Branko Kokanovic
6 years ago
committed by
GitHub
commit
54f3b75139
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MIT License
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Copyright (c) 2018 pdlib contributors
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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#include "../php_pdlib.h"
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#include "face_recognition.h"
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#include <zend_exceptions.h>
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#include <dlib/image_io.h>
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using namespace std;
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using namespace dlib;
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static inline face_recognition *php_face_recognition_from_obj(zend_object *obj) {
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return (face_recognition*)((char*)(obj) - XtOffsetOf(face_recognition, std));
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}
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#define Z_FACE_RECOGNITION_P(zv) php_face_recognition_from_obj(Z_OBJ_P((zv)))
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PHP_METHOD(FaceRecognition, __construct)
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{
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char *sz_face_recognition_model_path;
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size_t face_recognition_model_path_len;
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face_recognition *fr = Z_FACE_RECOGNITION_P(getThis());
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if (NULL == fr) {
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php_error_docref(NULL TSRMLS_CC, E_ERROR, "Unable to find obj in FaceRecognition::__construct()");
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return;
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}
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if (zend_parse_parameters(ZEND_NUM_ARGS(), "s",
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&sz_face_recognition_model_path, &face_recognition_model_path_len) == FAILURE){
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zend_throw_exception_ex(zend_ce_exception, 0 TSRMLS_CC, "Unable to parse face_recognition_model_path");
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return;
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}
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try {
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string face_recognition_model_path(sz_face_recognition_model_path, face_recognition_model_path_len);
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fr->net = new anet_type;
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deserialize(face_recognition_model_path) >> *(fr->net);
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} catch (exception& e) {
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zend_throw_exception_ex(zend_ce_exception, 0 TSRMLS_CC, e.what());
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return;
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}
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}
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std::vector<matrix<rgb_pixel>> pdlib_jitter_image(
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const matrix<rgb_pixel>& img,
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const int num_jitters,
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dlib::rand& rnd) {
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std::vector<matrix<rgb_pixel>> crops;
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for (int i = 0; i < num_jitters; ++i)
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crops.push_back(dlib::jitter_image(img,rnd));
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return crops;
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}
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// Helper macro to automatically have parsing of "top"/"bottom"/"left"/"right"
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//
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#define PARSE_BOUNDING_BOX_EDGE(side) \
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PARSE_LONG_FROM_ARRAY(rect_hash, side, \
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"Shape's rect array is missing " #side "key", "Shape's rect array's " #side " key is not long type")
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// Helper macro to parse "x"/"y"
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//
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#define PARSE_POINT(coord) \
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PARSE_LONG_FROM_ARRAY(part_hash, coord, \
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#coord " coordinate key is missing in parts array", #coord " coordinate key is not of long type")
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PHP_METHOD(FaceRecognition, computeDescriptor)
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{
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char *img_path;
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size_t img_path_len;
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zval *shape;
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long num_jitters = 1;
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if (zend_parse_parameters(ZEND_NUM_ARGS(), "sa|l", &img_path, &img_path_len, &shape, &num_jitters) == FAILURE){
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zend_throw_exception_ex(zend_ce_exception, 0 TSRMLS_CC, "Unable to parse computeDescriptor arguments");
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return;
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}
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HashTable *shape_hash = Z_ARRVAL_P(shape);
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uint32_t shape_hash_num_elements = zend_hash_num_elements(shape_hash);
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if (shape_hash_num_elements != 2) {
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zend_throw_exception_ex(zend_ce_exception, 0 TSRMLS_CC, "Shape (second argument) needs to have exactly 2 elements - keys \"rect\" and \"parts\"");
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return;
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}
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zval *rect_zval = zend_hash_str_find(shape_hash, "rect", sizeof("rect")-1);
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if (rect_zval == nullptr) {
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zend_throw_exception_ex(zend_ce_exception, 0 TSRMLS_CC, "Shape (second argument) array needs to have \"rect\" key"); \
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return;
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}
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if (Z_TYPE_P(rect_zval) != IS_ARRAY) {
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zend_throw_exception_ex(zend_ce_exception, 0 TSRMLS_CC, "Value of shape's key \"rect\" must be array");
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return;
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}
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HashTable *rect_hash = Z_ARRVAL_P(rect_zval);
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PARSE_BOUNDING_BOX_EDGE(top)
