├── .gitignore ├── README.md ├── category_maps ├── data_from_games_categories.txt └── stanford_bground_categories.txt ├── model.py ├── pinheiro-collobert-rcnns-scene-labelling-2014.pdf ├── preprocessing.py ├── requirements.txt ├── test ├── .DS_Store ├── test_category_maps │ └── stanford_bground_categories.txt ├── test_data_stanford │ ├── .DS_Store │ ├── images │ │ ├── 0000047.jpg │ │ ├── 0000051.jpg │ │ ├── 0000059.jpg │ │ ├── 0000072.jpg │ │ ├── 0000087.jpg │ │ ├── 0000176.jpg │ │ ├── 0000382.jpg │ │ ├── 0000631.jpg │ │ ├── 0000643.jpg │ │ └── 0000697.jpg │ └── labels │ │ ├── 0000047.regions.txt │ │ ├── 0000051.regions.txt │ │ ├── 0000059.regions.txt │ │ ├── 0000072.regions.txt │ │ ├── 0000087.regions.txt │ │ ├── 0000176.regions.txt │ │ ├── 0000382.regions.txt │ │ ├── 0000631.regions.txt │ │ ├── 0000643.regions.txt │ │ └── 0000697.regions.txt └── test_preprocessing.py └── train.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *,cover 46 | .hypothesis/ 47 | 48 | # Translations 49 | *.mo 50 | *.pot 51 | 52 | # Django stuff: 53 | *.log 54 | local_settings.py 55 | 56 | # Flask stuff: 57 | instance/ 58 | .webassets-cache 59 | 60 | # Scrapy stuff: 61 | .scrapy 62 | 63 | # Sphinx documentation 64 | docs/_build/ 65 | 66 | # PyBuilder 67 | target/ 68 | 69 | # IPython Notebook 70 | .ipynb_checkpoints 71 | 72 | # pyenv 73 | .python-version 74 | 75 | # celery beat schedule file 76 | celerybeat-schedule 77 | 78 | # dotenv 79 | .env 80 | 81 | # virtualenv 82 | venv/ 83 | ENV/ 84 | 85 | # Spyder project settings 86 | .spyderproject 87 | 88 | # Rope project settings 89 | .ropeproject 90 | 91 | # vim swap files: 92 | *.swp 93 | *.swo 94 | 95 | # project specific stuff: 96 | output.mid 97 | test.mid 98 | scratch.py 99 | 100 | .idea 101 | 102 | models 103 | test_results 104 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Scene Labeling with Recurrent Convolutional Neural Networks 2 | 3 | ## Overview 4 | This project implements a "Recurrent Convolutional Neural Network" (rCNN) for scene labeling, 5 | as seen in [Pinheiro et al, 2014](http://www.jmlr.org/proceedings/papers/v32/pinheiro14.pdf). 6 | A copy of the paper is also available in this repo's root directory, `pinheiro-collobert-rcnns-scene-labelling-2014.pdf`. 7 | 8 | ### Summary of files 9 | README.md -- README file 10 | category_maps/ -- Text files containing category info for Stanford & "Data from Games" datasets 11 | model.py -- Code for rCNN model 12 | preprocessing.py -- Code for processing input data 13 | requirements.txt -- Lists python package requirements 14 | test/ -- Contains unit tests and sample data 15 | train.py -- Script for training and evaluating model 16 | 17 | ## Installation 18 | 1. Clone or download this repository to your computer: 19 | 20 | ```git clone https://github.com/rkargon/Scene-Labeling.git``` 21 | 22 | 2. Install the necessary Python requirements through `pip`: 23 | 24 | ```pip install -r requirements.txt``` 25 | 26 | This project only requires Tensorflow version 11, and PIL for image manipulation. 27 | 28 | 3. Download a dataset with which to use the model. 29 | We used the [Stanford Background Dataset](http://dags.stanford.edu/projects/scenedataset.html) which has 700+ 320x240 images and 8 classes. 30 | The code also works with the [Data from Games](https://download.visinf.tu-darmstadt.de/data/from_games/) 31 | dataset, which has 25,000 1914 × 1052 with 38 categories. 32 | 33 | To use another dataset, make sure it is organized similarly to one of the above two, and specify while training and testing which dataset it is "mimicking". 34 | Specifically, both datasets had the data in one folder, with subfolders "labels" and "images" for labels and images, respectively. 35 | The stanford dataset had labels in the format of space-separated digits in a text file, while the "Data from Games" dataset had labels in the form of paletted images, 36 | where each color corresponds to a different label. 37 | 38 | 4. Generate a text file that maps colors to category numbers nad labels. Each line of the file has five space-separated values: 39 | 40 | 41 | R G B category_num category_id 42 | 43 | R,G,B values should be in the range [0,1]. 44 | Category files for the Stanford Background and Data From Games datasets are provided in the folder `category_maps`. 45 | 46 | ## Running 47 | 48 | ### Training 49 | For training, use the `train.py` script with the `--training` flag. The following command trains the model on the Stanford dataset: 50 | 51 | python train.py --training --dataset stanford-bground --category_map category_maps/stanford_bground_categories.txt --data_dir --model_save_path 52 | 53 | 54 | Running `train.py -h` will show additional parameters for the script, including different hyperparameters. 55 | 56 | ### Testing 57 | For testing, use the `train.py` script without the `--training` flag. 58 | This script will get per-class accuracies for each image, as well as output predicted labels as image files. 59 | The following command loads a saved model and evaluates accuracy on the stanford data set: 60 | 61 | python train.py --model --category_map category_maps/stanford_bground_categories.txt --dataset stanford-bground --data_dir --output_dir 62 | 63 | 64 | This outputs per-clas accuracies for each layer of the recurrent rCNN, and also saves predicted labels for each layer. 65 | 66 | ## Credits 67 | 68 | This was originally submitted as a final project for Dr. Thomas Serre's Computational Vision (CLPS1520) course at Brown University. 