├── .gitignore ├── LICENSE ├── README.md ├── assets ├── cameraui.jpg ├── cctv_system.jpg ├── dl.png ├── encrypt.png ├── index.jpeg ├── locations.png ├── logo.png └── search_tab.png ├── camera ├── caption_gen.py ├── feed.py ├── models.py ├── results.csv └── word_map.json ├── encryption ├── decrypt.py └── encrypt.py ├── model ├── __init__.py ├── datasets.py ├── eval.py ├── model.py └── utils.py ├── requirements.txt ├── search ├── search.py └── search_word.py └── test ├── coordinates.csv ├── coordinates.py ├── results.csv └── test.csv /.gitignore: -------------------------------------------------------------------------------- 1 | # Editors 2 | .vscode/ 3 | .idea/ 4 | 5 | # Vagrant 6 | .vagrant/ 7 | 8 | # Mac/OSX 9 | .DS_Store 10 | 11 | # Windows 12 | Thumbs.db 13 | 14 | # Source for the following rules: https://raw.githubusercontent.com/github/gitignore/master/Python.gitignore 15 | # Byte-compiled / optimized / DLL files 16 | __pycache__/ 17 | *.py[cod] 18 | *$py.class 19 | 20 | # C extensions 21 | *.so 22 | 23 | # Distribution / packaging 24 | .Python 25 | build/ 26 | develop-eggs/ 27 | dist/ 28 | downloads/ 29 | eggs/ 30 | .eggs/ 31 | lib/ 32 | lib64/ 33 | parts/ 34 | sdist/ 35 | var/ 36 | wheels/ 37 | *.egg-info/ 38 | .installed.cfg 39 | *.egg 40 | MANIFEST 41 | 42 | # PyInstaller 43 | # Usually these files are written by a python script from a template 44 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 45 | *.manifest 46 | *.spec 47 | 48 | # Installer logs 49 | pip-log.txt 50 | pip-delete-this-directory.txt 51 | 52 | # Unit test / coverage reports 53 | htmlcov/ 54 | .tox/ 55 | .nox/ 56 | .coverage 57 | .coverage.* 58 | .cache 59 | nosetests.xml 60 | coverage.xml 61 | *.cover 62 | .hypothesis/ 63 | .pytest_cache/ 64 | 65 | # Translations 66 | *.mo 67 | *.pot 68 | 69 | # Django stuff: 70 | *.log 71 | local_settings.py 72 | db.sqlite3 73 | 74 | # Flask stuff: 75 | instance/ 76 | .webassets-cache 77 | 78 | # Scrapy stuff: 79 | .scrapy 80 | 81 | # Sphinx documentation 82 | docs/_build/ 83 | 84 | # PyBuilder 85 | target/ 86 | 87 | # Jupyter Notebook 88 | .ipynb_checkpoints 89 | 90 | # IPython 91 | profile_default/ 92 | ipython_config.py 93 | 94 | # pyenv 95 | .python-version 96 | 97 | # celery beat schedule file 98 | celerybeat-schedule 99 | 100 | # SageMath parsed files 101 | *.sage.py 102 | 103 | # Environments 104 | .env 105 | .venv 106 | env/ 107 | venv/ 108 | ENV/ 109 | env.bak/ 110 | venv.bak/ 111 | 112 | # Spyder project settings 113 | .spyderproject 114 | .spyproject 115 | 116 | # Rope project settings 117 | .ropeproject 118 | 119 | # mkdocs documentation 120 | /site 121 | 122 | # mypy 123 | .mypy_cache/ 124 | .dmypy.json 125 | dmypy.json -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. For the purposes of this definition, 18 | "control" means (i) the power, direct or indirect, to cause the 19 | direction or management of such entity, whether by contract or 20 | otherwise, or (ii) ownership of fifty percent (50%) or more of the 21 | outstanding shares, or (iii) beneficial ownership of such entity. 22 | 23 | "You" (or "Your") shall mean an individual or Legal Entity 24 | exercising permissions granted by this License. 25 | 26 | "Source" form shall mean the preferred form for making modifications, 27 | including but not limited to software source code, documentation 28 | source, and configuration files. 29 | 30 | "Object" form shall mean any form resulting from mechanical 31 | transformation or translation of a Source form, including but 32 | not limited to compiled object code, generated documentation, 33 | and conversions to other media types. 34 | 35 | "Work" shall mean the work of authorship, whether in Source or 36 | Object form, made available under the License, as indicated by a 37 | copyright notice that is included in or attached to the work 38 | (an example is provided in the Appendix below). 39 | 40 | "Derivative Works" shall mean any work, whether in Source or Object 41 | form, that is based on (or derived from) the Work and for which the 42 | editorial revisions, annotations, elaborations, or other modifications 43 | represent, as a whole, an original work of authorship. For the purposes 44 | of this License, Derivative Works shall not include works that remain 45 | separable from, or merely link (or bind by name) to the interfaces of, 46 | the Work and Derivative Works thereof. 47 | 48 | "Contribution" shall mean any work of authorship, including 49 | the original version of the Work and any modifications or additions 50 | to that Work or Derivative Works thereof, that is intentionally 51 | submitted to Licensor for inclusion in the Work by the copyright owner 52 | or by an individual or Legal Entity authorized to submit on behalf of 53 | the copyright owner. For the purposes of this definition, "submitted" 54 | means any form of electronic, verbal, or written communication sent 55 | to the Licensor or its representatives, including but not limited to 56 | communication on electronic mailing lists, source code control systems, 57 | and issue tracking systems that are managed by, or on behalf of, the 58 | Licensor for the purpose of discussing and improving the Work, but 59 | excluding communication that is conspicuously marked or otherwise 60 | designated in writing by the copyright owner as "Not a Contribution." 61 | 62 | "Contributor" shall mean Licensor and any individual or Legal Entity 63 | on behalf of whom a Contribution has been received by Licensor and 64 | subsequently incorporated within the Work. 65 | 66 | 2. Grant of Copyright License. Subject to the terms and conditions of 67 | this License, each Contributor hereby grants to You a perpetual, 68 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 69 | copyright license to reproduce, prepare Derivative Works of, 70 | publicly display, publicly perform, sublicense, and distribute the 71 | Work and such Derivative Works in Source or Object form. 72 | 73 | 3. Grant of Patent License. Subject to the terms and conditions of 74 | this License, each Contributor hereby grants to You a perpetual, 75 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 76 | (except as stated in this section) patent license to make, have made, 77 | use, offer to sell, sell, import, and otherwise transfer the Work, 78 | where such license applies only to those patent claims licensable 79 | by such Contributor that are necessarily infringed by their 80 | Contribution(s) alone or by combination of their Contribution(s) 81 | with the Work to which such Contribution(s) was submitted. If You 82 | institute patent litigation against any entity (including a 83 | cross-claim or counterclaim in a lawsuit) alleging that the Work 84 | or a Contribution incorporated within the Work constitutes direct 85 | or contributory patent infringement, then any patent licenses 86 | granted to You under this License for that Work shall terminate 87 | as of the date such litigation is filed. 88 | 89 | 4. Redistribution. You may reproduce and distribute copies of the 90 | Work or Derivative Works thereof in any medium, with or without 91 | modifications, and in Source or Object form, provided that You 92 | meet the following conditions: 93 | 94 | (a) You must give any other recipients of the Work or 95 | Derivative Works a copy of this License; and 96 | 97 | (b) You must cause any modified files to carry prominent notices 98 | stating that You changed the files; and 99 | 100 | (c) You must retain, in the Source form of any Derivative Works 101 | that You distribute, all copyright, patent, trademark, and 102 | attribution notices from the Source form of the Work, 103 | excluding those notices that do not pertain to any part of 104 | the Derivative Works; and 105 | 106 | (d) If the Work includes a "NOTICE" text file as part of its 107 | distribution, then any Derivative Works that You distribute must 108 | include a readable copy of the attribution notices contained 109 | within such NOTICE file, excluding those notices that do not 110 | pertain to any part of the Derivative Works, in at least one 111 | of the following places: within a NOTICE text file distributed 112 | as part of the Derivative Works; within the Source form or 113 | documentation, if provided along with the Derivative Works; or, 114 | within a display generated by the Derivative Works, if and 115 | wherever such third-party notices normally appear. The contents 116 | of the NOTICE file are for informational purposes only and 117 | do not modify the License. You may add Your own attribution 118 | notices within Derivative Works that You distribute, alongside 119 | or as an addendum to the NOTICE text from the Work, provided 120 | that such additional attribution notices cannot be construed 121 | as modifying the License. 122 | 123 | You may add Your own copyright statement to Your modifications and 124 | may provide additional or different license terms and conditions 125 | for use, reproduction, or distribution of Your modifications, or 126 | for any such Derivative Works as a whole, provided Your use, 127 | reproduction, and distribution of the Work otherwise complies with 128 | the conditions stated in this License. 129 | 130 | 5. Submission of Contributions. Unless You explicitly state otherwise, 131 | any Contribution intentionally submitted for inclusion in the Work 132 | by You to the Licensor shall be under the terms and conditions of 133 | this License, without any additional terms or conditions. 134 | Notwithstanding the above, nothing herein shall supersede or modify 135 | the terms of any separate license agreement you may have executed 136 | with Licensor regarding such Contributions. 137 | 138 | 6. Trademarks. This License does not grant permission to use the trade 139 | names, trademarks, service marks, or product names of the Licensor, 140 | except as required for reasonable and customary use in describing the 141 | origin of the Work and reproducing the content of the NOTICE file. 142 | 143 | 7. Disclaimer of Warranty. Unless required by applicable law or 144 | agreed to in writing, Licensor provides the Work (and each 145 | Contributor provides its Contributions) on an "AS IS" BASIS, 146 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or 147 | implied, including, without limitation, any warranties or conditions 148 | of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A 149 | PARTICULAR PURPOSE. You are solely responsible for determining the 150 | appropriateness of using or redistributing the Work and assume any 151 | risks associated with Your exercise of permissions under this License. 152 | 153 | 8. Limitation of Liability. In no event and under no legal theory, 154 | whether in tort (including negligence), contract, or otherwise, 155 | unless required by applicable law (such as deliberate and grossly 156 | negligent acts) or agreed to in writing, shall any Contributor be 157 | liable to You for damages, including any direct, indirect, special, 158 | incidental, or consequential damages of any character arising as a 159 | result of this License or out of the use or inability to use the 160 | Work (including but not limited to damages for loss of goodwill, 161 | work stoppage, computer failure or malfunction, or any and all 162 | other commercial damages or losses), even if such Contributor 163 | has been advised of the possibility of such damages. 164 | 165 | 9. Accepting Warranty or Additional Liability. While redistributing 166 | the Work or Derivative Works thereof, You may choose to offer, 167 | and charge a fee for, acceptance of support, warranty, indemnity, 168 | or other liability obligations and/or rights consistent with this 169 | License. However, in accepting such obligations, You may act only 170 | on Your own behalf and on Your sole responsibility, not on behalf 171 | of any other Contributor, and only if You agree to indemnify, 172 | defend, and hold each Contributor harmless for any liability 173 | incurred by, or claims asserted against, such Contributor by reason 174 | of your accepting any such warranty or additional liability. 175 | 176 | END OF TERMS AND CONDITIONS 177 | 178 | APPENDIX: How to apply the Apache License to your work. 179 | 180 | To apply the Apache License to your work, attach the following 181 | boilerplate notice, with the fields enclosed by brackets "[]" 182 | replaced with your own identifying information. (Don't include 183 | the brackets!) The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # capbot 2 | Repository to hold code for the [cap-bot](https://github.com/aryankargwal/cap-bot) varient that is being presented at the SIIC Defence Hackathon 2021. 3 |
4 | 5 | ## Problem Inspiration
6 |
7 | A plethora of surveillance devices are being used by the Defense Services for supervision and monitoring. However, most of them are manually operated at the cost of enormous amounts of time and manual labour.
8 | Check out the [video proposal](https://www.youtube.com/watch?v=nBATYSIb7fs) for the Problem Statement. 9 |
10 | 11 | ## Problem Description
12 | Present state-of-the-art Surveillance Devices require both consistent manual assistance and time for their successful operation. This results in a considerable loss of manual and technical resources. 13 |
14 | 15 | ## Proposed Solution
16 | We propose a Deep Learning Application that will be able to solve the above mentioned problems. 17 | - Our application named ‘Cap-Bot’ is capable of running Image Captioning on multiple CCTV footages and storing the captions along with the camera number and the time of capture in a convenient log.
18 |
19 | - The file of saved captions can then be used to look up for incidents from any instant of time just by entering a few keywords. The returned camera number and time slot can then be used to obtain the required CCTV footage.
20 |
21 | 22 | ### Check out the [Project Proposal](https://www.youtube.com/watch?v=Sr8dNQMBRZI) for our product. 23 | 24 | ## Advantages and Features 25 | - Interface to map CCTV Location in a defined area and eventually help single out points of interest.
26 |
27 | - Since our model relies on Deep Learning, the time can be reduced considerably as we are resorting to an automatic searching operation.
28 |
29 | - Since the information is purely textual, the encryption of information is way easier than pictorial.
30 |
31 | 32 | ## Steps of Deployment 33 | - [x] Training the Model 34 | - [x] Write the Search Module 35 | - [x] Captioning UI 36 | - [x] Search UI 37 | - [x] Perfecting Search feature 38 | - [x] Resolving Backend 39 | - [x] Encryption of Generation Captions
40 | Extra Feature 41 | - [ ] CCTV Localization with results 42 | 43 | 44 | ## Using the deployed version of the web application 45 | Please download the Model Checkpoints and move the file to the camera folder. 46 | 47 | - Setting up the Python Environment with dependencies 48 | 49 | pip install -r requirements.txt 50 | - Cloning the Repository: 51 | 52 | git clone https://github.com/aryankargwal/capbot2.0 53 | - Entering the directory for captioning: 54 | 55 | cd capbot2.0/camera 56 | - Running the captioning web application: 57 | 58 | streamlit run feed.py 59 | - Entering the directory for searching: 60 | 61 | cd capbot2.0/search 62 | - Running the searching web application: 63 | 64 | streamlit run search.py 65 | - Stopping the web application from the terminal 66 | 67 | Ctrl+C 68 | 69 |
70 | 71 | ## License 72 | This project is under the Apache License. See [LICENSE](LICENSE) for Details. 73 | 74 | ## Contributors 75 | 76 | 77 | 78 | 92 | 93 | 107 | 108 | 122 | 123 | 137 |
79 | 80 | Aryan Kargwal 81 | 82 |

