFolder, fname) 44 | 45 | json.dump(output_dict, open(fname, 'w'), indent=4) 46 | 47 | 48 | def inference(_): 49 | # Creating the graphs 50 | evalGraph = tf.Graph() 51 | polyGraph = tf.Graph() 52 | 53 | # Evaluator Network 54 | tf.logging.info("Building EvalNet...") 55 | with evalGraph.as_default(): 56 | with tf.variable_scope("discriminator_network"): 57 | evaluator = EvalNet(_BATCH_SIZE) 58 | evaluator.build_graph() 59 | saver = tf.train.Saver() 60 | 61 | # Start session 62 | evalSess = tf.Session(config=tf.ConfigProto( 63 | allow_soft_placement=True 64 | ), graph=evalGraph) 65 | saver.restore(evalSess, FLAGS.EvalNet_checkpoint) 66 | 67 | # PolygonRNN++ 68 | tf.logging.info("Building PolygonRNN++ ...") 69 | model = PolygonModel(FLAGS.PolyRNN_metagraph, polyGraph) 70 | 71 | model.register_eval_fn(lambda input_: evaluator.do_test(evalSess, input_)) 72 | 73 | polySess = tf.Session(config=tf.ConfigProto( 74 | allow_soft_placement=True 75 | ), graph=polyGraph) 76 | 77 | model.saver.restore(polySess, FLAGS.PolyRNN_checkpoint) 78 | 79 | if FLAGS.Use_ggnn: 80 | ggnnGraph = tf.Graph() 81 | tf.logging.info("Building GGNN ...") 82 | ggnnModel = GGNNPolygonModel(FLAGS.GGNN_metagraph, ggnnGraph) 83 | ggnnSess = tf.Session(config=tf.ConfigProto( 84 | allow_soft_placement=True 85 | ), graph=ggnnGraph) 86 | 87 | ggnnModel.saver.restore(ggnnSess, FLAGS.GGNN_checkpoint) 88 | 89 | tf.logging.info("Testing...") 90 | if not os.path.isdir(FLAGS.OutputFolder): 91 | tf.gfile.MakeDirs(FLAGS.OutputFolder) 92 | crops_path = glob.glob(os.path.join(FLAGS.InputFolder, '*.png')) 93 | 94 | for crop_path in tqdm.tqdm(crops_path): 95 | image_np = io.imread(crop_path) 96 | image_np = np.expand_dims(image_np, axis=0) 97 | preds = [model.do_test(polySess, image_np, top_k) for top_k in range(_FIRST_TOP_K)] 98 | 99 | # sort predictions based on the eval score and pick the best 100 | preds = sorted(preds, key=lambda x: x['scores'][0], reverse=True)[0] 101 | 102 | if FLAGS.Use_ggnn: 103 | polys = np.copy(preds['polys'][0]) 104 | feature_indexs, poly, mask = utils.preprocess_ggnn_input(polys) 105 | preds_gnn = ggnnModel.do_test(ggnnSess, image_np, feature_indexs, poly, mask) 106 | output = {'polys': preds['polys'], 'polys_ggnn': preds_gnn['polys_ggnn']} 107 | else: 108 | output = {'polys': preds['polys']} 109 | 110 | # dumping to json files 111 | save_to_json(crop_path, output) 112 | 113 | 114 | if __name__ == '__main__': 115 | tf.app.run(inference) 116 | -------------------------------------------------------------------------------- /src/poly_utils.py: -------------------------------------------------------------------------------- 1 | # import matplotlib.pyplot as plt 2 | import matplotlib.patches as patches 3 | import numpy as np 4 | import cv2 5 | import skimage.draw as draw 6 | 7 | 8 | def vis_polys(ax, img, poly, title=''): 9 | h, w = img.shape[:2] 10 | ax.imshow(img, aspect='equal') 11 | patch_poly = patches.Polygon(poly, alpha=0.6, color='blue') 12 | ax.add_patch(patch_poly) 13 | poly = np.append(poly, [poly[0, :]], axis=0) 14 | # 15 | ax.plot(poly[:, 0] * w, poly[:, 1] * h, '-o', linewidth=2, color='orange') 16 | # first point different color 17 | ax.plot(poly[0, 0] * w, poly[0, 1] * h, '-o', linewidth=3, color='blue') 18 | ax.set_title(title) 19 | ax.axis('off') 20 | 21 | 22 | def draw_poly(mask, poly): 23 | """ 24 | Draw a polygon in the img. 25 | 26 | Args: 27 | img: np array of type np.uint8 28 | poly: np array of shape N x 2 29 | """ 30 | cv2.fillPoly(mask, [poly], 255) 31 | 32 | return mask 33 | 34 | 35 | def polygon_perimeter(polygon, img_side=28): 36 | """ 37 | Generate the perimeter of a polygon including the vertices. 38 | """ 39 | # Create empty image 40 | img_shape = [img_side, img_side] 41 | img = np.zeros(img_shape, dtype=np.float32) 42 | 43 | prev_idx, cur_idx = -1, 0 44 | poly_len = len(polygon) 45 | while cur_idx < poly_len: 46 | # Get vertices 47 | prev_vertex = polygon[prev_idx] 48 | cur_vertex = polygon[cur_idx] 49 | 50 | # Get line pixels 51 | prev_rr, prev_cc = draw.line( 52 | prev_vertex[1], prev_vertex[0], 53 | cur_vertex[1], cur_vertex[0] 54 | ) 55 | # Draw lines 56 | img[prev_rr, prev_cc] = 1. 