69 | The code for the original project may be found at: https://github.com/NP-coder/CLPS1520Project 70 | 71 | - Tyler Barnes-Diana 72 | - Zhenhao Hou (Andrew) 73 | - Jae Hwan Hwang (James) 74 | - Raphael Kargon 75 | -------------------------------------------------------------------------------- /category_maps/data_from_games_categories.txt: -------------------------------------------------------------------------------- 1 | 0 0 0 0 unlabeled 2 | 0 0 0 1 ego_vehicle 3 | 0 0 0 2 rectification_border 4 | 0 0 0 3 out_of_roi 5 | 0.0784 0.0784 0.0784 4 static 6 | 0.4353 0.2902 0 5 dynamic 7 | 0.3176 0 0.3176 6 ground 8 | 0.5020 0.2510 0.5020 7 road 9 | 0.9569 0.1373 0.9098 8 sidewalk 10 | 0.9804 0.6667 0.6275 9 parking 11 | 0.9020 0.5882 0.5490 10 rail_track 12 | 0.2745 0.2745 0.2745 11 building 13 | 0.4000 0.4000 0.6118 12 wall 14 | 0.7451 0.6000 0.6000 13 fence 15 | 0.7059 0.6471 0.7059 14 guard_rail 16 | 0.5882 0.3922 0.3922 15 bridge 17 | 0.5882 0.4706 0.3529 16 tunnel 18 | 0.6000 0.6000 0.6000 17 pole 19 | 0.6000 0.6000 0.6000 18 polegroup 20 | 0.9804 0.6667 0.1176 19 traffic_light 21 | 0.8627 0.8627 0 20 traffic_sign 22 | 0.4196 0.5569 0.1373 21 vegetation 23 | 0.5961 0.9843 0.5961 22 terrain 24 | 0.2745 0.5098 0.7059 23 sky 25 | 0.8627 0.0784 0.2353 24 person 26 | 1.0000 0 0 25 rider 27 | 0 0 0.5569 26 car 28 | 0 0 0.2745 27 truck 29 | 0 0.2353 0.3922 28 bus 30 | 0 0 0.3529 29 caravan 31 | 0 0 0.4314 30 trailer 32 | 0 0.3137 0.3922 31 train 33 | 0 0 0.9020 32 motorcycle 34 | 0.4667 0.0431 0.1255 33 bicycle 35 | 0 0 0.5569 34 license_plate 36 | -------------------------------------------------------------------------------- /category_maps/stanford_bground_categories.txt: -------------------------------------------------------------------------------- 1 | 0.0000 0.0000 0.0000 -1 unknown 2 | 0.3700 0.7300 0.9900 0 sky 3 | 0.1500 0.3100 0.0700 1 tree/bush 4 | 0.2600 0.2600 0.2600 2 road/path 5 | 0.4200 0.6600 0.3100 3 grass 6 | 0.0700 0.3300 0.8000 4 water 7 | 0.8800 0.4000 0.4000 5 building 8 | 0.8100 0.8900 0.9500 6 mountain 9 | 0.9500 0.7600 0.2000 7 foreground_object 10 | -------------------------------------------------------------------------------- /model.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | import tensorflow as tf 4 | 5 | 6 | class RCNNModel: 7 | """ 8 | A recurrent convolutional neural network for scene labeling. A single convolutional net is iteratively applied to 9 | an image, using as input the pixel colors as well as the label predictions from the previous iteration. This 10 | allows the model to use context of nearby labels to improve predictions. 11 | """ 12 | 13 | def __init__(self, hidden_size_1: int, hidden_size_2: int, num_classes: int, learning_rate: float, 14 | num_layers: int): 15 | # padding? 16 | self.hidden_size_1 = hidden_size_1 17 | self.hidden_size_2 = hidden_size_2 18 | self.learning_rate = learning_rate 19 | self.num_classes = num_classes 20 | self.num_layers = num_layers 21 | 22 | # Set up placeholders for input and output 23 | self.inpt = tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3 + self.num_classes]) 24 | self.output = tf.placeholder(tf.int32, [None, None, None]) 25 | 26 | # Set up variable weights for model. These are shared across recurrent layers 27 | self.w_conv1 = tf.Variable(tf.truncated_normal([8, 8, 3 + self.num_classes, self.hidden_size_1], stddev=0.1)) 28 | b_conv1 = tf.Variable(tf.constant(0.1, shape=[self.hidden_size_1])) 29 | 30 | w_conv2 = tf.Variable(tf.truncated_normal([8, 8, self.hidden_size_1, self.hidden_size_2], stddev=0.1)) 31 | b_conv2 = tf.Variable(tf.constant(0.1, shape=[self.hidden_size_2])) 32 | 33 | w_conv3 = tf.Variable(tf.truncated_normal([1, 1, self.hidden_size_2, self.num_classes], stddev=0.1)) 34 | b_conv3 = tf.Variable(tf.constant(0.1, shape=[self.num_classes])) 35 | 36 | self.logits = [] 37 | self.errors = [] 38 | current_input = self.inpt 39 | current_output = self.output 40 | for i in range(self.num_layers): 41 | # scale output down by a stride of 2, to match convolution output 42 | current_output = tf.strided_slice(current_output, [0, 0, 0], [0, 0, 0], strides=[1, 2, 2], end_mask=7) 43 | 44 | # convolution steps 45 | h_conv1 = tf.nn.conv2d(current_input, self.w_conv1, strides=[1, 1, 1, 1], padding='SAME') + b_conv1 46 | h_pool1 = tf.nn.max_pool(h_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') 47 | tanh = tf.tanh(h_pool1) 48 | h_conv2 = tf.nn.conv2d(tanh, w_conv2, strides=[1, 1, 1, 1], padding='SAME') + b_conv2 49 | h_conv3 = tf.nn.conv2d(h_conv2, w_conv3, strides=[1, 1, 1, 1], padding='SAME') + b_conv3 50 | current_logits = h_conv3 51 | 52 | # tensorflow 11 doesn't have multidimensional softmax, we need to get predictions manually :-( 53 | # (predictions are what's passed to the next iteration/layer of the CNN 54 | exp_logits = tf.exp(current_logits) 55 | predictions = exp_logits / tf.reduce_sum(exp_logits, reduction_indices=[3], keep_dims=True) 56 | 57 | cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(current_logits, current_output) 58 | error_for_all_pixel = tf.reduce_mean(cross_entropy, reduction_indices=[0]) 59 | error_for_image = tf.reduce_mean(error_for_all_pixel) 60 | self.logits.append(current_logits) 61 | self.errors.append(error_for_image) 62 | 63 | # extracts RGB channels from input image. Only keeps every other pixel, since convolution scales down the 64 | # output. The shape of this should have the same height and width and the logits. 