fight_fall_cls.cpp
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/*
* @Author: yangzilong
* @Date: 2021-12-02 14:52:24
* @Last Modified by: yangzilong
* @Last Modified time: Do not edit
* @Email: yangzilong@objecteye.com
* @Description:
*/
#include <algorithm>
#include "sy_common.h"
#include "./fight_fall_cls.hpp"
#include "../reprocessing_module/CropImg.h"
namespace ai_engine_module
{
namespace fight_fall_cls
{
long long FightfallCls::gid_ = 0;
algorithm_type_t FightfallCls::fall_algor_type_ = algorithm_type_t::PEDESTRIAN_FALL;
algorithm_type_t FightfallCls::fight_algor_type_ = algorithm_type_t::PEDESTRIAN_FIGHT;
FightfallCls::FightfallCls()
: task_param_manager_(nullptr)
{
}
FightfallCls::~FightfallCls()
{
fight_det_release(&tools_);
if (!tools_)
{
delete tools_;
tools_ = nullptr;
}
}
bool FightfallCls::init(int gpu_id, char *trt_serialize_file)
{
init_ = false;
fight_det_param param;
{
param.mode = gpu_id >= 0 ? DEVICE_GPU : DEVICE_CPU;
param.gpuid = gpu_id;
param.score_thresld = 0.8;
param.auth_license = "sy_tsl_aiplatform_sdk_2021"; // TODO:
param.max_batch = MAX_BATCH;
param.serialize_file = "./serialize_file/fight_fall";
}
int status;
if (!(init_ = (0 == (status = fight_det_init(&tools_, param)))))
LOG_ERROR("Init FightFellClsSdk failed error code is {}", status);
else
if (!task_param_manager_)
task_param_manager_ = task_param_manager::getInstance();
return init_;
}
bool FightfallCls::check_initied()
{
if (!init_)
LOG_ERROR("[FightfallCls] call init function please.");
return init_;
}
float iou(const box_t &box1, const box_t &box2)
{
float insect_top = max(box1.top, box2.top);
float insect_left = max(box1.left, box2.left);
float insect_right = min(box1.right, box2.right);
float insect_bottom = min(box1.bottom, box2.bottom);
if (insect_right <= insect_left || insect_bottom <= insect_top)
return 0.0f;
float insect_area = (insect_right - insect_left) * (insect_bottom - insect_top);
float union_area = (box1.right - box1.left) * (box1.bottom - box1.top) +
(box2.right - box2.left) * (box2.bottom - box2.top) -
insect_area;
return insect_area / union_area;
}
box_t union_box(const box_t &box1, const box_t &box2)
{
box_t box;
{
box.top = std::min(box1.top, box2.top);
box.left = std::min(box1.left, box2.left);
box.right = std::max(box1.right, box2.right);
box.bottom = std::max(box1.bottom, box2.bottom);
}
return box;
}
bool FightfallCls::process_mstreams(const std::set<taskid_t> &taskIds, const sy_img *det_input_images, const std::vector<onelevel_det_result> &det_results,
std::vector<result_data_t> &results)
{
if (!check_initied())
return false;
if (det_results.empty())
{
LOG_ERROR("[FightfallCls] call init function please.");
LOG_ERROR("detection result is empty.");
return false;
}
int n_images = det_results.size(); // or n_stream
unsigned flattened_idx = 0;
std::map<int, int> flattened_idx_to_batch_idx;
/* 1. Crop & keep some interest class. */
/* 1. preprocess. */
auto taskId_iter = taskIds.begin();
std::vector<sy_img> flattened_imgs(0);
std::vector<input_data_wrap_t> flattened_interest_data(0); //
for (int n = 0; n < n_images; ++n)
{
int n_interest_obj = 0;
const sy_img &src_img = det_input_images[n];
auto &boxes_of_one_image = det_results[n].obj;
for (int i = 0; i < det_results[n].obj_count; ++i)
{
auto &box = boxes_of_one_image[i];
if (static_cast<det_class_label_t>(box.index) == det_class_label_t::HUMAN)
{
auto &taskId = *taskId_iter;
input_data_wrap_t data;
int top = std::max(int(box.top - (IMAGE_CROP_EXPAND_RATIO * box.top)), 0);
int left = std::max(int(box.left - (IMAGE_CROP_EXPAND_RATIO * box.left)), 0);
int right = std::min(int(box.right + (IMAGE_CROP_EXPAND_RATIO * box.right)), src_img.w_);
int bottom = std::min(int(box.bottom + (IMAGE_CROP_EXPAND_RATIO * box.bottom)), src_img.h_);
int width = right - left;
int height = bottom - top;
auto fall_algor_param_wrap = task_param_manager_->get_task_other_param(taskId, this->fall_algor_type_);
auto fall_algor_param = (fall_algor_param_wrap != nullptr) ? ((fall_algor_param_type)fall_algor_param_wrap->algor_param) : nullptr;
auto fall_basic_param = (fall_algor_param_wrap != nullptr) ? (fall_algor_param_wrap->basic_param) : nullptr;
