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https://github.com/davisking/dlib.git
synced 2024-11-01 10:14:53 +08:00
Minor changes to avoid bugs in some compilers.
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9ff911696c
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3e9d361f89
@ -122,7 +122,7 @@ std::vector<double> normalized_predict_vec (
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{
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std::vector<double> out;
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out.reserve(samps.size());
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for (auto& x : samps)
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for (const auto& x : samps)
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out.push_back(normalized_predict(df,x));
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return out;
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}
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@ -64,10 +64,10 @@ public:
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throw dlib::error("The array of images and the array of array of locations must be of the same size");
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int total_chips = 0;
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for (auto& faces : batch_faces)
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for (const auto& faces : batch_faces)
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{
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total_chips += faces.size();
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for (auto& f : faces)
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for (const auto& f : faces)
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{
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if (f.num_parts() != 68 && f.num_parts() != 5)
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throw dlib::error("The full_object_detection must use the iBUG 300W 68 point face landmark style or dlib's 5 point style.");
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@ -82,7 +82,7 @@ public:
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auto& img = batch_imgs[i];
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std::vector<chip_details> dets;
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for (auto& f : faces)
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for (const auto& f : faces)
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dets.push_back(get_face_chip_details(f, 150, 0.25));
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dlib::array<matrix<rgb_pixel>> this_img_face_chips;
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extract_image_chips(img, dets, this_img_face_chips);
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@ -214,12 +214,12 @@ void save_face_chips (
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int num_faces = faces.size();
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std::vector<chip_details> dets;
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for (auto& f : faces)
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for (const auto& f : faces)
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dets.push_back(get_face_chip_details(f, size, padding));
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dlib::array<matrix<rgb_pixel>> face_chips;
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extract_image_chips(numpy_image<rgb_pixel>(img), dets, face_chips);
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int i=0;
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for (auto& chip : face_chips)
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for (const auto& chip : face_chips)
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{
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i++;
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if(num_faces > 1)
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@ -203,7 +203,7 @@ std::shared_ptr<global_function_search> py_global_function_search1 (
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)
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{
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std::vector<function_spec> tmp;
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for (auto i : functions)
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for (const auto& i : functions)
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tmp.emplace_back(i.cast<function_spec>());
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return std::make_shared<global_function_search>(tmp);
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@ -216,14 +216,14 @@ std::shared_ptr<global_function_search> py_global_function_search2 (
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)
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{
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std::vector<function_spec> specs;
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for (auto i : functions)
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for (const auto& i : functions)
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specs.emplace_back(i.cast<function_spec>());
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std::vector<std::vector<function_evaluation>> func_evals;
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for (auto i : initial_function_evals)
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for (const auto& i : initial_function_evals)
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{
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std::vector<function_evaluation> evals;
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for (auto j : i)
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for (const auto& j : i)
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{
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evals.emplace_back(j.cast<function_evaluation>());
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}
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@ -410,12 +410,12 @@ simply a struct that records x and the scalar value F(x). )RAW")
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std::vector<std::vector<function_evaluation>> function_evals;
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self.get_function_evaluations(specs,function_evals);
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py::list py_specs, py_func_evals;
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for (auto& s : specs)
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for (const auto& s : specs)
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py_specs.append(s);
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for (auto& i : function_evals)
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for (const auto& i : function_evals)
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{
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py::list tmp;
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for (auto& j : i)
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for (const auto& j : i)
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tmp.append(j);
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py_func_evals.append(tmp);
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}
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@ -97,7 +97,7 @@ void add_overlay_pylist (
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)
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{
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std::vector<rectangle> rects;
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for (auto& obj : objs)
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for (const auto& obj : objs)
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{