83 | Aryan Kargwal 84 |

85 |

86 | 87 | 88 | 89 | 90 |

91 | 		94 | 95 | Indira Dutta 96 | 97 |

98 | Indira Dutta 99 |

100 |

101 | 102 | 103 | 104 | 105 |

106 | 		109 | 110 | Kunal Mundada 111 | 112 |

113 | Rusali Saha 114 |

115 |

116 | 117 | 118 | 119 | 120 |

121 | 		124 | 125 | Srijarko Roy 126 | 127 |

128 | person 129 |

130 |

131 | 132 | 133 | 134 | 135 |

136 |
138 | 139 | 140 | 141 | 142 |

143 | crafted with ♥ by team Missing-Colon 144 |

145 | -------------------------------------------------------------------------------- /assets/cameraui.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aryankargwal/cap-bot/bce709ad8a2c77c95890450d3b571419786417ee/assets/cameraui.jpg -------------------------------------------------------------------------------- /assets/cctv_system.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aryankargwal/cap-bot/bce709ad8a2c77c95890450d3b571419786417ee/assets/cctv_system.jpg -------------------------------------------------------------------------------- /assets/dl.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aryankargwal/cap-bot/bce709ad8a2c77c95890450d3b571419786417ee/assets/dl.png -------------------------------------------------------------------------------- /assets/encrypt.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aryankargwal/cap-bot/bce709ad8a2c77c95890450d3b571419786417ee/assets/encrypt.png -------------------------------------------------------------------------------- /assets/index.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aryankargwal/cap-bot/bce709ad8a2c77c95890450d3b571419786417ee/assets/index.jpeg -------------------------------------------------------------------------------- /assets/locations.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aryankargwal/cap-bot/bce709ad8a2c77c95890450d3b571419786417ee/assets/locations.png -------------------------------------------------------------------------------- /assets/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aryankargwal/cap-bot/bce709ad8a2c77c95890450d3b571419786417ee/assets/logo.png -------------------------------------------------------------------------------- /assets/search_tab.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aryankargwal/cap-bot/bce709ad8a2c77c95890450d3b571419786417ee/assets/search_tab.png -------------------------------------------------------------------------------- /camera/caption_gen.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | import numpy as np 4 | import json 5 | import cv2 6 | from skimage import transform 7 | import torchvision.transforms as transforms 8 | from imageio import imread 9 | from PIL import Image 10 | 11 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 12 | 13 | 14 | def caption_image_beam_search(encoder, decoder, image_path, word_map, beam_size=8): 15 | 16 | k = beam_size 17 | vocab_size = len(word_map) 18 | 19 | # Read image and process 20 | img = image_path 21 | if len(img.shape) == 2: 22 | img = img[:, :, np.newaxis] 23 | img = np.concatenate([img, img, img], axis=2) 24 | img = np.array(Image.fromarray(img).resize((256, 256))) 25 | img = img.transpose(2, 0, 1) 26 | img = img / 255. 27 | img = torch.FloatTensor(img).to(device) 28 | normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], 29 | std=[0.229, 0.224, 0.225]) 30 | transform = transforms.Compose([normalize]) 31 | image = transform(img) # (3, 256, 256) 32 | 33 | # Encode 34 | image = image.unsqueeze(0) # (1, 3, 256, 256) 35 | # (1, enc_image_size, enc_image_size, encoder_dim) 36 | encoder_out = encoder(image) 37 | enc_image_size = encoder_out.size(1) 38 | encoder_dim = encoder_out.size(3) 39 | 40 | # Flatten encoding 41 | # (1, num_pixels, encoder_dim) 42 | encoder_out = encoder_out.view(1, -1, encoder_dim) 43 | num_pixels = encoder_out.size(1) 44 | 45 | # We'll treat the problem as having a batch size of k 46 | # (k, num_pixels, encoder_dim) 47 | encoder_out = encoder_out.expand(k, num_pixels, encoder_dim) 48 | 49 | # Tensor to store top k previous words at each step; now they're just 50 | k_prev_words = torch.LongTensor( 51 | [[word_map['']]] * k).to(device) # (k, 1) 52 | 53 | # Tensor to store top k sequences; now they're just 54 | seqs = k_prev_words # (k, 1) 55 | 56 | # Tensor to store top k sequences' scores; now they're just 0 57 | top_k_scores = torch.zeros(k, 1).to(device) # (k, 1) 58 | 59 | # Tensor to store top k sequences' alphas; now they're just 1s 60 | seqs_alpha = torch.ones(k, 1, enc_image_size, enc_image_size).to( 61 | device) # (k, 1, enc_image_size, enc_image_size) 62 | 63 | # Lists to store completed sequences, their alphas and scores 64 | complete_seqs = list() 65 | complete_seqs_alpha = list() 66 | complete_seqs_scores = list() 67 | 68 | # Start decoding 69 | step = 1 70 | h, c = decoder.init_hidden_state(encoder_out) 71 | 72 | # s is a number less than or equal to k, because sequences are removed from this process once they hit 73 | while True: 74 | 75 | embeddings = decoder.embedding( 76 | k_prev_words).squeeze(1) # (s, embed_dim) 77 | 78 | # (s, encoder_dim), (s, num_pixels) 79 | awe, alpha = decoder.attention(encoder_out, h) 80 | 81 | # (s, enc_image_size, enc_image_size) 82 | alpha = alpha.view(-1, enc_image_size, enc_image_size) 83 | 84 | # gating scalar, (s, encoder_dim) 85 | gate = decoder.sigmoid(decoder.f_beta(h)) 86 | awe = gate * awe 87 | 88 | h, c = decoder.decode_step( 89 | torch.cat([embeddings, awe], dim=1), (h, c)) # (s, decoder_dim) 90 | 91 | scores = decoder.fc(h) # (s, vocab_size) 92 | scores = F.log_softmax(scores, dim=1) 93 | 94 | # Add 95 | scores = top_k_scores.expand_as(scores) + scores # (s, vocab_size) 96 | 97 | # For the first step, all k points will have the same scores (since same k previous words, h, c) 98 | if step == 1: 99 | top_k_scores, top_k_words = scores[0].topk(k, 0, True, True) # (s) 100 | else: 101 | # Unroll and find top scores, and their unrolled indices 102 | # (s) 103 | top_k_scores, top_k_words = scores.view(-1).topk(k, 0, True, True) 104 | 105 | # Convert unrolled indices to actual indices of scores 106 | prev_word_inds = top_k_words / vocab_size # (s) 107 | next_word_inds = top_k_words % vocab_size # (s) 108 | 109 | # Add new words to sequences, alphas 110 | seqs = torch.cat( 111 | [seqs[prev_word_inds], next_word_inds.unsqueeze(1)], dim=1) # (s, step+1) 112 | seqs_alpha = torch.cat([seqs_alpha[prev_word_inds], alpha[prev_word_inds].unsqueeze(1)], 113 | dim=1) # (s, step+1, enc_image_size, enc_image_size) 114 | 115 | # Which sequences are incomplete (didn't reach )? 116 | incomplete_inds = [ind for ind, next_word in enumerate(next_word_inds) if 117 | next_word != word_map['']] 118 | complete_inds = list( 119 | set(range(len(next_word_inds))) - set(incomplete_inds)) 120 | 121 | # Set aside complete sequences 122 | if len(complete_inds) > 0: 123 | complete_seqs.extend(seqs[complete_inds].tolist()) 124 | complete_seqs_alpha.extend(seqs_alpha[complete_inds].tolist()) 125 | complete_seqs_scores.extend(top_k_scores[complete_inds]) 126 | k -= len(complete_inds) # reduce beam length accordingly 127 | 128 | # Proceed with incomplete sequences 129 | if k == 0: 130 | break 131 | seqs = seqs[incomplete_inds] 132 | seqs_alpha = seqs_alpha[incomplete_inds] 133 | h = h[prev_word_inds[incomplete_inds]] 134 | c = c[prev_word_inds[incomplete_inds]] 135 | encoder_out = encoder_out[prev_word_inds[incomplete_inds]] 136 | top_k_scores = top_k_scores[incomplete_inds].unsqueeze(1) 137 | k_prev_words = next_word_inds[incomplete_inds].unsqueeze(1) 138 | 139 | # Break if things have been going on too long 140 | if step > 50: 141 | break 142 | step += 1 143 | 144 | i = complete_seqs_scores.index(max(complete_seqs_scores)) 145 | seq = complete_seqs[i] 146 | 147 | return seq 148 | 149 | 150 | def cap_gen(img): 151 | 152 | model = 'BEST_checkpoint_coco_5_cap_per_img_5_min_word_freq.pth.tar' 153 | word_map = 'word_map.json' 154 | 155 | # Load model 156 | checkpoint = torch.load(model, map_location=str(device)) 157 | decoder = checkpoint['decoder'] 158 | decoder = decoder.to(device) 159 | decoder.eval() 160 | encoder = checkpoint['encoder'] 161 | encoder = encoder.to(device) 162 | encoder.eval() 163 | 164 | # Load word map (word2ix) 165 | with open(word_map, 'r') as j: 166 | word_map = json.load(j) 167 | rev_word_map = {v: k for k, v in word_map.items()} # ix2word 168 | 169 | # Encode, decode with attention and beam search 170 | seq = caption_image_beam_search(encoder, decoder, img, word_map,) 171 | 172 | words = [rev_word_map[ind] for ind in seq] 173 | return words 174 | -------------------------------------------------------------------------------- /camera/feed.py: -------------------------------------------------------------------------------- 1 | from caption_gen import * 2 | import streamlit as st 3 | import cv2 4 | import csv 5 | from PIL import Image 6 | import pandas as pd 7 | import time 8 | from datetime import datetime 9 | import tempfile 10 | from imageio import imread 11 | 12 | # To display the webcam feed 13 | FRAME_WINDOW = st.image([]) 14 | 15 | 16 | def run_app(): 17 | # sidebar 18 | st.sidebar.image("../assets/logo.png") 19 | st.sidebar.header("Log maker") 20 | st.sidebar.markdown( 21 | "An interactive logging application to upload/connect camera to start the captioning and save the captions in an encrypted form for added secuirity." 22 | ) 23 | st.sidebar.markdown( 24 | "[Github Repository](https://github.com/aryankargwal/capbot2.0)" 25 | ) 26 | st.sidebar.markdown("[Proposal Video](https://www.youtube.com/watch?v=Sr8dNQMBRZI)") 27 | 28 | # source selector 29 | st.header("Select the source of the feed:") 30 | source = st.selectbox("", ("Live Camera", "Upload")) 31 | 32 | if source == "Upload": 33 | video_file = st.file_uploader( 34 | "surveillance feed", accept_multiple_files=False, type=["mp4"] 35 | ) 36 | tfile = tempfile.NamedTemporaryFile(delete=False) 37 | if video_file is not None: 38 | tfile.write(video_file.read()) 39 | vid = cv2.VideoCapture(tfile.name) 40 | if source == "Live Camera": 41 | vid = cv2.VideoCapture(0) 42 | run = st.checkbox("Run", key="start") 43 | show_frame = st.checkbox("Show frames", key="frame") 44 | csvw = CSVWorker() 45 | # Starts the app, when the button is clicked 46 | while run: 47 | _, frame = vid.read() 48 | frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) 49 | if show_frame: 50 | FRAME_WINDOW.image(frame) 51 | # img = Image.fromarray(frame) 52 | # img = img.resize((224, 224)) 53 | pred = cap_gen(frame) 54 | print(pred) 55 | csvw.write(pred) 56 | caption = " " 57 | caption.join(pred) 58 | st.write(caption) 59 | time.sleep(5) 60 | vid.release() 61 | cv2.destroyAllWindows() 62 | 63 | 64 | def main(): 65 | run_app() 66 | 67 | 68 | @st.cache(show_spinner=False) 69 | class CSVWorker: 70 | def __init__(self): 71 | self.fields = [ 72 | "w1", 73 | "w2", 74 | "w3", 75 | "w4", 76 | "w5", 77 | "w6", 78 | "w7", 79 | "w8", 80 | "w9", 81 | "w10", 82 | "time", 83 | "camera", 84 | ] 85 | self.filename = "results.csv" 86 | self.create_csv() 87 | 88 | def create_csv(self): 89 | df = pd.DataFrame(list(), columns=self.fields) 90 | df.to_csv(self.filename) 91 | 92 | def write(self, pred): 93 | df = pd.read_csv(self.filename) 94 | pred = pred[1:-1] 95 | if len(pred) >= 10: 96 | entry = [ 97 | pred[0], 98 | pred[1], 99 | pred[2], 100 | pred[3], 101 | pred[4], 102 | pred[5], 103 | pred[6], 104 | pred[7], 105 | pred[8], 106 | pred[9], 107 | datetime.now(), 108 | 1, 109 | ] 110 | elif len(pred) < 10: 111 | entry = [] 112 | for i in range(len(pred)): 113 | entry.append(pred[i]) 114 | for i in range(len(entry) - 1, 11): 115 | entry.append("") 116 | entry.append(datetime.now) 117 | entry.append(1) 118 | with open(self.filename, "a") as csvfile: 119 | # creating a csv writer object 120 | csvwriter = csv.writer(csvfile) 121 | # writing data rows 122 | csvwriter.writerow(entry) 123 | 124 | 125 | if __name__ == "__main__": 126 | main() 127 | -------------------------------------------------------------------------------- /camera/models.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | import torchvision 4 | 5 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 6 | 7 | 8 | class Encoder(nn.Module): 9 | """ 10 | Encoder. 11 | """ 12 | 13 | def __init__(self, encoded_image_size=14): 14 | super(Encoder, self).__init__() 15 | self.enc_image_size = encoded_image_size 16 | 17 | resnet = torchvision.models.resnet101(pretrained=True) # pretrained ImageNet ResNet-101 18 | 19 | # Remove linear and pool layers (since we're not doing classification) 20 | modules = list(resnet.children())[:-2] 21 | self.resnet = nn.Sequential(*modules) 22 | 23 | # Resize image to fixed size to allow input images of variable size 24 | self.adaptive_pool = nn.AdaptiveAvgPool2d((encoded_image_size, encoded_image_size)) 25 | 26 | self.fine_tune() 27 | 28 | def forward(self, images): 29 | """ 30 | Forward propagation. 31 | :param images: images, a tensor of dimensions (batch_size, 3, image_size, image_size) 32 | :return: encoded images 33 | """ 34 | out = self.resnet(images) # (batch_size, 2048, image_size/32, image_size/32) 35 | out = self.adaptive_pool(out) # (batch_size, 2048, encoded_image_size, encoded_image_size) 36 | out = out.permute(0, 2, 3, 1) # (batch_size, encoded_image_size, encoded_image_size, 2048) 37 | return out 38 | 39 | def fine_tune(self, fine_tune=True): 40 | """ 41 | Allow or prevent the computation of gradients for convolutional blocks 2 through 4 of the encoder. 42 | :param fine_tune: Allow? 43 | """ 44 | for p in self.resnet.parameters(): 45 | p.requires_grad = False 46 | # If fine-tuning, only fine-tune convolutional blocks 2 through 4 47 | for c in list(self.resnet.children())[5:]: 48 | for p in c.parameters(): 49 | p.requires_grad = fine_tune 50 | 51 | 52 | class Attention(nn.Module): 53 | """ 54 | Attention Network. 55 | """ 56 | 57 | def __init__(self, encoder_dim, decoder_dim, attention_dim): 58 | """ 59 | :param encoder_dim: feature size of encoded images 60 | :param decoder_dim: size of decoder's RNN 61 | :param attention_dim: size of the attention network 62 | """ 63 | super(Attention, self).__init__() 64 | self.encoder_att = nn.Linear(encoder_dim, attention_dim) # linear layer to transform encoded image 65 | self.decoder_att = nn.Linear(decoder_dim, attention_dim) # linear layer to transform decoder's output 66 | self.full_att = nn.Linear(attention_dim, 1) # linear layer to calculate values to be softmax-ed 67 | self.relu = nn.ReLU() 68 | self.softmax = nn.Softmax(dim=1) # softmax layer to calculate weights 69 | 70 | def forward(self, encoder_out, decoder_hidden): 71 | """ 72 | Forward propagation. 73 | :param encoder_out: encoded images, a tensor of dimension (batch_size, num_pixels, encoder_dim) 74 | :param decoder_hidden: previous decoder output, a tensor of dimension (batch_size, decoder_dim) 75 | :return: attention weighted encoding, weights 76 | """ 77 | att1 = self.encoder_att(encoder_out) # (batch_size, num_pixels, attention_dim) 78 | att2 = self.decoder_att(decoder_hidden) # (batch_size, attention_dim) 79 | att = self.full_att(self.relu(att1 + att2.unsqueeze(1))).squeeze(2) # (batch_size, num_pixels) 80 | alpha = self.softmax(att) # (batch_size, num_pixels) 81 | attention_weighted_encoding = (encoder_out * alpha.unsqueeze(2)).sum(dim=1) # (batch_size, encoder_dim) 82 | 83 | return attention_weighted_encoding, alpha 84 | 85 | 86 | class DecoderWithAttention(nn.Module): 87 | """ 88 | Decoder. 89 | """ 90 | 91 | def __init__(self, attention_dim, embed_dim, decoder_dim, vocab_size, encoder_dim=2048, dropout=0.5): 92 | """ 93 | :param attention_dim: size of attention network 94 | :param embed_dim: embedding size 95 | :param decoder_dim: size of decoder's RNN 96 | :param vocab_size: size of vocabulary 97 | :param encoder_dim: feature size of encoded images 98 | :param dropout: dropout 99 | """ 100 | super(DecoderWithAttention, self).__init__() 101 | 102 | self.encoder_dim = encoder_dim 103 | self.attention_dim = attention_dim 104 | self.embed_dim = embed_dim 105 | self.decoder_dim = decoder_dim 106 | self.vocab_size = vocab_size 107 | self.dropout = dropout 108 | 109 | self.attention = Attention(encoder_dim, decoder_dim, attention_dim) # attention network 110 | 111 | self.embedding = nn.Embedding(vocab_size, embed_dim) # embedding layer 112 | self.dropout = nn.Dropout(p=self.dropout) 113 | self.decode_step = nn.LSTMCell(embed_dim + encoder_dim, decoder_dim, bias=True) # decoding LSTMCell 114 | self.init_h = nn.Linear(encoder_dim, decoder_dim) # linear layer to find initial hidden state of LSTMCell 115 | self.init_c = nn.Linear(encoder_dim, decoder_dim) # linear layer to find initial cell state of LSTMCell 116 | self.f_beta = nn.Linear(decoder_dim, encoder_dim) # linear layer to create a sigmoid-activated gate 117 | self.sigmoid = nn.Sigmoid() 118 | self.fc = nn.Linear(decoder_dim, vocab_size) # linear layer to find scores over vocabulary 119 | self.init_weights() # initialize some layers with the uniform distribution 120 | 121 | def init_weights(self): 122 | """ 123 | Initializes some parameters with values from the uniform distribution, for easier convergence. 124 | """ 125 | self.embedding.weight.data.uniform_(-0.1, 0.1) 126 | self.fc.bias.data.fill_(0) 127 | self.fc.weight.data.uniform_(-0.1, 0.1) 128 | 129 | def load_pretrained_embeddings(self, embeddings): 130 | """ 131 | Loads embedding layer with pre-trained embeddings. 132 | :param embeddings: pre-trained embeddings 133 | """ 134 | self.embedding.weight = nn.Parameter(embeddings) 135 | 136 | def fine_tune_embeddings(self, fine_tune=True): 137 | """ 138 | Allow fine-tuning of embedding layer? (Only makes sense to not-allow if using pre-trained embeddings). 139 | :param fine_tune: Allow? 140 | """ 141 | for p in self.embedding.parameters(): 142 | p.requires_grad = fine_tune 143 | 144 | def init_hidden_state(self, encoder_out): 145 | """ 146 | Creates the initial hidden and cell states for the decoder's LSTM based on the encoded images. 147 | :param encoder_out: encoded images, a tensor of dimension (batch_size, num_pixels, encoder_dim) 148 | :return: hidden state, cell state 149 | """ 150 | mean_encoder_out = encoder_out.mean(dim=1) 151 | h = self.init_h(mean_encoder_out) # (batch_size, decoder_dim) 152 | c = self.init_c(mean_encoder_out) 153 | return h, c 154 | 155 | def forward(self, encoder_out, encoded_captions, caption_lengths): 156 | """ 157 | Forward propagation. 158 | :param encoder_out: encoded images, a tensor of dimension (batch_size, enc_image_size, enc_image_size, encoder_dim) 159 | :param encoded_captions: encoded captions, a tensor of dimension (batch_size, max_caption_length) 160 | :param caption_lengths: caption lengths, a tensor of dimension (batch_size, 1) 161 | :return: scores for vocabulary, sorted encoded captions, decode lengths, weights, sort indices 162 | """ 163 | 164 | batch_size = encoder_out.size(0) 165 | encoder_dim = encoder_out.size(-1) 166 | vocab_size = self.vocab_size 167 | 168 | # Flatten image 169 | encoder_out = encoder_out.view(batch_size, -1, encoder_dim) # (batch_size, num_pixels, encoder_dim) 170 | num_pixels = encoder_out.size(1) 171 | 172 | # Sort input data by decreasing lengths; why? apparent below 173 | caption_lengths, sort_ind = caption_lengths.squeeze(1).sort(dim=0, descending=True) 174 | encoder_out = encoder_out[sort_ind] 175 | encoded_captions = encoded_captions[sort_ind] 176 | 177 | # Embedding 178 | embeddings = self.embedding(encoded_captions) # (batch_size, max_caption_length, embed_dim) 179 | 180 | # Initialize LSTM state 181 | h, c = self.init_hidden_state(encoder_out) # (batch_size, decoder_dim) 182 | 183 | # We won't decode at the position, since we've finished generating as soon as we generate 184 | # So, decoding lengths are actual lengths - 1 185 | decode_lengths = (caption_lengths - 1).tolist() 186 | 187 | # Create tensors to hold word predicion scores and alphas 188 | predictions = torch.zeros(batch_size, max(decode_lengths), vocab_size).to(device) 189 | alphas = torch.zeros(batch_size, max(decode_lengths), num_pixels).to(device) 190 | 191 | # At each time-step, decode by 192 | # attention-weighing the encoder's output based on the decoder's previous hidden state output 193 | # then generate a new word in the decoder with the previous word and the attention weighted encoding 194 | for t in range(max(decode_lengths)): 195 | batch_size_t = sum([l > t for l in decode_lengths]) 196 | attention_weighted_encoding, alpha = self.attention(encoder_out[:batch_size_t], 197 | h[:batch_size_t]) 198 | gate = self.sigmoid(self.f_beta(h[:batch_size_t])) # gating scalar, (batch_size_t, encoder_dim) 199 | attention_weighted_encoding = gate * attention_weighted_encoding 200 | h, c = self.decode_step( 201 | torch.cat([embeddings[:batch_size_t, t, :], attention_weighted_encoding], dim=1), 202 | (h[:batch_size_t], c[:batch_size_t])) # (batch_size_t, decoder_dim) 203 | preds = self.fc(self.dropout(h)) # (batch_size_t, vocab_size) 204 | predictions[:batch_size_t, t, :] = preds 205 | alphas[:batch_size_t, t, :] = alpha 206 | 207 | return predictions, encoded_captions, decode_lengths, alphas, sort_ind 208 | -------------------------------------------------------------------------------- /camera/results.csv: -------------------------------------------------------------------------------- 1 | ,w1,w2,w3,w4,w5,w6,w7,w8,w9,w10,time,camera 2 | -------------------------------------------------------------------------------- /camera/word_map.json: -------------------------------------------------------------------------------- 1 | {"a": 1, "man": 2, "with": 3, "red": 4, "helmet": 5, "on": 6, "small": 7, "moped": 8, "dirt": 9, "road": 10, "riding": 11, "motor": 12, "bike": 13, "the": 14, "countryside": 15, "back": 16, "of": 17, "motorcycle": 18, "path": 19, "young": 20, "person": 21, "rests": 22, "to": 23, "foreground": 24, "verdant": 25, "area": 26, "bridge": 27, "and": 28, "background": 29, "cloud": 30, "mountains": 31, "in": 32, "shirt": 33, "hat": 34, "is": 35, "hill": 36, "side": 37, "woman": 38, "wearing": 39, "net": 40, "her": 41, "head": 42, "cutting": 43, "cake": 44, "large": 45, "white": 46, "sheet": 47, "hair": 48, "there": 49, "that": 50, "marking": 51, "chefs": 52, "knife": 53, "child": 54, "holding": 55, "flowered": 56, "umbrella": 57, "petting": 58, "yak": 59, "an": 60, "next": 61, "herd": 62, "cattle": 63, "boy": 64, "barefoot": 65, "touching": 66, "horn": 67, "cow": 68, "who": 69, "while": 70, "standing": 71, "livestock": 72, "front": 73, "computer": 74, "keyboard": 75, "little": 76, "headphones": 77, "looking": 78, "at": 79, "monitor": 80, "he": 81, "listening": 82, "intently": 83, "school": 84, "stares": 85, "up": 86, "kid": 87, "phones": 88, "using": 89, "one": 90, "long": 91, "row": 92, "computers": 93, "earphones": 94, "something": 95, "group": 96, "people": 97, "sitting": 98, "desk": 99, "children": 100, "stations": 101, "table": 102, "plays": 103, "kitchen": 104, "making": 105, "pizzas": 106, "apron": 107, "oven": 108, "pans": 109, "baker": 110, "working": 111, "rolling": 112, "dough": 113, "by": 114, "stove": 115, "pies": 116, "being": 117, "made": 118, "near": 119, "wall": 120, "pots": 121, "hanging": 122, "room": 123, "cat": 124, "girl": 125, "smiles": 126, "as": 127, "she": 128, "holds": 129, "wears": 130, "brightly": 131, "colored": 132, "skirt": 133, "carrying": 134, "soft": 135, "toy": 136, "intent": 137, "blowing": 138, "out": 139, "candle": 140, "preparing": 141, "blow": 142, "single": 143, "bowl": 144, "birthday": 145, "ice": 146, "cream": 147, "getting": 148, "ready": 149, "dessert": 150, "commercial": 151, "stainless": 152, "pot": 153, "food": 154, "cooking": 155, "some": 156, "sits": 157, "has": 158, "all": 159, "steel": 160, "appliances": 161, "counters": 162, "sink": 163, "many": 164, "machines": 165, "cooks": 166, "two": 167, "men": 168, "aprons": 169, "style": 170, "professional": 171, "metallic": 172, "around": 173, "prepare": 174, "several": 175, "plates": 176, "shirts": 177, "restaurant": 178, "are": 179, "someone": 180, "ordered": 181, "this": 182, "chef": 183, "very": 184, "dark": 185, "picture": 186, "shelf": 187, "cluttered": 188, "view": 189, "messy": 190, "shelves": 191, "storage": 192, "wood": 193, "walls": 194, "dim": 195, "lit": 196, "consisting": 197, "objects": 198, "put": 199, "together": 200, "filled": 201, "black": 202, "lots": 203, "counter": 204, "top": 205, "space": 206, "brown": 207, "cabinets": 208, "tea": 209, "kettle": 210, "microwave": 211, "glass": 212, "wooden": 213, "modern": 214, "may": 215, "different": 216, "items": 217, "sinks": 218, "toilet": 219, "various": 220, "cleaning": 221, "dish": 222, "washing": 223, "station": 224, "it": 225, "mop": 226, "bucket": 227, "industrial": 228, "bicycle": 229, "train": 230, "but": 231, "guy": 232, "his": 233, "past": 234, "traveling": 235, "along": 236, "tracks": 237, "narrow": 238, "utensils": 239, "galley": 240, "both": 241, "sides": 242, "hallway": 243, "leading": 244, "into": 245, "doorway": 246, "refrigerator": 247, "pantry": 248, "door": 249, "closed": 250, "floors": 251, "furniture": 252, "beautiful": 253, "open": 254, "dining": 255, "features": 256, "island": 257, "center": 258, "windows": 259, "mostly": 260, "laptop": 261, "spacious": 262, "full": 263, "eating": 264, "vegetables": 265, "forks": 266, "mouth": 267, "assortment": 268, "mixed": 269, "eats": 270, "plate": 271, "fresh": 272, "from": 273, "shown": 274, "variety": 275, "plaid": 276, "curtains": 277, "metal": 278, "older": 279, "tops": 280, "empty": 281, "glasses": 282, "bottles": 283, "placed": 284, "performing": 285, "kickflip": 286, "skateboard": 287, "city": 288, "street": 289, "doing": 290, "trick": 291, "jumps": 292, "air": 293, "beneath": 294, "him": 295, "skateboarder": 296, "flipping": 297, "board": 298, "kitchenette": 299, "uses": 300, "great": 301, "efficiency": 302, "image": 303, "setting": 304, "fruit": 305, "clean": 306, "for": 307, "us": 308, "see": 309, "decorated": 310, "loaded": 311, "pickup": 312, "truck": 313, "number": 314, "things": 315, "crowded": 316, "overloaded": 317, "old": 318, "pick": 319, "over": 320, "cargo": 321, "carries": 322, "amount": 323, "few": 324, "ball": 325, "stick": 326, "spoons": 327, "selection": 328, "tools": 329, "lined": 330, "multiple": 331, "surrounded": 332, "chairs": 333, "laid": 334, "across": 335, "boat": 336, "mean": 337, "wheels": 338, "bunch": 339, "aboard": 340, "rolled": 341, "trailer": 342, "cart": 343, "students": 344, "workstation": 345, "examines": 346, "lap": 347, "hand": 348, "other": 349, "mouse": 350, "use": 351, "reading": 352, "material": 353, "elephant": 354, "water": 355, "creek": 356, "forest": 357, "river": 358, "rides": 359, "couple": 360, "women": 361, "home": 362, "dinner": 363, "lights": 364, "huge": 365, "pan": 366, "inside": 367, "granite": 368, "baby": 369, "laying": 370, "down": 371, "teddy": 372, "bear": 373, "crib": 374, "stuffed": 375, "gloves": 376, "lying": 377, "left": 378, "taking": 379, "lays": 380, "beside": 381, "lies": 382, "blue": 383, "green": 384, "bedding": 385, "shopping": 386, "walk": 387, "homeless": 388, "begs": 389, "cash": 390, "walking": 391, "begging": 392, "cup": 393, "bikers": 394, "building": 395, "shots": 396, "bicycles": 397, "streets": 398, "tall": 399, "bikes": 400, "photos": 401, "skate": 402, "park": 403, "performs": 404, "athletes": 405, "tricks": 406, "falls": 407, "off": 408, "parked": 409, "chained": 410, "fixture": 411, "sidewalk": 412, "locked": 413, "post": 414, "approaching": 415, "bird": 416, "three": 417, "riders": 418, "trees": 419, "pigeon": 420, "coming": 421, "smiling": 422, "colorful": 423, "greets": 424, "bicyclists": 425, "kitten": 426, "fence": 427, "tank": 428, "yard": 429, "yellow": 430, "kitty": 431, "bathroom": 432, "just": 433, "toliet": 434, "section": 435, "lighting": 436, "cabinet": 437, "mirror": 438, "been": 439, "cleaned": 440, "store": 441, "shows": 442, "males": 443, "leaning": 444, "toward": 445, "adjust": 446, "shop": 447, "employee": 448, "helping": 449, "customer": 450, "talking": 451, "about": 452, "broken": 453, "shower": 454, "looks": 455, "missing": 456, "tile": 457, "stall": 458, "needs": 459, "be": 460, "fixed": 461, "basement": 462, "big": 463, "whit": 464, "rest": 465, "shabby": 466, "interesting": 467, "focal": 468, "point": 469, "perspective": 470, "washroom": 471, "under": 472, "i": 473, "stand": 474, "persons": 475, "reflection": 476, "behind": 477, "mirrored": 478, "corner": 479, "bathtub": 480, "mirrors": 481, "tub": 482, "doors": 483, "beige": 484, "bedroom": 485, "another": 486, "double": 487, "carpeted": 488, "floor": 489, "vanity": 490, "opens": 491, "diamond": 492, "patterned": 493, "onto": 494, "closet": 495, "jacuzzi": 496, "tiled": 497, "adjoining": 498, "flowers": 499, "shot": 500, "includes": 501, "letter": 502, "framed": 503, "potted": 504, "plant": 505, "tissue": 506, "commode": 507, "enclosed": 508, "featuring": 509, "ride": 510, "busy": 511, "lane": 512, "passing": 513, "burger": 514, "king": 515, "most": 516, "not": 517, "nice": 518, "displayed": 519, "piece": 520, "interior": 521, "scene": 522, "furnishings": 523, "including": 524, "dogs": 525, "they": 526, "restroom": 527, "camera": 528, "currently": 529, "repair": 530, "photograph": 531, "undergoing": 532, "major": 533, "renovations": 534, "process": 535, "remodeling": 536, "remolded": 537, "where": 538, "installed": 539, "construction": 540, "unfinished": 541, "plumbing": 542, "guests": 543, "bath": 544, "standup": 545, "which": 546, "covered": 547, "tiles": 548, "without": 549, "curtain": 550, "held": 551, "walled": 552, "through": 553, "faded": 554, "paint": 555, "no": 556, "panel": 557, "painted": 558, "buckets": 559, "toilets": 560, "paper": 561, "holder": 562, "soap": 563, "window": 564, "like": 565, "dust": 566, "brush": 567, "low": 568, "rough": 569, "close": 570, "pale": 571, "cement": 572, "broom": 573, "fixtures": 574, "contains": 575, "well": 576, "above": 577, "them": 578, "equipment": 579, "fire": 580, "extinguisher": 581, "photo": 582, "porcelain": 583, "posed": 584, "tan": 585, "flower": 586, "ground": 587, "curb": 588, "house": 589, "lawn": 590, "outside": 591, "gray": 592, "median": 593, "lady": 594, "divider": 595, "takes": 596, "public": 597, "flooring": 598, "each": 599, "used": 600, "multicolored": 601, "pedestal": 602, "cars": 603, "driving": 604, "highway": 605, "traffic": 606, "stop": 607, "intersection": 608, "sit": 609, "asia": 610, "taxi": 611, "cabs": 612, "buildings": 613, "heavy": 614, "going": 615, "direction": 616, "stuck": 617, "high": 618, "way": 619, "parking": 620, "late": 621, "evening": 622, "wires": 623, "break": 624, "glow": 625, "waits": 626, "light": 627, "signs": 628, "opened": 629, "shapped": 630, "mug": 631, "himself": 632, "self": 633, "cell": 634, "phone": 635, "selfie": 636, "bare": 637, "varying": 638, "awaits": 639, "cabinetry": 640, "meters": 