Branko Kokanovic
7de8d060b3
This change extends existing landmark detection in new ways: 1. Existing logic is hiding HOG model (frontal_face_detector) underneath and user cannot use other models (CNN model, for example). 2. Bounding box is exposed as additional argument, and user can define custom bounding box (which is needed, if image used to detect faces is changed (for example scaled), and we want to crop only face from original image to feed into shape predictor). 3. This approach is class-based, so no need for multiple loadings of shape predictor model (only once, in ctor)
111 lines
2.2 KiB
Markdown
111 lines
2.2 KiB
Markdown
# PDlib - A PHP extension for Dlib
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A PHP extension
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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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## Dependence
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### Dlib
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Install Dlib as share 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
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```
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vim youpath/php.ini
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```
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Write the below content 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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## Usage
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#### face detection
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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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#### 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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#### 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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- [ ] 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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