auto fight_algor_param_wrap = task_param_manager_->get_task_other_param(taskId, this->fight_algor_type_);
auto fight_algor_param = (fight_algor_param_wrap != nullptr) ? ((fight_algor_param_type)fight_algor_param_wrap->algor_param) : nullptr;
auto fight_basic_param = (fight_algor_param_wrap != nullptr) ? (fight_algor_param_wrap->basic_param) : nullptr;
auto minimum_width = std::min((fall_algor_param == nullptr ? DEFAULT_MIN_WIDTH : fall_algor_param->pedestrian_min_width),
(fight_algor_param == nullptr ? DEFAULT_MIN_WIDTH : fight_algor_param->pedestrian_min_width));
auto minimum_height = std::min((fall_algor_param == nullptr ? DEFAULT_MIN_HEIGHT : fall_algor_param->pedestrian_min_height),
(fight_algor_param == nullptr ? DEFAULT_MIN_HEIGHT : fight_algor_param->pedestrian_min_height));
auto minimum_threshold = std::min((fall_algor_param == nullptr ? DEFAULT_PTHRESHOLD : fall_algor_param->threshold),
(fight_algor_param == nullptr ? DEFAULT_PTHRESHOLD : fight_algor_param->threshold));
sy_rect intel_rect;
if (fight_basic_param == nullptr)
{
if (fall_basic_param != nullptr)
intel_rect = fall_basic_param->algor_valid_rect;
}
else
{
intel_rect = fight_basic_param->algor_valid_rect;
}
if ((width < minimum_width || height < minimum_height || box.confidence < minimum_threshold) ||
!snapshot_legal_inarea(intel_rect, left, top, right, bottom))
continue;
data.box.top = top;
data.box.left = left;
data.box.right = right;
data.box.bottom = bottom;
data.box.score = box.confidence;
data.taskId = taskId;
data.objId = box.id;
sy_img img;
img.w_ = width;
img.h_ = height;
img.c_ = src_img.c_;
cudaError_t cuda_status;
const unsigned nbytes = img.c_ * img.h_ * img.w_ * sizeof(unsigned char);
if (CUDA_SUCCESS != (cuda_status = cudaMalloc((void**)&img.data_, nbytes)))
{
LOG_ERROR("cudaMalloc failed: {} malloc nbytes is {} mb is {} ", cudaGetErrorString(cuda_status), nbytes, nbytes / (1024 * 1024));
continue;
}
if (CUDA_SUCCESS != ( cuda_status = cudacommon::CropImgGpu(src_img.data_, src_img.w_, src_img.h_, img.data_, left, top, width, height)))
{
LOG_ERROR("Crop image GPU failed error is %s wh is [{}, {}] ltrb is [{} {} {} {}]",
cudaGetErrorString(cuda_status), src_img.w_, src_img.h_, data.box.left, data.box.top, data.box.right, data.box.bottom);
CHECK(cudaFree(img.data_));
continue;
}
flattened_imgs.emplace_back(std::move(img));
flattened_interest_data.emplace_back(std::move(data));
flattened_idx_to_batch_idx[flattened_idx++] = n;
}
}
++taskId_iter;
}
/* 2. inference. */
int n_input_image = flattened_imgs.size();
fight_det_result model_results[n_input_image];
{
int steps = (n_input_image + MAX_BATCH - 1) / MAX_BATCH;
for (int step = 0; step < steps; ++step)
{
int offset = step * MAX_BATCH;
int batch_size = (step == steps - 1) ? n_input_image - offset : MAX_BATCH;
fight_det_process_batch(tools_, flattened_imgs.data() + offset, batch_size, model_results + offset);
}
}
/* 3. postprocess. */
{
/* a. review to 2d format. */
std::unordered_map<taskid_t, std::vector<fight_det_result>> taskid_to_sdk_result;
{
for (int n = 0; n < n_input_image; ++n)
taskid_to_sdk_result[flattened_interest_data[n].taskId].emplace_back(std::move(model_results[n]));
}
/* b. post process */
int n = 0;
for (auto iter = taskIds.begin(); iter != taskIds.end(); ++iter)
{
result_data_t result_data;
auto &taskId = *iter;
auto fall_algor_param_wrap = task_param_manager_->get_task_other_param(taskId, this->fall_algor_type_);
auto fall_algor_param = (fall_algor_param_wrap != nullptr) ? ((fall_algor_param_type)fall_algor_param_wrap->algor_param) : nullptr;
auto fight_algor_param_wrap = task_param_manager_->get_task_other_param(taskId, this->fight_algor_type_);
auto fight_algor_param = (fight_algor_param_wrap != nullptr) ? ((fight_algor_param_type)fight_algor_param_wrap->algor_param) : nullptr;
auto &model_result = taskid_to_sdk_result[taskId];
const unsigned model_result_size = model_result.size();
bool has_fight_pair[model_result_size];
memset(has_fight_pair, false, model_result_size * sizeof(bool));
for (int i = 0; i < model_result_size; ++i, ++n)
{
const sy_img& src_img = det_input_images[flattened_idx_to_batch_idx[n]];
auto &cropped_img = flattened_imgs[n]; // croped image.