65 | rgb = tf.strided_slice(current_input, [0, 0, 0, 0], [0, 0, 0, 3], strides=[1, 2, 2, 1], end_mask=7) 66 | current_input = tf.concat(concat_dim=3, values=[rgb, predictions]) 67 | 68 | self.loss = tf.add_n(self.errors) 69 | self.train_step = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss) 70 | 71 | 72 | def save_model(sess: tf.Session, path: str, saver: tf.train.Saver = None) -> tf.train.Saver: 73 | """ 74 | Saves a tensorflow session to the given path. 75 | NOTE: This currently saves *all* variables in the session, unless one passes in a custom Saver object. 76 | :param sess: The tensorflow session to save from 77 | :param path: The path to store the saved data 78 | :param saver: A custom saver object to use. This can be used to only save certain variables. If None, 79 | creates a saver object that saves all variables. 80 | :return: The saver object used. 81 | """ 82 | if saver is None: 83 | saver = tf.train.Saver(tf.all_variables()) 84 | saver.save(sess, path) 85 | return saver 86 | 87 | 88 | def restore_model(sess: tf.Session, path: str, saver: tf.train.Saver = None) -> tf.train.Saver: 89 | """ 90 | Loads a tensorflow session from the given path. 91 | NOTE: This currently loads *all* variables in the saved file, unless one passes in a custom Saver object. 92 | :param sess: The tensorflow checkpoint to load from 93 | :param path: The path to the saved data 94 | :param saver: A custom saver object to use. This can be used to only load certain variables. If None, 95 | creates a saver object that loads all variables. 96 | :return: The saver object used. 97 | """ 98 | if saver is None: 99 | saver = tf.train.Saver(tf.all_variables()) 100 | saver.restore(sess, path) 101 | return saver 102 | -------------------------------------------------------------------------------- /pinheiro-collobert-rcnns-scene-labelling-2014.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rkargon/Scene-Labeling/5d0ce6b6dbcc902569905bdbbf63e05af11ec0ef/pinheiro-collobert-rcnns-scene-labelling-2014.pdf -------------------------------------------------------------------------------- /preprocessing.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | """ 4 | Code for preprocessing and loading image and label data. 5 | """ 6 | 7 | import os 8 | import sys 9 | from os.path import isfile 10 | from typing import List, Generator, Tuple, Sequence 11 | 12 | import numpy as np 13 | from PIL import Image 14 | 15 | 16 | def read_object_classes(classes_map_filename: str) -> Tuple[List[Tuple[float, float, float]], List[str]]: 17 | """ 18 | Reads an index of object classes and their corresponding names and colors. 19 | Each line of the file has 5 elements: R,G,B values as floats, an integer ID, and a name as a string. 20 | :param classes_map_filename: The filename storing the index 21 | :return: a tuple of 4 items: 22 | 1. an array of ID -> category color as RGB tuple (in [0, 255]) 23 | 2. a dictionary of category color (as an RGB tuple) -> ID 24 | 3. an array of ID -> category name 25 | 2. a dictionary of category name -> ID 26 | """ 27 | format_description = "Each line should contain 5 elements: (float R, float G, float B, int ID, str Name)." 28 | ids = set() 29 | ids_to_cols = {} 30 | ids_to_names = {} 31 | with open(classes_map_filename, 'r') as classes_file: 32 | for line in classes_file: 33 | try: 34 | vals = line.split() 35 | if len(vals) == 0: 36 | continue 37 | elif len(vals) == 2: 38 | has_cols = False 39 | category_num = int(vals[0]) 40 | category_name = vals[1] 41 | elif len(vals) == 5: 42 | has_cols = True 43 | rgb = tuple([int(255 * float(s)) for s in vals[:3]]) 44 | category_num = int(vals[3]) 45 | category_name = vals[4] 46 | else: 47 | raise ValueError("Category map must have either 2 or 5 columns") 48 | 49 | if category_num < 0: 50 | continue 51 | 52 | # check for duplicate categories 53 | if category_num in ids: 54 | sys.stderr.write("A category with this number (%d) already exists.\n" % category_num) 55 | continue 56 | 57 | ids.add(category_num) 58 | ids_to_names[category_num] = category_name 59 | if has_cols: 60 | ids_to_cols[category_num] = rgb 61 | 62 | except (ValueError, IndexError) as e: 63 | sys.stderr.write("%s %s\n" % (format_description, e)) 64 | continue 65 | 66 | max_id = max(ids) 67 | category_colors = [tuple()] * (max_id + 1) 68 | category_names = [""] * (max_id + 1) 69 | for cat_id in ids: 70 | category_names[cat_id] = ids_to_names[cat_id] 71 | if has_cols: 72 | category_colors[cat_id] = ids_to_cols[cat_id] 73 | 74 | return category_colors, category_names 75 | 76 | 77 | def image_to_np_array(img_filename: str, float_cols: bool = True) -> np.ndarray: 78 | """ 79 | Reads an image into a numpy array, with shape [height x width x 3] 80 | Each pixel is represented by 3 RGB values, either as floats in [0, 1] or as ints in [0, 255] 81 | :param img_filename: The filename of the image to load 82 | :param float_cols: Whether to load colors as floats in [0, 1] or as ints in [0, 255] 83 | :return: A numpy array containing the image data 84 | """ 85 | img = Image.open(img_filename) 86 | img.load() 87 | if float_cols: 88 | data = np.asarray(img, dtype="float32") / 255.0 89 | else: 90 | data = np.asarray(img, dtype="uint8") 91 | return data 92 | 93 | 94 | def np_array_to_image(array: np.ndarray, output_filename: str): 95 | """ 96 | Saves a numpy array to an image. 97 | :param array: A numpy array of shape (height x width x 3), representing an RGB image. Its values should be in the 98 | range [0,1]. 99 | :param output_filename: The filename in which to store the image. 