├── .gitignore ├── requirements.txt ├── test_image ├── 5550609955.png ├── 680481205.png └── 1000001600535997.jpeg ├── Model_01.py ├── README.md ├── eval.py └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | .vscode/ 2 | __pycache__/ 3 | model/ 4 | output/ -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pandas 2 | Pillow 3 | torch 4 | torchvision 5 | huggingface_hub 6 | transformers 7 | PIL -------------------------------------------------------------------------------- /test_image/5550609955.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/strawmelon11/human-perception-place-pulse/HEAD/test_image/5550609955.png -------------------------------------------------------------------------------- /test_image/680481205.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/strawmelon11/human-perception-place-pulse/HEAD/test_image/680481205.png -------------------------------------------------------------------------------- /test_image/1000001600535997.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/strawmelon11/human-perception-place-pulse/HEAD/test_image/1000001600535997.jpeg -------------------------------------------------------------------------------- /Model_01.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | from torchvision.models import vit_b_16, ViT_B_16_Weights 4 | 5 | 6 | class Net(nn.Module): 7 | def __init__(self, num_class): 8 | super(Net, self).__init__() 9 | 10 | self.model = vit_b_16(weights=ViT_B_16_Weights.IMAGENET1K_SWAG_E2E_V1) 11 | num_fc = self.model.heads.head.in_features 12 | self.model.heads.head = nn.Sequential( 13 | nn.Linear(num_fc, 512, bias=True), 14 | nn.ReLU(True), 15 | nn.Linear(512, 256, bias=True), 16 | nn.ReLU(True), 17 | nn.Linear(256, num_class, bias=True) 18 | ) 19 | nn.init.xavier_uniform_(self.model.heads.head[0].weight) 20 | nn.init.xavier_uniform_(self.model.heads.head[2].weight) 21 | nn.init.xavier_uniform_(self.model.heads.head[4].weight) 22 | 23 | 24 | 25 | def forward(self, x): 26 | x = self.model(x) 27 | return x 28 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # human-perception-place-pulse 2 | 3 | ## What does the model do 4 | ### safety, lively, beautiful, wealthy, boring and depressing. 5 | Getting human perception scores from street-level imagery. 6 | 7 | The scores are in scale of 0-10. 8 | 9 | ` Safety, lively, beautiful, wealthy` high score indicates strong **positive** feeling 10 | 11 | ` Boring, depressing` high score indicates strong **negative** feeling 12 | 13 | Model Accuracy： 14 | | Model | safe | lively | wealthy | beautiful | boring | depressing | 15 | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | 16 | | Accuracy | 76.7% | 77.1% | 72.9% | 76.9% | 61.6% | 67.2% | 17 | 18 | 19 | ## Model 20 | The models are pre-trained on the MIT Place Pulse 2.0 dataset. The backbone of the models are vision transformer (ViT) pretrianed on ImageNet (ViT_B_16_Weights.IMAGENET1K_SWAG_E2E_V1). 21 | 22 | In the ViT heads, 3 Linear layers with ReLU activiation are added for classification. 23 | 24 | The models will be automatically downloaded when run *eval.py* (recommended method). You can also manually download the models [here](https://huggingface.co/Jiani11/human-perception-place-pulse) 25 | 26 | 27 | ## How to run the model 28 | Install packages from requirements.txt 29 | 30 | ` pip install -r requirements.txt` 31 | 32 | Change the file path in *eval.py* 33 | 34 | ``` 35 | model_load_path = "./model" # path to save downloaded models 36 | images_path = "./test_image" # input image path 37 | out_Path = "./output" # output scores path 38 | ``` 39 | Run the file *eval.py* 40 | 41 | `python eval.py` 42 | 43 | ## Citation 44 | Please cite our papers if you use this code or any of the models. Find more streetscapes [here](https://github.com/ualsg/global-streetscapes) 