641, "series": 642, "located": 643, "meter": 644, "car": 645, "elegant": 646, "decorations": 647, "fashion": 648, "gold": 649, "feet": 650, "lovely": 651, "vintage": 652, "styled": 653, "claw": 654, "footed": 655, "foot": 656, "seen": 657, "stuff": 658, "shelving": 659, "unit": 660, "between": 661, "hygiene": 662, "products": 663, "wrong": 664, "additional": 665, "medicine": 666, "urinal": 667, "mounted": 668, "pair": 669, "tongs": 670, "thing": 671, "glazed": 672, "donut": 673, "rod": 674, "tasty": 675, "treat": 676, "antenna": 677, "orange": 678, "striped": 679, "tabby": 680, "vehicles": 681, "wheel": 682, "fluffy": 683, "tail": 684, "tire": 685, "vehicle": 686, "hiding": 687, "fender": 688, "jetliner": 689, "flying": 690, "airplane": 691, "flies": 692, "sky": 693, "comes": 694, "land": 695, "plane": 696, "silver": 697, "bus": 698, "lot": 699, "parks": 700, "metro": 701, "outdoor": 702, "resting": 703, "playing": 704, "stepping": 705, "playground": 706, "skateboarding": 707, "jacket": 708, "attire": 709, "tie": 710, "eyes": 711, "dressed": 712, "necktie": 713, "nicely": 714, "surfboards": 715, "field": 716, "fashioned": 717, "retro": 718, "wagon": 719, "surfboard": 720, "cop": 721, "police": 722, "officer": 723, "cops": 724, "backpack": 725, "book": 726, "bag": 727, "stone": 728, "floored": 729, "possibly": 730, "lid": 731, "blurry": 732, "seat": 733, "perched": 734, "was": 735, "position": 736, "revealing": 737, "shiny": 738, "display": 739, "new": 740, "brand": 741, "show": 742, "end": 743, "dirty": 744, "non": 745, "transparent": 746, "plain": 747, "birds": 748, "church": 749, "spire": 750, "decorative": 751, "roof": 752, "clock": 753, "tower": 754, "towering": 755, "its": 756, "steeple": 757, "fly": 758, "driveway": 759, "license": 760, "luxurious": 761, "hedge": 762, "showing": 763, "container": 764, "crosses": 765, "base": 766, "designed": 767, "neck": 768, "shut": 769, "sort": 770, "religious": 771, "object": 772, "picking": 773, "bananas": 774, "bunches": 775, "attending": 776, "bundles": 777, "grabbing": 778, "stack": 779, "pile": 780, "sun": 781, "plaque": 782, "middle": 783, "decor": 784, "decoration": 785, "decal": 786, "cramped": 787, "combination": 788, "advanced": 789, "control": 790, "fountain": 791, "faucet": 792, "controls": 793, "horses": 794, "grazing": 795, "pasture": 796, "filed": 797, "grass": 798, "horse": 799, "drawn": 800, "carriage": 801, "pass": 802, "outdoors": 803, "pavement": 804, "discarded": 805, "someones": 806, "crowd": 807, "graze": 808, "amid": 809, "snow": 810, "melting": 811, "spot": 812, "estate": 813, "wind": 814, "indicator": 815, "weather": 816, "vane": 817, "clear": 818, "cycle": 819, "tree": 820, "unattended": 821, "roadway": 822, "trash": 823, "can": 824, "compact": 825, "waste": 826, "bin": 827, "toilette": 828, "purple": 829, "brick": 830, "sport": 831, "motorbike": 832, "blur": 833, "against": 834, "office": 835, "works": 836, "motorcycles": 837, "hats": 838, "their": 839, "motorists": 840, "gathered": 841, "stores": 842, "apartment": 843, "motorcyclists": 844, "bowls": 845, "bottle": 846, "cinnamon": 847, "sugar": 848, "cereal": 849, "milk": 850, "desert": 851, "mashed": 852, "oatmeal": 853, "eat": 854, "rows": 855, "helmets": 856, "motorbikes": 857, "line": 858, "set": 859, "makes": 860, "tightly": 861, "columns": 862, "grouped": 863, "ally": 864, "neighborhood": 865, "alley": 866, "lush": 867, "mopeds": 868, "scooter": 869, "happy": 870, "work": 871, "hope": 872, "setup": 873, "printer": 874, "scanner": 875, "extra": 876, "tablet": 877, "vases": 878, "run": 879, "planters": 880, "sign": 881, "plaster": 882, "external": 883, "images": 884, "attached": 885, "overlooking": 886, "chair": 887, "faces": 888, "away": 889, "fro": 890, "television": 891, "pillow": 892, "living": 893, "ornate": 894, "books": 895, "tv": 896, "straight": 897, "ahead": 898, "lanes": 899, "heading": 900, "motorized": 901, "scooters": 902, "stopped": 903, "lamp": 904, "screen": 905, "flat": 906, "surround": 907, "sound": 908, "country": 909, "swiftly": 910, "painting": 911, "oranges": 912, "pitcher": 913, "vase": 914, "pieces": 915, "frame": 916, "case": 917, "packed": 918, "boxed": 919, "delivery": 920, "market": 921, "packaged": 922, "boxes": 923, "cardboard": 924, "box": 925, "plastic": 926, "unopened": 927, "perfectly": 928, "ripe": 929, "barrel": 930, "or": 931, "plantains": 932, "piled": 933, "containers": 934, "depicting": 935, "writing": 936, "ticket": 937, "rider": 938, "race": 939, "track": 940, "racetrack": 941, "racer": 942, "performance": 943, "beverage": 944, "slices": 945, "cold": 946, "juice": 947, "beer": 948, "stands": 949, "containing": 950, "liquid": 951, "sliced": 952, "wide": 953, "nearing": 954, "nearby": 955, "electric": 956, "kfc": 957, "facade": 958, "town": 959, "bread": 960, "basket": 961, "finch": 962, "newspaper": 963, "grey": 964, "wine": 965, "winery": 966, "among": 967, "bench": 968, "entrance": 969, "warped": 970, "cottage": 971, "cabin": 972, "garden": 973, "dog": 974, "watches": 975, "animal": 976, "facing": 977, "watching": 978, "wildlife": 979, "hound": 980, "rug": 981, "advertising": 982, "arizona": 983, "airport": 984, "baggage": 985, "claim": 986, "terminal": 987, "displaying": 988, "ad": 989, "jumping": 990, "news": 991, "passes": 992, "find": 993, "taken": 994, "four": 995, "engine": 996, "jet": 997, "transport": 998, "grassy": 999, "jumbo": 1000, "landing": 1001, "flight": 1002, "china": 1003, "runway": 1004, "airfield": 1005, "safety": 1006, "vest": 1007, "arms": 1008, "giving": 1009, "directions": 1010, "controller": 1011, "directing": 1012, "reflective": 1013, "gear": 1014, "airliner": 1015, "sticks": 1016, "airline": 1017, "employees": 1018, "aircraft": 1019, "gate": 1020, "connected": 1021, "passengers": 1022, "propeller": 1023, "planes": 1024, "renovated": 1025, "propellor": 1026, "fighter": 1027, "tarmac": 1028, "pilot": 1029, "war": 1030, "type": 1031, "p": 1032, "40": 1033, "taxis": 1034, "take": 1035, "towards": 1036, "wet": 1037, "unloading": 1038, "arrival": 1039, "gates": 1040, "airplanes": 1041, "carrier": 1042, "pulls": 1043, "disconnected": 1044, "boarding": 1045, "fancy": 1046, "emptied": 1047, "plan": 1048, "animals": 1049, "bull": 1050, "giraffe": 1051, "deer": 1052, "parakeet": 1053, "childrens": 1054, "strewn": 1055, "toys": 1056, "personal": 1057, "cross": 1058, "pulled": 1059, "crossing": 1060, "signal": 1061, "crosswalk": 1062, "night": 1063, "appearing": 1064, "confusing": 1065, "electronic": 1066, "glows": 1067, "time": 1068, "arrows": 1069, "stripes": 1070, "warplane": 1071, "airstrip": 1072, "model": 1073, "american": 1074, "insignia": 1075, "wings": 1076, "bending": 1077, "check": 1078, "radio": 1079, "operated": 1080, "crouched": 1081, "kneeling": 1082, "miniature": 1083, "cloudy": 1084, "partially": 1085, "day": 1086, "biplane": 1087, "siting": 1088, "sunny": 1089, "lone": 1090, "underneath": 1091, "boats": 1092, "harbor": 1093, "beach": 1094, "sea": 1095, "leaves": 1096, "native": 1097, "slowly": 1098, "giraffes": 1099, "place": 1100, "wing": 1101, "pointed": 1102, "landscape": 1103, "sandy": 1104, "hills": 1105, "rain": 1106, "mountain": 1107, "ascends": 1108, "passenger": 1109, "beginning": 1110, "ascent": 1111, "sheep": 1112, "farm": 1113, "flock": 1114, "goats": 1115, "walks": 1116, "habitat": 1117, "herding": 1118, "keep": 1119, "watch": 1120, "bright": 1121, "sheeps": 1122, "chewing": 1123, "hay": 1124, "mouthful": 1125, "hungry": 1126, "zoo": 1127, "alone": 1128, "rocky": 1129, "shines": 1130, "drive": 1131, "skyscrapers": 1132, "shining": 1133, "sunlight": 1134, "elderly": 1135, "bottom": 1136, "poster": 1137, "billboard": 1138, "art": 1139, "installation": 1140, "stopping": 1141, "get": 1142, "doesnt": 1143, "appear": 1144, "anyone": 1145, "waiting": 1146, "political": 1147, "advertisement": 1148, "coach": 1149, "here": 1150, "scott": 1151, "browns": 1152, "campaign": 1153, "tour": 1154, "curvy": 1155, "curved": 1156, "curve": 1157, "following": 1158, "alongside": 1159, "fish": 1160, "eye": 1161, "rounding": 1162, "africa": 1163, "goes": 1164, "vast": 1165, "expanse": 1166, "puffy": 1167, "clouds": 1168, "fill": 1169, "den": 1170, "licks": 1171, "limb": 1172, "strangely": 1173, "lick": 1174, "branch": 1175, "bark": 1176, "glowing": 1177, "clearly": 1178, "visible": 1179, "pole": 1180, "sets": 1181, "drinking": 1182, "drink": 1183, "pond": 1184, "awkwardly": 1185, "sips": 1186, "puddle": 1187, "knees": 1188, "rocks": 1189, "stroll": 1190, "bush": 1191, "rub": 1192, "necks": 1193, "paths": 1194, "pin": 1195, "enclosure": 1196, "hits": 1197, "body": 1198, "smaller": 1199, "exhibit": 1200, "posted": 1201, "south": 1202, "streetlight": 1203, "directional": 1204, "naked": 1205, "stoplight": 1206, "signals": 1207, "c": 1208, "3": 1209, "plants": 1210, "overgrown": 1211, "hose": 1212, "wilderness": 1213, "creepy": 1214, "shaped": 1215, "rear": 1216, "below": 1217, "situated": 1218, "woods": 1219, "buses": 1220, "pedestrians": 1221, "busses": 1222, "dusk": 1223, "cathedral": 1224, "direct": 1225, "illuminated": 1226, "second": 1227, "if": 1228, "u": 1229, "busted": 1230, "pedestrian": 1231, "symbol": 1232, "sunglasses": 1233, "herself": 1234, "commuter": 1235, "urban": 1236, "caravan": 1237, "opposite": 1238, "bent": 1239, "submerged": 1240, "flood": 1241, "waters": 1242, "grill": 1243, "flooded": 1244, "muddy": 1245, "pier": 1246, "structure": 1247, "reaching": 1248, "greet": 1249, "extends": 1250, "onlooker": 1251, "feeding": 1252, "approaches": 1253, "almost": 1254, "touches": 1255, "speaking": 1256, "kids": 1257, "chips": 1258, "rusted": 1259, "pink": 1260, "hydrant": 1261, "slightly": 1262, "rusty": 1263, "make": 1264, "look": 1265, "more": 1266, "festive": 1267, "stickers": 1268, "face": 1269, "sandwich": 1270, "sticky": 1271, "sculpture": 1272, "pushing": 1273, "inner": 1274, "courtyard": 1275, "snaps": 1276, "having": 1277, "fun": 1278, "newly": 1279, "married": 1280, "emo": 1281, "hipster": 1282, "wedding": 1283, "bride": 1284, "groom": 1285, "pose": 1286, "pictures": 1287, "leaking": 1288, "gushing": 1289, "pouring": 1290, "spilling": 1291, "spouting": 1292, "pen": 1293, "dusted": 1294, "girls": 1295, "firefighter": 1296, "fireman": 1297, "poking": 1298, "fired": 1299, "balloon": 1300, "zebra": 1301, "ducks": 1302, "duck": 1303, "freeway": 1304, "states": 1305, "las": 1306, "vegas": 1307, "north": 1308, "float": 1309, "parade": 1310, "towing": 1311, "wheeled": 1312, "band": 1313, "part": 1314, "swan": 1315, "floating": 1316, "flags": 1317, "tents": 1318, "docked": 1319, "bank": 1320, "shore": 1321, "lake": 1322, "backdrop": 1323, "dry": 1324, "seagulls": 1325, "floats": 1326, "patch": 1327, "neighboring": 1328, "threw": 1329, "palm": 1330, "neon": 1331, "reflections": 1332, "streaming": 1333, "thru": 1334, "crane": 1335, "because": 1336, "warning": 1337, "languages": 1338, "closeup": 1339, "vertical": 1340, "triangle": 1341, "english": 1342, "foreign": 1343, "same": 1344, "wavy": 1345, "swimming": 1346, "ocean": 1347, "waves": 1348, "beak": 1349, "swims": 1350, "closely": 1351, "dispenser": 1352, "gather": 1353, "fed": 1354, "surrounding": 1355, "raised": 1356, "mural": 1357, "heard": 1358, "scattered": 1359, "gathering": 1360, "quiet": 1361, "suburbs": 1362, "growing": 1363, "multi": 1364, "cones": 1365, "far": 1366, "right": 1367, "drives": 1368, "slow": 1369, "apple": 1370, "travels": 1371, "historical": 1372, "travel": 1373, "tropical": 1374, "forward": 1375, "spots": 1376, "zebras": 1377, "turns": 1378, "pack": 1379, "rd": 1380, "stacked": 1381, "shelve": 1382, "rack": 1383, "types": 1384, "id": 1385, "age": 1386, "says": 1387, "you": 1388, "must": 1389, "18": 1390, "enter": 1391, "cover": 1392, "cab": 1393, "guiding": 1394, "pictured": 1395, "indicating": 1396, "distance": 1397, "statue": 1398, "lion": 1399, "york": 1400, "library": 1401, "lions": 1402, "statues": 1403, "appears": 1404, "signage": 1405, "repairing": 1406, "attaching": 1407, "less": 1408, "than": 1409, "mile": 1410, "trail": 1411, "hangs": 1412, "perches": 1413, "claws": 1414, "skates": 1415, "skateboards": 1416, "vandalized": 1417, "prohibiting": 1418, "skating": 1419, "arrow": 1420, "points": 1421, "oil": 1422, "flag": 1423, "reads": 1424, "damaged": 1425, "word": 1426, "barn": 1427, "mother": 1428, "larger": 1429, "sanctuary": 1430, "grown": 1431, "staring": 1432, "directly": 1433, "lens": 1434, "massive": 1435, "pennsylvania": 1436, "avenue": 1437, "stately": 1438, "adorned": 1439, "st": 1440, "ones": 1441, "feathers": 1442, "lettering": 1443, "adorns": 1444, "concrete": 1445, "live": 1446, "life": 1447, "sneakers": 1448, "hang": 1449, "av": 1450, "first": 1451, "shoes": 1452, "power": 1453, "lines": 1454, "cages": 1455, "sheets": 1456, "have": 1457, "clothes": 1458, "covering": 1459, "cloth": 1460, "tarps": 1461, "strapped": 1462, "advantage": 1463, "birdbath": 1464, "diving": 1465, "splashes": 1466, "asian": 1467, "language": 1468, "interstate": 1469, "somewhere": 1470, "poll": 1471, "gliding": 1472, "snowy": 1473, "range": 1474, "photographer": 1475, "swooping": 1476, "rather": 1477, "name": 1478, "indicates": 1479, "during": 1480, "winter": 1481, "panting": 1482, "tongue": 1483, "cute": 1484, "adult": 1485, "rock": 1486, "sick": 1487, "cane": 1488, "hurdle": 1489, "ramp": 1490, "crouching": 1491, "balancing": 1492, "printed": 1493, "traffice": 1494, "poles": 1495, "sticking": 1496, "posts": 1497, "similar": 1498, "eiffel": 1499, "pigeons": 1500, "ledge": 1501, "railing": 1502, "seated": 1503, "besides": 1504, "prominent": 1505, "landmark": 1506, "hilltop": 1507, "fields": 1508, "canadian": 1509, "geese": 1510, "swim": 1511, "wading": 1512, "cage": 1513, "unusual": 1514, "headed": 1515, "story": 1516, "dr": 1517, "focused": 1518, "atop": 1519, "dead": 1520, "detroit": 1521, "give": 1522, "names": 1523, "utility": 1524, "sports": 1525, "ram": 1526, "bends": 1527, "woolly": 1528, "broadway": 1529, "star": 1530, "nypd": 1531, "security": 1532, "edge": 1533, "cliff": 1534, "numerous": 1535, "interact": 1536, "explaining": 1537, "landscaping": 1538, "shops": 1539, "selling": 1540, "dress": 1541, "poses": 1542, "walkway": 1543, "posing": 1544, "legs": 1545, "flamingos": 1546, "shallow": 1547, "foraging": 1548, "female": 1549, "peacocks": 1550, "strange": 1551, "huddled": 1552, "wild": 1553, "turkeys": 1554, "dried": 1555, "railroad": 1556, "trains": 1557, "scouts": 1558, "uniform": 1559, "scout": 1560, "cannon": 1561, "tiny": 1562, "angry": 1563, "guys": 1564, "bookshelf": 1565, "conversation": 1566, "these": 1567, "costumes": 1568, "snowboard": 1569, "mask": 1570, "crazy": 1571, "hands": 1572, "scary": 1573, "costume": 1574, "snowboarder": 1575, "hugged": 1576, "upward": 1577, "bend": 1578, "moving": 1579, "graffiti": 1580, "weathered": 1581, "boxcar": 1582, "spray": 1583, "elaborate": 1584, "freight": 1585, "propane": 1586, "marked": 1587, "gas": 1588, "parrots": 1589, "log": 1590, "watering": 1591, "tin": 1592, "moves": 1593, "last": 1594, "grain": 1595, "pulling": 1596, "short": 1597, "speeds": 1598, "rails": 1599, "arriving": 1600, "planter": 1601, "real": 1602, "bed": 1603, "shape": 1604, "clutter": 1605, "junction": 1606, "posters": 1607, "houses": 1608, "running": 1609, "speed": 1610, "platform": 1611, "photographs": 1612, "peeks": 1613, "tunnel": 1614, "overpass": 1615, "go": 1616, "depot": 1617, "milling": 1618, "upside": 1619, "sighn": 1620, "propped": 1621, "sigh": 1622, "sittin": 1623, "somewhat": 1624, "telephone": 1625, "dented": 1626, "humorous": 1627, "sox": 1628, "supporting": 1629, "words": 1630, "written": 1631, "marker": 1632, "freshly": 1633, "plowed": 1634, "wonderland": 1635, "suburban": 1636, "recently": 1637, "clings": 1638, "rural": 1639, "carts": 1640, "videos": 1641, "worker": 1642, "jack": 1643, "hammer": 1644, "operating": 1645, "blond": 1646, "knitting": 1647, "sweater": 1648, "coin": 1649, "paying": 1650, "pays": 1651, "decorating": 1652, "fake": 1653, "putting": 1654, "wrapping": 1655, "designs": 1656, "sand": 1657, "accept": 1658, "credit": 1659, "cards": 1660, "remaining": 1661, "porch": 1662, "dock": 1663, "beam": 1664, "sunbathing": 1665, "milks": 1666, "milking": 1667, "eventually": 1668, "authentic": 1669, "couch": 1670, "curled": 1671, "napping": 1672, "sleeping": 1673, "bulldozer": 1674, "asphalt": 1675, "machine": 1676, "paving": 1677, "vests": 1678, "dump": 1679, "pay": 1680, "loader": 1681, "workers": 1682, "own": 1683, "fat": 1684, "hauling": 1685, "easier": 1686, "funny": 1687, "logging": 1688, "logs": 1689, "travelling": 1690, "semi": 1691, "shoe": 1692, "laces": 1693, "shoelace": 1694, "navy": 1695, "strings": 1696, "tiger": 1697, "cats": 1698, "business": 1699, "gravel": 1700, "antique": 1701, "classic": 1702, "booth": 1703, "event": 1704, "vendor": 1705, "ox": 1706, "tow": 1707, "card": 1708, "tows": 1709, "care": 1710, "towed": 1711, "luggage": 1712, "suitcase": 1713, "owner": 1714, "amongst": 1715, "clothing": 1716, "folded": 1717, "furry": 1718, "sofa": 1719, "arm": 1720, "sad": 1721, "expression": 1722, "cows": 1723, "kind": 1724, "banana": 1725, "bite": 1726, "perch": 1727, "peek": 1728, "maroon": 1729, "coffee": 1730, "recliner": 1731, "monitors": 1732, "horizon": 1733, "grazes": 1734, "scared": 1735, "bulls": 1736, "calmly": 1737, "fair": 1738, "piles": 1739, "stable": 1740, "lie": 1741, "stalls": 1742, "sill": 1743, "curious": 1744, "wandering": 1745, "meadow": 1746, "lay": 1747, "fast": 1748, "asleep": 1749, "lounging": 1750, "itself": 1751, "firetruck": 1752, "hides": 1753, "drawers": 1754, "peaking": 1755, "flatbed": 1756, "transporting": 1757, "load": 1758, "carry": 1759, "faced": 1760, "bushes": 1761, "lose": 1762, "kinds": 1763, "electronics": 1764, "stereo": 1765, "others": 1766, "calf": 1767, "upon": 1768, "mud": 1769, "climbing": 1770, "entertainment": 1771, "halo": 1772, "heads": 1773, "silo": 1774, "featured": 1775, "skies": 1776, "remote": 1777, "blanket": 1778, "trucks": 1779, "stretched": 1780, "fishermen": 1781, "nets": 1782, "fishing": 1783, "twin": 1784, "pontoon": 1785, "wrecked": 1786, "umbrellas": 1787, "shaded": 1788, "steer": 1789, "alert": 1790, "tied": 1791, "peeking": 1792, "ceiling": 1793, "desktop": 1794, "savannah": 1795, "elephants": 1796, "mama": 1797, "roam": 1798, "freely": 1799, "wildebeests": 1800, "golden": 1801, "grasses": 1802, "assorted": 1803, "ties": 1804, "racks": 1805, "neatly": 1806, "arrange": 1807, "sale": 1808, "mens": 1809, "dozens": 1810, "colors": 1811, "figurines": 1812, "ceramic": 1813, "figurine": 1814, "photographed": 1815, "signing": 1816, "important": 1817, "documents": 1818, "papers": 1819, "suit": 1820, "suits": 1821, "also": 1822, "medium": 1823, "sized": 1824, "marina": 1825, "moored": 1826, "sizes": 1827, "moustache": 1828, "individual": 1829, "comical": 1830, "mustache": 1831, "serious": 1832, "facial": 1833, "tying": 1834, "do": 1835, "bow": 1836, "assisted": 1837, "sailboat": 1838, "lighthouse": 1839, "ship": 1840, "hil": 1841, "turned": 1842, "shoreline": 1843, "flipped": 1844, "piercings": 1845, "brunette": 1846, "drawing": 1847, "adjusted": 1848, "entering": 1849, "groups": 1850, "trunk": 1851, "toned": 1852, "insects": 1853, "throwing": 1854, "spraying": 1855, "afield": 1856, "cylinders": 1857, "pails": 1858, "indian": 1859, "nd": 1860, "jugs": 1861, "tanks": 1862, "ridden": 1863, "item": 1864, "ferry": 1865, "teddybear": 1866, "tug": 1867, "jungle": 1868, "sprouting": 1869, "yacht": 1870, "mingle": 1871, "coast": 1872, "party": 1873, "pushes": 1874, "daytime": 1875, "prairie": 1876, "dusty": 1877, "shrubbery": 1878, "male": 1879, "tusks": 1880, "family": 1881, "drinks": 1882, "stream": 1883, "ferries": 1884, "scape": 1885, "sailing": 1886, "scenic": 1887, "daily": 1888, "thames": 1889, "england": 1890, "formally": 1891, "onlookers": 1892, "docks": 1893, "cranes": 1894, "reach": 1895, "patiently": 1896, "gazing": 1897, "port": 1898, "bring": 1899, "gated": 1900, "adults": 1901, "gun": 1902, "formal": 1903, "suspenders": 1904, "sprawled": 1905, "bags": 1906, "accessories": 1907, "spread": 1908, "thrown": 1909, "shoulder": 1910, "shade": 1911, "environment": 1912, "wooded": 1913, "boulders": 1914, "paddles": 1915, "canoe": 1916, "kayak": 1917, "rowing": 1918, "glides": 1919, "kayaks": 1920, "canal": 1921, "british": 1922, "design": 1923, "color": 1924, "corrected": 1925, "congested": 1926, "within": 1927, "ships": 1928, "docking": 1929, "yachts": 1930, "rhinoceros": 1931, "rhino": 1932, "hold": 1933, "share": 1934, "neckties": 1935, "patterns": 1936, "six": 1937, "levels": 1938, "pattern": 1939, "crystal": 1940, "decker": 1941, "waving": 1942, "sails": 1943, "still": 1944, "extremely": 1945, "calm": 1946, "abandoned": 1947, "leads": 1948, "parasol": 1949, "gloomy": 1950, "foggy": 1951, "embracing": 1952, "hugging": 1953, "whos": 1954, "keeper": 1955, "caged": 1956, "bathing": 1957, "hot": 1958, "stretching": 1959, "upwards": 1960, "feed": 1961, "grove": 1962, "tusked": 1963, "shrubs": 1964, "adorable": 1965, "son": 1966, "rubbing": 1967, "captive": 1968, "branches": 1969, "raises": 1970, "grab": 1971, "twigs": 1972, "lifts": 1973, "wade": 1974, "wire": 1975, "touch": 1976, "trunks": 1977, "laughing": 1978, "teenage": 1979, "hover": 1980, "talk": 1981, "laugh": 1982, "smile": 1983, "shades": 1984, "chest": 1985, "polka": 1986, "dot": 1987, "belt": 1988, "driver": 1989, "brief": 1990, "tattoos": 1991, "eastern": 1992, "decorate": 1993, "briefcase": 1994, "lunch": 1995, "sorts": 1996, "passangers": 1997, "tourists": 1998, "stagecoach": 1999, "cases": 2000, "block": 2001, "suitcases": 2002, "louis": 2003, "animation": 2004, "scarf": 2005, "dimensional": 2006, "stylish": 2007, "drawings": 2008, "handles": 2009, "deployed": 2010, "help": 2011, "when": 2012, "lock": 2013, "handle": 2014, "frisbee": 2015, "catch": 2016, "trying": 2017, "dig": 2018, "prepares": 2019, "jump": 2020, "squatting": 2021, "toddler": 2022, "persian": 2023, "carpet": 2024, "protecting": 2025, "covers": 2026, "infant": 2027, "really": 2028, "need": 2029, "match": 2030, "hotel": 2031, "wrestles": 2032, "deck": 2033, "adjusting": 2034, "placing": 2035, "too": 2036, "anything": 2037, "retriever": 2038, "pool": 2039, "sleeps": 2040, "backyard": 2041, "stories": 2042, "celebrity": 2043, "creature": 2044, "magazine": 2045, "always": 2046, "print": 2047, "latest": 2048, "cartoon": 2049, "characters": 2050, "stare": 2051, "puppy": 2052, "snuggled": 2053, "cushions": 2054, "shaggy": 2055, "gets": 2056, "served": 2057, "broccoli": 2058, "given": 2059, "railway": 2060, "polar": 2061, "fours": 2062, "backpacks": 2063, "hunting": 2064, "salmon": 2065, "bears": 2066, "fallen": 2067, "waist": 2068, "deep": 2069, "toss": 2070, "disc": 2071, "smelling": 2072, "vibrant": 2073, "game": 2074, "motion": 2075, "throw": 2076, "catching": 2077, "five": 2078, "circular": 2079, "play": 2080, "waterfront": 2081, "friends": 2082, "early": 2083, "hour": 2084, "winged": 2085, "lands": 2086, "purses": 2087, "peacefully": 2088, "pillows": 2089, "sleepy": 2090, "disk": 2091, "catches": 2092, "towel": 2093, "wetsuit": 2094, "sunset": 2095, "rumpled": 2096, "mattress": 2097, "terrier": 2098, "sheeted": 2099, "blankets": 2100, "frumpled": 2101, "reflected": 2102, "campsite": 2103, "barbecue": 2104, "grate": 2105, "fenced": 2106, "bars": 2107, "younger": 2108, "teens": 2109, "dune": 2110, "racing": 2111, "captivity": 2112, "cliffs": 2113, "followed": 2114, "hiker": 2115, "canyon": 2116, "hillside": 2117, "shady": 2118, "wooly": 2119, "curly": 2120, "half": 2121, "hybrid": 2122, "biting": 2123, "fighting": 2124, "involved": 2125, "fight": 2126, "playful": 2127, "teeth": 2128, "terrain": 2129, "hind": 2130, "wrestling": 2131, "groomed": 2132, "kite": 2133, "throws": 2134, "yelling": 2135, "leans": 2136, "plains": 2137, "grassland": 2138, "we": 2139, "shrub": 2140, "knee": 2141, "battling": 2142, "leg": 2143, "donkey": 2144, "collage": 2145, "places": 2146, "scrub": 2147, "summer": 2148, "savanna": 2149, "afternoon": 2150, "jockeys": 2151, "cowboys": 2152, "uniforms": 2153, "6": 2154, "races": 2155, "attention": 2156, "gazelles": 2157, "portion": 2158, "buggy": 2159, "thats": 2160, "team": 2161, "farmer": 2162, "plowing": 2163, "plow": 2164, "trotting": 2165, "hilly": 2166, "majestically": 2167, "leather": 2168, "coat": 2169, "pet": 2170, "ranch": 2171, "trainer": 2172, "horseback": 2173, "pathway": 2174, "led": 2175, "saddle": 2176, "trolley": 2177, "conductor": 2178, "pony": 2179, "hairy": 2180, "bushy": 2181, "tails": 2182, "ponies": 2183, "dressage": 2184, "practicing": 2185, "certain": 2186, "tending": 2187, "trolly": 2188, "stops": 2189, "before": 2190, "continues": 2191, "pull": 2192, "angles": 2193, "markings": 2194, "gaze": 2195, "course": 2196, "jockey": 2197, "wait": 2198, "only": 2199, "trails": 2200, "gallops": 2201, "competition": 2202, "corral": 2203, "knights": 2204, "performers": 2205, "medieval": 2206, "times": 2207, "perform": 2208, "observers": 2209, "arena": 2210, "jousting": 2211, "knight": 2212, "outfits": 2213, "character": 2214, "surface": 2215, "haunches": 2216, "elegantly": 2217, "haired": 2218, "sword": 2219, "figures": 2220, "corn": 2221, "bridle": 2222, "lead": 2223, "rope": 2224, "impressed": 2225, "grace": 2226, "bun": 2227, "lettuce": 2228, "hotdog": 2229, "ketchup": 2230, "topped": 2231, "foods": 2232, "cheese": 2233, "meat": 2234, "cheeses": 2235, "sausages": 2236, "fruits": 2237, "meats": 2238, "baseball": 2239, "player": 2240, "bat": 2241, "hit": 2242, "powdered": 2243, "breakfast": 2244, "pancakes": 2245, "toppings": 2246, "sauce": 2247, "whipped": 2248, "swinging": 2249, "players": 2250, "pitch": 2251, "hitting": 2252, "try": 2253, "tray": 2254, "tangerines": 2255, "pear": 2256, "apples": 2257, "pears": 2258, "array": 2259, "boys": 2260, "league": 2261, "balls": 2262, "forth": 2263, "softball": 2264, "clusters": 2265, "hung": 2266, "magazines": 2267, "slice": 2268, "ornament": 2269, "scoop": 2270, "grocery": 2271, "supermarket": 2272, "produce": 2273, "indoor": 2274, "kicked": 2275, "winding": 2276, "catchers": 2277, "mitt": 2278, "attentively": 2279, "third": 2280, "engages": 2281, "defensive": 2282, "cap": 2283, "skis": 2284, "participating": 2285, "surfing": 2286, "surfer": 2287, "skiing": 2288, "chain": 2289, "link": 2290, "baskets": 2291, "etc": 2292, "include": 2293, "potatoes": 2294, "price": 2295, "arrangement": 2296, "saying": 2297, "how": 2298, "much": 2299, "cost": 2300, "apricots": 2301, "arranged": 2302, "cartons": 2303, "pitchers": 2304, "mound": 2305, "jersey": 2306, "action": 2307, "desserts": 2308, "chocolate": 2309, "deserts": 2310, "stalk": 2311, "peak": 2312, "slope": 2313, "mounting": 2314, "ski": 2315, "skier": 2316, "slopes": 2317, "casts": 2318, "shadow": 2319, "bundled": 2320, "headdress": 2321, "necklace": 2322, "outfit": 2323, "shoulders": 2324, "performer": 2325, "sauteed": 2326, "spoon": 2327, "stir": 2328, "fry": 2329, "frying": 2330, "accompanied": 2331, "fried": 2332, "onions": 2333, "stirs": 2334, "skiers": 2335, "glove": 2336, "lunges": 2337, "reaches": 2338, "pitching": 2339, "rays": 2340, "salads": 2341, "cut": 2342, "pizza": 2343, "salad": 2344, "soda": 2345, "rice": 2346, "cola": 2347, "meal": 2348, "doughnuts": 2349, "donuts": 2350, "frosting": 2351, "bagels": 2352, "seafood": 2353, "cooked": 2354, "shrimp": 2355, "vegetable": 2356, "carrots": 2357, "warm": 2358, "learning": 2359, "skiies": 2360, "greeting": 2361, "fiving": 2362, "celebrating": 2363, "after": 2364, "win": 2365, "fives": 2366, "jackets": 2367, "mangoes": 2368, "pasta": 2369, "finely": 2370, "shredded": 2371, "beef": 2372, "main": 2373, "entree": 2374, "steak": 2375, "steamed": 2376, "caterpillar": 2377, "emerging": 2378, "bug": 2379, "fork": 2380, "twig": 2381, "worm": 2382, "candies": 2383, "junk": 2384, "sprinkles": 2385, "cherries": 2386, "sundae": 2387, "sweet": 2388, "treats": 2389, "marshmallows": 2390, "delicious": 2391, "chicken": 2392, "soup": 2393, "cauliflower": 2394, "scallions": 2395, "onion": 2396, "slicer": 2397, "celery": 2398, "mini": 2399, "stalks": 2400, "steps": 2401, "grinding": 2402, "stairway": 2403, "chinese": 2404, "teenager": 2405, "shells": 2406, "eaten": 2407, "mixture": 2408, "beans": 2409, "evergreens": 2410, "aquarium": 2411, "tape": 2412, "measure": 2413, "pants": 2414, "thick": 2415, "snowboarding": 2416, "d": 2417, "noodles": 2418, "dressing": 2419, "veggies": 2420, "square": 2421, "carrot": 2422, "giant": 2423, "cricket": 2424, "insect": 2425, "trap": 2426, "trapped": 2427, "ramps": 2428, "practices": 2429, "skatepark": 2430, "protective": 2431, "patio": 2432, "hooded": 2433, "hollywood": 2434, "fame": 2435, "electrical": 2436, "demonstrates": 2437, "exposure": 2438, "youngster": 2439, "simple": 2440, "tip": 2441, "stunt": 2442, "skills": 2443, "picked": 2444, "soil": 2445, "woven": 2446, "earth": 2447, "cock": 2448, "boots": 2449, "toothbrushes": 2450, "tube": 2451, "toothpaste": 2452, "tooth": 2453, "brushes": 2454, "paste": 2455, "skateboarders": 2456, "dc": 2457, "rail": 2458, "bar": 2459, "jean": 2460, "jumped": 2461, "pipe": 2462, "mid": 2463, "skater": 2464, "does": 2465, "startled": 2466, "railings": 2467, "manual": 2468, "tennis": 2469, "shorts": 2470, "skiier": 2471, "snowball": 2472, "demolished": 2473, "snowman": 2474, "microphone": 2475, "relish": 2476, "condiments": 2477, "monochrome": 2478, "boards": 2479, "slanted": 2480, "burrito": 2481, "teenagers": 2482, "hoodie": 2483, "boarder": 2484, "indoors": 2485, "slides": 2486, "pickle": 2487, "nachos": 2488, "snowboards": 2489, "fort": 2490, "enjoying": 2491, "bites": 2492, "african": 2493, "wintery": 2494, "sandwiches": 2495, "deli": 2496, "tags": 2497, "plenty": 2498, "buy": 2499, "pastries": 2500, "muffins": 2501, "storm": 2502, "incline": 2503, "boarders": 2504, "sausage": 2505, "chili": 2506, "garage": 2507, "crate": 2508, "buns": 2509, "regular": 2510, "styrofoam": 2511, "wedges": 2512, "eight": 2513, "serving": 2514, "doughnut": 2515, "bespectacled": 2516, "zodiac": 2517, "sweatshirt": 2518, "boiled": 2519, "eggs": 2520, "picnic": 2521, "wax": 2522, "napkins": 2523, "frosted": 2524, "sprinkled": 2525, "maker": 2526, "weird": 2527, "what": 2528, "hikes": 2529, "challenging": 2530, "sliding": 2531, "bean": 2532, "chew": 2533, "beanbag": 2534, "baked": 2535, "goods": 2536, "trays": 2537, "enjoys": 2538, "snowboarders": 2539, "conversing": 2540, "geared": 2541, "rink": 2542, "gently": 2543, "roofed": 2544, "facility": 2545, "lodge": 2546, "layed": 2547, "dozen": 2548, "snack": 2549, "sweets": 2550, "bakery": 2551, "napkin": 2552, "carseat": 2553, "varieties": 2554, "icing": 2555, "purchase": 2556, "garnish": 2557, "layered": 2558, "avocado": 2559, "ham": 2560, "kale": 2561, "twp": 2562, "fingers": 2563, "enormous": 2564, "guest": 2565, "ten": 2566, "flavored": 2567, "toasted": 2568, "grilled": 2569, "panini": 2570, "fries": 2571, "platter": 2572, "toothpicks": 