MTCNN face detection implementation for TensorFlow, as a PIP package.

Overview

MTCNN

https://travis-ci.org/ipazc/mtcnn.svg?branch=master

Implementation of the MTCNN face detector for Keras in Python3.4+. It is written from scratch, using as a reference the implementation of MTCNN from David Sandberg (FaceNet's MTCNN) in Facenet. It is based on the paper Zhang, K et al. (2016) [ZHANG2016].

https://github.com/ipazc/mtcnn/raw/master/result.jpg

INSTALLATION

Currently it is only supported Python3.4 onwards. It can be installed through pip:

$ pip install mtcnn

This implementation requires OpenCV>=4.1 and Keras>=2.0.0 (any Tensorflow supported by Keras will be supported by this MTCNN package). If this is the first time you use tensorflow, you will probably need to install it in your system:

$ pip install tensorflow

or with conda

$ conda install tensorflow

Note that tensorflow-gpu version can be used instead if a GPU device is available on the system, which will speedup the results.

USAGE

The following example illustrates the ease of use of this package:

>>> from mtcnn import MTCNN
>>> import cv2
>>>
>>> img = cv2.cvtColor(cv2.imread("ivan.jpg"), cv2.COLOR_BGR2RGB)
>>> detector = MTCNN()
>>> detector.detect_faces(img)
[
    {
        'box': [277, 90, 48, 63],
        'keypoints':
        {
            'nose': (303, 131),
            'mouth_right': (313, 141),
            'right_eye': (314, 114),
            'left_eye': (291, 117),
            'mouth_left': (296, 143)
        },
        'confidence': 0.99851983785629272
    }
]

The detector returns a list of JSON objects. Each JSON object contains three main keys: 'box', 'confidence' and 'keypoints':

  • The bounding box is formatted as [x, y, width, height] under the key 'box'.
  • The confidence is the probability for a bounding box to be matching a face.
  • The keypoints are formatted into a JSON object with the keys 'left_eye', 'right_eye', 'nose', 'mouth_left', 'mouth_right'. Each keypoint is identified by a pixel position (x, y).

Another good example of usage can be found in the file "example.py." located in the root of this repository. Also, you can run the Jupyter Notebook "example.ipynb" for another example of usage.

BENCHMARK

The following tables shows the benchmark of this mtcnn implementation running on an Intel i7-3612QM CPU @ 2.10GHz, with a CPU-based Tensorflow 1.4.1.

  • Pictures containing a single frontal face:
Image size Total pixels Process time FPS
460x259 119,140 0.118 seconds 8.5
561x561 314,721 0.227 seconds 4.5
667x1000 667,000 0.456 seconds 2.2
1920x1200 2,304,000 1.093 seconds 0.9
4799x3599 17,271,601 8.798 seconds 0.1
  • Pictures containing 10 frontal faces:
Image size Total pixels Process time FPS
474x224 106,176 0.185 seconds 5.4
736x348 256,128 0.290 seconds 3.4
2100x994 2,087,400 1.286 seconds 0.7

MODEL

By default the MTCNN bundles a face detection weights model.

The model is adapted from the Facenet's MTCNN implementation, merged in a single file located inside the folder 'data' relative to the module's path. It can be overriden by injecting it into the MTCNN() constructor during instantiation.

The model must be numpy-based containing the 3 main keys "pnet", "rnet" and "onet", having each of them the weights of each of the layers of the network.

For more reference about the network definition, take a close look at the paper from Zhang et al. (2016) [ZHANG2016].

LICENSE

MIT License.

REFERENCE

[ZHANG2016] (1, 2) Zhang, K., Zhang, Z., Li, Z., and Qiao, Y. (2016). Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23(10):1499–1503.
Owner
Iván de Paz Centeno
Lead Data Scientist, R&D Engineer at Smarkia.
Iván de Paz Centeno
Async API for controlling Hue Lights

Hue API Async API for controlling Hue Lights Documentation: hue-api.nirantak.com Source: github.com/nirantak/hue-api Installation This is an async cli

Nirantak Raghav 4 Nov 16, 2022
Implementation of Fast Transformer in Pytorch

Fast Transformer - Pytorch Implementation of Fast Transformer in Pytorch. This only work as an encoder. Yannic video AI Epiphany Install $ pip install

Phil Wang 167 Dec 27, 2022
Code for the CVPR2021 workshop paper "Noise Conditional Flow Model for Learning the Super-Resolution Space"

NCSR: Noise Conditional Flow Model for Learning the Super-Resolution Space Official NCSR training PyTorch Code for the CVPR2021 workshop paper "Noise

57 Oct 03, 2022
領域を指定し、キーを入力することで画像を保存するツールです。クラス分類用のデータセット作成を想定しています。

image-capture-class-annotation 領域を指定し、キーを入力することで画像を保存するツールです。 クラス分類用のデータセット作成を想定しています。 Requirement OpenCV 3.4.2 or later Usage 実行方法は以下です。 起動後はマウスクリック4

KazuhitoTakahashi 5 May 28, 2021
Neural style in TensorFlow! 🎨

neural-style An implementation of neural style in TensorFlow. This implementation is a lot simpler than a lot of the other ones out there, thanks to T

Anish Athalye 5.5k Dec 29, 2022
The implementation our EMNLP 2021 paper "Enhanced Language Representation with Label Knowledge for Span Extraction".