100 | """ 101 | img = Image.fromarray((array * 255).astype(dtype=np.uint8), mode='RGB') 102 | img.save(output_filename) 103 | 104 | 105 | def labels_to_np_array(lab_filename: str) -> np.ndarray: 106 | """ 107 | Reads an image of category labels as a numpy array of category IDs. 108 | NOTE: The image data must already be in a color palette such that color # corresponds to label ID. 109 | The "Playing for Data" dataset is configured in this way (http://download.visinf.tu-darmstadt.de/data/from_games/) 110 | :param lab_filename: The filename of the label image to load 111 | :return: A numpy array containing the label ID for each pixel 112 | """ 113 | img = Image.open(lab_filename) 114 | img.load() 115 | data = np.asarray(img, dtype="uint8") 116 | return data 117 | 118 | 119 | def text_labels_to_np_array(lab_filename: str) -> np.ndarray: 120 | """ 121 | Reads a text file representing a set of labels for an image, and converts it into a numpy array. 122 | The text file must be organized into rows, and space-separated columns. Each element is a number corresponding to 123 | a label number for that pixel. 124 | NOTE: Numbers must be at least 0. 125 | :param lab_filename: The name of the label file 126 | :return: A numpy array of shape (height x width) containing numerical labels. 127 | """ 128 | label_file = open(lab_filename, 'r') 129 | labels = [list(map(lambda n: max(0, int(n)), l.split())) for l in label_file.readlines()] 130 | return np.array(labels, dtype=np.int8) 131 | 132 | 133 | def save_labels_array(labels: np.ndarray, output_filename: str, colors: List[Tuple[float, float, float]]): 134 | """ 135 | Saves a numpy array of labels to an paletted image. 136 | :param colors: An array of colors for each index. Should correspond to label ID's in 'labels' 137 | :param labels: A 2D array of labels 138 | :param output_filename: The filename of the image to output 139 | """ 140 | img = Image.fromarray(obj=labels, mode="P") 141 | # palette is a flattened array of r,g,b values, representing the colors in the palette in order. 142 | palette = [] 143 | for c in colors: 144 | palette.extend(c) 145 | img.putpalette(palette) 146 | img.save(output_filename) 147 | 148 | 149 | def get_patch(array: np.ndarray, center: Sequence[int], patch_size: int) -> np.ndarray: 150 | """ 151 | Returns a square 2D patch of an array with a given size and center. Also returns other dimensions of the array, 152 | uncropped. 153 | NOTE: does not do bounds checking. 154 | :param array: A numpy array 155 | :param center: The coordinates of the center, as a list or array of length 2 156 | :param patch_size: A single number representing the width and height of the patch. 157 | :return: A square patch of the image with the given center and size. 158 | """ 159 | rounded_width = int(patch_size / 2) 160 | return array[center[0] - rounded_width: center[0] + rounded_width + 1, 161 | center[1] - rounded_width: center[1] + rounded_width + 1] 162 | 163 | 164 | def interleave_images(images, stride): 165 | """ 166 | Interleaves a series of images with the given stride. The pixels of each image correspond to one out of `stride` 167 | pixels of every row and column of the output image. 168 | The top left corner of each image in `images` corresponds to a pixel in [0:stride, 0:stride] in the output, 169 | in row-major order. 170 | That is, if the stride is 2, the images' corners will be at pixels [(0,0), (0,1), (1,0), (1,1)] in the output, 171 | respectively. 172 | If the image higher dimensions (e.g. 3 RGB channels), these must have the same sizes across all images. Only the 173 | first two dimensions are interleaved when creating output. 174 | 175 | e.g. `images` = 176 | [ [[ 1, 2, 3, 4], 177 | [ 5, 6, 7, 8], 178 | [ 9, 10, 11, 12]], 179 | 180 | [[13, 14, 15, 16], 181 | [17, 18, 19, 20], 182 | [21, 22, 23, 24]], 183 | 184 | [[25, 26, 27, 28], 185 | [29, 30, 31, 32], 186 | [33, 34, 35, 36]], 187 | 188 | [[37, 38, 39, 40], 189 | [41, 42, 43, 44], 190 | [45, 46, 47, 48]] 191 | 192 | `stride` = 2 193 | 194 | output is: 195 | 196 | [[ 1, 13, 2, 14, 3, 15, 4, 16], 197 | [25, 37, 26, 38, 27, 39, 28, 40], 198 | [ 5, 17, 6, 18, 7, 19, 8, 20], 199 | [29, 41, 30, 42, 31, 43, 32, 44], 200 | [ 9, 21, 10, 22, 11, 23, 12, 24], 201 | [33, 45, 34, 46, 35, 47, 36, 48]] 202 | 203 | :param images: A sequence of images of length (stride * stride). 204 | :param stride: The space between pixels of the same input image in the output. 205 | :return: An array with width and height equal to the sum of the width and height of the input images, respectively. 206 | The array will contain the interleaved pixels of the input images. 207 | """ 208 | h = sum([i.shape[0] for i in images[::stride]]) 209 | w = sum([i.shape[1] for i in images[:stride]]) 210 | # preserve higher-order shape of input 211 | shape = (h, w) + images[0].shape[2:] 212 | output = np.zeros(shape=shape) 213 | i = 0 214 | for dy in range(stride): 215 | for dx in range(stride): 216 | output[dy::stride, dx::stride] = images[i] 217 | i += 1 218 | return output 219 | 220 | 221 | def get_sorted_files_in_folder(folder: str, extension: str = None) -> List[str]: 222 | """ 223 | Returns a sorted list of all the non-hidden files in a folder, possibly filtered by extension. 224 | (Note: the search isn't recursive, only the files directly in the given folder are returned) 225 | :param folder: The folder in which to search 226 | :param extension: Optional, specifies an extension that all filenames must end with. 227 | :return: A list of filenames "folder_path/file_name" 228 | """ 229 | files = [os.path.join(folder, f) for f in os.listdir(folder) if 230 | isfile(os.path.join(folder, f)) and not f.startswith('.')] 