Face recognition

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)..

tests/face_recognition_ctor_error.phpt

@@ -0,0 +1,15 @@
+--TEST--
+Testing FaceRecognition constructor without arguments
+--SKIPIF--
+<?php if (!extension_loaded("pdlib")) print "skip"; ?>
+--FILE--
+<?php
+try {
+	new FaceRecognition();
+} catch (Exception $e) {
+	var_dump($e->getMessage());
+}
+?>
+--EXPECT--
+Warning: FaceRecognition::__construct() expects exactly 1 parameter, 0 given in /home/branko/pdlib/tests/face_recognition_ctor_error.php on line 3
+string(43) "Unable to parse face_recognition_model_path"

tests/integration_face_recognition.phpt

@@ -0,0 +1,60 @@
+--TEST--
+Full test for face recognition - download models, detect faces, landmark detection and face recognition.
+--SKIPIF--
+<?php if (!extension_loaded("pdlib") || (function_exists("bzopen"))) print "skip"; ?>
+--FILE--
+<?php
+$models = array(
+  "detection" => array("uri"=>"http://dlib.net/files/mmod_human_face_detector.dat.bz2"),
+  "prediction" => array("uri"=>"http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2"),
+  "recognition" => array("uri"=>"http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2")
+);
+
+// Check if there are models in local tmp. Download them if not (lazy caching).
+//
+foreach ($models as $modelName => $modelBag) {
+  printf("Processing %s model\n", $modelName);
+  $bz2_filename = array_values(array_slice(explode("/", $modelBag["uri"]), -1))[0];
+  $temp_bz2_file = sys_get_temp_dir() . "/" . $bz2_filename;
+  $dat_filename = array_values(array_slice(explode(".", $bz2_filename), 0))[0] . ".dat";
+  $temp_dat_file = sys_get_temp_dir() . "/" . $dat_filename;
+  $models[$modelName]["local_path"] = $temp_dat_file;
+
+  if (file_exists($temp_dat_file)) {
+    continue;
+  }
+  file_put_contents($temp_bz2_file, fopen($modelBag["uri"], 'r'));
+  $bz = bzopen($temp_bz2_file, "r");
+  $decompressed_file = "";
+  while (!feof($bz)) {
+    $decompressed_file .= bzread($bz, 4096);
+  }
+  bzclose($bz);
+
+  file_put_contents($temp_dat_file, $decompressed_file);
+}
+
+printf("Detection\n");
+$fd = new CnnFaceDetection($models["detection"]["local_path"]);
+$detected_faces = $fd->detect(__DIR__ . "/lenna.jpg");
+printf("Faces found = %d\n", count($detected_faces));
+foreach($detected_faces as $index => $detected_face) {
+  printf("Face[%d] in bounding box (left=%d, top=%d, right=%d, bottom=%d)\n", $index,
+    $detected_face["left"], $detected_face["top"], $detected_face["right"], $detected_face["bottom"]);
+  $fld = new FaceLandmarkDetection($models["prediction"]["local_path"]);
+  $landmarks = $fld->detect(__DIR__ . "/lenna.jpg", $detected_face);
+  printf("Since we used model with 5 shape predictions, we found %d landmark parts\n", count($landmarks["parts"]));
+  $fr = new FaceRecognition($models["recognition"]["local_path"]);
+  $descriptor = $fr->computeDescriptor(__DIR__ . "/lenna.jpg", $landmarks);
+  printf("Descriptor is vector of %d dimensions\n", count($descriptor));
+}
+?>
+--EXPECT--
+Processing detection model
+Processing prediction model
+Processing recognition model
+Detection
+Faces found = 1
+Face[0] in bounding box (left=187, top=186, right=357, bottom=355)
+Since we used model with 5 shape predictions, we found 5 landmark parts
+Descriptor is vector of 128 dimensions