2573, "french": 2574, "cookie": 2575, "bitten": 2576, "oddly": 2577, "wrapped": 2578, "bagged": 2579, "yummy": 2580, "sprinkle": 2581, "basketball": 2582, "tattoo": 2583, "uniformed": 2584, "hoop": 2585, "goal": 2586, "kicking": 2587, "soccer": 2588, "kicks": 2589, "candles": 2590, "2nd": 2591, "year": 2592, "done": 2593, "baking": 2594, "rising": 2595, "wave": 2596, "bodysuit": 2597, "halves": 2598, "doily": 2599, "2": 2600, "teams": 2601, "competing": 2602, "chasing": 2603, "praying": 2604, "youth": 2605, "huddling": 2606, "kick": 2607, "contending": 2608, "surfers": 2609, "morning": 2610, "sunrise": 2611, "tractor": 2612, "highchair": 2613, "layer": 2614, "prepared": 2615, "accents": 2616, "surfboarder": 2617, "bringing": 2618, "breaking": 2619, "crashing": 2620, "football": 2621, "members": 2622, "whle": 2623, "soccerball": 2624, "deciding": 2625, "returns": 2626, "goalie": 2627, "mate": 2628, "mom": 2629, "hospital": 2630, "iv": 2631, "hooked": 2632, "pointing": 2633, "finger": 2634, "notebook": 2635, "childs": 2636, "toes": 2637, "gestures": 2638, "fudge": 2639, "pastry": 2640, "creme": 2641, "whip": 2642, "could": 2643, "remodeled": 2644, "doctors": 2645, "rags": 2646, "possible": 2647, "drying": 2648, "upright": 2649, "frames": 2650, "mattresses": 2651, "rooms": 2652, "fireplace": 2653, "displays": 2654, "so": 2655, "drizzled": 2656, "attempts": 2657, "read": 2658, "novel": 2659, "grabs": 2660, "grips": 2661, "instructing": 2662, "dad": 2663, "coaches": 2664, "beds": 2665, "headboard": 2666, "differently": 2667, "cozy": 2668, "surf": 2669, "canopy": 2670, "linens": 2671, "drape": 2672, "mosquito": 2673, "netting": 2674, "overhead": 2675, "size": 2676, "neutral": 2677, "mickey": 2678, "ears": 2679, "mouses": 2680, "resort": 2681, "privacy": 2682, "rental": 2683, "cabana": 2684, "frowning": 2685, "grimacing": 2686, "pf": 2687, "surfboarding": 2688, "volley": 2689, "paddling": 2690, "whom": 2691, "finished": 2692, "breasted": 2693, "companion": 2694, "enjoy": 2695, "floral": 2696, "carnival": 2697, "spreads": 2698, "were": 2699, "date": 2700, "silverware": 2701, "wife": 2702, "got": 2703, "bowler": 2704, "lab": 2705, "love": 2706, "mic": 2707, "racket": 2708, "racquet": 2709, "court": 2710, "raquet": 2711, "poured": 2712, "italian": 2713, "presented": 2714, "tomatoes": 2715, "olives": 2716, "christmas": 2717, "gifts": 2718, "holiday": 2719, "veggie": 2720, "egg": 2721, "pie": 2722, "racquets": 2723, "identical": 2724, "rackets": 2725, "pepperoni": 2726, "sub": 2727, "pretty": 2728, "gigantic": 2729, "novelty": 2730, "females": 2731, "fencing": 2732, "seating": 2733, "vegetarian": 2734, "gone": 2735, "nearly": 2736, "come": 2737, "serve": 2738, "coke": 2739, "swirl": 2740, "audience": 2741, "best": 2742, "lunging": 2743, "traditional": 2744, "fully": 2745, "whole": 2746, "uncut": 2747, "paddle": 2748, "cook": 2749, "slicing": 2750, "easy": 2751, "tables": 2752, "comfy": 2753, "brushing": 2754, "toothbrush": 2755, "halfway": 2756, "draped": 2757, "bored": 2758, "ottoman": 2759, "remotes": 2760, "oval": 2761, "pineapple": 2762, "compartment": 2763, "hut": 2764, "utensil": 2765, "floss": 2766, "cleaner": 2767, "kept": 2768, "mouthwash": 2769, "foaming": 2770, "cool": 2771, "numbered": 2772, "affixed": 2773, "pajamas": 2774, "wii": 2775, "projection": 2776, "projector": 2777, "gentleman": 2778, "video": 2779, "games": 2780, "dimly": 2781, "chilling": 2782, "nintendo": 2783, "controllers": 2784, "nightstand": 2785, "bookshelves": 2786, "extending": 2787, "collection": 2788, "residence": 2789, "folks": 2790, "bowling": 2791, "preparation": 2792, "interactive": 2793, "sectional": 2794, "stairs": 2795, "hardwood": 2796, "manipulating": 2797, "ribbon": 2798, "fabric": 2799, "pressing": 2800, "buttons": 2801, "wand": 2802, "theme": 2803, "orderly": 2804, "ceilings": 2805, "florida": 2806, "location": 2807, "couches": 2808, "lamps": 2809, "inflatable": 2810, "sofas": 2811, "nook": 2812, "since": 2813, "shinning": 2814, "hockey": 2815, "hard": 2816, "provide": 2817, "contrast": 2818, "mac": 2819, "macintosh": 2820, "wired": 2821, "wireless": 2822, "matching": 2823, "glimpse": 2824, "corridor": 2825, "desks": 2826, "descent": 2827, "screens": 2828, "information": 2829, "furnished": 2830, "partial": 2831, "dribbling": 2832, "project": 2833, "chandelier": 2834, "laptops": 2835, "matress": 2836, "releases": 2837, "else": 2838, "string": 2839, "tries": 2840, "let": 2841, "kites": 2842, "flown": 2843, "choppy": 2844, "pottery": 2845, "attempting": 2846, "launch": 2847, "parachute": 2848, "onlooking": 2849, "para": 2850, "parasailing": 2851, "windsurfing": 2852, "sail": 2853, "skinned": 2854, "pushed": 2855, "swing": 2856, "rises": 2857, "higher": 2858, "any": 2859, "vicinity": 2860, "brass": 2861, "bureau": 2862, "classroom": 2863, "split": 2864, "studying": 2865, "cookies": 2866, "triangular": 2867, "excitedly": 2868, "laughs": 2869, "runs": 2870, "patrol": 2871, "jeep": 2872, "dragon": 2873, "bit": 2874, "erected": 2875, "local": 2876, "fronts": 2877, "blurred": 2878, "lamppost": 2879, "batter": 2880, "catcher": 2881, "umpire": 2882, "turn": 2883, "swung": 2884, "stadium": 2885, "spectators": 2886, "wiffle": 2887, "bats": 2888, "referee": 2889, "recording": 2890, "batters": 2891, "foul": 2892, "swings": 2893, "misses": 2894, "castle": 2895, "ben": 2896, "clocktower": 2897, "barge": 2898, "skyline": 2899, "rivers": 2900, "architectural": 2901, "scaffolding": 2902, "warms": 2903, "mimicking": 2904, "stance": 2905, "rolls": 2906, "call": 2907, "warming": 2908, "t": 2909, "teaching": 2910, "tee": 2911, "barry": 2912, "bonds": 2913, "fans": 2914, "photographers": 2915, "films": 2916, "receive": 2917, "entertaining": 2918, "blood": 2919, "battle": 2920, "everywhere": 2921, "cardinals": 2922, "dugout": 2923, "circle": 2924, "poised": 2925, "mushroom": 2926, "bake": 2927, "fulled": 2928, "balances": 2929, "casserole": 2930, "crackers": 2931, "clothe": 2932, "homemade": 2933, "form": 2934, "returning": 2935, "return": 2936, "opponent": 2937, "rustic": 2938, "came": 2939, "serves": 2940, "celebrates": 2941, "reacts": 2942, "fists": 2943, "viewed": 2944, "tournament": 2945, "doubles": 2946, "converse": 2947, "dresses": 2948, "checkered": 2949, "pesto": 2950, "peppers": 2951, "artichokes": 2952, "checked": 2953, "follows": 2954, "mushrooms": 2955, "anchovies": 2956, "strike": 2957, "incoming": 2958, "shutters": 2959, "bulb": 2960, "theres": 2961, "seems": 2962, "font": 2963, "barred": 2964, "mat": 2965, "prints": 2966, "envelope": 2967, "supplies": 2968, "adjacent": 2969, "mugs": 2970, "tablets": 2971, "ladies": 2972, "o": 2973, "f": 2974, "blown": 2975, "jars": 2976, "shapes": 2977, "typing": 2978, "operates": 2979, "extension": 2980, "cords": 2981, "toaster": 2982, "conference": 2983, "devices": 2984, "gesture": 2985, "eagerly": 2986, "rabbit": 2987, "bunny": 2988, "tight": 2989, "happily": 2990, "ribbons": 2991, "alcohol": 2992, "retail": 2993, "curio": 2994, "symbols": 2995, "tears": 2996, "pit": 2997, "falling": 2998, "apart": 2999, "wants": 3000, "plush": 3001, "beers": 3002, "mall": 3003, "smart": 3004, "smartphone": 3005, "iphone": 3006, "round": 3007, "device": 3008, "ipod": 3009, "charging": 3010, "cellphone": 3011, "clay": 3012, "formed": 3013, "dripping": 3014, "sidwalk": 3015, "heels": 3016, "cubicle": 3017, "indiana": 3018, "jones": 3019, "talks": 3020, "spaghetti": 3021, "stood": 3022, "recorder": 3023, "pouch": 3024, "gadgets": 3025, "pocket": 3026, "speaks": 3027, "festival": 3028, "texting": 3029, "cel": 3030, "ear": 3031, "mobile": 3032, "attachments": 3033, "cameras": 3034, "messing": 3035, "opening": 3036, "babys": 3037, "bulletin": 3038, "refridgerator": 3039, "mementos": 3040, "fridge": 3041, "magnets": 3042, "countertop": 3043, "studio": 3044, "freezer": 3045, "homes": 3046, "thin": 3047, "otherwise": 3048, "build": 3049, "touring": 3050, "stocked": 3051, "lower": 3052, "combo": 3053, "dated": 3054, "lemons": 3055, "lemon": 3056, "stock": 3057, "hued": 3058, "pail": 3059, "beautifully": 3060, "dishes": 3061, "daisy": 3062, "greenery": 3063, "variations": 3064, "pillar": 3065, "details": 3066, "scissors": 3067, "labeled": 3068, "pinned": 3069, "pairs": 3070, "prices": 3071, "surgical": 3072, "community": 3073, "babies": 3074, "training": 3075, "potty": 3076, "twins": 3077, "toddlers": 3078, "radiator": 3079, "heater": 3080, "towers": 3081, "steep": 3082, "infamous": 3083, "clocks": 3084, "angle": 3085, "built": 3086, "nighttime": 3087, "stars": 3088, "dolls": 3089, "doll": 3090, "numbers": 3091, "indicate": 3092, "twelve": 3093, "thirty": 3094, "roman": 3095, "numerals": 3096, "bells": 3097, "stained": 3098, "either": 3099, "balconies": 3100, "western": 3101, "west": 3102, "jar": 3103, "intricately": 3104, "detailed": 3105, "tulips": 3106, "bouquet": 3107, "stemmed": 3108, "stems": 3109, "centerpiece": 3110, "artsy": 3111, "representing": 3112, "scissor": 3113, "clippers": 3114, "nuts": 3115, "factory": 3116, "cigar": 3117, "trimming": 3118, "shavings": 3119, "pencil": 3120, "ruler": 3121, "spool": 3122, "thread": 3123, "craft": 3124, "sewing": 3125, "contents": 3126, "developed": 3127, "creative": 3128, "gummy": 3129, "candy": 3130, "spiral": 3131, "graham": 3132, "cracker": 3133, "themed": 3134, "santa": 3135, "kittens": 3136, "nap": 3137, "laundry": 3138, "knitted": 3139, "consists": 3140, "dip": 3141, "laden": 3142, "meeting": 3143, "pumping": 3144, "liquor": 3145, "stem": 3146, "benches": 3147, "merchandise": 3148, "flea": 3149, "motocycle": 3150, "aisle": 3151, "seats": 3152, "depart": 3153, "occupied": 3154, "travelers": 3155, "sleeveless": 3156, "cocktail": 3157, "toe": 3158, "heel": 3159, "pumps": 3160, "neat": 3161, "tidy": 3162, "residential": 3163, "cupboards": 3164, "dishwasher": 3165, "crock": 3166, "charming": 3167, "peering": 3168, "remodel": 3169, "dinging": 3170, "trimmed": 3171, "peeled": 3172, "blender": 3173, "blended": 3174, "washer": 3175, "inspects": 3176, "groceries": 3177, "fixes": 3178, "cooker": 3179, "smooth": 3180, "countertops": 3181, "mixing": 3182, "stirring": 3183, "booster": 3184, "stool": 3185, "stripped": 3186, "bathes": 3187, "newborn": 3188, "purpose": 3189, "basin": 3190, "observing": 3191, "basic": 3192, "odd": 3193, "awful": 3194, "marble": 3195, "farmhouse": 3196, "purse": 3197, "rectangle": 3198, "knives": 3199, "magnet": 3200, "seven": 3201, "sharp": 3202, "lime": 3203, "raw": 3204, "threes": 3205, "lived": 3206, "remains": 3207, "despite": 3208, "showcasing": 3209, "dinette": 3210, "settings": 3211, "means": 3212, "later": 3213, "corkscrew": 3214, "coasting": 3215, "reflects": 3216, "boiling": 3217, "burner": 3218, "checking": 3219, "concoction": 3220, "scratching": 3221, "pad": 3222, "organized": 3223, "bicyclist": 3224, "showering": 3225, "bricked": 3226, "fisheye": 3227, "dorm": 3228, "roll": 3229, "hanger": 3230, "usually": 3231, "found": 3232, "nursing": 3233, "will": 3234, "wheelchair": 3235, "accessible": 3236, "sidewalks": 3237, "cityscape": 3238, "european": 3239, "leopard": 3240, "facet": 3241, "robe": 3242, "towels": 3243, "powder": 3244, "sleek": 3245, "efficient": 3246, "overheard": 3247, "aerial": 3248, "fuel": 3249, "pump": 3250, "tool": 3251, "dresser": 3252, "carriages": 3253, "suv": 3254, "vw": 3255, "van": 3256, "awesome": 3257, "volkswagon": 3258, "pipes": 3259, "nasty": 3260, "cable": 3261, "certainly": 3262, "appropriate": 3263, "cans": 3264, "rubbish": 3265, "garbage": 3266, "bundle": 3267, "presses": 3268, "urinals": 3269, "smell": 3270, "automatic": 3271, "freshener": 3272, "rotunda": 3273, "uk": 3274, "bathed": 3275, "copper": 3276, "cupcake": 3277, "crotch": 3278, "rocket": 3279, "cot": 3280, "toiled": 3281, "tuna": 3282, "dutch": 3283, "key": 3284, "hole": 3285, "calico": 3286, "curls": 3287, "sleep": 3288, "wheeler": 3289, "fit": 3290, "cooler": 3291, "debris": 3292, "order": 3293, "typical": 3294, "such": 3295, "snacks": 3296, "motorcyles": 3297, "highlighted": 3298, "ripped": 3299, "mantle": 3300, "better": 3301, "officers": 3302, "lightly": 3303, "attend": 3304, "grand": 3305, "architecture": 3306, "dots": 3307, "motorcylce": 3308, "cellular": 3309, "cigarette": 3310, "driven": 3311, "motorcyclist": 3312, "united": 3313, "tons": 3314, "bandanna": 3315, "gang": 3316, "today": 3317, "rainy": 3318, "individuals": 3319, "damp": 3320, "subway": 3321, "am": 3322, "unable": 3323, "crew": 3324, "loading": 3325, "squadron": 3326, "jets": 3327, "formation": 3328, "smoke": 3329, "cloudless": 3330, "trailing": 3331, "leaving": 3332, "vapor": 3333, "shuttle": 3334, "aircrafts": 3335, "force": 3336, "lufthansa": 3337, "humongous": 3338, "strip": 3339, "engines": 3340, "hangar": 3341, "bomber": 3342, "seattle": 3343, "needle": 3344, "washington": 3345, "quite": 3346, "compared": 3347, "scraper": 3348, "unison": 3349, "world": 3350, "1": 3351, "german": 3352, "biplanes": 3353, "military": 3354, "underside": 3355, "ends": 3356, "crafts": 3357, "streams": 3358, "soaring": 3359, "approach": 3360, "hogs": 3361, "vegetation": 3362, "nature": 3363, "reserve": 3364, "buffalo": 3365, "wholly": 3366, "ha": 3367, "lambs": 3368, "paddock": 3369, "v": 3370, "angel": 3371, "leafless": 3372, "zooming": 3373, "push": 3374, "button": 3375, "turquoise": 3376, "president": 3377, "begins": 3378, "delta": 3379, "scrubby": 3380, "rise": 3381, "juvenile": 3382, "clearing": 3383, "huddle": 3384, "surroundings": 3385, "liner": 3386, "crops": 3387, "sailboats": 3388, "okay": 3389, "strung": 3390, "contemplating": 3391, "markers": 3392, "pointy": 3393, "rested": 3394, "jeans": 3395, "sexy": 3396, "extreme": 3397, "knoll": 3398, "iron": 3399, "cast": 3400, "bronze": 3401, "blonde": 3402, "leaf": 3403, "stretches": 3404, "lapse": 3405, "photography": 3406, "transit": 3407, "vehicular": 3408, "picks": 3409, "bits": 3410, "london": 3411, "occupy": 3412, "grassing": 3413, "overlooks": 3414, "roaming": 3415, "coats": 3416, "shearing": 3417, "feeder": 3418, "fairly": 3419, "natural": 3420, "camper": 3421, "nudging": 3422, "forested": 3423, "chopped": 3424, "plank": 3425, "separate": 3426, "girrafe": 3427, "overlook": 3428, "bay": 3429, "bricks": 3430, "weeds": 3431, "barriers": 3432, "decked": 3433, "dalmatian": 3434, "department": 3435, "usa": 3436, "hikers": 3437, "allows": 3438, "unused": 3439, "stones": 3440, "probably": 3441, "separated": 3442, "steam": 3443, "waterway": 3444, "leafy": 3445, "buss": 3446, "site": 3447, "exit": 3448, "feild": 3449, "schoolbus": 3450, "graffitied": 3451, "proud": 3452, "free": 3453, "fall": 3454, "fishnet": 3455, "stockings": 3456, "finds": 3457, "sparse": 3458, "stump": 3459, "armchairs": 3460, "hydrants": 3461, "bolt": 3462, "nut": 3463, "fir": 3464, "decaying": 3465, "knob": 3466, "worn": 3467, "sculptures": 3468, "gulls": 3469, "camp": 3470, "grounds": 3471, "tagged": 3472, "weighed": 3473, "tag": 3474, "modified": 3475, "changed": 3476, "handwritten": 3477, "letters": 3478, "peeping": 3479, "seed": 3480, "seeds": 3481, "metropolitan": 3482, "ways": 3483, "tilted": 3484, "lean": 3485, "extended": 3486, "perfect": 3487, "warns": 3488, "owners": 3489, "ct": 3490, "raising": 3491, "detour": 3492, "say": 3493, "tide": 3494, "pelican": 3495, "observed": 3496, "roadside": 3497, "miss": 3498, "route": 3499, "taped": 3500, "duct": 3501, "relax": 3502, "boar": 3503, "did": 3504, "camouflaged": 3505, "hummingbird": 3506, "humming": 3507, "grouping": 3508, "falcon": 3509, "parrot": 3510, "hawk": 3511, "exotic": 3512, "silly": 3513, "slight": 3514, "owl": 3515, "tips": 3516, "citrus": 3517, "fog": 3518, "blurs": 3519, "beyond": 3520, "legged": 3521, "seagull": 3522, "sheared": 3523, "goat": 3524, "fied": 3525, "skinny": 3526, "unhealthy": 3527, "wheat": 3528, "secluded": 3529, "blending": 3530, "wiht": 3531, "lakeside": 3532, "seashore": 3533, "prominently": 3534, "llamas": 3535, "misty": 3536, "gentlemen": 3537, "eachother": 3538, "bushel": 3539, "dumpster": 3540, "barrels": 3541, "bails": 3542, "twine": 3543, "random": 3544, "cleans": 3545, "good": 3546, "nibbling": 3547, "hook": 3548, "supply": 3549, "barricades": 3550, "blocked": 3551, "shadows": 3552, "grates": 3553, "caboose": 3554, "observation": 3555, "decks": 3556, "movie": 3557, "rounds": 3558, "dull": 3559, "54": 3560, "necessary": 3561, "encased": 3562, "buried": 3563, "punk": 3564, "tagging": 3565, "graffitti": 3566, "caricature": 3567, "beaver": 3568, "blossoms": 3569, "waking": 3570, "fifteen": 3571, "minutes": 3572, "sticker": 3573, "rainbow": 3574, "murals": 3575, "vans": 3576, "paws": 3577, "dont": 3578, "often": 3579, "warehouse": 3580, "equipped": 3581, "cherry": 3582, "picker": 3583, "tuxedo": 3584, "sheer": 3585, "bandana": 3586, "wander": 3587, "shine": 3588, "fading": 3589, "goblet": 3590, "statute": 3591, "paw": 3592, "onward": 3593, "trailers": 3594, "calfs": 3595, "reddish": 3596, "brownish": 3597, "chrome": 3598, "rims": 3599, "naps": 3600, "eyed": 3601, "spotted": 3602, "balcony": 3603, "windowsill": 3604, "package": 3605, "inches": 3606, "paved": 3607, "barnyard": 3608, "browsing": 3609, "mass": 3610, "unidentifiable": 3611, "tethered": 3612, "hes": 3613, "played": 3614, "enough": 3615, "now": 3616, "pug": 3617, "bearded": 3618, "bulldog": 3619, "anchored": 3620, "1960s": 3621, "socialize": 3622, "wallpaper": 3623, "isle": 3624, "pews": 3625, "ceremony": 3626, "garb": 3627, "cuddled": 3628, "longhorn": 3629, "horns": 3630, "focus": 3631, "reception": 3632, "blazer": 3633, "portrait": 3634, "bald": 3635, "corsage": 3636, "sections": 3637, "rowboats": 3638, "pockets": 3639, "beached": 3640, "soaked": 3641, "dads": 3642, "mans": 3643, "artist": 3644, "depiction": 3645, "stylized": 3646, "tiki": 3647, "coverings": 3648, "folding": 3649, "toasting": 3650, "celebration": 3651, "raise": 3652, "toast": 3653, "carefully": 3654, "customers": 3655, "sells": 3656, "bottled": 3657, "tapes": 3658, "wrap": 3659, "lobby": 3660, "hall": 3661, "professionally": 3662, "backseat": 3663, "gown": 3664, "slide": 3665, "unique": 3666, "projected": 3667, "presentation": 3668, "delivering": 3669, "lecture": 3670, "film": 3671, "motorboat": 3672, "drug": 3673, "buggies": 3674, "husky": 3675, "sleigh": 3676, "husband": 3677, "sweaters": 3678, "archway": 3679, "couples": 3680, "wear": 3681, "content": 3682, "businessman": 3683, "collar": 3684, "pensive": 3685, "eyeglasses": 3686, "profile": 3687, "handful": 3688, "robot": 3689, "wars": 3690, "club": 3691, "cafe": 3692, "shading": 3693, "tress": 3694, "channel": 3695, "cubes": 3696, "kissing": 3697, "whiile": 3698, "kiss": 3699, "sharing": 3700, "dryer": 3701, "fur": 3702, "user": 3703, "web": 3704, "bordered": 3705, "arrives": 3706, "backsides": 3707, "lining": 3708, "heeled": 3709, "lagoon": 3710, "wit": 3711, "gull": 3712, "separating": 3713, "basking": 3714, "snuggling": 3715, "though": 3716, "alleyway": 3717, "cobbled": 3718, "dragging": 3719, "cobble": 3720, "concerned": 3721, "whatever": 3722, "breast": 3723, "snout": 3724, "designated": 3725, "cinder": 3726, "tailgate": 3727, "stays": 3728, "sandal": 3729, "artificial": 3730, "pugs": 3731, "cheek": 3732, "inverted": 3733, "upturned": 3734, "leash": 3735, "highland": 3736, "sunshine": 3737, "caption": 3738, "sanding": 3739, "rubber": 3740, "dummy": 3741, "frisby": 3742, "frisbe": 3743, "relaxes": 3744, "pines": 3745, "leashes": 3746, "brands": 3747, "lace": 3748, "packs": 3749, "duffle": 3750, "boardwalk": 3751, "upholstered": 3752, "comforter": 3753, "trio": 3754, "dvds": 3755, "movies": 3756, "dvd": 3757, "muzzle": 3758, "lip": 3759, "carousel": 3760, "areas": 3761, "conveyor": 3762, "steal": 3763, "reclines": 3764, "conveyer": 3765, "had": 3766, "surrounds": 3767, "darth": 3768, "vader": 3769, "saber": 3770, "grizzly": 3771, "installing": 3772, "hoof": 3773, "nest": 3774, "cave": 3775, "lazily": 3776, "boulder": 3777, "doggy": 3778, "mountainous": 3779, "&": 3780, "cub": 3781, "chillin": 3782, "peer": 3783, "families": 3784, "service": 3785, "visiting": 3786, "session": 3787, "shit": 3788, "boars": 3789, "leaping": 3790, "plaza": 3791, "corgi": 3792, "keeps": 3793, "antelopes": 3794, "hog": 3795, "draw": 3796, "examine": 3797, "tossing": 3798, "legos": 3799, "blocks": 3800, "rump": 3801, "frisbees": 3802, "cluster": 3803, "ostrich": 3804, "tender": 3805, "moment": 3806, "rich": 3807, "murky": 3808, "splashing": 3809, "reins": 3810, "colt": 3811, "nurses": 3812, "momma": 3813, "leaned": 3814, "monument": 3815, "cowboy": 3816, "fo": 3817, "sure": 3818, "shes": 3819, "safe": 3820, "backs": 3821, "rodeo": 3822, "sturdy": 3823, "fitted": 3824, "twist": 3825, "greens": 3826, "progress": 3827, "lego": 3828, "dachshund": 3829, "map": 3830, "crates": 3831, "pineapples": 3832, "smoothies": 3833, "cups": 3834, "gravy": 3835, "blueberry": 3836, "glaze": 3837, "waffle": 3838, "cone": 3839, "daughter": 3840, "topping": 3841, "vanilla": 3842, "strawberry": 3843, "shoot": 3844, "spewing": 3845, "speak": 3846, "climate": 3847, "gears": 3848, "hiking": 3849, "skiis": 3850, "host": 3851, "yams": 3852, "bins": 3853, "grapefruit": 3854, "pine": 3855, "glide": 3856, "wheelbarrow": 3857, "collecting": 3858, "evergreen": 3859, "directed": 3860, "watched": 3861, "macaroni": 3862, "sushi": 3863, "cantaloupe": 3864, "peaches": 3865, "powered": 3866, "pork": 3867, "meaty": 3868, "uniquely": 3869, "friend": 3870, "jelly": 3871, "packets": 3872, "bagel": 3873, "healthy": 3874, "potato": 3875, "bento": 3876, "sking": 3877, "spatula": 3878, "brocolli": 3879, "pea": 3880, "pods": 3881, "skillet": 3882, "mountainside": 3883, "chopsticks": 3884, "tent": 3885, "buying": 3886, "farmers": 3887, "cakes": 3888, "eagle": 3889, "tackling": 3890, "mark": 3891, "hunched": 3892, "flip": 3893, "roasted": 3894, "cheesy": 3895, "coated": 3896, "lift": 3897, "sideways": 3898, "then": 3899, "telling": 3900, "gloved": 3901, "cabbage": 3902, "root": 3903, "turnips": 3904, "crown": 3905, "bounds": 3906, "shack": 3907, "knifes": 3908, "withered": 3909, "stabbed": 3910, "illusion": 3911, "rotini": 3912, "shell": 3913, "tossed": 3914, "parmesan": 3915, "lite": 3916, "cranberry": 3917, "curry": 3918, "untouched": 3919, "competitors": 3920, "sporting": 3921, "skiiers": 3922, "engaged": 3923, "competitive": 3924, "dome": 3925, "destination": 3926, "born": 3927, "crumbs": 3928, "croutons": 3929, "spill": 3930, "adolescent": 3931, "garnished": 3932, "parsley": 3933, "spears": 3934, "practice": 3935, "concentrates": 3936, "midair": 3937, "gain": 3938, "quickly": 3939, "speeding": 3940, "stunts": 3941, "wake": 3942, "tomato": 3943, "gnarly": 3944, "downhill": 3945, "spending": 3946, "wonderful": 3947, "sustenance": 3948, "strawberries": 3949, "cucumbers": 3950, "sloped": 3951, "barbeque": 3952, "shirtless": 3953, "grapes": 3954, "tablecloth": 3955, "berries": 3956, "bell": 3957, "patches": 3958, "cotton": 3959, "loose": 3960, "stuffing": 3961, "cutouts": 3962, "capped": 3963, "skinning": 3964, "depicts": 3965, "balance": 3966, "grinds": 3967, "pic": 3968, "chairlift": 3969, "trek": 3970, "trekking": 3971, "column": 3972, "slider": 3973, "burgers": 3974, "hamburgers": 3975, "pickles": 3976, "mustard": 3977, "instructor": 3978, "preforming": 3979, "hovers": 3980, "gourmet": 3981, "spicy": 3982, "wrapper": 3983, "nestled": 3984, "crusty": 3985, "hotdogs": 3986, "aluminum": 3987, "foil": 3988, "tinfoil": 3989, "sauerkraut": 3990, "inserted": 3991, "filling": 3992, "breaded": 3993, "senior": 3994, "citizen": 3995, "jam": 3996, "sloppy": 3997, "joe": 3998, "snowsuit": 3999, "holes": 4000, "saucer": 4001, "parents": 4002, "father": 4003, "feast": 4004, "penguin": 4005, "blade": 4006, "tier": 4007, "biscuits": 4008, "rushing": 4009, "flavors": 4010, "pepper": 4011, "turkey": 4012, "guacamole": 4013, "cole": 4014, "slaw": 4015, "kit": 4016, "should": 4017, "coco": 4018, "celebrate": 4019, "ion": 4020, "munching": 4021, "mess": 4022, "jointly": 4023, "college": 4024, "swimsuit": 4025, "newlywed": 4026, "robotic": 4027, "rectangular": 4028, "iced": 4029, "proudly": 4030, "presents": 4031, "special": 4032, "unkempt": 4033, "m": 4034, "unmade": 4035, "suite": 4036, "queen": 4037, "tone": 4038, "pregnant": 4039, "wigs": 4040, "drag": 4041, "queens": 4042, "bedside": 4043, "traveler": 4044, "checkerboard": 4045, "dressers": 4046, "ex": 4047, "entirely": 4048, "shipping": 4049, "envelopes": 4050, "fedex": 4051, "modernized": 4052, "drawer": 4053, "oar": 4054, "posh": 4055, "extravagant": 4056, "ornately": 4057, "slabs": 4058, "carpeting": 4059, "colonial": 4060, "boogie": 4061, "wakeboard": 4062, "current": 4063, "raft": 4064, "overlooked": 4065, "rundown": 4066, "uncooked": 4067, "crust": 4068, "exiting": 4069, "beard": 4070, "w": 4071, "swimmers": 4072, "wallpapered": 4073, "aiming": 4074, "aimed": 4075, "email": 4076, "ith": 4077, "student": 4078, "study": 4079, "spinach": 4080, "heavily": 4081, "peel": 4082, "handmade": 4083, "already": 4084, "sleeve": 4085, "margarita": 4086, "lookign": 4087, "chicago": 4088, "fits": 4089, "grated": 4090, "fedora": 4091, "restaraunt": 4092, "oriental": 4093, "flatbread": 4094, "bacon": 4095, "pita": 4096, "prosciutto": 4097, "bakes": 4098, "winds": 4099, "gooey": 4100, "olive": 4101, "super": 4102, "mario": 4103, "booklet": 4104, "wiimote": 4105, "instruction": 4106, "outdated": 4107, "season": 4108, "overweight": 4109, "fan": 4110, "prepped": 4111, "trim": 4112, "controlled": 4113, "gaming": 4114, "system": 4115, "statement": 4116, "boxing": 4117, "golf": 4118, "keyboards": 4119, "desktops": 4120, "piano": 4121, "relaxing": 4122, "comparison": 4123, "jutting": 4124, "handling": 4125, "chute": 4126, "fling": 4127, "whale": 4128, "glider": 4129, "ballon": 4130, "n": 4131, "controlling": 4132, "ornaments": 4133, "plateau": 4134, "partly": 4135, "valley": 4136, "posture": 4137, "move": 4138, "note": 4139, "maid": 4140, "regarding": 4141, "damage": 4142, "guard": 4143, "ropes": 4144, "upscale": 4145, "roped": 4146, "awaiting": 4147, "strikes": 4148, "hoping": 4149, "zombie": 4150, "actors": 4151, "opposing": 4152, "striking": 4153, "vines": 4154, "ginger": 4155, "batting": 4156, "sandals": 4157, "ingredients": 4158, "trey": 4159, "intercept": 4160, "australian": 4161, "turf": 4162, "baseline": 4163, "bleachers": 4164, "exterior": 4165, "cobblestone": 4166, "protruding": 4167, "feature": 4168, "keys": 4169, "mp3": 4170, "charge": 4171, "minimal": 4172, "peripheral": 4173, "human": 4174, "parts": 4175, "snake": 4176, "whose": 4177, "reacting": 4178, "music": 4179, "audio": 4180, "historic": 4181, "notes": 4182, "outlet": 4183, "plugged": 4184, "job": 4185, "standard": 4186, "compute": 4187, "bot": 4188, "homework": 4189, "destroyed": 4190, "alcoholic": 4191, "frosty": 4192, "pens": 4193, "pencils": 4194, "graces": 4195, "deco": 4196, "amused": 4197, "tripod": 4198, "vending": 4199, "even": 4200, "recycling": 4201, "faux": 4202, "yet": 4203, "assembled": 4204, "crossed": 4205, "checks": 4206, "booths": 4207, "wih": 4208, "rimmed": 4209, "grinning": 4210, "blackberry": 4211, "menu": 4212, "icons": 4213, "samsung": 4214, "missed": 4215, "message": 4216, "gps": 4217, "sprint": 4218, "caucasian": 4219, "cellphones": 4220, "makeshift": 4221, "appliance": 4222, "roaster": 4223, "roast": 4224, "removing": 4225, "dilapidated": 4226, "crumbling": 4227, "disrepair": 4228, "arranging": 4229, "museum": 4230, "substance": 4231, "smoothie": 4232, "goo": 4233, "helps": 4234, "combing": 4235, "takeout": 4236, "appetizers": 4237, "creamy": 4238, "breads": 4239, "peas": 4240, "pins": 4241, "clothesline": 4242, "pigtails": 4243, "sugary": 4244, "platters": 4245, "cupcakes": 4246, "britain": 4247, "watery": 4248, "blooming": 4249, "daffodils": 4250, "petals": 4251, "models": 4252, "gourds": 4253, "captured": 4254, "wilting": 4255, "roses": 4256, "wilted": 4257, "drooping": 4258, "tabletop": 4259, "poodle": 4260, "livingroom": 4261, "selecting": 4262, "buffet": 4263, "glassware": 4264, "pleased": 4265, "hearty": 4266, "croissant": 4267, "melon": 4268, "croissants": 4269, "finishing": 4270, "bearing": 4271, "tasting": 4272, "testing": 4273, "amounts": 4274, "eager": 4275, "exhibition": 4276, "vineyard": 4277, "diagonal": 4278, "wheelie": 4279, "handed": 4280, "gamer": 4281, "hugs": 4282, "g": 4283, "chunk": 4284, "butcher": 4285, "trashcans": 4286, "untidy": 4287, "halfpipe": 4288, "chopping": 4289, "burners": 4290, "griddle": 4291, "berry": 4292, "blackberries": 4293, "crumb": 4294, "juicer": 4295, "wiping": 4296, "leave": 4297, "lavatory": 4298, "tiling": 4299, "cyclists": 4300, "filthy": 4301, "outline": 4302, "entire": 4303, "completely": 4304, "constructed": 4305, "incomplete": 4306, "unpainted": 4307, "victorian": 4308, "casting": 4309, "pedaling": 4310, "bathmat": 4311, "bathrooms": 4312, "blues": 4313, "ring": 4314, "crooked": 4315, "litter": 4316, "automobile": 4317, "torn": 4318, "asking": 4319, "licking": 4320, "wanting": 4321, "hood": 4322, "lifted": 4323, "chevrolet": 4324, "tucked": 4325, "rush": 4326, "int": 4327, "nozzle": 4328, "zone": 4329, "altar": 4330, "ladle": 4331, "mystery": 4332, "stew": 4333, "sectioned": 4334, "muffin": 4335, "worked": 4336, "packages": 4337, "unwrapped": 4338, "wrappers": 4339, "exposed": 4340, "entry": 4341, "reflect": 4342, "streaks": 4343, "towl": 4344, "seasoning": 4345, "swirly": 4346, "antennae": 4347, "windshield": 4348, "bookcase": 4349, "cycles": 4350, "pagoda": 4351, "becomes": 4352, "airborne": 4353, "handlebars": 4354, "motocross": 4355, "rv": 4356, "lifting": 4357, "lookout": 4358, "kill": 4359, "masters": 4360, "footstool": 4361, "speakers": 4362, "spinning": 4363, "spitting": 4364, "letting": 4365, "landed": 4366, "tarp": 4367, "prop": 4368, "support": 4369, "propellers": 4370, "kangaroo": 4371, "nine": 4372, "angels": 4373, "pilots": 4374, "greenish": 4375, "mast": 4376, "ascending": 4377, "bust": 4378, "unpaved": 4379, "minivan": 4380, "know": 4381, "moon": 4382, "luxury": 4383, "heart": 4384, "costumed": 4385, "herds": 4386, "linger": 4387, "tram": 4388, "scenery": 4389, "distant": 4390, "structures": 4391, "spreading": 4392, "turtle": 4393, "stripe": 4394, "pumpkin": 4395, "pumpkins": 4396, "marks": 4397, "harvest": 4398, "squash": 4399, "takeoff": 4400, "twilight": 4401, "dawn": 4402, "hours": 4403, "fron": 4404, "california": 4405, "guide": 4406, "trough": 4407, "lamb": 4408, "snap": 4409, "mad": 4410, "passed": 4411, "vista": 4412, "stormy": 4413, "yaks": 4414, "once": 4415, "14": 4416, "tourist": 4417, "studies": 4418, "staff": 4419, "shepherd": 4420, "tends": 4421, "drivers": 4422, "navigate": 4423, "oncoming": 4424, "sloping": 4425, "amtrak": 4426, "beat": 4427, "wool": 4428, "shaved": 4429, "sheered": 4430, "idly": 4431, "hydrogen": 4432, "scratches": 4433, "create": 4434, "effect": 4435, "ladder": 4436, "rubs": 4437, "straw": 4438, "caution": 4439, "gracefully": 4440, "outstretched": 4441, "span": 4442, "womans": 4443, "punching": 4444, "innocent": 4445, "pretending": 4446, "punch": 4447, "tough": 4448, "nosing": 