231 | if extension is not None: 232 | files = filter(lambda f: f.endswith(extension), files) 233 | return sorted(files) 234 | 235 | 236 | # define a type alias for the output of these iterators 237 | DataSetIter = Generator[Tuple[np.ndarray, np.ndarray, str], None, None] 238 | 239 | 240 | def from_games_dataset(data_dir: str, data_fraction: float = None, num_samples: int = None) -> DataSetIter: 241 | """ 242 | Iterates over images and labels in the "Playing for Data" dataset from 243 | . 244 | 245 | The given directory must have two folders, "images" and "labels", that contain images and corresponding labels, 246 | respectively. 247 | Images and labels should both be .png images, with labels being paletted PNGs. 248 | Each image and corresponding label must have the same filename. 249 | 250 | :param data_dir: The directory in which the data is stored. 251 | :param data_fraction: Optional - what fraction of the data to return. Can be used to partition data into training 252 | and 253 | testing. Can be negative to only return data at the end of the list. e.g. a value of 0.3 will return the first 254 | 30% from the list of files, while -0.2 will return the last 20%. 255 | :param num_samples: Optional - how many data samples to return. Overrides data_fraction. 256 | :return: Yields a series of tuples (image, labels, img_id). 257 | - Image is a numpy array of shape (height x width x 3). 258 | - Labels is a numpy array of shape (height x width). 259 | - img_id is a string, identifying the image-label pair. 260 | """ 261 | labels_dir = os.path.join(data_dir, 'labels') 262 | images_dir = os.path.join(data_dir, 'images') 263 | 264 | labels = get_sorted_files_in_folder(labels_dir) 265 | images = get_sorted_files_in_folder(images_dir) 266 | train_files = list(zip(labels, images)) 267 | 268 | # if specified, only choose subset of training data 269 | if data_fraction is not None and num_samples is None: 270 | num_samples = int(len(train_files) * data_fraction) 271 | if num_samples is not None: 272 | train_files = train_files[:num_samples] 273 | 274 | for label_f, image_f in train_files: 275 | img_id = os.path.basename(image_f).split('.')[0] 276 | label_id = os.path.basename(label_f).split('.')[0] 277 | print("Current image:", os.path.basename(image_f)) 278 | if img_id != label_id: 279 | print("UNEQUAL IMAGE AND LABEL NAMES!") 280 | image = image_to_np_array(image_f) 281 | labels = labels_to_np_array(label_f) 282 | yield image, labels, img_id 283 | 284 | 285 | def stanford_bgrounds_dataset(data_dir: str, data_fraction: float = None, num_samples: int = None) -> DataSetIter: 286 | """ 287 | Iterates over images and labels in the Stanford Background dataset from 288 | . 289 | 290 | The given directory must have two folders, "images" and "labels", that contain images and corresponding labels, 291 | respectively. 292 | Images should be .jpg images, and labels should be text files with the same ID as the image, but with the 293 | extension ".regions.txt." Text files should contain category labels as a series of space-separate rows of digits. 294 | 295 | :param data_dir: The directory in which the data is stored. 296 | :param data_fraction: Optional - what fraction of the data to return. Can be used to partition data into training 297 | and 298 | testing. Can be negative to only return data at the end of the list. e.g. a value of 0.3 will return the first 299 | 30% from the list of files, while -0.2 will return the last 20%. 300 | :param num_samples: Optional - how many data samples to return. Overrides data_fraction. 301 | :return: Yields a series of tuples (image, labels, img_id). 302 | - Image is a numpy array of shape (height x width x 3). 303 | - Labels is a numpy array of shape (height x width). 304 | - img_id is a string, identifying the image-label pair. 305 | """ 306 | labels_dir = os.path.join(data_dir, 'labels') 307 | images_dir = os.path.join(data_dir, 'images') 308 | 309 | labels = get_sorted_files_in_folder(labels_dir, extension='.regions.txt') 310 | images = get_sorted_files_in_folder(images_dir) 311 | train_files = list(zip(labels, images)) 312 | 313 | # if specified, only choose subset of training data 314 | if data_fraction is not None and num_samples is None: 315 | num_samples = int(len(train_files) * data_fraction) 316 | if num_samples is not None: 317 | if num_samples >= 0: 318 | train_files = train_files[:num_samples] 319 | else: 320 | train_files = train_files[num_samples:] 321 | 322 | for label_f, image_f in train_files: 323 | if os.path.basename(label_f).split('.')[0] != os.path.basename(image_f).split('.')[0]: 324 | print("UNEQUAL IMAGE NAMES!", label_f, image_f) 325 | img_id = os.path.basename(label_f).split('.')[0] 326 | image = image_to_np_array(image_f) 327 | labels = text_labels_to_np_array(label_f) 328 | yield image, labels, img_id 329 | 330 | 331 | # list of datasets for which we have iterators 332 | FROM_GAMES = 'from-games' 333 | SIFT_FLOW = 'sift-flow' 334 | STANFORD_BGROUND = 'stanford-bground' 335 | DATASETS = {FROM_GAMES: from_games_dataset, SIFT_FLOW: None, STANFORD_BGROUND: stanford_bgrounds_dataset} 336 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | Pillow 2 | tensorflow==0.10.0 3 | -------------------------------------------------------------------------------- /test/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rkargon/Scene-Labeling/5d0ce6b6dbcc902569905bdbbf63e05af11ec0ef/test/.DS_Store -------------------------------------------------------------------------------- /test/test_category_maps/stanford_bground_categories.txt: -------------------------------------------------------------------------------- 1 | 