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celeb-detector-0.0.24
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مشاهده بیشتر
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مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
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مشاهده بیشترتوضیحات
Model to recognize celebrities using a face matching algorithm
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | celeb-detector-0.0.24 |
| نام | celeb-detector |
| نسخه کتابخانه | 0.0.24 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Shobhit Gupta |
| ایمیل نویسنده | shobhit9618@gmail.com |
| آدرس صفحه اصلی | https://github.com/shobhit9618/celeb_recognition |
| آدرس اینترنتی | https://pypi.org/project/celeb-detector/ |
| مجوز | - |
# Celebrity Recognition [](https://badge.fury.io/py/celeb-detector) [](https://celeb-recognition.readthedocs.io/en/main/) [](https://anaconda.org/shobhit9618/celeb-detector)
Model to recognize celebrities using a face matching algorithm.
Refer [this](https://celeb-recognition.readthedocs.io/en/main/) for detailed documentation.
You can also read my article on medium [here](https://medium.com/@shobhitgupta/celebrity-recognition-using-vggface-and-annoy-363c5df31f1e).
## Basic working of the algorithm includes the following:
- Face detection is done using MTCNN face detection model.
- Face encodings are created using [VGGFace](https://github.com/rcmalli/keras-vggface) model in keras.
- Face matching is done using [annoy](https://github.com/spotify/annoy) library (spotify).
## Installing dependencies
- Run `pip install -r requirements.txt` to install all the dependencies (preferably in a virtual environment).
## PyPI package
### Installation
- To ensure you have all the required additional packages, run `pip install -r requirements.txt` first.
- To install pip package, run:
```bash
# pip release version
pip install celeb-detector
# also install additional dependencies with this (if not installed via requirements.txt file)
pip install annoy keras-vggface keras-applications
# Directly from repo
pip install git+https://github.com/shobhit9618/celeb_recognition.git
```
- If you are using conda on linux or ubuntu, you can use the following commands to create and use a new environment called celeb-detector:
```bash
conda env create shobhit9618/celeb-detector
conda activate celeb-detector
```
This will install all the required dependencies. To ensure you are using the latest version of the package, also run (inside the environment):
```bash
pip install --upgrade celeb-detector
```
### Using pip pakcage
- For using my model for predictions, use the following lines of code after installation:
```python
import celeb_detector # on running for the first time, this will download vggface model
img_path = 'sample_image.jpg' # this supports both local path and web url like https://sample/sample_image_url.jpg
celeb_detector.celeb_recognition(img_path) # on running for the first time, 2 files (celeb_mapping.json and celeb_index_60.ann) will downloaded to your home directory
```
This returns a list of dictionaries, each dictionary contains bbox coordinates, celeb name and confidence for each face detected in the image (celeb name will be unknown if no matching face detected).
- For using your own custom model, also provide path to json and ann files as shown below:
```python
import celeb_detector
img_path = 'sample_image.jpg'
ann_path = 'sample_index.ann'
celeb_map = 'sample_mapping.json'
celeb_detector.celeb_recognition(img_path, ann_path, celeb_map)
```
- For creating your own model (refer [this](#create-your-own-celeb-model) for more details on usage) and run as follows:
```python
import celeb_detector
folder_path = 'celeb_images'
celeb_detector.create_celeb_model(folder_path)
```
## Create your own celeb model
- Create a dataset of celebs in the following directory structure:
```bash
celeb_images/
celeb-a/
celeb-a_1.jpg
celeb-a_2.jpg
...
celeb-b/
celeb-b_1.jpg
celeb-b_1.jpg
...
...
```
- Each folder name will be considered as the corresponding celeb name for the model (WARNING: Do not provide any special characters or spaces in the names).
- Make sure each image has only 1 face (of the desired celebrity), if there are multiple faces, only the first detected face will be considered.
- Provide path to the dataset folder (for example, `celeb_images` folder) in the [create_celeb_model.py](create_celeb_model.py) file.
- Run [create_celeb_model.py](create_celeb_model.py) file.
- Upon successful completion of the code, we get `celeb_mapping.json` (for storing indexes vs celeb names), `celeb_index.ann` (ann file for searching encodings) and `celeb_name_encoding.pkl` files (for storing encodings vs indexes for each celeb).
(WARNING: You need to provide paths for storing each of these files, default is to store in the current directory)
## Model predictions in jupyter
- Provide paths to `celeb_mapping.json` and `celeb_index.ann` files in [celeb_recognition.ipynb](celeb_recognition.ipynb) file. If you want to try my model, ignore this step.
- Run all the cells in the [celeb_recognition.ipynb](celeb_recognition.ipynb) file, the final cell will provide widgets for uploading images and making predictions
(this will also download the necessary model files).
- NOTE: [celeb_recognition.ipynb](celeb_recognition.ipynb) is a standalone file and does not require any other files from the repo for running.
## Model predictions in python
- Provide paths to `celeb_mapping.json` and `celeb_index.ann` files in [celeb_recognition.py](celeb_recognition.py) and [celeb_utils.py](celeb_utils/celeb_utils.py) files. If you want to try my model, ignore this step.
- Run [celeb_recognition.py](celeb_recognition.py) file, provide path to image in the file.
- Output includes a list of the identified faces, bounding boxes and the predicted celeb name (unknown if not found).
- It also displays the output with bounding boxes.
## Sample image output
## Binder
You can run a binder application by clicking the following link:
[](https://mybinder.org/v2/gh/shobhit9618/celeb_recognition/main)
You can also launch a voila binder application (which only has widgets for image upload and celeb prediction) by clicking [here](https://mybinder.org/v2/gh/shobhit9618/celeb_recognition/main?urlpath=%2Fvoila%2Frender%2Fceleb_recognition.ipynb).
## Google Colab
To open and run [celeb_recognition.ipynb](celeb_recognition.ipynb) file in google colab, click the following link:
[](https://colab.research.google.com/github/shobhit9618/celeb_recognition/blob/main/celeb_recognition.ipynb)
نیازمندی
| مقدار | نام |
|---|---|
| - | tensorflow |
| - | mtcnn |
| >=2.4.3 | keras |
| - | imutils |
| >=4.0 | opencv-python |
| - | matplotlib |
| - | numpy |
| - | tqdm |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl celeb-detector-0.0.24:
pip install celeb-detector-0.0.24.whl
نصب پکیج tar.gz celeb-detector-0.0.24:
pip install celeb-detector-0.0.24.tar.gz