4449, "nose": 4450, "peels": 4451, "crow": 4452, "mix": 4453, "banner": 4454, "barren": 4455, "stork": 4456, "marshy": 4457, "vulture": 4458, "pop": 4459, "pepsi": 4460, "fences": 4461, "japanese": 4462, "furnace": 4463, "named": 4464, "district": 4465, "downtown": 4466, "indicated": 4467, "titled": 4468, "jackson": 4469, "east": 4470, "witha": 4471, "ny": 4472, "my": 4473, "disembarking": 4474, "outcropping": 4475, "bunched": 4476, "unhappy": 4477, "army": 4478, "cgi": 4479, "bad": 4480, "patient": 4481, "thomas": 4482, "reflecting": 4483, "rust": 4484, "necked": 4485, "ruffled": 4486, "underground": 4487, "sniffing": 4488, "sniffs": 4489, "bamboo": 4490, "fine": 4491, "locomotive": 4492, "locomotives": 4493, "enters": 4494, "billowing": 4495, "toronto": 4496, "la": 4497, "cuts": 4498, "roads": 4499, "overhang": 4500, "thai": 4501, "blocking": 4502, "until": 4503, "except": 4504, "6th": 4505, "happen": 4506, "anywhere": 4507, "barbwire": 4508, "crepes": 4509, "icecream": 4510, "gelato": 4511, "boarded": 4512, "seperate": 4513, "crossroads": 4514, "corners": 4515, "meaning": 4516, "universal": 4517, "attendants": 4518, "kart": 4519, "gallery": 4520, "sing": 4521, "page": 4522, "describe": 4523, "headlights": 4524, "frog": 4525, "puppet": 4526, "jammed": 4527, "promoting": 4528, "buds": 4529, "would": 4530, "positioned": 4531, "comfortably": 4532, "cushioned": 4533, "buckled": 4534, "stored": 4535, "dreary": 4536, "annoyed": 4537, "wicker": 4538, "mill": 4539, "padded": 4540, "lounge": 4541, "species": 4542, "curbside": 4543, "comfortable": 4544, "hairless": 4545, "patchwork": 4546, "quilt": 4547, "sweeper": 4548, "central": 4549, "file": 4550, "files": 4551, "text": 4552, "vcr": 4553, "interacting": 4554, "village": 4555, "pets": 4556, "pats": 4557, "stroking": 4558, "saw": 4559, "horned": 4560, "medal": 4561, "shed": 4562, "hipsters": 4563, "nick": 4564, "knack": 4565, "circus": 4566, "topper": 4567, "ugly": 4568, "affection": 4569, "mouths": 4570, "uncomfortable": 4571, "clip": 4572, "hitching": 4573, "underbrush": 4574, "enclosures": 4575, "balloons": 4576, "alot": 4577, "peaceful": 4578, "splash": 4579, "ample": 4580, "national": 4581, "offering": 4582, "handing": 4583, "marching": 4584, "triumphantly": 4585, "growth": 4586, "rafts": 4587, "flotation": 4588, "skin": 4589, "obscured": 4590, "boston": 4591, "spring": 4592, "drizzle": 4593, "swans": 4594, "parasols": 4595, "vacation": 4596, "duffel": 4597, "mount": 4598, "ditch": 4599, "aged": 4600, "stacks": 4601, "exercise": 4602, "countries": 4603, "replica": 4604, "socializing": 4605, "spare": 4606, "bangs": 4607, "flowery": 4608, "step": 4609, "shepard": 4610, "crafted": 4611, "santas": 4612, "frozen": 4613, "essentials": 4614, "walkways": 4615, "scruffy": 4616, "filtered": 4617, "effort": 4618, "pokes": 4619, "spokes": 4620, "rocking": 4621, "zoomed": 4622, "noses": 4623, "bone": 4624, "contraption": 4625, "conventional": 4626, "shaving": 4627, "leftover": 4628, "framing": 4629, "addition": 4630, "add": 4631, "labrador": 4632, "dancing": 4633, "policemen": 4634, "feather": 4635, "plumes": 4636, "those": 4637, "lost": 4638, "unicorn": 4639, "rose": 4640, "mane": 4641, "figure": 4642, "bridal": 4643, "merry": 4644, "squared": 4645, "layers": 4646, "tiered": 4647, "holly": 4648, "touched": 4649, "lowering": 4650, "saddled": 4651, "coral": 4652, "saddles": 4653, "idle": 4654, "visitors": 4655, "oats": 4656, "clump": 4657, "granola": 4658, "chip": 4659, "parchment": 4660, "climb": 4661, "attempt": 4662, "san": 4663, "francisco": 4664, "giants": 4665, "stretch": 4666, "fastball": 4667, "shaking": 4668, "unripe": 4669, "flops": 4670, "midst": 4671, "watermelon": 4672, "melons": 4673, "sealed": 4674, "mesh": 4675, "amateur": 4676, "bases": 4677, "advancing": 4678, "poultry": 4679, "baseman": 4680, "lips": 4681, "gadget": 4682, "knit": 4683, "garment": 4684, "springs": 4685, "ferns": 4686, "bruised": 4687, "strainer": 4688, "upclose": 4689, "dense": 4690, "strips": 4691, "superman": 4692, "cucumber": 4693, "zucchini": 4694, "planted": 4695, "plot": 4696, "twisted": 4697, "fixing": 4698, "dwarfed": 4699, "walker": 4700, "harness": 4701, "burned": 4702, "diced": 4703, "recipe": 4704, "noodle": 4705, "salami": 4706, "pauses": 4707, "drifts": 4708, "wedge": 4709, "leaps": 4710, "packaging": 4711, "florets": 4712, "blend": 4713, "sour": 4714, "shares": 4715, "broth": 4716, "rim": 4717, "hamburger": 4718, "ridding": 4719, "consumption": 4720, "asparagus": 4721, "navigates": 4722, "olympic": 4723, "turning": 4724, "respective": 4725, "quick": 4726, "pace": 4727, "competitor": 4728, "olympics": 4729, "mountaintop": 4730, "problem": 4731, "circles": 4732, "gentle": 4733, "rip": 4734, "chops": 4735, "chop": 4736, "wok": 4737, "allow": 4738, "pain": 4739, "plated": 4740, "herbs": 4741, "joins": 4742, "pate": 4743, "finish": 4744, "arch": 4745, "won": 4746, "reached": 4747, "uphill": 4748, "silhouette": 4749, "launches": 4750, "unusually": 4751, "tack": 4752, "excited": 4753, "squirrel": 4754, "nothing": 4755, "orders": 4756, "sled": 4757, "dunkin": 4758, "goggles": 4759, "pausing": 4760, "rooster": 4761, "joke": 4762, "chickens": 4763, "peanut": 4764, "butter": 4765, "halved": 4766, "sandwhich": 4767, "waxed": 4768, "mozzarella": 4769, "cooling": 4770, "entrees": 4771, "peculiar": 4772, "fronted": 4773, "polaroid": 4774, "possession": 4775, "congratulations": 4776, "your": 4777, "upcoming": 4778, "congratulating": 4779, "torsos": 4780, "walnut": 4781, "walnuts": 4782, "maybe": 4783, "bows": 4784, "chin": 4785, "differnt": 4786, "browned": 4787, "cracked": 4788, "filing": 4789, "cresting": 4790, "excitement": 4791, "blinds": 4792, "shutter": 4793, "pleasant": 4794, "joined": 4795, "surfs": 4796, "foamy": 4797, "breaks": 4798, "skill": 4799, "hammock": 4800, "burning": 4801, "labels": 4802, "tech": 4803, "master": 4804, "server": 4805, "barely": 4806, "knocked": 4807, "altered": 4808, "nightstands": 4809, "disheveled": 4810, "sneaker": 4811, "bottoms": 4812, "level": 4813, "lavish": 4814, "futuristic": 4815, "nude": 4816, "tangled": 4817, "silk": 4818, "pizzeria": 4819, "feta": 4820, "appetizing": 4821, "paneled": 4822, "struggles": 4823, "connect": 4824, "xbox": 4825, "sample": 4826, "booze": 4827, "basil": 4828, "leafs": 4829, "macbook": 4830, "pro": 4831, "contact": 4832, "patrons": 4833, "athlete": 4834, "shirted": 4835, "tosses": 4836, "strong": 4837, "glue": 4838, "sweat": 4839, "clips": 4840, "nail": 4841, "polish": 4842, "toiletries": 4843, "stocking": 4844, "hairbrush": 4845, "chews": 4846, "rodent": 4847, "ferret": 4848, "gum": 4849, "brother": 4850, "dance": 4851, "panda": 4852, "koala": 4853, "shocked": 4854, "mote": 4855, "snuggle": 4856, "gives": 4857, "thumbs": 4858, "rugs": 4859, "videogame": 4860, "pastel": 4861, "nun": 4862, "chuck": 4863, "attachment": 4864, "trade": 4865, "panoramic": 4866, "junky": 4867, "motions": 4868, "concert": 4869, "via": 4870, "official": 4871, "government": 4872, "flagpole": 4873, "texas": 4874, "th": 4875, "peacock": 4876, "ray": 4877, "overcast": 4878, "jetty": 4879, "fliers": 4880, "activity": 4881, "memorial": 4882, "storefronts": 4883, "inspect": 4884, "ornamental": 4885, "bullpen": 4886, "took": 4887, "misshapen": 4888, "beverages": 4889, "producing": 4890, "browning": 4891, "cutter": 4892, "meet": 4893, "rackett": 4894, "act": 4895, "smack": 4896, "refrigerated": 4897, "unbaked": 4898, "quiche": 4899, "judge": 4900, "paperwork": 4901, "everyone": 4902, "doodling": 4903, "cylinder": 4904, "suspended": 4905, "stage": 4906, "sided": 4907, "demonic": 4908, "smirk": 4909, "crochet": 4910, "eccentric": 4911, "raincoat": 4912, "ovens": 4913, "microwaves": 4914, "makeup": 4915, "flyers": 4916, "storefront": 4917, "portable": 4918, "glossy": 4919, "almond": 4920, "pudding": 4921, "yogurt": 4922, "taller": 4923, "sprayed": 4924, "littered": 4925, "cupboard": 4926, "retrieving": 4927, "magenta": 4928, "holders": 4929, "hairdryer": 4930, "singing": 4931, "corded": 4932, "cloths": 4933, "mothers": 4934, "brushed": 4935, "kiwi": 4936, "peach": 4937, "teapot": 4938, "dial": 4939, "numeral": 4940, "skyscraper": 4941, "condition": 4942, "artwork": 4943, "perhaps": 4944, "daisies": 4945, "irises": 4946, "seal": 4947, "overview": 4948, "terra": 4949, "cotta": 4950, "binder": 4951, "handled": 4952, "doctor": 4953, "injured": 4954, "instrument": 4955, "blank": 4956, "carved": 4957, "loaf": 4958, "meatloaf": 4959, "tortillas": 4960, "sauces": 4961, "tortilla": 4962, "mexican": 4963, "salsa": 4964, "quesadilla": 4965, "decent": 4966, "omelette": 4967, "omelet": 4968, "pancake": 4969, "coffe": 4970, "scallops": 4971, "terrace": 4972, "class": 4973, "rescue": 4974, "overturned": 4975, "crash": 4976, "cuddling": 4977, "minor": 4978, "skilled": 4979, "bitty": 4980, "easily": 4981, "closing": 4982, "12": 4983, "pinkish": 4984, "spice": 4985, "woodwork": 4986, "rods": 4987, "grow": 4988, "marbled": 4989, "mint": 4990, "seperated": 4991, "divided": 4992, "useful": 4993, "rounded": 4994, "biking": 4995, "biker": 4996, "walkers": 4997, "dude": 4998, "quietly": 4999, "pared": 5000, "shave": 5001, "toiletry": 5002, "resembling": 5003, "vespas": 5004, "sparsely": 5005, "niche": 5006, "fern": 5007, "tinted": 5008, "wreaths": 5009, "digital": 5010, "dove": 5011, "mascot": 5012, "motel": 5013, "belongings": 5014, "packing": 5015, "receptacle": 5016, "rollers": 5017, "checker": 5018, "scrubber": 5019, "toiler": 5020, "s": 5021, "pew": 5022, "peers": 5023, "flush": 5024, "flushing": 5025, "glaring": 5026, "scrubbing": 5027, "upper": 5028, "presenting": 5029, "bidet": 5030, "removed": 5031, "crawling": 5032, "changing": 5033, "onboard": 5034, "honey": 5035, "trashcan": 5036, "switch": 5037, "hosts": 5038, "dispensers": 5039, "motorcyle": 5040, "usual": 5041, "sensor": 5042, "never": 5043, "crude": 5044, "chopper": 5045, "businesses": 5046, "honda": 5047, "wizard": 5048, "options": 5049, "stoned": 5050, "elevator": 5051, "mosaic": 5052, "entryway": 5053, "movement": 5054, "viewer": 5055, "wrought": 5056, "length": 5057, "mache": 5058, "socks": 5059, "televisions": 5060, "contrail": 5061, "france": 5062, "aeroplane": 5063, "crowds": 5064, "releasing": 5065, "acrobatic": 5066, "routine": 5067, "serviced": 5068, "hoses": 5069, "girafee": 5070, "hidden": 5071, "spider": 5072, "closer": 5073, "interested": 5074, "foliage": 5075, "exploring": 5076, "airy": 5077, "streetlights": 5078, "mingling": 5079, "thicket": 5080, "outcrop": 5081, "examining": 5082, "private": 5083, "propellors": 5084, "licked": 5085, "sequence": 5086, "searching": 5087, "lonely": 5088, "leashed": 5089, "antelope": 5090, "congregate": 5091, "stoplights": 5092, "towns": 5093, "luggages": 5094, "icy": 5095, "lapsed": 5096, "gorgeous": 5097, "slug": 5098, "animated": 5099, "changes": 5100, "carvings": 5101, "humans": 5102, "grasslands": 5103, "bustling": 5104, "ban": 5105, "banks": 5106, "woolen": 5107, "fleet": 5108, "circled": 5109, "blossoming": 5110, "fireplug": 5111, "establishment": 5112, "cactus": 5113, "overexposed": 5114, "ash": 5115, "sunken": 5116, "barricade": 5117, "tired": 5118, "hundreds": 5119, "jogging": 5120, "available": 5121, "stranded": 5122, "wrench": 5123, "lever": 5124, "attendant": 5125, "cockatoo": 5126, "pure": 5127, "parachuting": 5128, "abundant": 5129, "ofa": 5130, "sin": 5131, "cylindrical": 5132, "sepia": 5133, "chalk": 5134, "welcoming": 5135, "feeling": 5136, "chalkboard": 5137, "miles": 5138, "dear": 5139, "littering": 5140, "stating": 5141, "ave": 5142, "investigating": 5143, "centre": 5144, "bumper": 5145, "hate": 5146, "sister": 5147, "labelled": 5148, "flowering": 5149, "convex": 5150, "seeing": 5151, "dinning": 5152, "virtual": 5153, "arabic": 5154, "pakistan": 5155, "mossy": 5156, "awning": 5157, "erect": 5158, "locations": 5159, "every": 5160, "kerouac": 5161, "boulevard": 5162, "clustered": 5163, "target": 5164, "property": 5165, "india": 5166, "southeast": 5167, "union": 5168, "pacific": 5169, "bog": 5170, "moss": 5171, "drain": 5172, "skull": 5173, "swords": 5174, "pirate": 5175, "expired": 5176, "artistic": 5177, "theater": 5178, "pouches": 5179, "idea": 5180, "everything": 5181, "trip": 5182, "trucking": 5183, "lacking": 5184, "headlight": 5185, "handbag": 5186, "shark": 5187, "fleece": 5188, "loves": 5189, "creating": 5190, "housed": 5191, "viewing": 5192, "complete": 5193, "cushion": 5194, "ordering": 5195, "taker": 5196, "squints": 5197, "siamese": 5198, "barbed": 5199, "hello": 5200, "convoy": 5201, "fuzzy": 5202, "curiously": 5203, "barges": 5204, "canoes": 5205, "admiring": 5206, "mower": 5207, "dairy": 5208, "barber": 5209, "mommy": 5210, "smeared": 5211, "whiskey": 5212, "martini": 5213, "gowns": 5214, "alternative": 5215, "puts": 5216, "capital": 5217, "capitol": 5218, "handler": 5219, "procession": 5220, "observes": 5221, "met": 5222, "solemn": 5223, "expressions": 5224, "smoggy": 5225, "hazy": 5226, "boating": 5227, "paid": 5228, "tones": 5229, "actually": 5230, "supports": 5231, "inlet": 5232, "sumo": 5233, "ref": 5234, "wrestle": 5235, "populated": 5236, "pavilion": 5237, "favorite": 5238, "boaters": 5239, "cruise": 5240, "expansive": 5241, "shoppers": 5242, "balding": 5243, "bowtie": 5244, "heights": 5245, "commuting": 5246, "fist": 5247, "intertwined": 5248, "trooper": 5249, "rowboat": 5250, "oars": 5251, "boundary": 5252, "dollar": 5253, "bill": 5254, "monkey": 5255, "looked": 5256, "curling": 5257, "teenaged": 5258, "literature": 5259, "loads": 5260, "raccoon": 5261, "pooh": 5262, "contrasting": 5263, "musical": 5264, "gathers": 5265, "armchair": 5266, "cubs": 5267, "elevated": 5268, "limbs": 5269, "belly": 5270, "company": 5271, "panorama": 5272, "visor": 5273, "riverbank": 5274, "patchy": 5275, "rhinos": 5276, "pigs": 5277, "investigates": 5278, "discs": 5279, "collide": 5280, "based": 5281, "youths": 5282, "ostriches": 5283, "nuzzles": 5284, "galloping": 5285, "confined": 5286, "gazelle": 5287, "hippo": 5288, "oxen": 5289, "autumn": 5290, "renaissance": 5291, "period": 5292, "caramel": 5293, "motorist": 5294, "label": 5295, "diner": 5296, "melted": 5297, "descending": 5298, "moms": 5299, "scale": 5300, "weight": 5301, "toothpick": 5302, "eatables": 5303, "slab": 5304, "broccolli": 5305, "feeds": 5306, "linen": 5307, "intense": 5308, "sold": 5309, "euro": 5310, "money": 5311, "brocclie": 5312, "standng": 5313, "shredding": 5314, "gnar": 5315, "tint": 5316, "climber": 5317, "meals": 5318, "cilantro": 5319, "sandwhiches": 5320, "receipts": 5321, "tangerine": 5322, "llama": 5323, "cheesecake": 5324, "pike": 5325, "quarter": 5326, "drop": 5327, "doubled": 5328, "bunt": 5329, "bundt": 5330, "goatee": 5331, "hovering": 5332, "smoky": 5333, "lighter": 5334, "camping": 5335, "flips": 5336, "teen": 5337, "capture": 5338, "colorfully": 5339, "edited": 5340, "skying": 5341, "boad": 5342, "carton": 5343, "subs": 5344, "gauges": 5345, "eatting": 5346, "fixings": 5347, "somebody": 5348, "ma": 5349, "blt": 5350, "eleven": 5351, "soars": 5352, "assembly": 5353, "processing": 5354, "fryer": 5355, "automated": 5356, "anticipation": 5357, "bathrobe": 5358, "skillfully": 5359, "grind": 5360, "instead": 5361, "leap": 5362, "fills": 5363, "tilting": 5364, "autographs": 5365, "rings": 5366, "philly": 5367, "backhand": 5368, "bobble": 5369, "danish": 5370, "parasailer": 5371, "windy": 5372, "rotating": 5373, "mason": 5374, "pretzel": 5375, "sparklers": 5376, "sparkler": 5377, "backing": 5378, "pretzels": 5379, "pastrami": 5380, "coleslaw": 5381, "dipping": 5382, "marinara": 5383, "dipped": 5384, "smoking": 5385, "sesame": 5386, "heaping": 5387, "leftovers": 5388, "want": 5389, "edges": 5390, "mills": 5391, "bunk": 5392, "bedspread": 5393, "woodland": 5394, "mitts": 5395, "drapes": 5396, "sponge": 5397, "bob": 5398, "cartoons": 5399, "jalapeno": 5400, "workspace": 5401, "inviting": 5402, "website": 5403, "sat": 5404, "whine": 5405, "glares": 5406, "anticipating": 5407, "squats": 5408, "requires": 5409, "react": 5410, "opponents": 5411, "lasagna": 5412, "me": 5413, "offers": 5414, "unoccupied": 5415, "taco": 5416, "wineglass": 5417, "oblong": 5418, "clothed": 5419, "pepperonis": 5420, "kneels": 5421, "circuit": 5422, "units": 5423, "science": 5424, "operate": 5425, "console": 5426, "bookstore": 5427, "cord": 5428, "largest": 5429, "affectionate": 5430, "snuggles": 5431, "carried": 5432, "motes": 5433, "learn": 5434, "contemporary": 5435, "paintings": 5436, "goofy": 5437, "convention": 5438, "cds": 5439, "pc": 5440, "flash": 5441, "test": 5442, "stroke": 5443, "deliver": 5444, "bounces": 5445, "bouncing": 5446, "weaving": 5447, "ktichen": 5448, "flys": 5449, "kits": 5450, "illustration": 5451, "rally": 5452, "kiteboarding": 5453, "resembles": 5454, "amazed": 5455, "thermos": 5456, "guinness": 5457, "detailing": 5458, "fold": 5459, "hitter": 5460, "caught": 5461, "womens": 5462, "leaguer": 5463, "pitches": 5464, "memorabilia": 5465, "caps": 5466, "braid": 5467, "focusing": 5468, "exchanging": 5469, "fastened": 5470, "10": 5471, "death": 5472, "comically": 5473, "textbook": 5474, "gift": 5475, "fisherman": 5476, "pliers": 5477, "active": 5478, "smallest": 5479, "passageway": 5480, "parent": 5481, "prep": 5482, "refrigerators": 5483, "starts": 5484, "remove": 5485, "broad": 5486, "daylight": 5487, "code": 5488, "tshirt": 5489, "tell": 5490, "sponsored": 5491, "intriguing": 5492, "utilizing": 5493, "policeman": 5494, "nokia": 5495, "calculator": 5496, "mixer": 5497, "article": 5498, "dries": 5499, "comb": 5500, "ancient": 5501, "slim": 5502, "hear": 5503, "lowered": 5504, "downward": 5505, "finding": 5506, "goldfish": 5507, "styles": 5508, "complex": 5509, "matches": 5510, "alter": 5511, "houseplant": 5512, "gin": 5513, "height": 5514, "dying": 5515, "sunflowers": 5516, "arts": 5517, "crayons": 5518, "redheaded": 5519, "plywood": 5520, "pedals": 5521, "brake": 5522, "pedal": 5523, "starting": 5524, "present": 5525, "cutlery": 5526, "salt": 5527, "wines": 5528, "taste": 5529, "champagne": 5530, "calendar": 5531, "attic": 5532, "themselves": 5533, "postal": 5534, "pillars": 5535, "feel": 5536, "mannequin": 5537, "photographing": 5538, "ca": 5539, "wash": 5540, "flowing": 5541, "wraps": 5542, "vessel": 5543, "stucco": 5544, "marketplace": 5545, "linoleum": 5546, "curtained": 5547, "accented": 5548, "handicap": 5549, "enclosing": 5550, "change": 5551, "surfaces": 5552, "likes": 5553, "mouthed": 5554, "rushes": 5555, "ford": 5556, "mustang": 5557, "lowers": 5558, "checkers": 5559, "gothic": 5560, "cruising": 5561, "surveys": 5562, "sunlit": 5563, "unknown": 5564, "outhouse": 5565, "speaker": 5566, "harley": 5567, "davidson": 5568, "stair": 5569, "ridge": 5570, "pecking": 5571, "remnants": 5572, "readies": 5573, "airports": 5574, "recognizable": 5575, "years": 5576, "michigan": 5577, "zookeeper": 5578, "follow": 5579, "ferris": 5580, "staning": 5581, "dug": 5582, "peoples": 5583, "transportation": 5584, "seem": 5585, "ringed": 5586, "criss": 5587, "soon": 5588, "props": 5589, "deckered": 5590, "beanie": 5591, "shelter": 5592, "manicured": 5593, "firehydrant": 5594, "canada": 5595, "valve": 5596, "co": 5597, "crack": 5598, "bugs": 5599, "clause": 5600, "claus": 5601, "famous": 5602, "vine": 5603, "buoy": 5604, "dangled": 5605, "embedded": 5606, "mcdonalds": 5607, "forrest": 5608, "recovery": 5609, "poolside": 5610, "burnt": 5611, "beaten": 5612, "mexico": 5613, "dumb": 5614, "delicate": 5615, "maintenance": 5616, "consumed": 5617, "consuming": 5618, "emitting": 5619, "billows": 5620, "enforcement": 5621, "personnel": 5622, "flashing": 5623, "mounds": 5624, "35": 5625, "jesus": 5626, "explains": 5627, "heron": 5628, "swamp": 5629, "rugged": 5630, "lighted": 5631, "arrive": 5632, "windmill": 5633, "tulip": 5634, "toll": 5635, "cant": 5636, "peeling": 5637, "telescope": 5638, "4": 5639, "backed": 5640, "protect": 5641, "attractive": 5642, "aqua": 5643, "mack": 5644, "eighteen": 5645, "moose": 5646, "overgrowth": 5647, "camouflage": 5648, "walked": 5649, "beagle": 5650, "vegetated": 5651, "relaxed": 5652, "mixers": 5653, "machinery": 5654, "delivers": 5655, "logo": 5656, "weedy": 5657, "nears": 5658, "strain": 5659, "penned": 5660, "shovel": 5661, "seemingly": 5662, "ashore": 5663, "noise": 5664, "watercraft": 5665, "satchel": 5666, "sidelines": 5667, "rapid": 5668, "rafting": 5669, "afro": 5670, "liberty": 5671, "manhattan": 5672, "shopped": 5673, "abstract": 5674, "reverse": 5675, "lobster": 5676, "reclining": 5677, "particular": 5678, "lounges": 5679, "fix": 5680, "depicted": 5681, "ravine": 5682, "looming": 5683, "arched": 5684, "classical": 5685, "trained": 5686, "entertain": 5687, "clowns": 5688, "ladys": 5689, "canes": 5690, "batch": 5691, "canvas": 5692, "stillness": 5693, "bonnet": 5694, "underwater": 5695, "provided": 5696, "fetching": 5697, "dyed": 5698, "dunk": 5699, "growling": 5700, "dives": 5701, "sorting": 5702, "handlers": 5703, "khaki": 5704, "crouches": 5705, "puppies": 5706, "cannot": 5707, "climbs": 5708, "butt": 5709, "wintry": 5710, "grizzle": 5711, "indifferent": 5712, "mating": 5713, "nad": 5714, "definitely": 5715, "infront": 5716, "tundra": 5717, "ruffle": 5718, "bee": 5719, "lets": 5720, "yawning": 5721, "yawns": 5722, "rearing": 5723, "fierce": 5724, "ultimate": 5725, "contest": 5726, "intended": 5727, "stomach": 5728, "freeze": 5729, "wagging": 5730, "darkened": 5731, "floppy": 5732, "trophy": 5733, "backside": 5734, "nibble": 5735, "clippings": 5736, "pressed": 5737, "camel": 5738, "lama": 5739, "faster": 5740, "knacks": 5741, "baseballs": 5742, "limes": 5743, "heaped": 5744, "dodgers": 5745, "digitally": 5746, "manipulated": 5747, "stages": 5748, "vendors": 5749, "sell": 5750, "dotted": 5751, "bushels": 5752, "runner": 5753, "hips": 5754, "infield": 5755, "ethnic": 5756, "monkeys": 5757, "steaming": 5758, "wound": 5759, "tofu": 5760, "juicy": 5761, "receiving": 5762, "expressing": 5763, "outfield": 5764, "brocoli": 5765, "coloring": 5766, "enjoyed": 5767, "bud": 5768, "slit": 5769, "bloody": 5770, "kabob": 5771, "harvesting": 5772, "hardly": 5773, "garlic": 5774, "fritter": 5775, "rare": 5776, "smothered": 5777, "nordic": 5778, "dishing": 5779, "radishes": 5780, "waterskiing": 5781, "kiteboard": 5782, "warmly": 5783, "spikes": 5784, "sight": 5785, "acting": 5786, "obstacle": 5787, "stairwell": 5788, "comfort": 5789, "helicopter": 5790, "accident": 5791, "catsup": 5792, "snowing": 5793, "amazing": 5794, "summit": 5795, "struggling": 5796, "nerd": 5797, "bakers": 5798, "offerings": 5799, "tented": 5800, "hash": 5801, "corned": 5802, "chases": 5803, "chase": 5804, "lollipop": 5805, "grooms": 5806, "applying": 5807, "waxing": 5808, "receipt": 5809, "sinking": 5810, "plying": 5811, "swell": 5812, "oceans": 5813, "placid": 5814, "bodyboarding": 5815, "shift": 5816, "underwear": 5817, "suited": 5818, "pipeline": 5819, "flickr": 5820, "denim": 5821, "overalls": 5822, "beachgoers": 5823, "crest": 5824, "manmade": 5825, "clad": 5826, "simulated": 5827, "wetsuits": 5828, "dive": 5829, "sailboard": 5830, "windsurfer": 5831, "wed": 5832, "brochure": 5833, "adding": 5834, "tarts": 5835, "filmed": 5836, "adorn": 5837, "fell": 5838, "slats": 5839, "guitar": 5840, "bigger": 5841, "hawaiian": 5842, "squinting": 5843, "sprinkling": 5844, "afghan": 5845, "sucks": 5846, "concept": 5847, "batteries": 5848, "tvs": 5849, "steering": 5850, "ridiculous": 5851, "compilation": 5852, "household": 5853, "futon": 5854, "playfully": 5855, "velvet": 5856, "burgundy": 5857, "guided": 5858, "quarters": 5859, "alarm": 5860, "oclock": 5861, "butterfly": 5862, "chilly": 5863, "obelisk": 5864, "panned": 5865, "parachutes": 5866, "minnesota": 5867, "dropping": 5868, "afar": 5869, "colgate": 5870, "bunting": 5871, "mets": 5872, "avoid": 5873, "calls": 5874, "drank": 5875, "elements": 5876, "8": 5877, "easel": 5878, "scratch": 5879, "spooning": 5880, "added": 5881, "crush": 5882, "latte": 5883, "stain": 5884, "glassed": 5885, "lieing": 5886, "start": 5887, "seminar": 5888, "technician": 5889, "technicians": 5890, "teh": 5891, "dell": 5892, "removes": 5893, "repairs": 5894, "stovetop": 5895, "state": 5896, "primarily": 5897, "disabled": 5898, "salon": 5899, "crocheted": 5900, "intensely": 5901, "iphones": 5902, "crossword": 5903, "comics": 5904, "quality": 5905, "product": 5906, "click": 5907, "chandeliers": 5908, "archways": 5909, "processor": 5910, "cube": 5911, "blenders": 5912, "analog": 5913, "grows": 5914, "clipped": 5915, "blades": 5916, "eatery": 5917, "needed": 5918, "crafting": 5919, "grains": 5920, "waffles": 5921, "syrup": 5922, "crumbled": 5923, "scrambled": 5924, "ravioli": 5925, "buttered": 5926, "bones": 5927, "landscaped": 5928, "dangling": 5929, "knick": 5930, "shears": 5931, "compete": 5932, "mainly": 5933, "ivy": 5934, "abundance": 5935, "dual": 5936, "basins": 5937, "unclear": 5938, "dingy": 5939, "cycling": 5940, "flame": 5941, "custom": 5942, "panels": 5943, "sanitizer": 5944, "nearest": 5945, "victoria": 5946, "causing": 5947, "1950s": 5948, "please": 5949, "sorry": 5950, "soldier": 5951, "turret": 5952, "cemetery": 5953, "graveyard": 5954, "sock": 5955, "mail": 5956, "spoonful": 5957, "straddles": 5958, "poor": 5959, "hedges": 5960, "thee": 5961, "flatscreen": 5962, "nostalgic": 5963, "cables": 5964, "seaplane": 5965, "smiley": 5966, "forefront": 5967, "border": 5968, "collie": 5969, "airlines": 5970, "747": 5971, "belongs": 5972, "treed": 5973, "petted": 5974, "loving": 5975, "amidst": 5976, "wondering": 5977, "pawing": 5978, "lightening": 5979, "lightning": 5980, "teachers": 5981, "rearview": 5982, "disco": 5983, "dwarfs": 5984, "visitor": 5985, "jug": 5986, "quaint": 5987, "solitary": 5988, "horizontally": 5989, "commuters": 5990, "casino": 5991, "logos": 5992, "antiques": 5993, "battered": 5994, "silhouettes": 5995, "parallel": 5996, "bloom": 5997, "herded": 5998, "grafitti": 5999, "splattered": 6000, "pebbles": 6001, "hyrdant": 6002, "protected": 6003, "girraffe": 6004, "giraff": 6005, "barrier": 6006, "exactly": 6007, "patriotic": 6008, "chains": 6009, "sprays": 6010, "motorhome": 6011, "cautionary": 6012, "storks": 6013, "beaks": 6014, "goose": 6015, "citizens": 6016, "safari": 6017, "dodge": 6018, "duke": 6019, "blvd": 6020, "suburb": 6021, "reigns": 6022, "derby": 6023, "guards": 6024, "ago": 6025, "centered": 6026, "chugs": 6027, "brothers": 6028, "directs": 6029, "advertisements": 6030, "34th": 6031, "according": 6032, "chugging": 6033, "stationary": 6034, "bullet": 6035, "billboards": 6036, "coal": 6037, "tacks": 6038, "nailed": 6039, "passanger": 6040, "calling": 6041, "graffitid": 6042, "withe": 6043, "intersecting": 6044, "signed": 6045, "replaced": 6046, "y": 6047, "r": 6048, "civilians": 6049, "scrap": 6050, "convertible": 6051, "catnip": 6052, "blind": 6053, "semis": 6054, "monster": 6055, "tuxedos": 6056, "streamers": 6057, "slatted": 6058, "fox": 6059, "trampoline": 6060, "hooks": 6061, "bison": 6062, "washed": 6063, "stowed": 6064, "sisters": 6065, "messed": 6066, "waitress": 6067, "chart": 6068, "spanning": 6069, "blouse": 6070, "coastline": 6071, "similarly": 6072, "undone": 6073, "evil": 6074, "collared": 6075, "paisley": 6076, "teacher": 6077, "auditorium": 6078, "arrayed": 6079, "chaise": 6080, "butts": 6081, "escort": 6082, "alien": 6083, "grade": 6084, "lapel": 6085, "pinning": 6086, "grandfather": 6087, "en": 6088, "briefcases": 6089, "corona": 6090, "chests": 6091, "appreciating": 6092, "incredible": 6093, "pup": 6094, "sorted": 6095, "keeping": 6096, "fetch": 6097, "gripping": 6098, "cautiously": 6099, "piercing": 6100, "grooming": 6101, "gym": 6102, "least": 6103, "affectionately": 6104, "bowing": 6105, "nuzzling": 6106, "equestrian": 6107, "draught": 6108, "bucking": 6109, "occupants": 6110, "balanced": 6111, "squeezed": 6112, "lemonade": 6113, "pause": 6114, "adjusts": 6115, "plantain": 6116, "fingertips": 6117, "tapping": 6118, "youngsters": 6119, "unripened": 6120, "budding": 6121, "bloomed": 6122, "mit": 6123, "contain": 6124, "raspberry": 6125, "deserted": 6126, "measuring": 6127, "stirred": 6128, "clown": 6129, "wig": 6130, "outing": 6131, "winters": 6132, "cashews": 6133, "neglected": 6134, "cruises": 6135, "maintain": 6136, "diagonally": 6137, "steaks": 6138, "runners": 6139, "skaters": 6140, "platforms": 6141, "sandwiched": 6142, "sales": 6143, "wares": 6144, "thighs": 6145, "laced": 6146, "gotten": 6147, "twenty": 6148, "campus": 6149, "skat": 6150, "eggplant": 6151, "tilts": 6152, "cameraman": 6153, "knotted": 6154, "foam": 6155, "failing": 6156, "unpeeled": 6157, "cob": 6158, "function": 6159, "ironic": 6160, "worms": 6161, "overflowing": 6162, "tater": 6163, "tots": 6164, "pub": 6165, "engraved": 6166, "brownies": 6167, "curl": 6168, "frothy": 6169, "carving": 6170, "potatos": 6171, "generated": 6172, "avatar": 6173, "triple": 6174, "blows": 6175, "teaches": 6176, "tucking": 6177, "tucks": 6178, "rendering": 6179, "positions": 6180, "bead": 6181, "windowed": 6182, "grandma": 6183, "closes": 6184, "wipes": 6185, "medical": 6186, "professionals": 6187, "scrubs": 6188, "tether": 6189, "modem": 6190, "bikini": 6191, "delivered": 6192, "seriously": 6193, "yorkie": 6194, "stoic": 6195, "stitch": 6196, "consoles": 6197, "returned": 6198, "athletic": 6199, "muscles": 6200, "backwards": 6201, "flexing": 6202, "observe": 6203, "uncovered": 6204, "admires": 6205, "confused": 6206, "exhausted": 6207, "nacks": 6208, "organizer": 6209, "caddy": 6210, "accessory": 6211, "clogged": 6212, "bristles": 6213, "un": 6214, "cough": 6215, "flow": 6216, "thumb": 6217, "oak": 6218, "fabrics": 6219, "disorganized": 6220, "mantel": 6221, "silhouetted": 6222, "gliders": 6223, "mlb": 6224, "teammates": 6225, "handicapped": 6226, "helped": 6227, "williams": 6228, "difficult": 6229, "attended": 6230, "crusts": 6231, "mice": 6232, "ergonomic": 6233, "usb": 6234, "compass": 6235, "tricycle": 6236, "gesturing": 6237, "sunflower": 6238, "online": 6239, "wallet": 6240, "bracelets": 6241, "coins": 6242, "cd": 6243, "typewriter": 6244, "record": 6245, "passport": 6246, "scrunched": 6247, "mature": 6248, "newer": 6249, "bicycling": 6250, "fantasy": 6251, "unaware": 6252, "mooring": 6253, "anchor": 6254, "lanyard": 6255, "onstage": 6256, "chimneys": 6257, "bought": 6258, "tissues": 6259, "materials": 6260, "crumpled": 6261, "cutters": 6262, "pegs": 6263, "peg": 6264, "meets": 6265, "enjoyable": 6266, "flutes": 6267, "menus": 6268, "grins": 6269, "handsome": 6270, "gap": 6271, "discussing": 6272, "discussion": 6273, "staged": 6274, "filming": 6275, "unfurnished": 6276, "pendant": 6277, "instruments": 6278, "dread": 6279, "locks": 6280, "renovation": 6281, "feminine": 6282, "fluid": 6283, "removable": 6284, "adjustable": 6285, "sprayer": 6286, "maple": 6287, "necklaces": 6288, "jewelry": 6289, "dividers": 6290, "hamper": 6291, "roller": 6292, "widow": 6293, "average": 6294, "khakis": 6295, "able": 6296, "crashed": 6297, "colliding": 6298, "flushed": 6299, "pours": 6300, "calves": 