0.0000 0.0000 0.0000 -1 unknown 2 | 0.3700 0.7300 0.9900 0 sky 3 | 0.1500 0.3100 0.0700 1 tree/bush 4 | 0.2600 0.2600 0.2600 2 road/path 5 | 0.4200 0.6600 0.3100 3 grass 6 | 0.0700 0.3300 0.8000 4 water 7 | 0.8800 0.4000 0.4000 5 building 8 | 0.8100 0.8900 0.9500 6 mountain 9 | 0.9500 0.7600 0.2000 7 foreground_object 10 | -------------------------------------------------------------------------------- /test/test_data_stanford/.DS_Store: -------------------------------------------------------------------------------- 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4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 178 | 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 179 | 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 180 | 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 181 | -------------------------------------------------------------------------------- /test/test_preprocessing.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | import unittest 4 | 5 | from preprocessing import read_object_classes 6 | 7 | 8 | class TestPreprocessing(unittest.TestCase): 9 | def test_read_object_classes(self): 10 | map_filename = 'test_category_maps/stanford_bground_categories.txt' 11 | category_colors, category_names = read_object_classes(map_filename) 12 | self.assertEqual(category_colors[0], (int(0.3700 * 255), int(0.7300 * 255), int(0.9900 * 255))) 13 | self.assertEqual(category_colors[1], (int(0.1500 * 255), int(0.3100 * 255), int(0.0700 * 255))) 14 | self.assertEqual(category_colors[2], (int(0.2600 * 255), int(0.2600 * 255), int(0.2600 * 255))) 15 | self.assertEqual(category_colors[3], (int(0.4200 * 255), int(0.6600 * 255), int(0.3100 * 255))) 16 | self.assertEqual(category_colors[4], (int(0.0700 * 255), int(0.3300 * 255), int(0.8000 * 255))) 17 | self.assertEqual(category_colors[5], (int(0.8800 * 255), int(0.4000 * 255), int(0.4000 * 255))) 18 | self.assertEqual(category_colors[6], (int(0.8100 * 255), int(0.8900 * 255), int(0.9500 * 255))) 19 | self.assertEqual(category_colors[7], (int(0.9500 * 255), int(0.7600 * 255), int(0.2000 * 255))) 20 | 21 | category_names_correct = ["sky", "tree/bush", "road/path", "grass", "water", "building", "mountain", 22 | "foreground_object"] 23 | self.assertListEqual(category_names, category_names_correct) 24 | 25 | def image_to_np_array(self): 26 | pass 27 | 28 | def np_array_to_image(self): 29 | pass 30 | 31 | def labels_to_np_array(self): 32 | pass 33 | 34 | def text_labels_to_np_array(self): 35 | pass 36 | 37 | def save_labels_array(self): 38 | pass 39 | 40 | def get_patch(self): 41 | pass 42 | 43 | def interleave_images(self): 44 | pass 45 | 46 | def get_sorted_files_in_folder(self): 47 | pass 48 | 49 | def from_games_dataset(self): 50 | pass 51 | 52 | def stanford_bgrounds_dataset(self): 53 | pass 54 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | """ 4 | a 5 | """ 6 | 7 | import argparse 8 | import os 9 | import random 10 | import time 11 | 12 | import numpy as np 13 | import tensorflow as tf 14 | 15 | from model import RCNNModel, save_model, restore_model 16 | from preprocessing import read_object_classes, DATASETS, FROM_GAMES, save_labels_array, np_array_to_image, \ 17 | interleave_images 18 | 19 | 20 | class AccuracyCounter: 21 | """ 22 | This class stores per-class accuracy counts for some task, such as testing a classifier. 23 | """ 24 | 25 | def __init__(self, num_classes): 26 | self.num_classes = num_classes 27 | self.class_correct_counts = np.zeros(self.num_classes) 28 | self.class_total_counts = np.zeros(self.num_classes) 29 | 30 | def add_sample(self, predicted_labels, true_labels): 31 | """ 32 | Adds a sample to the accuracy counts. 33 | :param predicted_labels: An array of predicted labels 34 | :param true_labels: An array of the true labels, in the same shape as `predicted_labels` 35 | :return: The % accuracy for the given sample across all classes 36 | """ 37 | correct_class_labels = np.equal(predicted_labels, true_labels) 38 | for c in range(self.num_classes): 39 | current_class_labels = np.equal(true_labels, c) 40 | self.class_total_counts[c] += np.sum(current_class_labels) 41 | self.class_correct_counts[c] += np.sum(current_class_labels * correct_class_labels) 42 | return np.mean(correct_class_labels) 43 | 44 | def get_results(self): 45 | """ 46 | Get results on the accuracy for the samples stored in this counter object. 47 | :return: A tuple of overall accuracy, per-class accuracy, and total counts for each class 48 | """ 49 | class_accuracies = self.class_correct_counts / self.class_total_counts 50 | total_accuracy = np.sum(self.class_correct_counts) / np.sum(self.class_total_counts) 51 | return total_accuracy, class_accuracies, self.class_total_counts 52 | 53 | 54 | def run_model_on_image(sess, model, image, labels, is_training: bool = False): 55 | """ 56 | Given an image and labels, runs the model and returns output. A training step is also optionally run. 57 | :param sess: The session within which to run the model 58 | :param model: The model to run 59 | :param image: The input RGB image, a numpy array of shape height x width x 3. 