123 lines
4.3 KiB
Markdown
123 lines
4.3 KiB
Markdown
# PDlib - A PHP extension for [Dlib](https://github.com/davisking/dlib)
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Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. See http://dlib.net for the main project documentation and API reference.
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PDlib is a PHP extension that implements the PHP binging for the dlib library, that now is used in the Nextcloud [FaceRecognition](https://github.com/matiasdelellis/facerecognition) application.
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## Note
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This is a fork of the [original pdlib extension](https://github.com/goodspb/pdlib), which we will use temporarily for the Nextcloud [FaceRecognition](https://github.com/matiasdelellis/facerecognition) application while the original author is not responding.
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## Installation
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Read [Installation](https://github.com/matiasdelellis/pdlib/wiki/Installation) documentation on the wiki for details.
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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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detected_faces = dlib_face_detection("image.jpg");
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// $detected_faces is indexed array, where values are assoc arrays with "top", "bottom", "left" and "right" values
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```
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If you want to use CNN approach (and CNN model):
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```php
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<?php
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$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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