Branko Kokanovic
1e830a3285
This change adds support to retrieve 128D face descriptor for a given landmark. Since now we have full pipeline, README.md has "general usage" section and integration test is added. Also, return from FaceLandmarkDetection is changed, so it can be given to FaceRecognition without changes. All obtained values are crosschecked to match with values from python versions (however, if num_jitters is > 1 in FaceRecognition, values don't match between PHP and Python, I suspect it is related to usage of dlib::rand, but still investigating)..
170 lines
4.1 KiB
Markdown
170 lines
4.1 KiB
Markdown
# PDlib - A PHP extension for Dlib
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## Requirements
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- Dlib 19.13+
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- PHP 7.0+
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- C++11
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## Dependencies
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### Dlib
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Install Dlib as shared library
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```bash
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git clone git@github.com:davisking/dlib.git
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cd dlib/dlib
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mkdir build
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cd build
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cmake -DBUILD_SHARED_LIBS=ON ..
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make
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sudo make install
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```
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## Installation
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```bash
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git clone https://github.com/goodspb/pdlib.git
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cd pdlib
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phpize
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./configure --enable-debug
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make
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sudo make install
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```
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### Configure PHP installation
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```bash
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vim youpath/php.ini
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```
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Append the content below into `php.ini`
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```
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[pdlib]
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extension="pdlib.so"
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```
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## Tests
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For tests, you will need to have bz2 extension installed. On Ubuntu, it boils to:
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```bash
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sudo apt-get install php-bz2
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```
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After you successfully compiled everything, just run:
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```bash
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make test
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```
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## Usage
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### General Usage
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Good starting point can be `tests/integration_face_recognition.phpt`. Check that first.
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Basically, if you just quickly want to get from your image to 128D descriptor of faces in image,
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here is really minimal example how:
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```php
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<?php
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$img_path = "image.jpg";
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$fd = new CnnFaceDetection("detection_cnn_model.dat");
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$detected_faces = $fd->detect($img_path);
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foreach($detected_faces as $detected_face) {
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$fld = new FaceLandmarkDetection("landmark_model.dat");
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$landmarks = $fld->detect($img_path, $detected_face);
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$fr = new FaceRecognition("recognition_model.dat");
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$descriptor = $fr->computeDescriptor($img_path, $landmarks);
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// Optionally use descriptor later in `dlib_chinese_whispers` function
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}
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```
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Location from where to get these models can be found on DLib website, as well as in `tests/integration_face_recognition.phpt` test.
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### Specific use cases
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#### face detection
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If you want to use HOG based approach:
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```php
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<?php
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// face detection
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$faceCount = dlib_face_detection("~/a.jpg");
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// how mary face in the picture.
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var_dump($faceCount);
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```
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If you want to use CNN approach (and CNN model):
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```php
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<?php
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$fd = new CnnFaceDetection("detection_cnn_model.dat");
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$detected_faces = $fd->detect("image.jpg");
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// $detected_face is indexed array, where values are assoc arrays with "top", "bottom", "left" and "right" values
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```
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CNN model can get you slightly better results, but is much, much more demanding (CPU and memory, GPU is also preferred).
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#### face landmark detection
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```php
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<?php
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// face landmark detection
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$landmarks = dlib_face_landmark_detection("~/a.jpg");
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var_dump($landmarks);
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```
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Additionally, you can also use class-based approach:
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```php
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$rect = array("left"=>value, "top"=>value, "right"=>value, "bottom"=>value);
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// You can download a trained facial shape predictor from:
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// http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2
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$fld = new FaceLandmarkDetection("path/to/shape/predictor/model");
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$parts = $fld->detect("path/to/image.jpg", $rect);
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// $parts is integer array where keys are associative values with "x" and "y" for keys
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```
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Note that, if you use class-based approach, you need to feed bounding box rectangle with values obtained from `dlib_face_detection`. If you use `dlib_face_landmark_detection`, everything is already done for you (and you are using HOG face detection model).
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#### face recognition (aka getting face descriptor)
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```php
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<?php
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$fr = new FaceRecognition($model_path);
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$landmarks = array(
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"rect" => $rect_of_faces_obtained_with_CnnFaceDetection,
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"parts" => $parts_obtained_with_FaceLandmarkDetection);
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$descriptor = $fr->computeDescriptor($img_path, $landmarks);
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// $descriptor is 128D array
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```
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#### chinese whispers
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Provides raw access to dlib's `chinese_whispers` function.
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Client need to build and provide edges. Edges are provided
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as numeric array. Each element of this array should also be
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numeric array with 2 elements of long type.
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Returned value is also numeric array, containing obtained labels.
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```php
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<?php
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// This example will cluster nodes 0 and 1, but would leave 2 out.
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// $labels will look like [0,0,1].
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$edges = [[0,0], [0,1], [1,1], [2,2]];
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$labels = dlib_chinese_whispers($edges);
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```
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## Features
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- [x] 1.Face Detection
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- [x] 2.Face Landmark Detection
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- [x] 3.Deep Face Recognition
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- [x] 4.Deep Learning Face Detection
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- [x] 5. Raw chinese_whispers
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