60 | :param labels: An array of labels corresponding to the image, of shape height x width 61 | :param is_training: If true, trains the model to minimize the loss. 62 | :return: The results of running the model. Specifically, a tuple containing logits for each layer, the numerical 63 | loss, and an optional element corresponding to the training step. 64 | """ 65 | # check for invalid labels in image 66 | error_classes = np.sum(np.less(labels, 0) + np.greater_equal(labels, model.num_classes)) 67 | if error_classes > 0: 68 | print("ERROR - Incorrect labels in image:", labels) 69 | return 70 | 71 | ops_to_run = model.logits + [model.loss] 72 | if is_training: 73 | ops_to_run.append(model.train_step) 74 | 75 | h, w = labels.shape 76 | input_image = np.append(image, np.zeros(shape=[h, w, model.num_classes], dtype=np.float32), axis=2) 77 | feed_dict = {model.inpt: [input_image], model.output: [labels]} 78 | return sess.run(ops_to_run, feed_dict=feed_dict) 79 | 80 | 81 | def run_model(sess, model, dataset_iter, num_epochs=1, training=False, save_path=None, color_map=None, 82 | output_dir=None): 83 | """ 84 | Runs a given model on the given data set. Outputs per-class accuracies for the given data, and optionally trains 85 | the model as well. 86 | :param sess: A tensorflow session in which to run the model 87 | :param model: A given rCNN model 88 | :param dataset_iter: An iterator over tuples of (image, labels, img_id) in the dataset 89 | :param num_epochs: Number of epochs to run over the entire dataset 90 | :param training: Whether to run a training step for each sample, or to only test. 91 | :param save_path: Path in which to store saved model. (Saves every epoch) 92 | :param color_map: Optional, an array of colors for each class label, used to output predicted labels. 93 | :param output_dir: Optional, directory in which to output predicted labels for each class. 94 | """ 95 | for i in range(num_epochs): 96 | print('Running epoch %d/%d...' % (i + 1, num_epochs)) 97 | layer_accuracies = [AccuracyCounter(model.num_classes) for _ in range(model.num_layers)] 98 | n = 0 99 | for image, labels, img_id in dataset_iter(): 100 | start_time = time.time() 101 | n += 1 102 | 103 | # create several shifted copies of the input 104 | shifted_images = [] 105 | shifted_labels = [] 106 | stride = 2 ** model.num_layers 107 | for dy in range(stride): 108 | for dx in range(stride): 109 | shifted_images.append(image[dy:, dx:, :]) 110 | shifted_labels.append(labels[dy:, dx:]) 111 | 112 | # get model output for each shifted image/label pair 113 | op_results = [run_model_on_image(sess, model, i, l, is_training=training) for i, l in 114 | zip(shifted_images, shifted_labels)] 115 | grouped_results = list(zip(*op_results)) 116 | 117 | # for each shifted image, logits are in trivial batches of size 1. 118 | layer_logits = [[shifted_logits[0] for shifted_logits in layer] for layer in 119 | grouped_results[:model.num_layers]] 120 | loss = np.mean(grouped_results[model.num_layers]) 121 | if loss != loss: 122 | print("Loss is NaN! Stopping training without saving.") 123 | return 124 | elapsed_time = time.time() - start_time 125 | print("%s on image #%d (%s): Loss: %f Elapsed time: %.1f" % ( 126 | "Trained" if training else "Tested", n, img_id, loss, elapsed_time)) 127 | 128 | # interlace logits to create labels that have the same size as the input labels 129 | merged_labels = [] 130 | for l in range(model.num_layers): 131 | current_stride = 2 ** (l + 1) 132 | layer_labels = [np.argmax(shifted_logits, axis=2) for shifted_logits in layer_logits[l]] 133 | # If this is not the final layer, not all shifted outputs are needed. 134 | # Specifically, if the outputs each correspond to a different pixel in the top left (2^num_layers x 135 | # 2^num_layers pixels), one only needs the first (2^current_layer x 2 ^ current_layer) 136 | if l < model.num_layers - 1: 137 | layer_labels = [layer_labels[dy * stride + dx] for dy in range(current_stride) for dx in range(current_stride)] 138 | merged_labels.append(interleave_images(layer_labels, stride=current_stride)) 139 | 140 | # accuracy 141 | for l in range(model.num_layers): 142 | acc = layer_accuracies[l].add_sample(predicted_labels=merged_labels[l], true_labels=labels) 143 | print("Accuracy for layer %d: %f" % (l + 1, acc)) 144 | 145 | # write outputs to disk 146 | if output_dir is not None and color_map is not None: 147 | for l in range(model.num_layers): 148 | output_filename = os.path.join(output_dir, img_id + '_test_%d.png' % (l + 1)) 149 | save_labels_array(merged_labels[l].astype(np.uint8), output_filename, colors=color_map) 150 | 151 | for l in range(model.num_layers): 152 | total_accuracy, class_accuracies, class_total_counts = layer_accuracies[l].get_results() 153 | print("Layer %d total accuracy:" % (l + 1), total_accuracy) 154 | print("Layer %d per-class accuracies:" % (l + 1), class_accuracies) 155 | print("Layer %d per-class total counts:" % (l + 1), class_total_counts) 156 | 157 | if save_path is not None: 158 | print("Epoch %i finished, saving trained model to %s..." % (i + 1, save_path)) 159 | save_model(sess, save_path) 160 | 161 | 162 | def optimize_input(sess, model, labels, step_size=2e-2, num_iterations=200): 163 | """ 164 | Optimizes input for the given model using gradient descent, in order to match the given output labels. 165 | This is done by minimizing loss between the given labels and the output from running the generated image through 166 | the model. 167 | Each time a gradient descent step fails to improve loss, the step size is halved. 168 | :param sess: A tensorflow session 169 | :param model: A given rCNN model 170 | :param labels: The labels to match, as a numpy array of shape (height x width) 171 | :param step_size: The initial step size for gradient descent. 