mmdeploy/csrc/apis/c/pose_detector.cpp

191 lines
6.1 KiB
C++

// Copyright (c) OpenMMLab. All rights reserved.
#include "pose_detector.h"
#include <numeric>
#include "codebase/mmpose/mmpose.h"
#include "core/device.h"
#include "core/graph.h"
#include "core/mat.h"
#include "core/tensor.h"
#include "core/utils/formatter.h"
#include "handle.h"
using namespace std;
using namespace mmdeploy;
namespace {
const Value& config_template() {
// clang-format off
static Value v {
{
"pipeline", {
{"input", {"img_with_boxes"}},
{"output", {"key_points_unflat"}},
{
"tasks", {
{
{"name", "flatten"},
{"type", "Flatten"},
{"input", {"img_with_boxes"}},
{"output", {"patch_flat", "patch_index"}},
},
{
{"name", "pose-detector"},
{"type", "Inference"},
{"params", {{"model", "TBD"},{"batch_size", 1}}},
{"input", {"patch_flat"}},
{"output", {"key_points"}}
},
{
{"name", "unflatten"},
{"type", "Unflatten"},
{"input", {"key_points", "patch_index"}},
{"output", {"key_points_unflat"}},
}
}
}
}
}
};
// clang-format on
return v;
}
template <class ModelType>
int mmdeploy_pose_detector_create_impl(ModelType&& m, const char* device_name, int device_id,
mm_handle_t* handle) {
try {
auto value = config_template();
value["pipeline"]["tasks"][1]["params"]["model"] = std::forward<ModelType>(m);
auto pose_estimator = std::make_unique<Handle>(device_name, device_id, std::move(value));
*handle = pose_estimator.release();
return MM_SUCCESS;
} catch (const std::exception& e) {
MMDEPLOY_ERROR("exception caught: {}", e.what());
} catch (...) {
MMDEPLOY_ERROR("unknown exception caught");
}
return MM_E_FAIL;
}
} // namespace
int mmdeploy_pose_detector_create(mm_model_t model, const char* device_name, int device_id,
mm_handle_t* handle) {
return mmdeploy_pose_detector_create_impl(*static_cast<Model*>(model), device_name, device_id,
handle);
}
int mmdeploy_pose_detector_create_by_path(const char* model_path, const char* device_name,
int device_id, mm_handle_t* handle) {
return mmdeploy_pose_detector_create_impl(model_path, device_name, device_id, handle);
}
int mmdeploy_pose_detector_apply(mm_handle_t handle, const mm_mat_t* mats, int mat_count,
mm_pose_detect_t** results) {
return mmdeploy_pose_detector_apply_bbox(handle, mats, mat_count, nullptr, nullptr, results);
}
int mmdeploy_pose_detector_apply_bbox(mm_handle_t handle, const mm_mat_t* mats, int mat_count,
const mm_rect_t* bboxes, const int* bbox_count,
mm_pose_detect_t** results) {
if (handle == nullptr || mats == nullptr || mat_count == 0 || results == nullptr) {
return MM_E_INVALID_ARG;
}
try {
auto pose_detector = static_cast<Handle*>(handle);
Value input{Value::kArray};
auto result_count = 0;
for (int i = 0; i < mat_count; ++i) {
mmdeploy::Mat _mat{mats[i].height, mats[i].width, PixelFormat(mats[i].format),
DataType(mats[i].type), mats[i].data, Device{"cpu"}};
Value img_with_boxes;
if (bboxes && bbox_count) {
for (int j = 0; j < bbox_count[i]; ++j) {
Value obj;
obj["ori_img"] = _mat;
float width = bboxes[j].right - bboxes[j].left + 1;
float height = bboxes[j].bottom - bboxes[j].top + 1;
obj["box"] = {bboxes[j].left, bboxes[j].top, width, height, 1.0};
obj["rotation"] = 0.f;
img_with_boxes.push_back(obj);
}
bboxes += bbox_count[i];
result_count += bbox_count[i];
} else {
// inference whole image
Value obj;
obj["ori_img"] = _mat;
obj["box"] = {0, 0, _mat.width(), _mat.height(), 1.0};
obj["rotation"] = 0.f;
img_with_boxes.push_back(obj);
result_count += 1;
}
input.front().push_back(img_with_boxes);
}
auto output = pose_detector->Run(std::move(input)).value().front();
auto pose_outputs = from_value<vector<vector<mmpose::PoseDetectorOutput>>>(output);
std::vector<int> counts;
if (bboxes && bbox_count) {
counts = std::vector<int>(bbox_count, bbox_count + mat_count);
} else {
counts.resize(mat_count, 1);
}
std::vector<int> offsets{0};
std::partial_sum(begin(counts), end(counts), back_inserter(offsets));
auto deleter = [&](mm_pose_detect_t* p) {
mmdeploy_pose_detector_release_result(p, offsets.back());
};
std::unique_ptr<mm_pose_detect_t[], decltype(deleter)> _results(
new mm_pose_detect_t[result_count]{}, deleter);
for (int i = 0; i < mat_count; ++i) {
auto& pose_output = pose_outputs[i];
for (int j = 0; j < pose_output.size(); ++j) {
auto& res = _results[offsets[i] + j];
auto& box_result = pose_output[j];
int sz = box_result.key_points.size();
res.point = new mm_pointf_t[sz];
res.score = new float[sz];
res.length = sz;
for (int k = 0; k < sz; k++) {
res.point[k].x = box_result.key_points[k].bbox[0];
res.point[k].y = box_result.key_points[k].bbox[1];
res.score[k] = box_result.key_points[k].score;
}
}
}
*results = _results.release();
return MM_SUCCESS;
} catch (const std::exception& e) {
MMDEPLOY_ERROR("exception caught: {}", e.what());
} catch (...) {
MMDEPLOY_ERROR("unknown exception caught");
}
return MM_E_FAIL;
}
void mmdeploy_pose_detector_release_result(mm_pose_detect_t* results, int count) {
for (int i = 0; i < count; ++i) {
delete[] results[i].point;
delete[] results[i].score;
}
delete[] results;
}
void mmdeploy_pose_detector_destroy(mm_handle_t handle) { delete static_cast<Handle*>(handle); }