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try { rects.push_back(obj.cast<rectangle>()); continue; } catch(py::cast_error&) { }
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try { rects.push_back(obj.cast<drectangle>()); continue; } catch(py::cast_error&) { }
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@ -65,7 +65,7 @@ std::vector<point> py_remove_incoherent_edge_pixels (
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DLIB_CASSERT(num_rows(horz_gradient) == num_rows(vert_gradient));
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DLIB_CASSERT(num_columns(horz_gradient) == num_columns(vert_gradient));
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DLIB_CASSERT(angle_threshold >= 0);
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for (auto& p : line)
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for (const auto& p : line)
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DLIB_CASSERT(get_rect(horz_gradient).contains(p), "All line points must be inside the given images.");
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return remove_incoherent_edge_pixels(line, horz_gradient, vert_gradient, angle_threshold);
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@ -152,7 +152,7 @@ py::list py_extract_image_chips (
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dlib::array<numpy_image<T>> out;
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extract_image_chips(img, python_list_to_vector<chip_details>(chip_locations), out);
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py::list ret;
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for (auto& i : out)
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for (const auto& i : out)
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ret.append(i);
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return ret;
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}
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@ -511,7 +511,7 @@ py::list py_extract_image_chips (
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dlib::array<numpy_image<T>> out;
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extract_image_chips(img, python_list_to_vector<chip_details>(chip_locations), out);
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py::list ret;
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for (auto& i : out)
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for (const auto& i : out)
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ret.append(i);
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return ret;
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}
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@ -97,7 +97,7 @@ dataset py_load_image_dataset_metadata(
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std::shared_ptr<std::map<std::string,point>> map_from_object(py::dict obj)
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{
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auto ret = std::make_shared<std::map<std::string,point>>();
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for (auto& v : obj)
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for (const auto& v : obj)
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{
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(*ret)[v.first.cast<std::string>()] = v.second.cast<point>();
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}
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@ -121,7 +121,7 @@ image_dataset_metadata::dataset py_make_bounding_box_regression_training_data (
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// otherwise, detections should be a list of std::vectors.
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py::list dets(detections);
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std::vector<std::vector<rectangle>> temp;
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for (auto& d : dets)
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for (const auto& d : dets)
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temp.emplace_back(d.cast<const std::vector<rectangle>&>());
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return make_bounding_box_regression_training_data(truth, temp);
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}
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@ -174,7 +174,7 @@ void bind_image_dataset_metadata(py::module &m_)
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auto partsstr = [](const std::map<std::string,point>& item) {
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std::ostringstream sout;
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sout << "{";
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for (auto& v : item)
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for (const auto& v : item)
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sout << "'" << v.first << "': " << v.second << ", ";
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sout << "}";
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return sout.str();
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@ -182,7 +182,7 @@ void bind_image_dataset_metadata(py::module &m_)
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auto partsrepr = [](const std::map<std::string,point>& item) {
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std::ostringstream sout;
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sout << "dlib.image_dataset_metadata.parts({\n";
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for (auto& v : item)
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for (const auto& v : item)
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sout << "'" << v.first << "': dlib.point" << v.second << ",\n";
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sout << "})";
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return sout.str();
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@ -97,12 +97,12 @@ py::list get_face_chips (
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py::list chips_list;
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std::vector<chip_details> dets;
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for (auto& f : faces)
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for (const auto& f : faces)
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dets.push_back(get_face_chip_details(f, size, padding));
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dlib::array<numpy_image<rgb_pixel>> face_chips;
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extract_image_chips(img, dets, face_chips);
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for (auto& chip : face_chips)
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for (const auto& chip : face_chips)
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{
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// Append image to chips list
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chips_list.append(chip);
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@ -72,7 +72,7 @@ std::shared_ptr<full_object_detection> full_obj_det_init(const rectangle& rect,
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{
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const unsigned long num_parts = py::len(pyparts);
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std::vector<point> parts;
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for (auto& item : pyparts)
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for (const auto& item : pyparts)
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parts.push_back(item.cast<point>());
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return std::make_shared<full_object_detection>(rect, parts);
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