172 | :param num_iterations: Number of gradient descent steps to run. 173 | :return: The resulting image as an RGB numpy array of shape (height x width x 3) 174 | """ 175 | # use loss from final layer 176 | loss = model.errors[-1] 177 | grad = tf.gradients(loss, model.inpt)[0] 178 | h, w, = labels.shape 179 | image = np.random.uniform(low=0.4, high=0.6, size=(h, w, 3)) 180 | # image = np.zeros(shape=(h, w, 3)) + 0.5 181 | image = np.append(image, np.zeros(shape=[h, w, model.num_classes], dtype=np.float32), axis=2) 182 | l_prev = None 183 | for i in range(num_iterations): 184 | g, l = sess.run([grad, loss], feed_dict={model.inpt: [image], model.output: [labels]}) 185 | if l_prev is not None and l_prev <= l: 186 | step_size /= 2 187 | g = g[0] # we're using a batch size of 1 here 188 | # normalize gradients 189 | g /= g.std() + 1e-8 190 | image[:, :, :3] -= g[:, :, :3] * step_size 191 | print("Iteration %d, step size: %f, score: %f" % (i, step_size, l)) 192 | l_prev = l 193 | return image[:, :, :3] 194 | 195 | 196 | def run_gradient_descent(sess, model, true_labels, output_file=None, category_colors=None): 197 | """ 198 | Given a set of labels, iteratively optimizes inputs to produce those given labels. Similar to a Google "Deep Dream". 199 | The produced image, as well as the resulting predicted labels, are returned and optionally saved. 200 | :param sess: A tensorflow session 201 | :param model: An rCNN model 202 | :param true_labels: A given set of labels for an image, of shape (height x width) 203 | :param output_file: Filename to store resulting image (as well resulting labels) 204 | :param category_colors: Map from category to color, for storing resulting labels 205 | :return: A tuple of (resulting image, resulting labels) 206 | """ 207 | image = optimize_input(sess, model, true_labels) 208 | if output_file is not None and category_colors is not None: 209 | np_array_to_image(image, output_filename=output_file) 210 | 211 | ops = run_model_on_image(sess, model, image, true_labels) 212 | last_logits = ops[model.num_layers - 1][0] 213 | predicted_labels = np.argmax(last_logits, axis=2) 214 | predicted_labels = np.kron(predicted_labels, np.ones(shape=[2 ** model.num_layers, 2 ** model.num_layers])) 215 | if output_file is not None and category_colors is not None: 216 | save_labels_array(predicted_labels.astype(np.uint8), output_file + 'descent_labels.png', colors=category_colors) 217 | return image, predicted_labels 218 | 219 | 220 | def main(): 221 | """ 222 | Trains or evaluates an rCNN model. 223 | """ 224 | # parse command line arguments 225 | parser = argparse.ArgumentParser(description='An rCNN scene labeling model.', 226 | formatter_class=argparse.ArgumentDefaultsHelpFormatter) 227 | parser.add_argument('--dataset', type=str, default=FROM_GAMES, choices=DATASETS.keys(), 228 | help='Type of dataset to use. This determines the expected format of the data directory') 229 | parser.add_argument('--category_map', type=str, help='File that maps colors ') 230 | parser.add_argument('--data_dir', type=str, help='Directory for image and label data') 231 | parser.add_argument('--data_fraction', type=float, default=0.8, 232 | help='Fraction of data to train on. If positive, trains on first X images, otherwise trains on ' 233 | 'last X images.') 234 | parser.add_argument('--hidden_size_1', type=int, default=25, help='First Hidden size for CNN model') 235 | parser.add_argument('--hidden_size_2', type=int, default=50, help='Second Hidden size for CNN model') 236 | parser.add_argument('--learning_rate', type=float, default=1e-4, help='Learning rate for training CNN model') 237 | parser.add_argument('--num_epochs', type=int, default=1, help='Number of epochs for training CNN model') 238 | parser.add_argument('--model_save_path', type=str, default=None, 239 | help='Optional location to store saved model in.') 240 | parser.add_argument('--model_load_path', type=str, default=None, 241 | help='Optional location to load saved model from.') 242 | parser.add_argument('--training', action='store_true', default=False, help='Whether or not to train model.') 243 | parser.add_argument('--output_dir', type=str, default=None, help='Directory in which to save test output images.') 244 | parser.add_argument('--dry_run', action='store_true', default=False, 245 | help='If true, only trains on one image, to test the training code quickly.') 246 | parser.add_argument('--fix_random_seed', action='store_true', default=False, 247 | help='Whether to reset random seed at start, for debugging.') 248 | 249 | args = parser.parse_args() 250 | 251 | if args.fix_random_seed: 252 | random.seed(0) 253 | 254 | # load class labels 255 | category_colors, category_names = read_object_classes(args.category_map) 256 | num_classes = len(category_names) 257 | 258 | # create function that when called, provides iterator to an epoch of the data 259 | dataset_func = DATASETS[args.dataset] 260 | if args.dry_run: 261 | def dataset_epoch_iter(): 262 | return dataset_func(args.data_dir, num_samples=1) 263 | else: 264 | def dataset_epoch_iter(): 265 | return dataset_func(args.data_dir, data_fraction=args.data_fraction) 266 | 267 | model = RCNNModel(args.hidden_size_1, args.hidden_size_2, num_classes, 268 | args.learning_rate, num_layers=2) 269 | 270 | sess = tf.Session() 271 | init = tf.initialize_all_variables() 272 | sess.run(init) 273 | if args.model_load_path is not None: 274 | restore_model(sess, args.model_load_path) 275 | 276 | run_model(sess, model, dataset_epoch_iter, num_epochs=args.num_epochs, training=args.training, 277 | save_path=args.model_save_path, output_dir=args.output_dir, color_map=category_colors) 278 | 279 | 280 | if __name__ == '__main__': 281 | main() 282 | --------------------------------------------------------------------------------