6301, "antlers": 6302, "5": 6303, "cyclist": 6304, "hero": 6305, "deers": 6306, "virgin": 6307, "mary": 6308, "chapel": 6309, "latin": 6310, "ruins": 6311, "reindeer": 6312, "muscle": 6313, "tires": 6314, "staircase": 6315, "lazy": 6316, "uncommonly": 6317, "fitting": 6318, "chaps": 6319, "access": 6320, "tale": 6321, "release": 6322, "airliners": 6323, "recreational": 6324, "mamma": 6325, "ghost": 6326, "neath": 6327, "protesting": 6328, "flashes": 6329, "instructions": 6330, "welcome": 6331, "pamphlets": 6332, "shorn": 6333, "brochures": 6334, "guides": 6335, "horizontal": 6336, "departing": 6337, "awnings": 6338, "mechanical": 6339, "temporary": 6340, "crosstown": 6341, "51": 6342, "wildflowers": 6343, "circling": 6344, "hyde": 6345, "hyrdrant": 6346, "coca": 6347, "stools": 6348, "angrily": 6349, "arid": 6350, "mt": 6351, "press": 6352, "birdcage": 6353, "fondant": 6354, "tiers": 6355, "protector": 6356, "version": 6357, "japan": 6358, "steet": 6359, "sip": 6360, "dunes": 6361, "sydney": 6362, "australia": 6363, "county": 6364, "headband": 6365, "tiara": 6366, "hug": 6367, "shadowed": 6368, "taping": 6369, "encompassing": 6370, "kisses": 6371, "pearls": 6372, "casual": 6373, "gorilla": 6374, "informational": 6375, "cities": 6376, "projects": 6377, "witch": 6378, "witches": 6379, "tended": 6380, "chimney": 6381, "trestle": 6382, "guarding": 6383, "signaling": 6384, "merge": 6385, "proceeding": 6386, "merging": 6387, "development": 6388, "cal": 6389, "boxcars": 6390, "drifting": 6391, "tugboat": 6392, "navigating": 6393, "solar": 6394, "atm": 6395, "satellite": 6396, "kiosk": 6397, "refuse": 6398, "mailbox": 6399, "fashionable": 6400, "wrangling": 6401, "why": 6402, "bubble": 6403, "tipped": 6404, "blanketed": 6405, "penn": 6406, "farmland": 6407, "potter": 6408, "cuddle": 6409, "amusement": 6410, "darkly": 6411, "bumping": 6412, "formations": 6413, "source": 6414, "halloween": 6415, "knot": 6416, "hangings": 6417, "protects": 6418, "tusk": 6419, "handbags": 6420, "lazing": 6421, "law": 6422, "snowstorm": 6423, "rains": 6424, "wrestler": 6425, "pokemon": 6426, "spectator": 6427, "isolated": 6428, "wolf": 6429, "visit": 6430, "brow": 6431, "freez": 6432, "partner": 6433, "proceed": 6434, "wagons": 6435, "blinder": 6436, "bridled": 6437, "mule": 6438, "blinders": 6439, "racers": 6440, "clydesdale": 6441, "uneaten": 6442, "kiwis": 6443, "tempting": 6444, "doo": 6445, "provides": 6446, "listens": 6447, "crepe": 6448, "dolphins": 6449, "dolphin": 6450, "organic": 6451, "princess": 6452, "describing": 6453, "cowgirl": 6454, "chives": 6455, "variation": 6456, "roasting": 6457, "mild": 6458, "alpine": 6459, "coupe": 6460, "marathon": 6461, "crashes": 6462, "scenes": 6463, "patty": 6464, "cheeseburger": 6465, "chilli": 6466, "careful": 6467, "propel": 6468, "deformed": 6469, "executing": 6470, "beginner": 6471, "radish": 6472, "twisting": 6473, "descend": 6474, "sodas": 6475, "choose": 6476, "partaking": 6477, "windsurfs": 6478, "dominates": 6479, "pyramid": 6480, "coconut": 6481, "overly": 6482, "liter": 6483, "dumplings": 6484, "tim": 6485, "skewers": 6486, "plat": 6487, "rapids": 6488, "casually": 6489, "simulator": 6490, "meatball": 6491, "meatballs": 6492, "crinkle": 6493, "goblets": 6494, "wishes": 6495, "submarine": 6496, "monogrammed": 6497, "sideline": 6498, "spear": 6499, "turntable": 6500, "earring": 6501, "beauty": 6502, "accent": 6503, "sweeps": 6504, "grandmother": 6505, "heating": 6506, "peperoni": 6507, "finishes": 6508, "begin": 6509, "song": 6510, "concentrating": 6511, "goers": 6512, "obscene": 6513, "upstairs": 6514, "dinnerware": 6515, "apartments": 6516, "globe": 6517, "amazon": 6518, "breeze": 6519, "parasails": 6520, "microphones": 6521, "brings": 6522, "grasps": 6523, "campers": 6524, "fail": 6525, "b": 6526, "oversized": 6527, "award": 6528, "armoire": 6529, "technological": 6530, "happens": 6531, "sex": 6532, "dwelling": 6533, "chatting": 6534, "teach": 6535, "colleagues": 6536, "whats": 6537, "pretends": 6538, "funky": 6539, "wildly": 6540, "luck": 6541, "scantily": 6542, "buliding": 6543, "telephones": 6544, "tri": 6545, "pirates": 6546, "send": 6547, "sleeved": 6548, "bump": 6549, "headset": 6550, "trashed": 6551, "await": 6552, "disposal": 6553, "teal": 6554, "elaborately": 6555, "gilded": 6556, "freezers": 6557, "milkshake": 6558, "alcove": 6559, "quote": 6560, "ping": 6561, "pong": 6562, "fathers": 6563, "pendulum": 6564, "gazes": 6565, "penny": 6566, "flannel": 6567, "squeezing": 6568, "raspberries": 6569, "goodies": 6570, "dips": 6571, "corporate": 6572, "smells": 6573, "gay": 6574, "superimposed": 6575, "bowels": 6576, "kitchenware": 6577, "saucepan": 6578, "seaside": 6579, "shawl": 6580, "backsplash": 6581, "functional": 6582, "beams": 6583, "pour": 6584, "social": 6585, "lucky": 6586, "pristine": 6587, "reveals": 6588, "shaves": 6589, "headless": 6590, "sees": 6591, "scheme": 6592, "partition": 6593, "flair": 6594, "description": 6595, "dashboard": 6596, "restored": 6597, "tubs": 6598, "sidecar": 6599, "od": 6600, "cruiser": 6601, "pane": 6602, "transported": 6603, "trinkets": 6604, "roofs": 6605, "vein": 6606, "designer": 6607, "mechanic": 6608, "squat": 6609, "auto": 6610, "earlier": 6611, "folds": 6612, "pages": 6613, "cemetary": 6614, "stting": 6615, "grave": 6616, "sleeper": 6617, "jumpsuit": 6618, "hoisted": 6619, "inspecting": 6620, "showcase": 6621, "triumph": 6622, "showroom": 6623, "radar": 6624, "domed": 6625, "taxiing": 6626, "initials": 6627, "dropped": 6628, "ana": 6629, "jetliners": 6630, "southwest": 6631, "helicopters": 6632, "terminals": 6633, "787": 6634, "treeline": 6635, "expensive": 6636, "beachside": 6637, "atmosphere": 6638, "unloaded": 6639, "badly": 6640, "tanker": 6641, "region": 6642, "streetcar": 6643, "ambulance": 6644, "university": 6645, "jeeps": 6646, "9": 6647, "h": 6648, "trams": 6649, "rusting": 6650, "advertise": 6651, "manhole": 6652, "gutter": 6653, "sewer": 6654, "updated": 6655, "articulated": 6656, "europe": 6657, "sightseeing": 6658, "rigged": 6659, "lily": 6660, "pads": 6661, "rams": 6662, "poop": 6663, "chick": 6664, "prey": 6665, "urinating": 6666, "providing": 6667, "ann": 6668, "chicks": 6669, "puddles": 6670, "bolted": 6671, "treetops": 6672, "trainers": 6673, "chinatown": 6674, "squares": 6675, "lavender": 6676, "width": 6677, "switching": 6678, "sipping": 6679, "sippy": 6680, "rue": 6681, "paul": 6682, "peter": 6683, "google": 6684, "nowhere": 6685, "siding": 6686, "instructional": 6687, "reader": 6688, "steers": 6689, "forklift": 6690, "pallets": 6691, "occupying": 6692, "involving": 6693, "dalmation": 6694, "prone": 6695, "classy": 6696, "tux": 6697, "stay": 6698, "ankles": 6699, "gondola": 6700, "caring": 6701, "aa": 6702, "boxer": 6703, "dancers": 6704, "cultural": 6705, "dancer": 6706, "ladybug": 6707, "called": 6708, "flights": 6709, "atrium": 6710, "addresses": 6711, "peaked": 6712, "interest": 6713, "tot": 6714, "linked": 6715, "stride": 6716, "jerseys": 6717, "wildebeest": 6718, "sands": 6719, "fourth": 6720, "foal": 6721, "cropped": 6722, "decrepit": 6723, "beets": 6724, "skulls": 6725, "photoshopped": 6726, "jello": 6727, "advertises": 6728, "peanuts": 6729, "sum": 6730, "measured": 6731, "empire": 6732, "glassy": 6733, "forehead": 6734, "temple": 6735, "jumper": 6736, "tomatoe": 6737, "applesauce": 6738, "packet": 6739, "servings": 6740, "slalom": 6741, "knows": 6742, "pound": 6743, "popular": 6744, "crowding": 6745, "portions": 6746, "ability": 6747, "handrails": 6748, "propping": 6749, "condiment": 6750, "peace": 6751, "coveralls": 6752, "couscous": 6753, "vegtables": 6754, "ont": 6755, "compartments": 6756, "lunchbox": 6757, "surprised": 6758, "slop": 6759, "choice": 6760, "poached": 6761, "went": 6762, "reveal": 6763, "dominos": 6764, "grape": 6765, "soldiers": 6766, "snail": 6767, "severely": 6768, "elder": 6769, "attractively": 6770, "again": 6771, "ironing": 6772, "hearts": 6773, "pools": 6774, "outlined": 6775, "sonic": 6776, "soccor": 6777, "carpets": 6778, "tipping": 6779, "minimalist": 6780, "cause": 6781, "paperback": 6782, "partitions": 6783, "further": 6784, "slept": 6785, "padding": 6786, "slippers": 6787, "pacifier": 6788, "awkward": 6789, "enthusiast": 6790, "l": 6791, "components": 6792, "lovingly": 6793, "persona": 6794, "cosmetic": 6795, "manicure": 6796, "tapestry": 6797, "studs": 6798, "technology": 6799, "banquet": 6800, "future": 6801, "expo": 6802, "component": 6803, "siblings": 6804, "lava": 6805, "webcam": 6806, "soul": 6807, "stroller": 6808, "forehand": 6809, "smash": 6810, "braids": 6811, "flamingo": 6812, "flapping": 6813, "said": 6814, "raring": 6815, "rared": 6816, "shake": 6817, "crushed": 6818, "flakes": 6819, "shaker": 6820, "spiky": 6821, "vegan": 6822, "podium": 6823, "addressing": 6824, "crying": 6825, "autographed": 6826, "captioned": 6827, "telegraph": 6828, "readying": 6829, "recycled": 6830, "tins": 6831, "mints": 6832, "mitten": 6833, "starbucks": 6834, "armor": 6835, "smokes": 6836, "keychain": 6837, "charm": 6838, "drab": 6839, "general": 6840, "advertised": 6841, "icebox": 6842, "international": 6843, "zones": 6844, "dye": 6845, "opener": 6846, "fountains": 6847, "scones": 6848, "pearl": 6849, "decorates": 6850, "organization": 6851, "razor": 6852, "mash": 6853, "naan": 6854, "cafeteria": 6855, "sampling": 6856, "informal": 6857, "sundaes": 6858, "solid": 6859, "violin": 6860, "waiter": 6861, "paneling": 6862, "stoves": 6863, "uninstalled": 6864, "cookware": 6865, "temperature": 6866, "clasping": 6867, "clasped": 6868, "views": 6869, "unattached": 6870, "completed": 6871, "escorted": 6872, "ventilation": 6873, "masks": 6874, "illegally": 6875, "ajar": 6876, "maneuver": 6877, "spaces": 6878, "densely": 6879, "bmw": 6880, "stret": 6881, "citys": 6882, "herder": 6883, "lids": 6884, "intricate": 6885, "slot": 6886, "chipped": 6887, "runways": 6888, "altitude": 6889, "alike": 6890, "spandex": 6891, "ewe": 6892, "hazard": 6893, "headscarf": 6894, "pics": 6895, "creatively": 6896, "harbour": 6897, "final": 6898, "shooting": 6899, "assisting": 6900, "sheering": 6901, "lampposts": 6902, "tours": 6903, "subject": 6904, "plug": 6905, "flanked": 6906, "flanking": 6907, "nuzzle": 6908, "search": 6909, "sedan": 6910, "centers": 6911, "underpass": 6912, "restaurants": 6913, "roosters": 6914, "anime": 6915, "deal": 6916, "automobiles": 6917, "unload": 6918, "hurt": 6919, "hollow": 6920, "parakeets": 6921, "bard": 6922, "nectar": 6923, "shadowy": 6924, "cay": 6925, "elf": 6926, "broke": 6927, "perpendicular": 6928, "barb": 6929, "piers": 6930, "lincoln": 6931, "e": 6932, "viewers": 6933, "westminster": 6934, "peddling": 6935, "allowed": 6936, "diesel": 6937, "drops": 6938, "adds": 6939, "sack": 6940, "loses": 6941, "wiring": 6942, "write": 6943, "tickets": 6944, "writes": 6945, "multistory": 6946, "lanterns": 6947, "banners": 6948, "squid": 6949, "lumber": 6950, "toyota": 6951, "suspicious": 6952, "starring": 6953, "tote": 6954, "wheelchairs": 6955, "rind": 6956, "planner": 6957, "plus": 6958, "saver": 6959, "marquee": 6960, "statutes": 6961, "musician": 6962, "rig": 6963, "splashed": 6964, "think": 6965, "suites": 6966, "politician": 6967, "shakes": 6968, "saluting": 6969, "bookcases": 6970, "vent": 6971, "hurry": 6972, "waterfall": 6973, "experience": 6974, "kayaking": 6975, "entwined": 6976, "thatched": 6977, "hub": 6978, "columned": 6979, "join": 6980, "stilts": 6981, "rooftop": 6982, "acoustic": 6983, "collars": 6984, "buidling": 6985, "staying": 6986, "convenience": 6987, "mules": 6988, "venturing": 6989, "ribs": 6990, "outfielder": 6991, "engaging": 6992, "fielder": 6993, "cheap": 6994, "clumps": 6995, "lesson": 6996, "astride": 6997, "attempted": 6998, "24": 6999, "brilliant": 7000, "ripeness": 7001, "ripen": 7002, "homeplate": 7003, "coconuts": 7004, "tar": 7005, "consist": 7006, "merchant": 7007, "wrinkled": 7008, "hey": 7009, "handrail": 7010, "retaining": 7011, "greasy": 7012, "cabbages": 7013, "custard": 7014, "mogul": 7015, "judges": 7016, "kraut": 7017, "crests": 7018, "helmeted": 7019, "crab": 7020, "rapped": 7021, "baguette": 7022, "amish": 7023, "sugared": 7024, "savory": 7025, "volleyball": 7026, "imitating": 7027, "spices": 7028, "carves": 7029, "stamp": 7030, "swimmer": 7031, "bib": 7032, "possessions": 7033, "successfully": 7034, "sprints": 7035, "gel": 7036, "flour": 7037, "jail": 7038, "prison": 7039, "become": 7040, "artifacts": 7041, "seasoned": 7042, "comforters": 7043, "whites": 7044, "graphic": 7045, "might": 7046, "distorted": 7047, "7": 7048, "buddy": 7049, "aim": 7050, "vigorously": 7051, "bbq": 7052, "grilling": 7053, "diners": 7054, "inspired": 7055, "vying": 7056, "chubby": 7057, "q": 7058, "combined": 7059, "booty": 7060, "manner": 7061, "cabins": 7062, "spiked": 7063, "ascend": 7064, "jordan": 7065, "sailer": 7066, "lilies": 7067, "chocolates": 7068, "cuddles": 7069, "vacant": 7070, "ballpark": 7071, "pitched": 7072, "scoreboard": 7073, "courts": 7074, "physical": 7075, "microsoft": 7076, "sporty": 7077, "notebooks": 7078, "answering": 7079, "dice": 7080, "dividing": 7081, "graduates": 7082, "forty": 7083, "resturant": 7084, "hike": 7085, "edged": 7086, "specialty": 7087, "keypad": 7088, "charger": 7089, "ass": 7090, "350": 7091, "dash": 7092, "tallest": 7093, "shorter": 7094, "slate": 7095, "rooftops": 7096, "drier": 7097, "gauge": 7098, "architecturally": 7099, "appearance": 7100, "placemat": 7101, "captain": 7102, "america": 7103, "notepad": 7104, "list": 7105, "stationed": 7106, "bulding": 7107, "slender": 7108, "beads": 7109, "strand": 7110, "listen": 7111, "diverse": 7112, "greek": 7113, "thinks": 7114, "related": 7115, "choices": 7116, "thanksgiving": 7117, "marine": 7118, "rowers": 7119, "speedboat": 7120, "shouting": 7121, "arches": 7122, "tolit": 7123, "example": 7124, "rat": 7125, "created": 7126, "uneven": 7127, "tasks": 7128, "chic": 7129, "precariously": 7130, "highest": 7131, "pained": 7132, "textured": 7133, "messenger": 7134, "newspapers": 7135, "inset": 7136, "cockpit": 7137, "atv": 7138, "trouble": 7139, "singapore": 7140, "nails": 7141, "fueled": 7142, "houseplants": 7143, "proceeds": 7144, "descends": 7145, "solo": 7146, "interacts": 7147, "pm": 7148, "wispy": 7149, "pig": 7150, "stake": 7151, "enclose": 7152, "splayed": 7153, "browse": 7154, "faucets": 7155, "respected": 7156, "march": 7157, "disney": 7158, "monorail": 7159, "resemble": 7160, "arrangements": 7161, "thermometer": 7162, "multitude": 7163, "laps": 7164, "murdered": 7165, "flap": 7166, "perching": 7167, "hen": 7168, "weed": 7169, "safeway": 7170, "diaper": 7171, "picturesque": 7172, "kingdom": 7173, "hawks": 7174, "hoods": 7175, "caked": 7176, "donations": 7177, "collect": 7178, "thank": 7179, "bumps": 7180, "connecting": 7181, "mechanism": 7182, "standstill": 7183, "engineer": 7184, "adventure": 7185, "arrived": 7186, "departure": 7187, "approached": 7188, "defaced": 7189, "commute": 7190, "dispensing": 7191, "canned": 7192, "emits": 7193, "maintained": 7194, "roaring": 7195, "americans": 7196, "god": 7197, "payment": 7198, "armored": 7199, "bovine": 7200, "inspection": 7201, "tanning": 7202, "workshop": 7203, "awards": 7204, "plaques": 7205, "passersby": 7206, "ran": 7207, "folder": 7208, "engage": 7209, "slacks": 7210, "loosely": 7211, "unfolded": 7212, "flashlight": 7213, "allot": 7214, "hip": 7215, "raining": 7216, "re": 7217, "longboard": 7218, "shetland": 7219, "hooves": 7220, "gaining": 7221, "stepped": 7222, "majestic": 7223, "swimwear": 7224, "spaniel": 7225, "frisbie": 7226, "days": 7227, "skimpy": 7228, "incredibly": 7229, "totally": 7230, "farming": 7231, "moved": 7232, "hitched": 7233, "draft": 7234, "trot": 7235, "obstacles": 7236, "aside": 7237, "polo": 7238, "mare": 7239, "hurdles": 7240, "yankees": 7241, "violet": 7242, "teammate": 7243, "per": 7244, "electricity": 7245, "prior": 7246, "pieced": 7247, "cheering": 7248, "tupperware": 7249, "executes": 7250, "pierced": 7251, "avoiding": 7252, "tricky": 7253, "bannister": 7254, "elongated": 7255, "googles": 7256, "rode": 7257, "impressive": 7258, "skatboard": 7259, "launched": 7260, "isnt": 7261, "concession": 7262, "links": 7263, "puffs": 7264, "campfire": 7265, "parka": 7266, "wiped": 7267, "chooses": 7268, "exposing": 7269, "bartender": 7270, "30th": 7271, "trophies": 7272, "tells": 7273, "bra": 7274, "sucking": 7275, "diapers": 7276, "levitating": 7277, "backward": 7278, "broadcast": 7279, "patients": 7280, "operation": 7281, "locker": 7282, "undressed": 7283, "aggressively": 7284, "composite": 7285, "grasping": 7286, "peripherals": 7287, "guitars": 7288, "eclectic": 7289, "arc": 7290, "sailors": 7291, "tassels": 7292, "te": 7293, "waterside": 7294, "parasailers": 7295, "examples": 7296, "grid": 7297, "proximity": 7298, "spires": 7299, "populate": 7300, "participate": 7301, "flattened": 7302, "clutching": 7303, "nasa": 7304, "demonstration": 7305, "motorola": 7306, "gatorade": 7307, "pedestals": 7308, "ruined": 7309, "grained": 7310, "markets": 7311, "samples": 7312, "volunteers": 7313, "rafters": 7314, "ballroom": 7315, "boom": 7316, "embroidered": 7317, "member": 7318, "buddha": 7319, "wedged": 7320, "skewered": 7321, "souffle": 7322, "watermelons": 7323, "thinking": 7324, "rag": 7325, "address": 7326, "conditions": 7327, "arctic": 7328, "octopus": 7329, "crowed": 7330, "implements": 7331, "thre": 7332, "holidng": 7333, "soaking": 7334, "layout": 7335, "century": 7336, "curving": 7337, "specially": 7338, "dealership": 7339, "ruck": 7340, "plethora": 7341, "ads": 7342, "copy": 7343, "korean": 7344, "harry": 7345, "kennel": 7346, "admire": 7347, "cam": 7348, "crop": 7349, "clinic": 7350, "notices": 7351, "flames": 7352, "familiar": 7353, "giraffee": 7354, "rollerblading": 7355, "waling": 7356, "strolls": 7357, "digging": 7358, "spouts": 7359, "serene": 7360, "nobody": 7361, "eps": 7362, "diffrent": 7363, "sparrows": 7364, "judged": 7365, "lizard": 7366, "fifth": 7367, "loitering": 7368, "feeders": 7369, "brooklyn": 7370, "ended": 7371, "signpost": 7372, "germany": 7373, "billed": 7374, "windup": 7375, "slows": 7376, "payphone": 7377, "plume": 7378, "viewpoint": 7379, "foothills": 7380, "screaming": 7381, "nutritious": 7382, "throughout": 7383, "al": 7384, "speech": 7385, "scottish": 7386, "winnie": 7387, "farther": 7388, "sons": 7389, "squeeze": 7390, "meander": 7391, "shelters": 7392, "monk": 7393, "rickshaw": 7394, "buddhist": 7395, "turbulent": 7396, "elk": 7397, "bail": 7398, "dolly": 7399, "bale": 7400, "loan": 7401, "shielding": 7402, "os": 7403, "drags": 7404, "frolicking": 7405, "frizbee": 7406, "escape": 7407, "attacked": 7408, "armed": 7409, "shields": 7410, "shield": 7411, "purposes": 7412, "#": 7413, "naval": 7414, "bowel": 7415, "enthusiasts": 7416, "colander": 7417, "navel": 7418, "paraphernalia": 7419, "triangles": 7420, "brace": 7421, "tattooed": 7422, "foodstuffs": 7423, "cider": 7424, "avocados": 7425, "hummus": 7426, "snowshoes": 7427, "snowmobile": 7428, "friday": 7429, "13th": 7430, "valentines": 7431, "hush": 7432, "including:": 7433, "shoveling": 7434, "sprouts": 7435, "dollars": 7436, "rippling": 7437, "lacy": 7438, "cocoa": 7439, "flavor": 7440, "seasonings": 7441, "ponytail": 7442, "ceremonial": 7443, "barrow": 7444, "celebratory": 7445, "arranges": 7446, "lampshade": 7447, "maneuvering": 7448, "whimsical": 7449, "bandages": 7450, "bandaged": 7451, "tubes": 7452, "nurse": 7453, "apparatus": 7454, "tend": 7455, "emergency": 7456, "injury": 7457, "due": 7458, "overcooked": 7459, "royal": 7460, "squatted": 7461, "successful": 7462, "disassembled": 7463, "sony": 7464, "belonging": 7465, "burns": 7466, "loft": 7467, "crt": 7468, "amp": 7469, "activities": 7470, "wreath": 7471, "correct": 7472, "constructing": 7473, "umpires": 7474, "ages": 7475, "powerful": 7476, "pondering": 7477, "fellow": 7478, "apparel": 7479, "friendly": 7480, "calculators": 7481, "reserved": 7482, "ranging": 7483, "muscular": 7484, "talbe": 7485, "theatre": 7486, "casing": 7487, "wrenches": 7488, "leafed": 7489, "decoratively": 7490, "artistically": 7491, "ware": 7492, "weathervane": 7493, "decadent": 7494, "biscuit": 7495, "soups": 7496, "cuisine": 7497, "bubbly": 7498, "beret": 7499, "ordinary": 7500, "bands": 7501, "drainer": 7502, "saucers": 7503, "poorly": 7504, "tp": 7505, "fifties": 7506, "cutout": 7507, "matter": 7508, "bulging": 7509, "eyeballs": 7510, "googly": 7511, "plunger": 7512, "meanders": 7513, "rotten": 7514, "filth": 7515, "rifle": 7516, "capable": 7517, "urine": 7518, "motorcyclers": 7519, "crucifix": 7520, "backlit": 7521, "sole": 7522, "doorways": 7523, "cubicles": 7524, "bridges": 7525, "pride": 7526, "runaway": 7527, "servicing": 7528, "vertically": 7529, "cathay": 7530, "embrace": 7531, "romantic": 7532, "harvested": 7533, "bi": 7534, "chased": 7535, "airways": 7536, "crammed": 7537, "avid": 7538, "dinosaur": 7539, "ranger": 7540, "streak": 7541, "chipping": 7542, "longer": 7543, "mission": 7544, "stoop": 7545, "rancher": 7546, "shear": 7547, "tattered": 7548, "3d": 7549, "speckled": 7550, "russian": 7551, "yoga": 7552, "borders": 7553, "designating": 7554, "puffed": 7555, "lad": 7556, "patting": 7557, "wreck": 7558, "blooms": 7559, "offspring": 7560, "protest": 7561, "strap": 7562, "toting": 7563, "ups": 7564, "paints": 7565, "ed": 7566, "dumping": 7567, "mulch": 7568, "trucked": 7569, "bookbag": 7570, "records": 7571, "goodbye": 7572, "trust": 7573, "somber": 7574, "former": 7575, "guardrail": 7576, "likely": 7577, "chance": 7578, "flooding": 7579, "boxers": 7580, "grounded": 7581, "skirts": 7582, "postcard": 7583, "rad": 7584, "coolers": 7585, "snapshot": 7586, "sundown": 7587, "houseboat": 7588, "bold": 7589, "buoys": 7590, "lingerie": 7591, "role": 7592, "kilt": 7593, "looms": 7594, "umping": 7595, "coaster": 7596, "wanders": 7597, "health": 7598, "preserve": 7599, "theyre": 7600, "blueberries": 7601, "ump": 7602, "receives": 7603, "happening": 7604, "started": 7605, "overripe": 7606, "butterflies": 7607, "juicing": 7608, "ripened": 7609, "chiquita": 7610, "ripening": 7611, "overhand": 7612, "housing": 7613, "grainy": 7614, "thinly": 7615, "contemplates": 7616, "escalator": 7617, "appealing": 7618, "creatures": 7619, "unrecognizable": 7620, "purchasing": 7621, "veil": 7622, "shoots": 7623, "loop": 7624, "ollie": 7625, "complicated": 7626, "trimmings": 7627, "dreadlocks": 7628, "rugby": 7629, "receiver": 7630, "mixes": 7631, "cobbler": 7632, "waring": 7633, "beaming": 7634, "devil": 7635, "highly": 7636, "quilted": 7637, "burritos": 7638, "cigarettes": 7639, "unbuttoned": 7640, "chunks": 7641, "clinging": 7642, "simultaneously": 7643, "journal": 7644, "crusted": 7645, "herb": 7646, "gross": 7647, "deviled": 7648, "artisan": 7649, "saloon": 7650, "combs": 7651, "converted": 7652, "struck": 7653, "crisp": 7654, "companions": 7655, "lilacs": 7656, "internet": 7657, "delight": 7658, "contestant": 7659, "origami": 7660, "heat": 7661, "fancily": 7662, "kiln": 7663, "contained": 7664, "hashbrowns": 7665, "donkeys": 7666, "thought": 7667, "dribbles": 7668, "toga": 7669, "comparing": 7670, "comic": 7671, "orchids": 7672, "bistro": 7673, "scrolls": 7674, "lantern": 7675, "restricted": 7676, "bouquets": 7677, "screwdriver": 7678, "smal": 7679, "tandem": 7680, "servers": 7681, "whisk": 7682, "sharpening": 7683, "strains": 7684, "kitchens": 7685, "lotion": 7686, "dental": 7687, "outfitted": 7688, "trike": 7689, "shuttered": 7690, "ipad": 7691, "boot": 7692, "loaves": 7693, "kawasaki": 7694, "tavern": 7695, "frontal": 7696, "boeing": 7697, "void": 7698, "identification": 7699, "launching": 7700, "disembark": 7701, "ale": 7702, "readied": 7703, "tights": 7704, "atlantic": 7705, "klm": 7706, "frown": 7707, "downwards": 7708, "adn": 7709, "territory": 7710, "intersections": 7711, "spans": 7712, "auction": 7713, "plastered": 7714, "writings": 7715, "sayings": 7716, "aloft": 7717, "joy": 7718, "scarfs": 7719, "cardinal": 7720, "pelicans": 7721, "italy": 7722, "listing": 7723, "embankment": 7724, "passage": 7725, "safely": 7726, "hauls": 7727, "bluebird": 7728, "mallet": 7729, "croquet": 7730, "saint": 7731, "destinations": 7732, "madison": 7733, "rights": 7734, "slogan": 7735, "attack": 7736, "booties": 7737, "whiskers": 7738, "riverside": 7739, "scraps": 7740, "feasting": 7741, "smashed": 7742, "flowerpot": 7743, "wades": 7744, "panes": 7745, "stnading": 7746, "mist": 7747, "wheelers": 7748, "operator": 7749, "somebodys": 7750, "mock": 7751, "yells": 7752, "cape": 7753, "transports": 7754, "untied": 7755, "loosened": 7756, "clapping": 7757, "sideburns": 7758, "foreheads": 7759, "cove": 7760, "stature": 7761, "riverboat": 7762, "hauled": 7763, "egyptian": 7764, "wristband": 7765, "wrist": 7766, "bracelet": 7767, "swampy": 7768, "tear": 7769, "cocker": 7770, "angled": 7771, "cheetah": 7772, "brushy": 7773, "sniff": 7774, "firsbee": 7775, "ina": 7776, "score": 7777, "swishing": 7778, "bridles": 7779, "eyeing": 7780, "roots": 7781, "bass": 7782, "victory": 7783, "brimmed": 7784, "floret": 7785, "sprout": 7786, "suspension": 7787, "leeks": 7788, "swiss": 7789, "pecks": 7790, "lentils": 7791, "charcoal": 7792, "disposable": 7793, "grills": 7794, "fires": 7795, "confetti": 7796, "wtih": 7797, "kayaker": 7798, "moderately": 7799, "ending": 7800, "currents": 7801, "composed": 7802, "pretend": 7803, "hotels": 7804, "taught": 7805, "doves": 7806, "bodyboard": 7807, "unplugged": 7808, "burn": 7809, "crouch": 7810, "duel": 7811, "cordless": 7812, "soar": 7813, "hobby": 7814, "lookers": 7815, "parasail": 7816, "kitesurfing": 7817, "imac": 7818, "ate": 7819, "screensaver": 7820, "overstuffed": 7821, "singer": 7822, "obama": 7823, "barack": 7824, "enthusiastic": 7825, "attraction": 7826, "courthouse": 7827, "fillings": 7828, "oversize": 7829, "squirting": 7830, "mohawk": 7831, "camo": 7832, "attentive": 7833, "teacup": 7834, "cappuccino": 7835, "teacups": 7836, "michael": 7837, "crossbones": 7838, "filter": 7839, "simply": 7840, "fluorescent": 7841, "musicians": 7842, "topless": 7843, "domestic": 7844, "proper": 7845, "rotting": 7846, "plentiful": 7847, "etched": 7848, "bleeding": 7849, "juices": 7850, "demonstrating": 7851, "bubbles": 7852, "indians": 7853, "monitoring": 7854, "officials": 7855, "ii": 7856, "overhanging": 7857, "foilage": 7858, "charter": 7859, "firm": 7860, "barking": 7861, "original": 7862, "haight": 7863, "cuckoo": 7864, "coo": 7865, "limit": 7866, "30": 7867, "teeshirt": 7868, "wording": 7869, "obese": 7870, "july": 7871, "1st": 7872, "brownie": 7873, "trainyard": 7874, "22": 7875, "ducklings": 7876, "extensive": 7877, "network": 7878, "demon": 7879, "streaking": 7880, "straps": 7881, "holstein": 7882, "hasnt": 7883, "distinctive": 7884, "armrest": 7885, "firefighters": 7886, "apparently": 7887, "firetrucks": 7888, "dumped": 7889, "program": 7890, "beast": 7891, "walkie": 7892, "talkie": 7893, "stables": 7894, "labeling": 7895, "truly": 7896, "broadly": 7897, "enthusiastically": 7898, "hanged": 7899, "straightening": 7900, "modeling": 7901, "sailor": 7902, "media": 7903, "interviewed": 7904, "torso": 7905, "hosing": 7906, "potential": 7907, "torches": 7908, "steamboat": 7909, "manager": 7910, "stamps": 7911, "messages": 7912, "scribbled": 7913, "drainage": 7914, "hump": 7915, "fatigues": 7916, "tugging": 7917, "deflated": 7918, "darker": 7919, "explore": 7920, "harnessed": 7921, "desolate": 7922, "pinto": 7923, "screened": 7924, "scoops": 7925, "atlanta": 7926, "braves": 7927, "mates": 7928, "spins": 7929, "blossom": 7930, "pilled": 7931, "ramen": 7932, "raisins": 7933, "handlebar": 7934, "elbow": 7935, "poked": 7936, "completing": 7937, "brave": 7938, "grinder": 7939, "hilton": 7940, "hoagie": 7941, "edible": 7942, "strolling": 7943, "dances": 7944, "snows": 7945, "gyro": 7946, "spanish": 7947, "krispy": 7948, "kreme": 7949, "occasion": 7950, "lifeguard": 7951, "overtaken": 7952, "barbershop": 7953, "plating": 7954, "malaysia": 7955, "2013": 7956, "malaysian": 7957, "solders": 7958, "anniversary": 7959, "gains": 7960, "wipe": 7961, "knelt": 7962, "enterprise": 7963, "seaweed": 7964, "miscellaneous": 7965, "aims": 7966, "lunge": 7967, "jalapenos": 7968, "whiteboard": 7969, "karaoke": 7970, "mats": 7971, "surge": 7972, "multicolor": 7973, "fifty": 7974, "technique": 7975, "photoshop": 7976, "illustrated": 7977, "combed": 7978, "kettles": 7979, "smartphones": 7980, "ton": 7981, "illuminating": 7982, "outs": 7983, "stony": 7984, "exceptionally": 7985, "fascinating": 7986, "distinct": 7987, "prevent": 7988, "mussels": 7989, "diagram": 7990, "kichen": 7991, "streamlined": 7992, "spirit": 7993, "vivid": 7994, "stark": 7995, "puzzled": 7996, "inter": 7997, "soiled": 7998, "individually": 7999, "peeing": 8000, "pee": 8001, "disarray": 8002, "marsh": 8003, "era": 8004, "20th": 8005, "performed": 8006, "tailed": 8007, "devoid": 8008, "slat": 8009, "illuminate": 8010, "sequential": 8011, "benched": 8012, "wont": 8013, "holing": 8014, "yes": 8015, "divers": 8016, "hong": 8017, "kong": 8018, "manger": 8019, "brought": 8020, "sparrow": 8021, "reeds": 8022, "meme": 8023, "egret": 8024, "mph": 8025, "resident": 8026, "derelict": 8027, "5th": 8028, "cupola": 8029, "pallet": 8030, "oin": 8031, "duvet": 8032, "feels": 8033, "magnifying": 8034, "maps": 8035, "thigh": 8036, "panties": 8037, "businessmen": 8038, "tye": 8039, "beaded": 8040, "shrine": 8041, "sprinkler": 8042, "bibs": 8043, "bye": 8044, "downed": 8045, "pandas": 8046, "graphics": 8047, "montage": 8048, "exciting": 8049, "brook": 8050, "patrolling": 8051, "situation": 8052, "interaction": 8053, "expert": 8054, "emptying": 8055, "none": 8056, "sending": 8057, "grapefruits": 8058, "cents": 8059, "sleds": 8060, "sledding": 8061, "iwth": 8062, "cookout": 8063, "brussel": 8064, "brussels": 8065, "sleeves": 8066, "losing": 8067, "delicacy": 8068, "earbuds": 8069, "rapidly": 8070, "purchased": 8071, "dougnuts": 8072, "wakeboarding": 8073, "trench": 8074, "skewer": 8075, "mayo": 8076, "mayonnaise": 8077, "assistance": 8078, "paragliding": 8079, "glare": 8080, "furnishing": 8081, "sliver": 8082, "spin": 8083, "wrinkles": 8084, "daybed": 8085, "cradle": 8086, "spaceous": 8087, "mousepad": 8088, "sponsor": 8089, "daughters": 8090, "glory": 8091, "protection": 8092, "fourteen": 8093, "interface": 8094, "converses": 8095, "chat": 8096, "extend": 8097, "appointed": 8098, "foyer": 8099, "lcd": 8100, "handset": 8101, "lg": 8102, "primitive": 8103, "spilled": 8104, "lunches": 8105, "paris": 8106, "hamster": 8107, "dandelion": 8108, "emerges": 8109, "artfully": 8110, "hearth": 8111, "underway": 8112, "sheriffs": 8113, "vender": 8114, "journey": 8115, "urn": 8116, "zombies": 8117, "loafs": 8118, "ultra": 8119, "paintbrush": 8120, "modeled": 8121, "amenities": 8122, "earrings": 8123, "fogged": 8124, "amusing": 8125, "applied": 8126, "pant": 8127, "sheriff": 8128, "gateway": 8129, "closest": 8130, "motionless": 8131, "plans": 8132, "taxing": 8133, "turbine": 8134, "qantas": 8135, "gazebo": 