https://github.com/ksachdeva/opencv-mtcnn
An implementation of MTCNN Face detector using OpenCV's DNN module
https://github.com/ksachdeva/opencv-mtcnn
dnn face-detection inference mtcnn opencl opencv opencv-dnn
Last synced: 5 months ago
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An implementation of MTCNN Face detector using OpenCV's DNN module
- Host: GitHub
- URL: https://github.com/ksachdeva/opencv-mtcnn
- Owner: ksachdeva
- License: apache-2.0
- Created: 2018-04-21T03:33:16.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2020-06-03T16:16:59.000Z (over 5 years ago)
- Last Synced: 2025-04-19T14:09:45.770Z (6 months ago)
- Topics: dnn, face-detection, inference, mtcnn, opencl, opencv, opencv-dnn
- Language: C++
- Homepage:
- Size: 2.1 MB
- Stars: 68
- Watchers: 5
- Forks: 22
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# opencv-mtcnn
This is an inference implementation of MTCNN (Multi-task Cascaded Convolutional Network) to perform Face Detection and Alignment using OpenCV's DNN module.
## MTCNN
[ZHANG2016] 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.
https://kpzhang93.github.io/MTCNN_face_detection_alignment/paper/spl.pdf
## OpenCV's DNN module
Since OpenCV 3.1 there is a module called DNN that provides the inference support. The module is capable of taking models & weights from various popular frameworks such as Caffe, tensorflow, darknet etc.
You can read more about it here - https://github.com/opencv/opencv/wiki/Deep-Learning-in-OpenCV
Note that at present there is no support to perform training in OpenCV's DNN module and if I understood correctly there is no intention either.
## Compile / Run
### Requirements
* OpenCV 3.4+
* Boost FileSystem (1.58+) [only required for the sample application]
* CMake 3.2+
I am using CMake as the build tool. Here are the steps to try the implementation -
```bash
# compiling the library and the sample application
git clone https://github.com/ksachdeva/opencv-mtcnn
cd opencv-mtcnn
mkdir build
cd build
cmake ..
cmake --build .
```
```bash
# running the sample application
cd build
./sample/app
# here are some example cmd lines to run with the model and images in the test repository
# An image with 0 human faces (have picture of 4 dogs)
./sample/app ../data/models ../data/dogs.jpg
# An image with 1 face
./sample/app ../data/models ../data/Aaron_Peirsol_0003.jpg
# An image with 7 faces
./sample/app ../data/models ../data/2007_007763.jpg
```
## Result
Here is an example of how the execution of the sample application looks like
## Acknowledgments
Most of the implementations of MTCNN are based on either Caffe or Tensorflow. I wanted to play with OpenCV's DNN implementation and understand the paper bit better. While implementing it, I looked at various other C++ implementations (again all of them use Caffe) and more specifically borrowed utilities from https://github.com/golunovas/mtcnn-cpp. IMHO, I found his implementation (in C++) that is based on Caffe to be the cleanest amongst many others.
The model files are taken from https://github.com/kpzhang93/MTCNN_face_detection_alignment/tree/master/code
The image file "Aaron_Peirsol_0003.jpg" is from the LFW database (http://vis-www.cs.umass.edu/lfw/)
The image files "dog.jpg" & "2007_007763.jpg" are from dlib's github repository (https://github.com/davisking/dlib/blob/master/examples/faces)