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PARSE_BOUNDING_BOX_EDGE(bottom)
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PARSE_BOUNDING_BOX_EDGE(left)
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PARSE_BOUNDING_BOX_EDGE(right)
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rectangle rect(left, top, right, bottom);
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zval *parts_zval = zend_hash_str_find(shape_hash, "parts", sizeof("parts")-1);
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if (parts_zval == nullptr) {
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zend_throw_exception_ex(zend_ce_exception, 0 TSRMLS_CC, "Shape (second argument) array needs to have \"parts\" key"); \
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return;
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}
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if (Z_TYPE_P(parts_zval) != IS_ARRAY) {
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zend_throw_exception_ex(zend_ce_exception, 0 TSRMLS_CC, "Value of shape's key \"parts\" must be array");
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return;
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}
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HashTable *parts_hash = Z_ARRVAL_P(parts_zval);
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HashPosition parts_pos;
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uint32_t parts_count = zend_hash_num_elements(parts_hash);
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point parts_points[parts_count];
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if ((parts_count != 5) && (parts_count != 68)) {
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zend_throw_exception_ex(zend_ce_exception, 0 TSRMLS_CC,
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"The full_object_detection must use the iBUG 300W 68 point face landmark style or dlib's 5 point style");
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return;
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}
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for (zend_hash_internal_pointer_reset_ex(parts_hash, &parts_pos);
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zend_hash_has_more_elements_ex(parts_hash, &parts_pos) == SUCCESS;
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zend_hash_move_forward_ex(parts_hash, &parts_pos)
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) {
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zend_string* str_index = {0};
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zend_ulong num_index;
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zval *part_zval = zend_hash_get_current_data_ex(parts_hash, &parts_pos);
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switch (zend_hash_get_current_key_ex(parts_hash, &str_index, &num_index, &parts_pos)) {
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case HASH_KEY_IS_LONG:
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if (Z_TYPE_P(part_zval) == IS_ARRAY)
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{
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HashTable *part_hash = Z_ARRVAL_P(part_zval);
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PARSE_POINT(x)
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PARSE_POINT(y)
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if (num_index > parts_count) {
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zend_throw_exception_ex(zend_ce_exception, 0 TSRMLS_CC, "Internal error, bad parsing of parts array");
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return;
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}
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parts_points[num_index] = point(x, y);
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} else {
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zend_throw_exception_ex(zend_ce_exception, 0 TSRMLS_CC, "Values from parts array must be arrays with \"x\" and \"y\" keys");
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return;
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}
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break;
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case HASH_KEY_IS_STRING:
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zend_throw_exception_ex(zend_ce_exception, 0 TSRMLS_CC, "Parts array must be indexed and it contains string keys");
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return;
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break;
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}
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}
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std::vector<point> parts;
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for (unsigned int i = 0; i < parts_count; i++) {
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parts.push_back(parts_points[i]);
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}
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try {
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face_recognition *fr = Z_FACE_RECOGNITION_P(getThis());
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full_object_detection fod(rect, parts);
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matrix<rgb_pixel> img;
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load_image(img, img_path);
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std::vector<chip_details> dets;
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dets.push_back(get_face_chip_details(fod, 150, 0.25));
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dlib::array<matrix<rgb_pixel>> face_chips;
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extract_image_chips(img, dets, face_chips);
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array_init(return_value);
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matrix<float,0,1> face_descriptor;
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if (num_jitters <= 1) {
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std::vector<matrix<float,0,1>> face_descriptors = fr->net->operator()(face_chips, 16);
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face_descriptor = face_descriptors[0];
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} else {
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matrix<rgb_pixel>& face_chip = face_chips[0];
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face_descriptor = mean(mat(fr->net->operator()(pdlib_jitter_image(face_chip, num_jitters, fr->rnd), 16)));
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}
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for (auto& d : face_descriptor) {
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add_next_index_double(return_value, d);
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}
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} catch (exception& e) {
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zend_throw_exception_ex(zend_ce_exception, 0 TSRMLS_CC, e.what());
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return;
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}
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}
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@ -0,0 +1,58 @@
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//
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// Created by branko at kokanovic dot org on 2018/8/26.