LEAR The implementation our EMNLP 2021 paper "Enhanced Language Representation with Label Knowledge for Span Extraction". **The code is in the "master

杨攀 93 Jan 07, 2023
Official Keras Implementation for UNet++ in IEEE Transactions on Medical Imaging and DLMIA 2018

UNet++: A Nested U-Net Architecture for Medical Image Segmentation UNet++ is a new general purpose image segmentation architecture for more accurate i

Zongwei Zhou 1.8k Dec 27, 2022
Simple keras FCN Encoder/Decoder model for MS-COCO (food subset) segmentation

FCN_MSCOCO_Food_Segmentation Simple keras FCN Encoder/Decoder model for MS-COCO (food subset) segmentation Input data: [http://mscoco.org/dataset/#ove

Alexander Kalinovsky 11 Jan 08, 2019
An automated algorithm to extract the linear blend skinning (LBS) from a set of example poses

Dem Bones This repository contains an implementation of Smooth Skinning Decomposition with Rigid Bones, an automated algorithm to extract the Linear B

Electronic Arts 684 Dec 26, 2022
A sample pytorch Implementation of ACL 2021 research paper "Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction".

Span-ASTE-Pytorch This repository is a pytorch version that implements Ali's ACL 2021 research paper Learning Span-Level Interactions for Aspect Senti

来自丹麦的天籁 10 Dec 06, 2022
This is the official source code for SLATE. We provide the code for the model, the training code, and a dataset loader for the 3D Shapes dataset. This code is implemented in Pytorch.

SLATE This is the official source code for SLATE. We provide the code for the model, the training code and a dataset loader for the 3D Shapes dataset.

Gautam Singh 66 Dec 26, 2022
Predict bus arrival time using VertexAI and Nvidia's Jetson Nano

bus_prediction predict bus arrival time using VertexAI and Nvidia's Jetson Nano imagenet the command for imagenet.py look like this python3 /path/to/i

10 Dec 22, 2022
DFM: A Performance Baseline for Deep Feature Matching

DFM: A Performance Baseline for Deep Feature Matching Python (Pytorch) and Matlab (MatConvNet) implementations of our paper DFM: A Performance Baselin

143 Jan 02, 2023
Paddle-Adversarial-Toolbox (PAT) is a Python library for Deep Learning Security based on PaddlePaddle.

Paddle-Adversarial-Toolbox Paddle-Adversarial-Toolbox (PAT) is a Python library for Deep Learning Security based on PaddlePaddle. Model Zoo Common FGS

AgentMaker 17 Nov 08, 2022
Meta graph convolutional neural network-assisted resilient swarm communications

Resilient UAV Swarm Communications with Graph Convolutional Neural Network This repository contains the source codes of Resilient UAV Swarm Communicat

62 Dec 06, 2022
Few-NERD: Not Only a Few-shot NER Dataset

Few-NERD: Not Only a Few-shot NER Dataset This is the source code of the ACL-IJCNLP 2021 paper: Few-NERD: A Few-shot Named Entity Recognition Dataset.

THUNLP 319 Dec 30, 2022
RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching

RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching This repository contains the source code for our paper: RAFT-Stereo: Multilevel

Princeton Vision & Learning Lab 328 Jan 09, 2023
Geometric Sensitivity Decomposition

Geometric Sensitivity Decomposition This repo is the official implementation of A Geometric Perspective towards Neural Calibration via Sensitivity Dec

16 Dec 26, 2022
Making a music video with Wav2CLIP and VQGAN-CLIP

music2video Overview A repo for making a music video with Wav2CLIP and VQGAN-CLIP. The base code was derived from VQGAN-CLIP The CLIP embedding for au

Joel Jang | 장요엘 163 Dec 26, 2022
DeLag: Detecting Latency Degradation Patterns in Service-based Systems

DeLag: Detecting Latency Degradation Patterns in Service-based Systems Replication package of the work "DeLag: Detecting Latency Degradation Patterns

SEALABQualityGroup @ University of L'Aquila 2 Mar 24, 2022