45 | ``` 46 | @article{2024_global_streetscapes, 47 | author = {Hou, Yujun and Quintana, Matias and Khomiakov, Maxim and Yap, Winston and Ouyang, Jiani and Ito, Koichi and Wang, Zeyu and Zhao, Tianhong and Biljecki, Filip}, 48 | doi = {10.1016/j.isprsjprs.2024.06.023}, 49 | journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, 50 | pages = {216-238}, 51 | title = {Global Streetscapes -- A comprehensive dataset of 10 million street-level images across 688 cities for urban science and analytics}, 52 | volume = {215}, 53 | year = {2024} 54 | } 55 | ``` 56 | 57 | -------------------------------------------------------------------------------- /eval.py: -------------------------------------------------------------------------------- 1 | # coding=UTF-8 2 | import os 3 | import pandas as pd 4 | import torch 5 | import torch.nn as nn 6 | from torchvision import transforms as T 7 | from PIL import Image 8 | from transformers import AutoModel 9 | from huggingface_hub import snapshot_download 10 | 11 | os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" 12 | 13 | perception = ['safety', 'lively', 'wealthy', 14 | 'beautiful', 'boring', 'depressing'] 15 | model_dict = { 16 | 'safety': 'safety.pth', 17 | 'lively': 'lively.pth', 18 | 'wealthy': 'wealthy.pth', 19 | 'beautiful': 'beautiful.pth', 20 | 'boring': 'boring.pth', 21 | 'depressing': 'depressing.pth', 22 | } 23 | 24 | 25 | train_transform = T.Compose([ 26 | T.Resize((384, 384)), 27 | T.ToTensor(), 28 | T.Normalize( 29 | mean=[0.485, 0.456, 0.406], 30 | std=[0.229, 0.224, 0.225]) 31 | ]) 32 | 33 | 34 | def predict(model, img_path, device): 35 | img = Image.open(img_path) 36 | if img.mode != "RGB": 37 | img = img.convert("RGB") 38 | img = train_transform(img) 39 | img = img.view(1, 3, 384, 384) 40 | # inference 41 | if device == 'cuda:0': 42 | pred = model(img.cuda()) 43 | else: 44 | pred = model(img) 45 | softmax = nn.Softmax(dim=1) 46 | pred = softmax(pred)[0][1].item() 47 | pred = round(pred*10, 2) 48 | 49 | return pred 50 | 51 | 52 | if __name__ == "__main__": 53 | 54 | model_load_path = "./model" # model dir path 55 | images_path = "./test_image" # input image path 56 | out_Path = "./output" # output path 57 | 58 | # download model 59 | print("Downloading models ...") 60 | snapshot_download(repo_id="Jiani11/human-perception-place-pulse", 61 | allow_patterns=["*.pth", "README.md"], local_dir=model_load_path) 62 | 63 | device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') 64 | print("using device:{} ".format(device)) 65 | for p in perception: 66 | # load model 67 | model_path = model_load_path + "/" + model_dict[p] 68 | model = torch.load(model_path, map_location=torch.device(device)) 69 | if torch.cuda.device_count() > 1: 70 | model = nn.DataParallel(model) 71 | model = model.to(device) 72 | print("######### {} #########".format(p)) 73 | model.eval() 74 | 75 | # Check if the directory exists, if not, create it 76 | if not os.path.exists(out_Path): 77 | os.makedirs(out_Path) 78 | 79 | # inferring img 80 | out_csvPath = out_Path + "/" + str(p) + ".csv" 81 | df = pd.DataFrame(columns=['img_path', str(p)+"_score"]) 82 | df.to_csv(out_csvPath, index=False) 83 | data_arr = [] 84 | 85 | for img in os.listdir(images_path): 86 | img_path = images_path + "/" + img 87 | print("current image: ", img_path) 88 | score = predict(model, img_path, device) 89 | data_arr = [img, score] 90 | df = pd.DataFrame(data_arr).T 91 | df.to_csv(out_csvPath, mode='a', header=False, 92 | index=False) # save scores into csv 93 | print("{} prediction done!".format(p)) 94 | -------------------------------------------------------------------------------- /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 | --------------------------------------------------------------------------------