PyPI 官网下载 | logodetect-1.1.2.tar.gz

<p align="center"> <img src="https://github.com/Heldenkombinat/Logos-Recognition/blob/master/docs/hkt_logo_detect.png"> </p> [](https://badge.fury.io/py/logodetect) Never be embarrassed again to say you have no big data! `logodetect` is a one-shot detection library to find logos of any kind in video and image data. Here's a quick example of football footage that can detect all logos on jerseys and the sports field. Go check out [our demo if you want to see it in action right away](https://logodetect.netlify.com/demo). <p align="center"> <img src="https://github.com/Heldenkombinat/Logos-Recognition/blob/master/docs/demo.gif"> </p> ## Introduction There is plenty of literature on the use of deep-learning for detecting logos, so, additionally to sharing with you a couple of algorithms to get started with one-shot logo-detection, the aim of this project is to develop a flexible architecture to facilitate the comparison of different algorithms for one-shot object recognition. The pipeline supports one or two stages. It is possible to only perform *object-recognition*, or to first perform *object-detection* and then *object-recognition* in a second stage. The idea is that you can use a generic detector for a single class of objects (e.g. logos, traffic signs or faces) and then compare each of its detections with the *exemplar*, i.e., the sub-class that you are trying to recognize, to determine if both belong to the same sub-class (e.g. a concrete brand, a stop sign or the face of a loved one). To get started, we include two algorithms that you can play with. Both have a Faster-RCNN [1] in the first stage that performs object-detection and they differ in the second stage that performs object-recognition. As a **baseline**, we bring the exemplars and the detections from the first stage to the same latent space (this reduces the course of dimensionality) and then simply measure the Euclidian or the cosine distance between both embeddings for object-recognition. Both inputs are considered to belong to the same sub-class if their distance is below a threshold determined in a preliminary analysis of the training dataset. The code also provides functionality to add various transformations, so you have the option to augment each exemplar with different transformations if you want. Simply add one or more exemplars into the `data/exemplars` folder that is generated after you've followed the installation instructions below, and you are good to go. <p align="center"> <img src="https://github.com/Heldenkombinat/logodetect/blob/master/docs/distances_analysis.png"> </p> As a **first reference** against the baseline, we also provide a modified ResNet [2] for object-recognition that directly takes the exemplars and the detections from the first stage and predicts if both belong to the same sub-class. Similarly to [3], this network infers a distance metric after being trained with examples of different sub-classes, but instead of sharing the same weights and processing each input in a separate pass as in [4], it concatenates both inputs and processes them in one pass. This concept follows more closely the architecture proposed by [5], where the assumption is that the exemplars often have more high-frequency components than the detections, and therefore the model can increase its accuracy by learning a separate set of weights for each input. The models that we are including in the repo achieved a reasonable performance after a few training epochs. However, if you would like to improve their performance you can find pointers to various datasets in [6], which can be used in the `training` part of this project. ## References [1] Ren et. al. [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/pdf/1506.01497.pdf) (2016)\ [2] He et. al. [Deep Residual Learning for Image Recognition](http://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf) (2016)\ [3] Hsieh et. al. [One-Shot Object Detection with Co-Attention and Co-Excitation](https://papers.nips.cc/paper/8540-one-shot-object-detection-with-co-attention-and-co-excitation.pdf) (2019)\ [4] Koch et. al. [Siamese Neural Networks for One-shot Image Recognition](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf) (2015)\ [5] Bhunia et. al. [A Deep One-Shot Network for Query-based Logo Retrieval](https://arxiv.org/pdf/1811.01395.pdf) (2019)\ [6] Hoi et. al. [LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks](https://arxiv.org/pdf/1511.02462.pdf) (2015) ## Installation This library is available on PyPI, so you can simply run `pip install logodetect` to install it. Make sure you have Python version 3.7 or later installed and to have an up-to-date `pip` version by running `pip install -U pip`. Also, we recommend working with virtual environments, but that is ultimately up to you. If you want to build `logodetect` from source, run ```bash_script git clone git@github.com:Heldenkombinat/logodetect.git cd logodetect pip install -e ".[tests, dev]" ``` Depending on your system and setup, you might have to run the install command as `sudo`. ## Usage After successful installation, a CLI tool called `logodetect` becomes available to you. If you invoke `logodetect` without any arguments, you will get help on how to use it. To automatically download all models and data needed to test the application first run the following command in your clone of this repository: ```bash_script export LOGOS_RECOGNITION=$(pwd) logodetect init ``` which will download all files to the current working directory. Note that if you prefer another folder to download the data, please use the environment variable `LOGOS_RECOGNITION` accordingly. Consider putting this variable in your `.bash_rc`, `.zshrc` or an equivalent configuration file on your system. If you don't specify a folder, it will default to `~/.hkt/logodetect`. After running the `logodetect init` CLI, you'll find data and models relative to the specified folder in the following structure: ```text data/ exemplars/ exemplars_100x100/ exemplars_100x100_aug/ exemplars_hq/ test_images/ test_videos/ models/ classifier_resnet18.pth detector.pth embedder.pth ``` If you're interested in training your own algorithms, it's a good idea to have a look at how the exemplar data is structured. For more on training, see the `training` folder and its readme. The `logodetect` CLI tool comes with two main commands, namely `video` and `image`, both of which work fairly similarly. In each case you need to provide the input data for which you would like to detect logos, and the logo exemplars that you want to detect in the footage. To get you started, we've provided some demo data that you can use out of the box. That means you can simply run: ```bash_script logodetect video ``` which should output the following text: ```text Rendering video: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17/17 [00:00<00:00, 707.42it/s] Moviepy - Building video /path/data/test_videos/test_video_small_50ms_output.mp4. Moviepy - Writing video /path/data/test_videos/test_video_small_50ms_output.mp4 Moviepy - Done ! Moviepy - video ready /path/data/test_videos/test_video_small_50ms_output.mp4 All done! ✨ 🍰 ✨ ``` to run one-shot detection on an example video, or you can run ```bash_script logodetect image ``` to do so for an example image, which results in the following output: ```text Saved resulting i