8136, "slap": 8137, "slowing": 8138, "flaps": 8139, "scratched": 8140, "robin": 8141, "missiles": 8142, "jal": 8143, "witting": 8144, "emblems": 8145, "choosing": 8146, "preserver": 8147, "immediate": 8148, "juts": 8149, "gardens": 8150, "normal": 8151, "barefooted": 8152, "harnesses": 8153, "joining": 8154, "downstairs": 8155, "parading": 8156, "varied": 8157, "steams": 8158, "picket": 8159, "sings": 8160, "outer": 8161, "turbines": 8162, "fireworks": 8163, "attracts": 8164, "luncheon": 8165, "drums": 8166, "firemen": 8167, "kilts": 8168, "gong": 8169, "alive": 8170, "masked": 8171, "protester": 8172, "ensemble": 8173, "hull": 8174, "halter": 8175, "cheerleader": 8176, "extraordinary": 8177, "bodies": 8178, "spell": 8179, "peope": 8180, "spelling": 8181, "suckling": 8182, "mowed": 8183, "duo": 8184, "fribee": 8185, "forage": 8186, "captures": 8187, "vinyl": 8188, "emblem": 8189, "maneuvers": 8190, "sharply": 8191, "pecans": 8192, "baord": 8193, "cheddar": 8194, "kneepads": 8195, "stemware": 8196, "chowder": 8197, "nourishment": 8198, "vert": 8199, "duplicate": 8200, "seas": 8201, "sunscreen": 8202, "ther": 8203, "shared": 8204, "loveseat": 8205, "grip": 8206, "arching": 8207, "settee": 8208, "venue": 8209, "southwestern": 8210, "assembling": 8211, "exercises": 8212, "bordering": 8213, "supreme": 8214, "salesman": 8215, "hp": 8216, "odds": 8217, "carnation": 8218, "joker": 8219, "stylus": 8220, "canister": 8221, "router": 8222, "jumble": 8223, "workbench": 8224, "loom": 8225, "negative": 8226, "depict": 8227, "palace": 8228, "collectibles": 8229, "modest": 8230, "spotless": 8231, "shampoo": 8232, "ankle": 8233, "fascinated": 8234, "peeler": 8235, "admired": 8236, "huts": 8237, "continental": 8238, "sever": 8239, "crows": 8240, "peopel": 8241, "peal": 8242, "gras": 8243, "vancouver": 8244, "attractions": 8245, "sunning": 8246, "division": 8247, "shores": 8248, "common": 8249, "25": 8250, "posting": 8251, "prince": 8252, "netted": 8253, "fe": 8254, "review": 8255, "acknowledging": 8256, "hi": 8257, "reason": 8258, "vessels": 8259, "kimono": 8260, "forms": 8261, "realistic": 8262, "astounding": 8263, "sling": 8264, "peaks": 8265, "separates": 8266, "clouded": 8267, "pomeranian": 8268, "obstructed": 8269, "lasso": 8270, "coaching": 8271, "coached": 8272, "slushy": 8273, "ans": 8274, "segments": 8275, "alfredo": 8276, "organizing": 8277, "warmer": 8278, "alfalfa": 8279, "patties": 8280, "paired": 8281, "skeleton": 8282, "nike": 8283, "lodged": 8284, "creams": 8285, "cornbread": 8286, "vat": 8287, "batches": 8288, "inn": 8289, "grater": 8290, "crispy": 8291, "birth": 8292, "spotlight": 8293, "floaters": 8294, "motif": 8295, "chested": 8296, "save": 8297, "reclined": 8298, "sophisticated": 8299, "netbook": 8300, "expose": 8301, "cheeks": 8302, "pod": 8303, "darkness": 8304, "smokey": 8305, "rhode": 8306, "peson": 8307, "turban": 8308, "supposed": 8309, "succulents": 8310, "ceramics": 8311, "felt": 8312, "yarn": 8313, "diorama": 8314, "dine": 8315, "lobsters": 8316, "barbecued": 8317, "exhaust": 8318, "chill": 8319, "heated": 8320, "skylight": 8321, "minimalistic": 8322, "heap": 8323, "backpacking": 8324, "cupping": 8325, "rears": 8326, "gump": 8327, "kickstand": 8328, "secure": 8329, "frontier": 8330, "fueling": 8331, "contrails": 8332, "x": 8333, "sheepdog": 8334, "trial": 8335, "danger": 8336, "dramatic": 8337, "greenhouse": 8338, "inclosure": 8339, "hide": 8340, "escorting": 8341, "woody": 8342, "pastures": 8343, "clover": 8344, "snowed": 8345, "sheers": 8346, "de": 8347, "believing": 8348, "rabbits": 8349, "flaming": 8350, "scarves": 8351, "largely": 8352, "stray": 8353, "unzipped": 8354, "vision": 8355, "barrack": 8356, "document": 8357, "attendees": 8358, "frolic": 8359, "silos": 8360, "pasties": 8361, "poem": 8362, "spout": 8363, "zippered": 8364, "tortoise": 8365, "vultures": 8366, "robes": 8367, "chihuahua": 8368, "frizbe": 8369, "breeds": 8370, "simpsons": 8371, "driftwood": 8372, "gallop": 8373, "equestrians": 8374, "ontop": 8375, "graceful": 8376, "straddling": 8377, "jams": 8378, "oj": 8379, "yellowish": 8380, "susan": 8381, "spoke": 8382, "tastes": 8383, "traversing": 8384, "mein": 8385, "techniques": 8386, "benedict": 8387, "cheer": 8388, "drum": 8389, "elders": 8390, "slathered": 8391, "rules": 8392, "coating": 8393, "selections": 8394, "lattice": 8395, "dangerous": 8396, "tart": 8397, "unlit": 8398, "ratchet": 8399, "juggling": 8400, "tangle": 8401, "rolex": 8402, "leggings": 8403, "energy": 8404, "southern": 8405, "wilson": 8406, "restuarant": 8407, "upset": 8408, "question": 8409, "recess": 8410, "shelfs": 8411, "13": 8412, "systems": 8413, "cracks": 8414, "checkout": 8415, "elizabeth": 8416, "partners": 8417, "detail": 8418, "webpage": 8419, "timer": 8420, "adidas": 8421, "budweiser": 8422, "micro": 8423, "bound": 8424, "tab": 8425, "11": 8426, "oxygen": 8427, "outlets": 8428, "drenched": 8429, "don": 8430, "appetizer": 8431, "cellar": 8432, "tackle": 8433, "pinstripe": 8434, "maze": 8435, "bedspreads": 8436, "newest": 8437, "merchants": 8438, "repaired": 8439, "assists": 8440, "fighters": 8441, "motorcylces": 8442, "bunkbed": 8443, "thorough": 8444, "accordion": 8445, "ar": 8446, "john": 8447, "protestors": 8448, "ward": 8449, "kiddie": 8450, "announcer": 8451, "crews": 8452, "tokyo": 8453, "protesters": 8454, "tuck": 8455, "zipper": 8456, "bomb": 8457, "squad": 8458, "james": 8459, "sites": 8460, "sash": 8461, "badges": 8462, "oakland": 8463, "algae": 8464, "die": 8465, "skimming": 8466, "sunbathers": 8467, "limo": 8468, "express": 8469, "grimaces": 8470, "collectible": 8471, "marilyn": 8472, "monroe": 8473, "butting": 8474, "doge": 8475, "ducking": 8476, "campground": 8477, "chariot": 8478, "bikinis": 8479, "swimsuits": 8480, "exercising": 8481, "draws": 8482, "wakeboarder": 8483, "gnawing": 8484, "gerbil": 8485, "puzzle": 8486, "taht": 8487, "ramping": 8488, "cranberries": 8489, "pickled": 8490, "garnishment": 8491, "wth": 8492, "munch": 8493, "blends": 8494, "aids": 8495, "pills": 8496, "graph": 8497, "easter": 8498, "playstation": 8499, "batman": 8500, "holidays": 8501, "coastal": 8502, "biggest": 8503, "guinea": 8504, "dj": 8505, "laminate": 8506, "mittens": 8507, "cooktop": 8508, "stains": 8509, "dinosaurs": 8510, "workout": 8511, "ranges": 8512, "transformed": 8513, "moto": 8514, "haircut": 8515, "sculpted": 8516, "portraits": 8517, "pattered": 8518, "grits": 8519, "rained": 8520, "cardigan": 8521, "organ": 8522, "cubby": 8523, "kneel": 8524, "waiters": 8525, "hutch": 8526, "bulbs": 8527, "ducky": 8528, "cobblestones": 8529, "50": 8530, "50th": 8531, "blacktop": 8532, "soaps": 8533, "seashells": 8534, "plungers": 8535, "dudes": 8536, "plums": 8537, "suzuki": 8538, "reporter": 8539, "roams": 8540, "barricaded": 8541, "glacier": 8542, "specialized": 8543, "creates": 8544, "valves": 8545, "traveled": 8546, "bathe": 8547, "fowl": 8548, "troll": 8549, "los": 8550, "angeles": 8551, "alaska": 8552, "droplets": 8553, "messily": 8554, "tequila": 8555, "grease": 8556, "shock": 8557, "furthest": 8558, "squished": 8559, "services": 8560, "determined": 8561, "canopies": 8562, "cubical": 8563, "geisha": 8564, "barbie": 8565, "zipping": 8566, "mr": 8567, "prize": 8568, "crook": 8569, "vice": 8570, "op": 8571, "waterhole": 8572, "orphanage": 8573, "vitamin": 8574, "mandarin": 8575, "miller": 8576, "needles": 8577, "grin": 8578, "gardening": 8579, "sparkling": 8580, "surboard": 8581, "kiteboards": 8582, "decals": 8583, "wieners": 8584, "consume": 8585, "lavishly": 8586, "gymnasium": 8587, "parlor": 8588, "headphone": 8589, "mime": 8590, "berlin": 8591, "dandelions": 8592, "selfies": 8593, "recorded": 8594, "tap": 8595, "cubed": 8596, "omelets": 8597, "canisters": 8598, "blackboard": 8599, "dollhouse": 8600, "outward": 8601, "candid": 8602, "assist": 8603, "figs": 8604, "dirtbike": 8605, "grayish": 8606, "illuminates": 8607, "speedometer": 8608, "idling": 8609, "jewish": 8610, "our": 8611, "roundabout": 8612, "birdhouse": 8613, "drift": 8614, "restrooms": 8615, "yield": 8616, "mm": 8617, "bout": 8618, "hitch": 8619, "catholic": 8620, "windmills": 8621, "carriers": 8622, "learns": 8623, "quad": 8624, "barns": 8625, "relief": 8626, "milked": 8627, "confinement": 8628, "pasted": 8629, "overcoat": 8630, "bolts": 8631, "rummaging": 8632, "wardrobe": 8633, "usage": 8634, "plump": 8635, "decided": 8636, "haul": 8637, "straws": 8638, "steady": 8639, "cheetos": 8640, "unidentified": 8641, "hookah": 8642, "combines": 8643, "chilies": 8644, "vegitables": 8645, "draining": 8646, "flask": 8647, "spam": 8648, "footlong": 8649, "cheerful": 8650, "shoving": 8651, "shakers": 8652, "inthe": 8653, "cheers": 8654, "programming": 8655, "ruby": 8656, "bandage": 8657, "idiot": 8658, "mechanics": 8659, "slip": 8660, "elbows": 8661, "homey": 8662, "browses": 8663, "flyer": 8664, "inflated": 8665, "chamber": 8666, "aid": 8667, "refigerator": 8668, "arent": 8669, "screwed": 8670, "pressure": 8671, "regal": 8672, "canon": 8673, "plugs": 8674, "disgusting": 8675, "roles": 8676, "piping": 8677, "dials": 8678, "pamphlet": 8679, "overtop": 8680, "volvo": 8681, "ever": 8682, "checkpoint": 8683, "tuned": 8684, "blackened": 8685, "malnourished": 8686, "folders": 8687, "manikin": 8688, "demonstrate": 8689, "graduation": 8690, "exits": 8691, "breed": 8692, "struts": 8693, "frilly": 8694, "trots": 8695, "hers": 8696, "treading": 8697, "participants": 8698, "chickpeas": 8699, "wiener": 8700, "mmm": 8701, "beet": 8702, "ona": 8703, "makings": 8704, "bunnies": 8705, "aggressive": 8706, "pillowcases": 8707, "arugula": 8708, "meant": 8709, "experiment": 8710, "slew": 8711, "hardware": 8712, "congregated": 8713, "winning": 8714, "slam": 8715, "colourful": 8716, "promotional": 8717, "clicking": 8718, "catering": 8719, "pda": 8720, "announcing": 8721, "canning": 8722, "gnome": 8723, "ambulances": 8724, "limited": 8725, "redone": 8726, "moldy": 8727, "grime": 8728, "ivory": 8729, "instructs": 8730, "mercedes": 8731, "benz": 8732, "title": 8733, "motorcade": 8734, "steeples": 8735, "atvs": 8736, "popping": 8737, "bred": 8738, "66": 8739, "photographic": 8740, "55": 8741, "trellis": 8742, "footpath": 8743, "branded": 8744, "20": 8745, "mauve": 8746, "baron": 8747, "elvis": 8748, "venice": 8749, "secured": 8750, "mansion": 8751, "boombox": 8752, "applies": 8753, "autograph": 8754, "mold": 8755, "mortar": 8756, "snacking": 8757, "minute": 8758, "hors": 8759, "diet": 8760, "airborn": 8761, "whizzes": 8762, "actual": 8763, "singular": 8764, "listed": 8765, "crested": 8766, "surgery": 8767, "actively": 8768, "chow": 8769, "delicately": 8770, "penalty": 8771, "pops": 8772, "communicating": 8773, "approval": 8774, "urns": 8775, "sewn": 8776, "especially": 8777, "papered": 8778, "outwards": 8779, "tranquil": 8780, "binoculars": 8781, "themself": 8782, "whirlpool": 8783, "necessities": 8784, "pylons": 8785, "meowing": 8786, "glances": 8787, "forming": 8788, "swirled": 8789, "yamaha": 8790, "granddaughter": 8791, "nonchalantly": 8792, "continue": 8793, "sync": 8794, "stooping": 8795, "volunteer": 8796, "lorry": 8797, "griaffe": 8798, "alto": 8799, "chevy": 8800, "offered": 8801, "nibbles": 8802, "craggy": 8803, "beachfront": 8804, "battleship": 8805, "congregating": 8806, "cocking": 8807, "buck": 8808, "cookbook": 8809, "skiiing": 8810, "discuss": 8811, "zooms": 8812, "badge": 8813, "surveying": 8814, "lessons": 8815, "mismatched": 8816, "relatively": 8817, "task": 8818, "texts": 8819, "preparations": 8820, "rubble": 8821, "battery": 8822, "awake": 8823, "protrudes": 8824, "im": 8825, "stoops": 8826, "handheld": 8827, "refreshments": 8828, "breasts": 8829, "barstools": 8830, "washbasin": 8831, "spa": 8832, "slid": 8833, "lack": 8834, "christ": 8835, "connection": 8836, "cessna": 8837, "betting": 8838, "bales": 8839, "bills": 8840, "beaked": 8841, "clams": 8842, "chess": 8843, "beacon": 8844, "streetlamp": 8845, "phrase": 8846, "donation": 8847, "outskirts": 8848, "udders": 8849, "bluff": 8850, "monks": 8851, "gingham": 8852, "perked": 8853, "attacking": 8854, "sombrero": 8855, "almonds": 8856, "chewed": 8857, "becoming": 8858, "handstand": 8859, "unpacked": 8860, "exact": 8861, "heritage": 8862, "charms": 8863, "glistening": 8864, "surfboarders": 8865, "haze": 8866, "swift": 8867, "rom": 8868, "allowing": 8869, "stomachs": 8870, "remain": 8871, "washes": 8872, "popcorn": 8873, "erase": 8874, "nerf": 8875, "sanctioned": 8876, "candlelight": 8877, "data": 8878, "ledges": 8879, "pre": 8880, "personalized": 8881, "courch": 8882, "sideboard": 8883, "postage": 8884, "wineglasses": 8885, "recessed": 8886, "knobs": 8887, "bleacher": 8888, "refurbished": 8889, "hopping": 8890, "digs": 8891, "leathers": 8892, "bluish": 8893, "bouncy": 8894, "dusting": 8895, "perimeter": 8896, "airshow": 8897, "begun": 8898, "nineteen": 8899, "cocked": 8900, "penguins": 8901, "rockaway": 8902, "george": 8903, "alertly": 8904, "climbers": 8905, "lipstick": 8906, "stern": 8907, "braces": 8908, "jewels": 8909, "fairy": 8910, "footboard": 8911, "flurry": 8912, "barley": 8913, "af": 8914, "blizzard": 8915, "glazing": 8916, "orchid": 8917, "planks": 8918, "sweating": 8919, "supported": 8920, "blast": 8921, "fanning": 8922, "refrigeration": 8923, "icon": 8924, "chars": 8925, "remarkable": 8926, "toped": 8927, "fin": 8928, "crutches": 8929, "insides": 8930, "hundred": 8931, "investigate": 8932, "wharf": 8933, "joint": 8934, "refueling": 8935, "windsurfers": 8936, "smoker": 8937, "stading": 8938, "espresso": 8939, "spools": 8940, "dedicated": 8941, "aer": 8942, "believe": 8943, "predators": 8944, "strokes": 8945, "duty": 8946, "roping": 8947, "pealed": 8948, "dollop": 8949, "ot": 8950, "crabs": 8951, "collected": 8952, "calzone": 8953, "nathans": 8954, "puff": 8955, "coordinating": 8956, "dramatically": 8957, "received": 8958, "nunchuck": 8959, "recreation": 8960, "effects": 8961, "ink": 8962, "spatulas": 8963, "mango": 8964, "knickknacks": 8965, "creations": 8966, "politicians": 8967, "skim": 8968, "styling": 8969, "skidding": 8970, "motorcross": 8971, "arial": 8972, "shamrock": 8973, "wonder": 8974, "k": 8975, "brim": 8976, "eof": 8977, "bourbon": 8978, "icicles": 8979, "sil": 8980, "fabulous": 8981, "processed": 8982, "boutonniere": 8983, "beards": 8984, "retrieves": 8985, "cleared": 8986, "souvenirs": 8987, "offer": 8988, "medley": 8989, "protein": 8990, "pinstriped": 8991, "orchard": 8992, "hops": 8993, "represent": 8994, "core": 8995, "rye": 8996, "interviewing": 8997, "badminton": 8998, "chunky": 8999, "hoody": 9000, "parliament": 9001, "leisurely": 9002, "flank": 9003, "belong": 9004, "camcorder": 9005, "crawls": 9006, "cookbooks": 9007, "fridges": 9008, "scooping": 9009, "garland": 9010, "lei": 9011, "wastebasket": 9012, "vespa": 9013, "functions": 9014, "vietnamese": 9015, "obscures": 9016, "bounded": 9017, "volkswagen": 9018, "palms": 9019, "manchester": 9020, "mermaid": 9021, "trumpet": 9022, "informing": 9023, "youre": 9024, "crescent": 9025, "handkerchief": 9026, "masts": 9027, "known": 9028, "plater": 9029, "poppy": 9030, "alligator": 9031, "sweaty": 9032, "arcade": 9033, "lovers": 9034, "binders": 9035, "ottomans": 9036, "philadelphia": 9037, "phillies": 9038, "octagonal": 9039, "lo": 9040, "pat": 9041, "noon": 9042, "brunch": 9043, "snoozing": 9044, "highlights": 9045, "customized": 9046, "gleaming": 9047, "chargers": 9048, "buldings": 9049, "needing": 9050, "whoa": 9051, "shacks": 9052, "turbans": 9053, "prom": 9054, "beetle": 9055, "anti": 9056, "brides": 9057, "pomegranates": 9058, "quantity": 9059, "basebal": 9060, "braided": 9061, "twists": 9062, "shallows": 9063, "calzones": 9064, "bassinet": 9065, "convenient": 9066, "pinking": 9067, "shattered": 9068, "granny": 9069, "smith": 9070, "notice": 9071, "magnificent": 9072, "jobs": 9073, "articles": 9074, "routes": 9075, "trolleys": 9076, "cosmetics": 9077, "menacingly": 9078, "guns": 9079, "locking": 9080, "balck": 9081, "released": 9082, "clutched": 9083, "afraid": 9084, "tartar": 9085, "plae": 9086, "response": 9087, "girlfriend": 9088, "sadly": 9089, "spend": 9090, "sharpie": 9091, "corks": 9092, "blindfolded": 9093, "blindfold": 9094, "chats": 9095, "wheeling": 9096, "culinary": 9097, "geometric": 9098, "gallon": 9099, "doorstep": 9100, "bu": 9101, "hatch": 9102, "fluffed": 9103, "slipper": 9104, "tractors": 9105, "tortoiseshell": 9106, "priest": 9107, "pastor": 9108, "doggie": 9109, "timey": 9110, "hangers": 9111, "surprise": 9112, "irish": 9113, "blower": 9114, "tummy": 9115, "chees": 9116, "taps": 9117, "institutional": 9118, "focuses": 9119, "porridge": 9120, "grating": 9121, "clam": 9122, "lockers": 9123, "jetway": 9124, "scotland": 9125, "movable": 9126, "civil": 9127, "reporters": 9128, "inscription": 9129, "mallard": 9130, "nesting": 9131, "feathered": 9132, "prizes": 9133, "idyllic": 9134, "popsicle": 9135, "boa": 9136, "forlorn": 9137, "tearing": 9138, "dummies": 9139, "mannequins": 9140, "matt": 9141, "mardi": 9142, "2012": 9143, "ease": 9144, "ted": 9145, "showed": 9146, "carnations": 9147, "prancing": 9148, "tankless": 9149, "horrible": 9150, "bystanders": 9151, "carport": 9152, "dangerously": 9153, "coup": 9154, "cacti": 9155, "ladders": 9156, "tried": 9157, "sacks": 9158, "compound": 9159, "grassed": 9160, "carcass": 9161, "mangos": 9162, "powdery": 9163, "shoved": 9164, "bratwurst": 9165, "confection": 9166, "manufacturing": 9167, "occupies": 9168, "pharmacy": 9169, "counting": 9170, "pill": 9171, "satin": 9172, "polished": 9173, "artful": 9174, "memory": 9175, "clipboard": 9176, "zip": 9177, "garnishes": 9178, "smoked": 9179, "forked": 9180, "sunroof": 9181, "anytime": 9182, "sneaks": 9183, "seuss": 9184, "grumpy": 9185, "contentedly": 9186, "toa": 9187, "3rd": 9188, "examined": 9189, "embellished": 9190, "footprints": 9191, "flippers": 9192, "coals": 9193, "sittign": 9194, "bet": 9195, "payer": 9196, "creation": 9197, "properly": 9198, "foldable": 9199, "peep": 9200, "bizarre": 9201, "splits": 9202, "motors": 9203, "representation": 9204, "sweeping": 9205, "mailboxes": 9206, "drunk": 9207, "spigot": 9208, "collector": 9209, "terrible": 9210, "crowns": 9211, "unseen": 9212, "disks": 9213, "colts": 9214, "pomegranate": 9215, "marmalade": 9216, "oreo": 9217, "scuba": 9218, "yourself": 9219, "margherita": 9220, "allover": 9221, "wi": 9222, "orioles": 9223, "facebook": 9224, "app": 9225, "blazing": 9226, "magnetic": 9227, "artists": 9228, "whether": 9229, "abandon": 9230, "del": 9231, "monte": 9232, "graduate": 9233, "umbrealla": 9234, "motoring": 9235, "pawn": 9236, "pitbull": 9237, "buffalos": 9238, "blaze": 9239, "nutella": 9240, "skateboarded": 9241, "reef": 9242, "ciabatta": 9243, "mariners": 9244, "stunning": 9245, "scrolling": 9246, "twirling": 9247, "adobe": 9248, "sharpener": 9249, "graveled": 9250, "courtroom": 9251, "wrote": 9252, "doubledecker": 9253, "piggy": 9254, "reduce": 9255, "freightliner": 9256, "curves": 9257, "lacrosse": 9258, "aging": 9259, "pavers": 9260, "dew": 9261, "hd": 9262, "29": 9263, "funeral": 9264, "album": 9265, "hotplate": 9266, "conditioner": 9267, "sown": 9268, "presidents": 9269, "drill": 9270, "owls": 9271, "true": 9272, "mysterious": 9273, "treks": 9274, "chestnut": 9275, "bannanas": 9276, "creamer": 9277, "plats": 9278, "deodorant": 9279, "connects": 9280, "hunches": 9281, "longingly": 9282, "conversations": 9283, "martin": 9284, "abraham": 9285, "brakes": 9286, "stree": 9287, "excellent": 9288, "bees": 9289, "carraige": 9290, "duster": 9291, "chilled": 9292, "candlesticks": 9293, "lollipops": 9294, "placemats": 9295, "taxidermy": 9296, "leak": 9297, "inscribed": 9298, "nw": 9299, "steve": 9300, "flows": 9301, "kings": 9302, "combat": 9303, "riderless": 9304, "snowsuits": 9305, "clementines": 9306, "dunking": 9307, "nursery": 9308, "tabe": 9309, "kleenex": 9310, "showerhead": 9311, "sterile": 9312, "banister": 9313, "clinton": 9314, "dodging": 9315, "skii": 9316, "bucks": 9317, "tomatos": 9318, "johns": 9319, "clementine": 9320, "stuffs": 9321, "cork": 9322, "prepping": 9323, "breath": 9324, "munches": 9325, "feathery": 9326, "medals": 9327, "shred": 9328, "j": 9329, "production": 9330, "aligned": 9331, "users": 9332, "dole": 9333, "vaulted": 9334, "bareback": 9335, "goofing": 9336, "playroom": 9337, "vote": 9338, "honk": 9339, "boring": 9340, "specific": 9341, "dismantled": 9342, "pleasure": 9343, "widescreen": 9344, "backup": 9345, "bid": 9346, "railed": 9347, "busily": 9348, "clutches": 9349, "vacuum": 9350, "bleak": 9351, "thorny": 9352, "patricks": 9353, "stitched": 9354, "limousine": 9355, "wile": 9356, "hatchback": 9357, "bazaar": 9358, "skatebaord": 9359, "minnie": 9360, "sibling": 9361, "tastefully": 9362, "snowmen": 9363, "exchange": 9364, "ok": 9365, "invisible": 9366, "pom": 9367, "greyhound": 9368, "railways": 9369, "executive": 9370, "condom": 9371, "lapt": 9372, "accompany": 9373, "grungy": 9374, "dam": 9375, "furred": 9376, "moderate": 9377, "drizzling": 9378, "register": 9379, "application": 9380, "soapy": 9381, "raggedy": 9382, "baltimore": 9383, "strapping": 9384, "actions": 9385, "straining": 9386, "rent": 9387, "snowfall": 9388, "emirates": 9389, "queue": 9390, "thatch": 9391, "baggy": 9392, "included": 9393, "ipads": 9394, "toolbox": 9395, "shortly": 9396, "bonsai": 9397, "puppets": 9398, "crossroad": 9399, "streaked": 9400, "catamaran": 9401, "preforms": 9402, "golfer": 9403, "artichoke": 9404, "spelled": 9405, "gingerbread": 9406, "dreads": 9407, "presidential": 9408, "hula": 9409, "birch": 9410, "retrieve": 9411, "tacos": 9412, "cartoonish": 9413, "kitcehn": 9414, "choo": 9415, "harper": 9416, "identically": 9417, "dick": 9418, "pursuit": 9419, "bulidings": 9420, "accepting": 9421, "thousands": 9422, "bible": 9423, "buiding": 9424, "gauze": 9425, "procedure": 9426, "charity": 9427, "loved": 9428, "sunday": 9429, "crater": 9430, "scatter": 9431, "printing": 9432, "grille": 9433, "keepers": 9434, "clipping": 9435, "forces": 9436, "inspected": 9437, "slippery": 9438, "collision": 9439, "winner": 9440, "ronald": 9441, "mcdonald": 9442, "pounce": 9443, "tigers": 9444, "paused": 9445, "stacking": 9446, "nativity": 9447, "vodka": 9448, "jay": 9449, "17": 9450, "staples": 9451, "slips": 9452, "funnel": 9453, "flesh": 9454, "airfrance": 9455, "dynamite": 9456, "stethoscope": 9457, "hyenas": 9458, "valleys": 9459, "myspace": 9460, "kerry": 9461, "delectable": 9462, "laser": 9463, "toasters": 9464, "buttery": 9465, "marijuana": 9466, "15": 9467, "pint": 9468, "vie": 9469, "workings": 9470, "brahma": 9471, "winners": 9472, "jeff": 9473, "dragged": 9474, "beware": 9475, "talbot": 9476, "thirteen": 9477, "choir": 9478, "monopoly": 9479, "eva": 9480, "coop": 9481, "bronco": 9482, "salvation": 9483, "scarecrow": 9484, "swaddled": 9485, "dentist": 9486, "": 9487, "": 9488, "": 9489, "": 0} -------------------------------------------------------------------------------- /encryption/decrypt.py: -------------------------------------------------------------------------------- 1 | from cryptography.fernet import Fernet 2 | fernet = Fernet(key) 3 | 4 | with open('result.csv', 'rb') as enc_file: 5 | encrypted = enc_file.read() 6 | 7 | decrypted = fernet.decrypt(encrypted) 8 | 9 | with open('result.csv', 'wb') as dec_file: 10 | dec_file.write(decrypted) 11 | -------------------------------------------------------------------------------- /encryption/encrypt.py: -------------------------------------------------------------------------------- 1 | from cryptography.fernet import Fernet 2 | key = Fernet.generate_key() 3 | 4 | with open('filekey.key', 'wb') as filekey: 5 | filekey.write(key) 6 | 7 | with open('filekey.key', 'rb') as filekey: 8 | key = filekey.read() 9 | 10 | fernet = Fernet(key) 11 | 12 | with open('result.csv', 'rb') as file: 13 | original = file.read() 14 | encrypted = fernet.encrypt(original) 15 | 16 | with open('result.csv', 'wb') as encrypted_file: 17 | encrypted_file.write(encrypted) -------------------------------------------------------------------------------- /model/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aryankargwal/cap-bot/bce709ad8a2c77c95890450d3b571419786417ee/model/__init__.py -------------------------------------------------------------------------------- /model/datasets.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.utils.data import Dataset 3 | import h5py 4 | import json 5 | import os 6 | 7 | 8 | class CaptionDataset(Dataset): 9 | """ 10 | A PyTorch Dataset class to be used in a PyTorch DataLoader to create batches. 11 | """ 12 | 13 | def __init__(self, data_folder, data_name, split, transform=None): 14 | """ 15 | :param data_folder: folder where data files are stored 16 | :param data_name: base name of processed datasets 17 | :param split: split, one of 'TRAIN', 'VAL', or 'TEST' 18 | :param transform: image transform pipeline 19 | """ 20 | self.split = split 21 | assert self.split in {'TRAIN', 'VAL', 'TEST'} 22 | 23 | # Open hdf5 file where images are stored 24 | self.h = h5py.File(os.path.join(data_folder, self.split + '_IMAGES_' + data_name + '.hdf5'), 'r') 25 | self.imgs = self.h['images'] 26 | 27 | # Captions per image 28 | self.cpi = self.h.attrs['captions_per_image'] 29 | 30 | # Load encoded captions (completely into memory) 31 | with open(os.path.join(data_folder, self.split + '_CAPTIONS_' + data_name + '.json'), 'r') as j: 32 | self.captions = json.load(j) 33 | 34 | # Load caption lengths (completely into memory) 35 | with open(os.path.join(data_folder, self.split + '_CAPLENS_' + data_name + '.json'), 'r') as j: 36 | self.caplens = json.load(j) 37 | 38 | # PyTorch transformation pipeline for the image (normalizing, etc.) 39 | self.transform = transform 40 | 41 | # Total number of datapoints 42 | self.dataset_size = len(self.captions) 43 | 44 | def __getitem__(self, i): 45 | # Remember, the Nth caption corresponds to the (N // captions_per_image)th image 46 | img = torch.FloatTensor(self.imgs[i // self.cpi] / 255.) 47 | if self.transform is not None: 48 | img = self.transform(img) 49 | 50 | caption = torch.LongTensor(self.captions[i]) 51 | 52 | caplen = torch.LongTensor([self.caplens[i]]) 53 | 54 | if self.split is 'TRAIN': 55 | return img, caption, caplen 56 | else: 57 | # For validation of testing, also return all 'captions_per_image' captions to find BLEU-4 score 58 | all_captions = torch.LongTensor( 59 | self.captions[((i // self.cpi) * self.cpi):(((i // self.cpi) * self.cpi) + self.cpi)]) 60 | return img, caption, caplen, all_captions 61 | 62 | def __len__(self): 63 | return self.dataset_size 64 | 65 | -------------------------------------------------------------------------------- /model/eval.py: -------------------------------------------------------------------------------- 1 | import torch.backends.cudnn as cudnn 2 | import torch.optim 3 | import torch.utils.data 4 | import torchvision.transforms as transforms 5 | from datasets import * 6 | from utils import * 7 | from nltk.translate.bleu_score import corpus_bleu 8 | import torch.nn.functional as F 9 | from tqdm import tqdm 10 | 11 | # Parameters 12 | data_folder = '/media/ssd/caption data' # folder with data files saved by create_input_files.py 13 | data_name = 'coco_5_cap_per_img_5_min_word_freq' # base name shared by data files 14 | checkpoint = '../BEST_checkpoint_coco_5_cap_per_img_5_min_word_freq.pth.tar' # model checkpoint 15 | word_map_file = './camera/word_map.json' # word map, ensure it's the same the data was encoded with and the model was trained with 16 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # sets device for model and PyTorch tensors 17 | cudnn.benchmark = True # set to true only if inputs to model are fixed size; otherwise lot of computational overhead 18 | 19 | # Load model 20 | checkpoint = torch.load(checkpoint) 21 | decoder = checkpoint['decoder'] 22 | decoder = decoder.to(device) 23 | decoder.eval() 24 | encoder = checkpoint['encoder'] 25 | encoder = encoder.to(device) 26 | encoder.eval() 27 | 28 | # Load word map (word2ix) 29 | with open(word_map_file, 'r') as j: 30 | word_map = json.load(j) 31 | rev_word_map = {v: k for k, v in word_map.items()} 32 | vocab_size = len(word_map) 33 | 34 | # Normalization transform 35 | normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], 36 | std=[0.229, 0.224, 0.225]) 37 | 38 | 39 | def evaluate(beam_size): 40 | """ 41 | Evaluation 42 | :param beam_size: beam size at which to generate captions for evaluation 43 | :return: BLEU-4 score 44 | """ 45 | # DataLoader 46 | loader = torch.utils.data.DataLoader( 47 | CaptionDataset(data_folder, data_name, 'TEST', transform=transforms.Compose([normalize])), 48 | batch_size=1, shuffle=True, num_workers=1, pin_memory=True) 49 | 50 | # TODO: Batched Beam Search 51 | # Therefore, do not use a batch_size greater than 1 - IMPORTANT! 52 | 53 | # Lists to store references (true captions), and hypothesis (prediction) for each image 54 | # If for n images, we have n hypotheses, and references a, b, c... for each image, we need - 55 | # references = [[ref1a, ref1b, ref1c], [ref2a, ref2b], ...], hypotheses = [hyp1, hyp2, ...] 56 | references = list() 57 | hypotheses = list() 58 | 59 | # For each image 60 | for i, (image, caps, caplens, allcaps) in enumerate( 61 | tqdm(loader, desc="EVALUATING AT BEAM SIZE " + str(beam_size))): 62 | 63 | k = beam_size 64 | 65 | # Move to GPU device, if available 66 | image = image.to(device) # (1, 3, 256, 256) 67 | 68 | # Encode 69 | encoder_out = encoder(image) # (1, enc_image_size, enc_image_size, encoder_dim) 70 | enc_image_size = encoder_out.size(1) 71 | encoder_dim = encoder_out.size(3) 72 | 73 | # Flatten encoding 74 | encoder_out = encoder_out.view(1, -1, encoder_dim) # (1, num_pixels, encoder_dim) 75 | num_pixels = encoder_out.size(1) 76 | 77 | # We'll treat the problem as having a batch size of k 78 | encoder_out = encoder_out.expand(k, num_pixels, encoder_dim) # (k, num_pixels, encoder_dim) 79 | 80 | # Tensor to store top k previous words at each step; now they're just 81 | k_prev_words = torch.LongTensor([[word_map['']]] * k).to(device) # (k, 1) 82 | 83 | # Tensor to store top k sequences; now they're just 84 | seqs = k_prev_words # (k, 1) 85 | 86 | # Tensor to store top k sequences' scores; now they're just 0 87 | top_k_scores = torch.zeros(k, 1).to(device) # (k, 1) 88 | 89 | # Lists to store completed sequences and scores 90 | complete_seqs = list() 91 | complete_seqs_scores = list() 92 | 93 | # Start decoding 94 | step = 1 95 | h, c = decoder.init_hidden_state(encoder_out) 96 | 97 | # s is a number less than or equal to k, because sequences are removed from this process once they hit 98 | while True: 99 | 100 | embeddings = decoder.embedding(k_prev_words).squeeze(1) # (s, embed_dim) 101 | 102 | awe, _ = decoder.attention(encoder_out, h) # (s, encoder_dim), (s, num_pixels) 103 | 104 | gate = decoder.sigmoid(decoder.f_beta(h)) # gating scalar, (s, encoder_dim) 105 | awe = gate * awe 106 | 107 | h, c = decoder.decode_step(torch.cat([embeddings, awe], dim=1), (h, c)) # (s, decoder_dim) 108 | 109 | scores = decoder.fc(h) # (s, vocab_size) 110 | scores = F.log_softmax(scores, dim=1) 111 | 112 | # Add 113 | scores = top_k_scores.expand_as(scores) + scores # (s, vocab_size) 114 | 115 | # For the first step, all k points will have the same scores (since same k previous words, h, c) 116 | if step == 1: 117 | top_k_scores, top_k_words = scores[0].topk(k, 0, True, True) # (s) 118 | else: 119 | # Unroll and find top scores, and their unrolled indices 120 | top_k_scores, top_k_words = scores.view(-1).topk(k, 0, True, True) # (s) 121 | 122 | # Convert unrolled indices to actual indices of scores 123 | prev_word_inds = top_k_words / vocab_size # (s) 124 | next_word_inds = top_k_words % vocab_size # (s) 125 | 126 | # Add new words to sequences 127 | seqs = torch.cat([seqs[prev_word_inds], next_word_inds.unsqueeze(1)], dim=1) # (s, step+1) 128 | 129 | # Which sequences are incomplete (didn't reach )? 