auto &preprocessed_data = flattened_interest_data[n];
auto &fight_result_iter = model_result[i];
for (int j = 0; j < FIGHT_MODEL_NUM; ++j)
{
auto &res = fight_result_iter.fight_infos[j];
if (fight_algor_param != nullptr)
if (!has_fight_pair[i])
for (int z = i + 1; z < model_result_size; ++z)
if (!has_fight_pair[z] && (res.fight_score > fight_algor_param->threshold ||
model_result[z].fight_infos[j].fight_score > fight_algor_param->threshold))
if (iou(preprocessed_data.box, flattened_interest_data[n + (z - i)].box) > fight_algor_param->iou_threshold)
{
// std::printf("[Debug] fight Pair ");
has_fight_pair[i] = has_fight_pair[z] = true;
auto &preprocessed_data2 = flattened_interest_data[n + (z - i)];
result_fight_data_t data;
{
data.taskid = taskId;
data.objectids.insert(preprocessed_data.objId);
data.objectids.insert(preprocessed_data2.objId);
data.box = union_box(preprocessed_data.box, preprocessed_data2.box);
// data.box.score = (res.fight_score + model_result[z].fight_infos[j].fight_score) * 0.5;
data.box.score = std::max(res.fight_score, model_result[z].fight_infos[j].fight_score);
// std::printf("\tltrb is [%d %d %d %d] wh of src img is [%d %d]", data.box.left, data.box.top, data.box.right, data.box.bottom, src_img.w_, src_img.h_);
sy_img img;
{
img.c_ = src_img.c_;
img.w_ = data.box.width();
img.h_ = data.box.height();
}
cudaError_t cuda_status;
const unsigned nbytes = img.c_ * img.h_ * img.w_ * sizeof(unsigned char);
if (CUDA_SUCCESS != (cuda_status = cudaMalloc((void**)&img.data_, nbytes)))
{
LOG_ERROR("cudaMalloc failed: {} malloc nbytes is {} mb is {} ", cudaGetErrorString(cuda_status), nbytes, nbytes / (1024 * 1024));
continue;
}
if (CUDA_SUCCESS != ( cuda_status = cudacommon::CropImgGpu(src_img.data_, src_img.w_, src_img.h_, img.data_, data.box.left, data.box.top, img.w_, img.h_)))
{
LOG_ERROR("Crop image GPU failed error is {} wh is [{} {}] ltrb is [{} {} {} {}]",
cudaGetErrorString(cuda_status), src_img.w_, src_img.h_, data.box.left, data.box.top, data.box.right, data.box.bottom);
CHECK(cudaFree(img.data_));
continue;
}
data.roi_img = img;
data.ori_img = src_img;
data.id = gid_++;
}
result_data.fight_data.emplace_back(std::move(data));
}
if (fall_algor_param != nullptr)
if (res.fall_score > fall_algor_param->threshold)
{
if ((float)(src_img.h_ - preprocessed_data.box.bottom) > (float)(src_img.h_ * 0.05))
{
result_fall_data_t data;
{
data.box = preprocessed_data.box;
data.objectid = preprocessed_data.objId;
data.taskid = taskId;
data.roi_img = cropped_img;
data.ori_img = src_img;
data.id = gid_++;
}
result_data.fall_data.emplace_back(std::move(data));
goto _continue;
}
}
}
cudaFree(cropped_img.data_);
_continue:
{
}
}
results.emplace_back(std::move(result_data));
}
}
return true;
} // func end
bool FightfallCls::process_mstreams2(const std::vector<taskid_t> &taskIds, const sy_img *det_input_images, const std::vector<onelevel_det_result> &det_results,
std::vector<result_data_t> &results)
{
if (!check_initied())
return false;
if (det_results.empty())
{
LOG_ERROR("[FightfallCls] call init function please.");
LOG_ERROR("detection result is empty.");
return false;
}
int n_images = det_results.size(); // or n_stream
unsigned flattened_idx = 0;
std::map<int, int> flattened_idx_to_batch_idx;
/* 1. Crop & keep some interest class. */
/* 1. preprocess. */
auto taskId_iter = taskIds.begin();
std::vector<sy_img> flattened_imgs(0);
std::vector<input_data_wrap_t> flattened_interest_data(0); //