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//
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#ifndef PHP_DLIB_FACE_RECOGNITION_H
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#define PHP_DLIB_FACE_RECOGNITION_H
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#include <dlib/dnn.h>
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using namespace dlib;
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template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
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using residual = add_prev1<block<N,BN,1,tag1<SUBNET>>>;
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template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
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using residual_down = add_prev2<avg_pool<2,2,2,2,skip1<tag2<block<N,BN,2,tag1<SUBNET>>>>>>;
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template <int N, template <typename> class BN, int stride, typename SUBNET>
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using block = BN<con<N,3,3,1,1,relu<BN<con<N,3,3,stride,stride,SUBNET>>>>>;
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template <int N, typename SUBNET> using ares = relu<residual<block,N,affine,SUBNET>>;
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template <int N, typename SUBNET> using ares_down = relu<residual_down<block,N,affine,SUBNET>>;
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template <typename SUBNET> using alevel0 = ares_down<256,SUBNET>;
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template <typename SUBNET> using alevel1 = ares<256,ares<256,ares_down<256,SUBNET>>>;
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template <typename SUBNET> using alevel2 = ares<128,ares<128,ares_down<128,SUBNET>>>;
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template <typename SUBNET> using alevel3 = ares<64,ares<64,ares<64,ares_down<64,SUBNET>>>>;
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template <typename SUBNET> using alevel4 = ares<32,ares<32,ares<32,SUBNET>>>;
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using anet_type = loss_metric<fc_no_bias<128,avg_pool_everything<
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alevel0<
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alevel1<
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alevel2<
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alevel3<
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alevel4<
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max_pool<3,3,2,2,relu<affine<con<32,7,7,2,2,
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input_rgb_image_sized<150>
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>>>>>>>>>>>>;
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typedef struct _face_recognition {
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anet_type *net;
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zend_object std;
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dlib::rand rnd;
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} face_recognition;
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ZEND_BEGIN_ARG_INFO_EX(face_recognition_ctor_arginfo, 0, 0, 1)
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ZEND_ARG_INFO(0, face_recognition_model_path)
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ZEND_END_ARG_INFO()
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PHP_METHOD(FaceRecognition, __construct);
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ZEND_BEGIN_ARG_INFO_EX(face_recognition_compute_descriptor_arginfo, 0, 0, 3)
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ZEND_ARG_INFO(0, img_path)
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ZEND_ARG_INFO(0, landmarks)
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ZEND_ARG_INFO(0, num_jitters)
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ZEND_END_ARG_INFO()
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PHP_METHOD(FaceRecognition, computeDescriptor);
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#endif //PHP_DLIB_FACE_RECOGNITION_H
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@ -0,0 +1,15 @@
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--TEST--
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Testing FaceRecognition constructor without arguments
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--SKIPIF--
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<?php if (!extension_loaded("pdlib")) print "skip"; ?>
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--FILE--
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<?php
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try {
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new FaceRecognition();
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} catch (Exception $e) {
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var_dump($e->getMessage());
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}
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?>
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--EXPECT--
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Warning: FaceRecognition::__construct() expects exactly 1 parameter, 0 given in /home/branko/pdlib/tests/face_recognition_ctor_error.php on line 3
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string(43) "Unable to parse face_recognition_model_path"
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@ -0,0 +1,60 @@
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--TEST--
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Full test for face recognition - download models, detect faces, landmark detection and face recognition.
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--SKIPIF--
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<?php if (!extension_loaded("pdlib") || (function_exists("bzopen"))) print "skip"; ?>
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--FILE--
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<?php
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$models = array(
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"detection" => array("uri"=>"http://dlib.net/files/mmod_human_face_detector.dat.bz2"),
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"prediction" => array("uri"=>"http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2"),
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"recognition" => array("uri"=>"http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2")
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);
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// Check if there are models in local tmp. Download them if not (lazy caching).
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//
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foreach ($models as $modelName => $modelBag) {
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printf("Processing %s model\n", $modelName);
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$bz2_filename = array_values(array_slice(explode("/", $modelBag["uri"]), -1))[0];
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$temp_bz2_file = sys_get_temp_dir() . "/" . $bz2_filename;
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$dat_filename = array_values(array_slice(explode(".", $bz2_filename), 0))[0] . ".dat";
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$temp_dat_file = sys_get_temp_dir() . "/" . $dat_filename;
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$models[$modelName]["local_path"] = $temp_dat_file;
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if (file_exists($temp_dat_file)) {
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continue;
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}
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file_put_contents($temp_bz2_file, fopen($modelBag["uri"], 'r'));
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$bz = bzopen($temp_bz2_file, "r");
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$decompressed_file = "";
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while (!feof($bz)) {
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$decompressed_file .= bzread($bz, 4096);
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}
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bzclose($bz);
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file_put_contents($temp_dat_file, $decompressed_file);
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}
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printf("Detection\n");
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$fd = new CnnFaceDetection($models["detection"]["local_path"]);
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$detected_faces = $fd->detect(__DIR__ . "/lenna.jpg");
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printf("Faces found = %d\n", count($detected_faces));
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foreach($detected_faces as $index => $detected_face) {
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printf("Face[%d] in bounding box (left=%d, top=%d, right=%d, bottom=%d)\n", $index,
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$detected_face["left"], $detected_face["top"], $detected_face["right"], $detected_face["bottom"]);
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$fld = new FaceLandmarkDetection($models["prediction"]["local_path"]);
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$landmarks = $fld->detect(__DIR__ . "/lenna.jpg", $detected_face);
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printf("Since we used model with 5 shape predictions, we found %d landmark parts\n", count($landmarks["parts"]));
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$fr = new FaceRecognition($models["recognition"]["local_path"]);
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$descriptor = $fr->computeDescriptor(__DIR__ . "/lenna.jpg", $landmarks);
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printf("Descriptor is vector of %d dimensions\n", count($descriptor));
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}
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?>
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--EXPECT--
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Processing detection model
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Processing prediction model
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Processing recognition model
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Detection
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Faces found = 1
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Face[0] in bounding box (left=187, top=186, right=357, bottom=355)
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Since we used model with 5 shape predictions, we found 5 landmark parts
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Descriptor is vector of 128 dimensions
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After Width: | Height: | Size: 460 KiB |
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