57 | 58 | # Increment prev_idx and cur_idx 59 | prev_idx += 1 60 | cur_idx += 1 61 | 62 | return img 63 | -------------------------------------------------------------------------------- /src/utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | _MAX_POLY_LEN = 142 4 | 5 | 6 | def _poly0g_to_poly01(polygon, grid_side=28): 7 | """ 8 | [0, grid_side] coordinates to [0, 1]. 9 | 10 | Note: we add 0.5 to the vertices so that the lie in the middle of the cell. 11 | """ 12 | result = (polygon.astype(np.float32) + 0.5) / grid_side 13 | 14 | return result 15 | 16 | 17 | def _mask_polys(polys, masks): 18 | """ 19 | Return masked polys. 20 | """ 21 | new_polys = [] 22 | for poly, mask in zip(polys, masks): 23 | cur_poly = poly[mask.astype(np.bool)] 24 | new_polys.append(cur_poly) 25 | 26 | return new_polys 27 | 28 | 29 | def _poly01_to_index(polygon, grid_side=112): 30 | """ 31 | Return poly index in a flat array. 32 | """ 33 | result = [] 34 | for item in polygon: 35 | result.append(item[0] + item[1] * grid_side) 36 | 37 | return result 38 | 39 | 40 | def preprocess_ggnn_input(pred_01_poly): 41 | """ 42 | Prepare data for GGNN 43 | """ 44 | 45 | enhanced_poly = [] 46 | for i in range(len(pred_01_poly)): 47 | if i < len(pred_01_poly) - 1: 48 | enhanced_poly.append(pred_01_poly[i]) 49 | 50 | enhanced_poly.append( 51 | np.array( 52 | [(pred_01_poly[i][0] + pred_01_poly[i + 1][0]) / 2, 53 | (pred_01_poly[i][1] + pred_01_poly[i + 1][1]) / 2]) 54 | ) 55 | else: 56 | enhanced_poly.append(pred_01_poly[i]) 57 | enhanced_poly.append( 58 | np.array( 59 | [(pred_01_poly[i][0] + pred_01_poly[0][0]) / 2, 60 | (pred_01_poly[i][1] + pred_01_poly[0][1]) / 2]) 61 | ) 62 | 63 | poly_for_feature_index = np.floor(np.array(enhanced_poly) * 112).astype(np.int32) 64 | feature_indexs = _poly01_to_index(poly_for_feature_index, 112) 65 | feature_indexs = np.array(feature_indexs) 66 | fwd_poly = np.floor(np.array(enhanced_poly) * 112).astype(np.int32) 67 | poly_len = len(fwd_poly) 68 | 69 | array_feature_indexs = np.ones(_MAX_POLY_LEN, np.float32) * 0. 70 | arr_fwd_poly = np.ones((_MAX_POLY_LEN, 2), np.float32) * -1. 71 | arr_mask = np.zeros(_MAX_POLY_LEN, np.int32) 72 | arr_fwd_poly[:poly_len] = fwd_poly 73 | arr_mask[:poly_len] = 1 74 | array_feature_indexs[:poly_len] = feature_indexs 75 | 76 | return np.array([array_feature_indexs]), np.array([arr_fwd_poly]), np.array([arr_mask]) 77 | -------------------------------------------------------------------------------- /src/vis_predictions.py: -------------------------------------------------------------------------------- 1 | import matplotlib 2 | matplotlib.use('Agg') 3 | 4 | import argparse 5 | import glob 6 | import os 7 | import json 8 | import matplotlib.pyplot as plt 9 | from poly_utils import vis_polys 10 | import skimage.io as io 11 | import numpy as np 12 | import tqdm 13 | 14 | def main(pred_dir, show_ggnn): 15 | preds_path = glob.glob(os.path.join(pred_dir, '*.json')) 16 | 17 | fig, axes = plt.subplots(1, 2 if show_ggnn else 1, num=0,figsize=(12,6)) 18 | axes = np.array(axes).flatten() 19 | for pred_path in tqdm.tqdm(preds_path): 20 | pred = json.load(open(pred_path, 'r')) 21 | file_name = pred_path.split('/')[-1].split('.')[0] 22 | 23 | im_crop, polys = io.imread(pred['img_source']), np.array(pred['polys']) 24 | vis_polys(axes[0], im_crop, polys, title='PolygonRNN++ : %s ' % file_name) 25 | if show_ggnn: 26 | vis_polys(axes[1], im_crop, np.array(pred['polys_ggnn']), title=' PolygonRNN++ + GGNN : %s' % file_name) 27 | 28 | fig_name = os.path.join(pred_dir, file_name) + '.png' 29 | fig.savefig(fig_name) 30 | 31 | [ax.cla() for ax in axes] 32 | 33 | if __name__ == "__main__": 34 | parser = argparse.ArgumentParser() 35 | parser.add_argument('-pred_dir', default='output/', help='dir with the predicted json files') 36 | parser.add_argument('--show_ggnn', action="store_true", default=False, help='visualize ggnn') 37 | # -- 38 | args = parser.parse_args() 39 | main(args.pred_dir, args.show_ggnn) 40 | --------------------------------------------------------------------------------