130 | incomplete_inds = [ind for ind, next_word in enumerate(next_word_inds) if 131 | next_word != word_map['']] 132 | complete_inds = list(set(range(len(next_word_inds))) - set(incomplete_inds)) 133 | 134 | # Set aside complete sequences 135 | if len(complete_inds) > 0: 136 | complete_seqs.extend(seqs[complete_inds].tolist()) 137 | complete_seqs_scores.extend(top_k_scores[complete_inds]) 138 | k -= len(complete_inds) # reduce beam length accordingly 139 | 140 | # Proceed with incomplete sequences 141 | if k == 0: 142 | break 143 | seqs = seqs[incomplete_inds] 144 | h = h[prev_word_inds[incomplete_inds]] 145 | c = c[prev_word_inds[incomplete_inds]] 146 | encoder_out = encoder_out[prev_word_inds[incomplete_inds]] 147 | top_k_scores = top_k_scores[incomplete_inds].unsqueeze(1) 148 | k_prev_words = next_word_inds[incomplete_inds].unsqueeze(1) 149 | 150 | # Break if things have been going on too long 151 | if step > 50: 152 | break 153 | step += 1 154 | 155 | i = complete_seqs_scores.index(max(complete_seqs_scores)) 156 | seq = complete_seqs[i] 157 | 158 | # References 159 | img_caps = allcaps[0].tolist() 160 | img_captions = list( 161 | map(lambda c: [w for w in c if w not in {word_map[''], word_map[''], word_map['']}], 162 | img_caps)) # remove and pads 163 | references.append(img_captions) 164 | 165 | # Hypotheses 166 | hypotheses.append([w for w in seq if w not in {word_map[''], word_map[''], word_map['']}]) 167 | 168 | assert len(references) == len(hypotheses) 169 | 170 | # Calculate BLEU-4 scores 171 | bleu4 = corpus_bleu(references, hypotheses) 172 | 173 | return bleu4 174 | 175 | 176 | if __name__ == '__main__': 177 | beam_size = [1, 3, 5] 178 | for beam in beam_size: 179 | print("\nBLEU-4 score @ beam size of %d is %.4f." % (beam, evaluate(beam))) 180 | -------------------------------------------------------------------------------- /model/model.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | import torchvision 4 | 5 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 6 | 7 | 8 | class Encoder(nn.Module): 9 | """ 10 | Encoder. 11 | """ 12 | 13 | def __init__(self, encoded_image_size=14): 14 | super(Encoder, self).__init__() 15 | self.enc_image_size = encoded_image_size 16 | 17 | resnet = torchvision.models.resnet101(pretrained=True) # pretrained ImageNet ResNet-101 18 | 19 | # Remove linear and pool layers (since we're not doing classification) 20 | modules = list(resnet.children())[:-2] 21 | self.resnet = nn.Sequential(*modules) 22 | 23 | # Resize image to fixed size to allow input images of variable size 24 | self.adaptive_pool = nn.AdaptiveAvgPool2d((encoded_image_size, encoded_image_size)) 25 | 26 | self.fine_tune() 27 | 28 | def forward(self, images): 29 | """ 30 | Forward propagation. 31 | :param images: images, a tensor of dimensions (batch_size, 3, image_size, image_size) 32 | :return: encoded images 33 | """ 34 | out = self.resnet(images) # (batch_size, 2048, image_size/32, image_size/32) 35 | out = self.adaptive_pool(out) # (batch_size, 2048, encoded_image_size, encoded_image_size) 36 | out = out.permute(0, 2, 3, 1) # (batch_size, encoded_image_size, encoded_image_size, 2048) 37 | return out 38 | 39 | def fine_tune(self, fine_tune=True): 40 | """ 41 | Allow or prevent the computation of gradients for convolutional blocks 2 through 4 of the encoder. 42 | :param fine_tune: Allow? 43 | """ 44 | for p in self.resnet.parameters(): 45 | p.requires_grad = False 46 | # If fine-tuning, only fine-tune convolutional blocks 2 through 4 47 | for c in list(self.resnet.children())[5:]: 48 | for p in c.parameters(): 49 | p.requires_grad = fine_tune 50 | 51 | 52 | class Attention(nn.Module): 53 | """ 54 | Attention Network. 55 | """ 56 | 57 | def __init__(self, encoder_dim, decoder_dim, attention_dim): 58 | """ 59 | :param encoder_dim: feature size of encoded images 60 | :param decoder_dim: size of decoder's RNN 61 | :param attention_dim: size of the attention network 62 | """ 63 | super(Attention, self).__init__() 64 | self.encoder_att = nn.Linear(encoder_dim, attention_dim) # linear layer to transform encoded image 65 | self.decoder_att = nn.Linear(decoder_dim, attention_dim) # linear layer to transform decoder's output 66 | self.full_att = nn.Linear(attention_dim, 1) # linear layer to calculate values to be softmax-ed 67 | self.relu = nn.ReLU() 68 | self.softmax = nn.Softmax(dim=1) # softmax layer to calculate weights 69 | 70 | def forward(self, encoder_out, decoder_hidden): 71 | """ 72 | Forward propagation. 73 | :param encoder_out: encoded images, a tensor of dimension (batch_size, num_pixels, encoder_dim) 74 | :param decoder_hidden: previous decoder output, a tensor of dimension (batch_size, decoder_dim) 75 | :return: attention weighted encoding, weights 76 | """ 77 | att1 = self.encoder_att(encoder_out) # (batch_size, num_pixels, attention_dim) 78 | att2 = self.decoder_att(decoder_hidden) # (batch_size, attention_dim) 79 | att = self.full_att(self.relu(att1 + att2.unsqueeze(1))).squeeze(2) # (batch_size, num_pixels) 80 | alpha = self.softmax(att) # (batch_size, num_pixels) 81 | attention_weighted_encoding = (encoder_out * alpha.unsqueeze(2)).sum(dim=1) # (batch_size, encoder_dim) 82 | 83 | return attention_weighted_encoding, alpha 84 | 85 | 86 | class DecoderWithAttention(nn.Module): 87 | """ 88 | Decoder. 89 | """ 90 | 91 | def __init__(self, attention_dim, embed_dim, decoder_dim, vocab_size, encoder_dim=2048, dropout=0.5): 92 | """ 93 | :param attention_dim: size of attention network 94 | :param embed_dim: embedding size 95 | :param decoder_dim: size of decoder's RNN 96 | :param vocab_size: size of vocabulary 97 | :param encoder_dim: feature size of encoded images 98 | :param dropout: dropout 99 | """ 100 | super(DecoderWithAttention, self).__init__() 101 | 102 | self.encoder_dim = encoder_dim 103 | self.attention_dim = attention_dim 104 | self.embed_dim = embed_dim 105 | self.decoder_dim = decoder_dim 106 | self.vocab_size = vocab_size 107 | self.dropout = dropout 108 | 109 | self.attention = Attention(encoder_dim, decoder_dim, attention_dim) # attention network 110 | 111 | self.embedding = nn.Embedding(vocab_size, embed_dim) # embedding layer 112 | self.dropout = nn.Dropout(p=self.dropout) 113 | self.decode_step = nn.LSTMCell(embed_dim + encoder_dim, decoder_dim, bias=True) # decoding LSTMCell 114 | self.init_h = nn.Linear(encoder_dim, decoder_dim) # linear layer to find initial hidden state of LSTMCell 115 | self.init_c = nn.Linear(encoder_dim, decoder_dim) # linear layer to find initial cell state of LSTMCell 116 | self.f_beta = nn.Linear(decoder_dim, encoder_dim) # linear layer to create a sigmoid-activated gate 117 | self.sigmoid = nn.Sigmoid() 118 | self.fc = nn.Linear(decoder_dim, vocab_size) # linear layer to find scores over vocabulary 119 | self.init_weights() # initialize some layers with the uniform distribution 120 | 121 | def init_weights(self): 122 | """ 123 | Initializes some parameters with values from the uniform distribution, for easier convergence. 124 | """ 125 | self.embedding.weight.data.uniform_(-0.1, 0.1) 126 | self.fc.bias.data.fill_(0) 127 | self.fc.weight.data.uniform_(-0.1, 0.1) 128 | 129 | def load_pretrained_embeddings(self, embeddings): 130 | """ 131 | Loads embedding layer with pre-trained embeddings. 132 | :param embeddings: pre-trained embeddings 133 | """ 134 | self.embedding.weight = nn.Parameter(embeddings) 135 | 136 | def fine_tune_embeddings(self, fine_tune=True): 137 | """ 138 | Allow fine-tuning of embedding layer? (Only makes sense to not-allow if using pre-trained embeddings). 139 | :param fine_tune: Allow? 140 | """ 141 | for p in self.embedding.parameters(): 142 | p.requires_grad = fine_tune 143 | 144 | def init_hidden_state(self, encoder_out): 145 | """ 146 | Creates the initial hidden and cell states for the decoder's LSTM based on the encoded images. 147 | :param encoder_out: encoded images, a tensor of dimension (batch_size, num_pixels, encoder_dim) 148 | :return: hidden state, cell state 149 | """ 150 | mean_encoder_out = encoder_out.mean(dim=1) 151 | h = self.init_h(mean_encoder_out) # (batch_size, decoder_dim) 152 | c = self.init_c(mean_encoder_out) 153 | return h, c 154 | 155 | def forward(self, encoder_out, encoded_captions, caption_lengths): 156 | """ 157 | Forward propagation. 158 | :param encoder_out: encoded images, a tensor of dimension (batch_size, enc_image_size, enc_image_size, encoder_dim) 159 | :param encoded_captions: encoded captions, a tensor of dimension (batch_size, max_caption_length) 160 | :param caption_lengths: caption lengths, a tensor of dimension (batch_size, 1) 161 | :return: scores for vocabulary, sorted encoded captions, decode lengths, weights, sort indices 162 | """ 163 | 164 | batch_size = encoder_out.size(0) 165 | encoder_dim = encoder_out.size(-1) 166 | vocab_size = self.vocab_size 167 | 168 | # Flatten image 169 | encoder_out = encoder_out.view(batch_size, -1, encoder_dim) # (batch_size, num_pixels, encoder_dim) 170 | num_pixels = encoder_out.size(1) 171 | 172 | # Sort input data by decreasing lengths; why? apparent below 173 | caption_lengths, sort_ind = caption_lengths.squeeze(1).sort(dim=0, descending=True) 174 | encoder_out = encoder_out[sort_ind] 175 | encoded_captions = encoded_captions[sort_ind] 176 | 177 | # Embedding 178 | embeddings = self.embedding(encoded_captions) # (batch_size, max_caption_length, embed_dim) 179 | 180 | # Initialize LSTM state 181 | h, c = self.init_hidden_state(encoder_out) # (batch_size, decoder_dim) 182 | 183 | # We won't decode at the position, since we've finished generating as soon as we generate 184 | # So, decoding lengths are actual lengths - 1 185 | decode_lengths = (caption_lengths - 1).tolist() 186 | 187 | # Create tensors to hold word predicion scores and alphas 188 | predictions = torch.zeros(batch_size, max(decode_lengths), vocab_size).to(device) 189 | alphas = torch.zeros(batch_size, max(decode_lengths), num_pixels).to(device) 190 | 191 | # At each time-step, decode by 192 | # attention-weighing the encoder's output based on the decoder's previous hidden state output 193 | # then generate a new word in the decoder with the previous word and the attention weighted encoding 194 | for t in range(max(decode_lengths)): 195 | batch_size_t = sum([l > t for l in decode_lengths]) 196 | attention_weighted_encoding, alpha = self.attention(encoder_out[:batch_size_t], 197 | h[:batch_size_t]) 198 | gate = self.sigmoid(self.f_beta(h[:batch_size_t])) # gating scalar, (batch_size_t, encoder_dim) 199 | attention_weighted_encoding = gate * attention_weighted_encoding 200 | h, c = self.decode_step( 201 | torch.cat([embeddings[:batch_size_t, t, :], attention_weighted_encoding], dim=1), 202 | (h[:batch_size_t], c[:batch_size_t])) # (batch_size_t, decoder_dim) 203 | preds = self.fc(self.dropout(h)) # (batch_size_t, vocab_size) 204 | predictions[:batch_size_t, t, :] = preds 205 | alphas[:batch_size_t, t, :] = alpha 206 | 207 | return predictions, encoded_captions, decode_lengths, alphas, sort_ind 208 | -------------------------------------------------------------------------------- /model/utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import h5py 4 | import json 5 | import torch 6 | from imageio import imread 7 | from tqdm import tqdm 8 | from collections import Counter 9 | from random import seed, choice, sample 10 | 11 | 12 | def create_input_files(dataset, karpathy_json_path, image_folder, captions_per_image, min_word_freq, output_folder, 13 | max_len=100): 14 | """ 15 | Creates input files for training, validation, and test data. 16 | :param dataset: name of dataset, one of 'coco', 'flickr8k', 'flickr30k' 17 | :param karpathy_json_path: path of Karpathy JSON file with splits and captions 18 | :param image_folder: folder with downloaded images 19 | :param captions_per_image: number of captions to sample per image 20 | :param min_word_freq: words occuring less frequently than this threshold are binned as s 21 | :param output_folder: folder to save files 22 | :param max_len: don't sample captions longer than this length 23 | """ 24 | 25 | assert dataset in {'coco', 'flickr8k', 'flickr30k'} 26 | 27 | # Read Karpathy JSON 28 | with open(karpathy_json_path, 'r') as j: 29 | data = json.load(j) 30 | 31 | # Read image paths and captions for each image 32 | train_image_paths = [] 33 | train_image_captions = [] 34 | val_image_paths = [] 35 | val_image_captions = [] 36 | test_image_paths = [] 37 | test_image_captions = [] 38 | word_freq = Counter() 39 | 40 | for img in data['images']: 41 | captions = [] 42 | for c in img['sentences']: 43 | # Update word frequency 44 | word_freq.update(c['tokens']) 45 | if len(c['tokens']) <= max_len: 46 | captions.append(c['tokens']) 47 | 48 | if len(captions) == 0: 49 | continue 50 | 51 | path = os.path.join(image_folder, img['filepath'], img['filename']) if dataset == 'coco' else os.path.join( 52 | image_folder, img['filename']) 53 | 54 | if img['split'] in {'train', 'restval'}: 55 | train_image_paths.append(path) 56 | train_image_captions.append(captions) 57 | elif img['split'] in {'val'}: 58 | val_image_paths.append(path) 59 | val_image_captions.append(captions) 60 | elif img['split'] in {'test'}: 61 | test_image_paths.append(path) 62 | test_image_captions.append(captions) 63 | 64 | # Sanity check 65 | assert len(train_image_paths) == len(train_image_captions) 66 | assert len(val_image_paths) == len(val_image_captions) 67 | assert len(test_image_paths) == len(test_image_captions) 68 | 69 | # Create word map 70 | words = [w for w in word_freq.keys() if word_freq[w] > min_word_freq] 71 | word_map = {k: v + 1 for v, k in enumerate(words)} 72 | word_map[''] = len(word_map) + 1 73 | word_map[''] = len(word_map) + 1 74 | word_map[''] = len(word_map) + 1 75 | word_map[''] = 0 76 | 77 | # Create a base/root name for all output files 78 | base_filename = dataset + '_' + str(captions_per_image) + '_cap_per_img_' + str(min_word_freq) + '_min_word_freq' 79 | 80 | # Save word map to a JSON 81 | with open(os.path.join(output_folder, 'WORDMAP_' + base_filename + '.json'), 'w') as j: 82 | json.dump(word_map, j) 83 | 84 | # Sample captions for each image, save images to HDF5 file, and captions and their lengths to JSON files 85 | seed(123) 86 | for impaths, imcaps, split in [(train_image_paths, train_image_captions, 'TRAIN'), 87 | (val_image_paths, val_image_captions, 'VAL'), 88 | (test_image_paths, test_image_captions, 'TEST')]: 89 | 90 | with h5py.File(os.path.join(output_folder, split + '_IMAGES_' + base_filename + '.hdf5'), 'a') as h: 91 | # Make a note of the number of captions we are sampling per image 92 | h.attrs['captions_per_image'] = captions_per_image 93 | 94 | # Create dataset inside HDF5 file to store images 95 | images = h.create_dataset('images', (len(impaths), 3, 256, 256), dtype='uint8') 96 | 97 | print("\nReading %s images and captions, storing to file...\n" % split) 98 | 99 | enc_captions = [] 100 | caplens = [] 101 | 102 | for i, path in enumerate(tqdm(impaths)): 103 | 104 | # Sample captions 105 | if len(imcaps[i]) < captions_per_image: 106 | captions = imcaps[i] + [choice(imcaps[i]) for _ in range(captions_per_image - len(imcaps[i]))] 107 | else: 108 | captions = sample(imcaps[i], k=captions_per_image) 109 | 110 | # Sanity check 111 | assert len(captions) == captions_per_image 112 | 113 | # Read images 114 | img = imread(impaths[i]) 115 | if len(img.shape) == 2: 116 | img = img[:, :, np.newaxis] 117 | img = np.concatenate([img, img, img], axis=2) 118 | img = np.array(Image.fromarray(img).resize((256,256))) 119 | img = img.transpose(2, 0, 1) 120 | assert img.shape == (3, 256, 256) 121 | assert np.max(img) <= 255 122 | 123 | # Save image to HDF5 file 124 | images[i] = img 125 | 126 | for j, c in enumerate(captions): 127 | # Encode captions 128 | enc_c = [word_map['']] + [word_map.get(word, word_map['']) for word in c] + [ 129 | word_map['']] + [word_map['']] * (max_len - len(c)) 130 | 131 | # Find caption lengths 132 | c_len = len(c) + 2 133 | 134 | enc_captions.append(enc_c) 135 | caplens.append(c_len) 136 | 137 | # Sanity check 138 | assert images.shape[0] * captions_per_image == len(enc_captions) == len(caplens) 139 | 140 | # Save encoded captions and their lengths to JSON files 141 | with open(os.path.join(output_folder, split + '_CAPTIONS_' + base_filename + '.json'), 'w') as j: 142 | json.dump(enc_captions, j) 143 | 144 | with open(os.path.join(output_folder, split + '_CAPLENS_' + base_filename + '.json'), 'w') as j: 145 | json.dump(caplens, j) 146 | 147 | 148 | def init_embedding(embeddings): 149 | """ 150 | Fills embedding tensor with values from the uniform distribution. 151 | :param embeddings: embedding tensor 152 | """ 153 | bias = np.sqrt(3.0 / embeddings.size(1)) 154 | torch.nn.init.uniform_(embeddings, -bias, bias) 155 | 156 | 157 | def load_embeddings(emb_file, word_map): 158 | """ 159 | Creates an embedding tensor for the specified word map, for loading into the model. 160 | :param emb_file: file containing embeddings (stored in GloVe format) 161 | :param word_map: word map 162 | :return: embeddings in the same order as the words in the word map, dimension of embeddings 163 | """ 164 | 165 | # Find embedding dimension 166 | with open(emb_file, 'r') as f: 167 | emb_dim = len(f.readline().split(' ')) - 1 168 | 169 | vocab = set(word_map.keys()) 170 | 171 | # Create tensor to hold embeddings, initialize 172 | embeddings = torch.FloatTensor(len(vocab), emb_dim) 173 | init_embedding(embeddings) 174 | 175 | # Read embedding file 176 | print("\nLoading embeddings...") 177 | for line in open(emb_file, 'r'): 178 | line = line.split(' ') 179 | 180 | emb_word = line[0] 181 | embedding = list(map(lambda t: float(t), filter(lambda n: n and not n.isspace(), line[1:]))) 182 | 183 | # Ignore word if not in train_vocab 184 | if emb_word not in vocab: 185 | continue 186 | 187 | embeddings[word_map[emb_word]] = torch.FloatTensor(embedding) 188 | 189 | return embeddings, emb_dim 190 | 191 | 192 | def clip_gradient(optimizer, grad_clip): 193 | """ 194 | Clips gradients computed during backpropagation to avoid explosion of gradients. 195 | :param optimizer: optimizer with the gradients to be clipped 196 | :param grad_clip: clip value 197 | """ 198 | for group in optimizer.param_groups: 199 | for param in group['params']: 200 | if param.grad is not None: 201 | param.grad.data.clamp_(-grad_clip, grad_clip) 202 | 203 | 204 | def save_checkpoint(data_name, epoch, epochs_since_improvement, encoder, decoder, encoder_optimizer, decoder_optimizer, 205 | bleu4, is_best): 206 | """ 207 | Saves model checkpoint. 208 | :param data_name: base name of processed dataset 209 | :param epoch: epoch number 210 | :param epochs_since_improvement: number of epochs since last improvement in BLEU-4 score 211 | :param encoder: encoder model 212 | :param decoder: decoder model 213 | :param encoder_optimizer: optimizer to update encoder's weights, if fine-tuning 214 | :param decoder_optimizer: optimizer to update decoder's weights 215 | :param bleu4: validation BLEU-4 score for this epoch 216 | :param is_best: is this checkpoint the best so far? 217 | """ 218 | state = {'epoch': epoch, 219 | 'epochs_since_improvement': epochs_since_improvement, 220 | 'bleu-4': bleu4, 221 | 'encoder': encoder, 222 | 'decoder': decoder, 223 | 'encoder_optimizer': encoder_optimizer, 224 | 'decoder_optimizer': decoder_optimizer} 225 | filename = 'checkpoint_' + data_name + '.pth.tar' 226 | torch.save(state, filename) 227 | # If this checkpoint is the best so far, store a copy so it doesn't get overwritten by a worse checkpoint 228 | if is_best: 229 | torch.save(state, 'BEST_' + filename) 230 | 231 | 232 | class AverageMeter(object): 233 | """ 234 | Keeps track of most recent, average, sum, and count of a metric. 235 | """ 236 | 237 | def __init__(self): 238 | self.reset() 239 | 240 | def reset(self): 241 | self.val = 0 242 | self.avg = 0 243 | self.sum = 0 244 | self.count = 0 245 | 246 | def update(self, val, n=1): 247 | self.val = val 248 | self.sum += val * n 249 | self.count += n 250 | self.avg = self.sum / self.count 251 | 252 | 253 | def adjust_learning_rate(optimizer, shrink_factor): 254 | """ 255 | Shrinks learning rate by a specified factor. 256 | :param optimizer: optimizer whose learning rate must be shrunk. 257 | :param shrink_factor: factor in interval (0, 1) to multiply learning rate with. 258 | """ 259 | 260 | print("\nDECAYING learning rate.") 261 | for param_group in optimizer.param_groups: 262 | param_group['lr'] = param_group['lr'] * shrink_factor 263 | print("The new learning rate is %f\n" % (optimizer.param_groups[0]['lr'],)) 264 | 265 | 266 | def accuracy(scores, targets, k): 267 | """ 268 | Computes top-k accuracy, from predicted and true labels. 269 | :param scores: scores from the model 270 | :param targets: true labels 271 | :param k: k in top-k accuracy 272 | :return: top-k accuracy 273 | """ 274 | 275 | batch_size = targets.size(0) 276 | _, ind = scores.topk(k, 1, True, True) 277 | correct = ind.eq(targets.view(-1, 1).expand_as(ind)) 278 | correct_total = correct.view(-1).float().sum() # 0D tensor 279 | return correct_total.item() * (100.0 / batch_size) 280 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit==0.79.0 2 | numpy==1.20.1 3 | pandas==1.2.3 4 | torch==1.8.0 5 | torchvision==0.9.0 6 | h5py==3.2.1 7 | Pillow==8.1.2 8 | opencv_python==4.5.1.48 9 | imageio==1.4 10 | nltk==3.5 11 | tqdm==4.59.0 12 | cryptography==3.4.6 13 | scikit-image==0.18.1 14 | -------------------------------------------------------------------------------- /search/search.py: -------------------------------------------------------------------------------- 1 | # imports 2 | import streamlit as st 3 | import cv2 4 | import csv 5 | from PIL import Image 6 | import pandas as pd 7 | import time 8 | import os 9 | import numpy as np 10 | from datetime import time 11 | from search_word import search_word 12 | 13 | 14 | # sidebar 15 | st.sidebar.image("../assets/logo.png") 16 | st.sidebar.header("Search Module") 17 | st.sidebar.markdown( 18 | "Utilizing data analytics and data algorithms, we provide a light yet efficient search and data analyzer for the **_'cap-bot'_** predicitons while ensuring a safe and convenient environment to aid the search" 19 | ) 20 | st.sidebar.header("Features") 21 | st.sidebar.markdown("""- Get a visualization of the CCTV Cameras""") 22 | st.sidebar.markdown("""- Define keywords of the event""") 23 | st.sidebar.markdown("""- Define time of the event""") 24 | st.sidebar.markdown("[Github Repository](https://github.com/aryankargwal/capbot2.0)") 25 | st.sidebar.markdown("[Proposal Video](https://www.youtube.com/watch?v=Sr8dNQMBRZI)") 26 | 27 | 28 | # Data Uploads 29 | st.header("Data Uploading") 30 | # uploading results 31 | st.subheader("Upload the CCTV Log here") 32 | file = st.file_uploader("logs") 33 | 34 | 35 | # loading results 36 | @st.cache 37 | def load_data(nrows): 38 | data = pd.read_csv(file, nrows=nrows) 39 | lower = lambda x: str(x).lower() 40 | data.rename(lower, axis="columns", inplace=True) 41 | return data 42 | 43 | 44 | if file is not None: 45 | data_load_state = st.text("") 46 | data = load_data(10000) 47 | # seeing data 48 | st.subheader("Raw data") 49 | st.write(data) 50 | 51 | # sample cctv map 52 | st.subheader("Upload the CCTV Co-ordinates here") 53 | cctv_map = st.file_uploader("co-ordinates") 54 | if cctv_map is not None: 55 | st.subheader("CCTV Map") 56 | df = pd.read_csv(cctv_map) 57 | st.map(df) 58 | 59 | 60 | # Search 61 | st.header("Searching Logs") 62 | 63 | # keywords 64 | st.subheader("Enter the keywords of the incident") 65 | keywords = st.text_input("") 66 | keywords = keywords.split(sep=" ") 67 | list(keywords) 68 | 69 | # time 70 | st.subheader("The time where the incident might have occured") 71 | footage_time = st.slider("", value=(time(9, 30), time(14, 45))) 72 | # button 73 | if st.button("Start Search"): 74 | row = search_word(data, keywords) 75 | for k in row: 76 | st.write(data.loc[[k], :]) 77 | -------------------------------------------------------------------------------- /search/search_word.py: -------------------------------------------------------------------------------- 1 | def search_word(df,keywords): 2 | l = [] 3 | for i in df.index: 4 | y = df.loc[i, :].values.tolist() 5 | y = set(y) 6 | if y.intersection(set(keywords)): 7 | l.append(i) 8 | return l -------------------------------------------------------------------------------- /test/coordinates.csv: -------------------------------------------------------------------------------- 1 | ,lat,lon 2 | 0,10.001894625688907,-9.963281846903001 3 | 1,10.000203766794348,-9.999519659046241 4 | 2,10.003667497881652,-9.980918928527075 5 | 3,9.994402584601014,-10.023811973194322 6 | 4,9.978416039573414,-9.992408682392336 7 | 5,10.021207143006142,-10.020802672960423 8 | 6,10.020730553569745,-10.030923611699805 9 | 7,10.003739153303957,-10.009927952580504 10 | 8,9.957844862885969,-9.993033862997207 11 | 9,9.982384424733533,-9.97871073993292 12 | 10,10.024744436909218,-10.009315511455016 13 | 11,9.995096871277493,-10.011780670282253 14 | 12,10.011816348109205,-10.024895468287708 15 | 13,10.01161961721637,-10.006947927407465 16 | 14,10.003005577874363,-9.97350432169051 17 | 15,10.033180099789615,-10.027671323333731 18 | 16,9.995521710941592,-10.047567792085179 19 | 17,9.999610025447875,-9.992783436910658 20 | 18,10.00200973960348,-10.016277069283115 21 | 19,10.035334367932741,-10.01839726966658 22 | 20,9.982507608795808,-9.988193908919351 23 | 21,9.987240929140512,-10.015773142281505 24 | 22,9.981748308214053,-10.01792308823075 25 | 23,9.980547102982971,-10.00583226524451 26 | 24,9.990355129525877,-9.997637893252733 27 | 25,9.992740381438011,-9.97794344903894 28 | 26,9.972098199425036,-9.981130519537977 29 | 27,9.990463241028031,-10.020254118993407 30 | 28,10.030351628881553,-10.006197842810526 31 | 29,9.993291833468135,-9.981156057781039 32 | 30,10.00417036982159,-9.98400980706904 33 | 31,10.023438708295124,-9.999473457731813 34 | 32,10.030192281297092,-10.003623956842677 35 | 33,10.034948525292979,-10.019409164656624 36 | 34,10.021635039963474,-9.96843090410144 37 | 35,9.991239672760974,-10.039267296066539 38 | 36,10.017183852472748,-9.998521414912613 39 | 37,9.999093995557878,-10.037998574631509 40 | 38,10.058493246809405,-9.988945386365923 41 | 39,10.011471438929057,-10.030548532955835 42 | 40,9.995497399824401,-10.015307393822315 43 | 41,9.981255435045584,-10.000290606542938 44 | 42,9.98189394017183,-10.008152131965717 45 | 43,10.000936276121472,-10.049648758397167 46 | 44,9.972629227663994,-9.993201307090377 47 | 45,10.01235968722261,-10.017095226167983 48 | 46,10.029919226823436,-9.988930567492806 49 | 47,10.030565531350135,-9.981922479158435 50 | 48,9.975335826984862,-10.006579083737185 51 | 49,9.986538439779984,-10.025585318308858 52 | 50,9.991218894678505,-9.998124066332146 53 | 51,9.96681753719071,-9.982708845188329 54 | 52,9.957692128351514,-9.991589911444594 55 | 53,10.010804037064295,-9.984478456397541 56 | 54,9.990352773487636,-9.991635502064653 57 | 55,9.982176526580268,-9.986063431046986 58 | 56,10.01026524413382,-9.98989966616809 59 | 57,10.009651443505536,-9.989463274197027 60 | 58,9.984029000031365,-10.012014104361032 61 | 59,10.016638640304556,-10.003303431411206 62 | 60,10.003159637400474,-9.98112522505928 63 | 61,10.017444444135846,-9.990279787372417 64 | 62,9.98766929680974,-9.972487408421769 65 | 63,10.046199833090807,-9.989721976392133 66 | 64,10.021486110525084,-10.016452065996386 67 | 65,10.027378916183373,-10.013572421444978 68 | 66,9.99805450900211,-10.063573994547175 69 | 67,9.989803108362535,-10.014573676189462 70 | 68,9.995298463972004,-9.988869040523106 71 | 69,9.977765159589826,-9.962077417991226 72 | 70,10.003861425339819,-9.992583815956056 73 | 71,9.995096768944432,-10.010530708120298 74 | 72,10.007452309057541,-10.022093866173286 75 | 73,9.967590668590343,-10.010349680717171 76 | 74,10.002814726832465,-10.011968669953534 77 | 75,10.002558858615105,-10.00812149759447 78 | 76,9.970127350771861,-9.966849470796127 79 | 77,9.997620862132274,-9.995395744869302 80 | 78,10.009084357092446,-10.017792318664823 81 | 79,10.001834988081443,-9.980396747010921 82 | 80,10.033246655961497,-9.997090806290869 83 | 81,9.987318490360751,-10.032513653565376 84 | 82,9.99741317826938,-10.01159813555461 85 | 83,9.983051683970931,-10.009971931388721 86 | 84,9.999222963752459,-9.977361753414838 87 | 85,10.011054359043627,-10.02506567915574 88 | 86,10.006037080521276,-10.01194265230492 89 | 87,9.985976120305427,-10.01027877823261 90 | 88,9.986870626428558,-9.967131997624142 91 | 89,10.013217550971424,-9.992807077826292 92 | 90,10.000164608165035,-9.999335289754832 93 | 91,10.001128493178154,-9.989214321579754 94 | 92,9.96609201491246,-10.009777110117161 95 | 93,10.011804783321653,-10.005159172692604 96 | 94,10.003809095171581,-10.014138174632594 97 | 95,10.044451596286098,-9.97769936571465 98 | 96,9.991842848564545,-10.033288941224622 99 | 97,10.013337106748502,-10.039822780457984 100 | 98,9.993438438805613,-9.997348700450063 101 | 99,9.955486757213606,-9.991953047716896 102 | -------------------------------------------------------------------------------- /test/coordinates.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | 4 | df = pd.DataFrame( 5 | np.random.randn(100, 2) / [50, 50] + [10, -10], columns=["lat", "lon"] 6 | ) 7 | df.to_csv("file1.csv") 8 | -------------------------------------------------------------------------------- /test/results.csv: -------------------------------------------------------------------------------- 1 | w1,w2,w3,w4,w5,w6,w7,w8,w9,w10,time,camera 2 | man,in,red,shirt,is,standing,in,front,of,an,00:36.3,1 3 | man,in,red,shirt,is,standing,in,front,of,skyscraper,00:46.7,1 4 | man,in,red,shirt,is,standing,in,front,of,an,00:57.0,1 5 | --------------------------------------------------------------------------------