for (int n = 0; n < n_images; ++n)
{
int n_interest_obj = 0;
const sy_img &src_img = det_input_images[n];
auto &boxes_of_one_image = det_results[n].obj;
for (int i = 0; i < det_results[n].obj_count; ++i)
{
auto &box = boxes_of_one_image[i];
if (static_cast<det_class_label_t>(box.index) == det_class_label_t::HUMAN)
{
auto &taskId = *taskId_iter;
input_data_wrap_t data;
int top = std::max(int(box.top - (IMAGE_CROP_EXPAND_RATIO * box.top)), 0);
int left = std::max(int(box.left - (IMAGE_CROP_EXPAND_RATIO * box.left)), 0);
int right = std::min(int(box.right + (IMAGE_CROP_EXPAND_RATIO * box.right)), src_img.w_);
int bottom = std::min(int(box.bottom + (IMAGE_CROP_EXPAND_RATIO * box.bottom)), src_img.h_);
int width = right - left;
int height = bottom - top;
auto fall_algor_param_wrap = task_param_manager_->get_task_other_param(taskId, this->fall_algor_type_);
auto fall_algor_param = (fall_algor_param_wrap != nullptr) ? ((fall_algor_param_type)fall_algor_param_wrap->algor_param) : nullptr;
auto fall_basic_param = (fall_algor_param_wrap != nullptr) ? (fall_algor_param_wrap->basic_param) : nullptr;
auto fight_algor_param_wrap = task_param_manager_->get_task_other_param(taskId, this->fight_algor_type_);
auto fight_algor_param = (fight_algor_param_wrap != nullptr) ? ((fight_algor_param_type)fight_algor_param_wrap->algor_param) : nullptr;
auto fight_basic_param = (fight_algor_param_wrap != nullptr) ? (fight_algor_param_wrap->basic_param) : nullptr;
auto minimum_width = std::min((fall_algor_param == nullptr ? DEFAULT_MIN_WIDTH : fall_algor_param->pedestrian_min_width),
(fight_algor_param == nullptr ? DEFAULT_MIN_WIDTH : fight_algor_param->pedestrian_min_width));
auto minimum_height = std::min((fall_algor_param == nullptr ? DEFAULT_MIN_HEIGHT : fall_algor_param->pedestrian_min_height),
(fight_algor_param == nullptr ? DEFAULT_MIN_HEIGHT : fight_algor_param->pedestrian_min_height));
auto minimum_threshold = std::min((fall_algor_param == nullptr ? DEFAULT_PTHRESHOLD : fall_algor_param->threshold),
(fight_algor_param == nullptr ? DEFAULT_PTHRESHOLD : fight_algor_param->threshold));
sy_rect intel_rect;
if (fight_basic_param == nullptr)
{
if (fall_basic_param != nullptr)
intel_rect = fall_basic_param->algor_valid_rect;
}
else
{
intel_rect = fight_basic_param->algor_valid_rect;
}
if ((width < minimum_width || height < minimum_height || box.confidence < minimum_threshold) ||
!snapshot_legal_inarea(intel_rect, left, top, right, bottom))
continue;
data.box.top = top;
data.box.left = left;
data.box.right = right;
data.box.bottom = bottom;
data.box.score = box.confidence;
data.taskId = taskId;
data.objId = box.id;
sy_img img;
img.w_ = width;
img.h_ = height;
img.c_ = src_img.c_;
cudaError_t cuda_status;
const unsigned nbytes = img.c_ * img.h_ * img.w_ * sizeof(unsigned char);
if (CUDA_SUCCESS != (cuda_status = cudaMalloc((void**)&img.data_, nbytes)))
{
LOG_ERROR("cudaMalloc failed: {} malloc nbytes is {} mb is {} ", cudaGetErrorString(cuda_status), nbytes, nbytes / (1024 * 1024));
continue;
}
if (CUDA_SUCCESS != ( cuda_status = cudacommon::CropImgGpu(src_img.data_, src_img.w_, src_img.h_, img.data_, left, top, width, height)))
{
LOG_ERROR("Crop image GPU failed error is %s wh is [{}, {}] ltrb is [{} {} {} {}]",
cudaGetErrorString(cuda_status), src_img.w_, src_img.h_, data.box.left, data.box.top, data.box.right, data.box.bottom);
CHECK(cudaFree(img.data_));
continue;
}
flattened_imgs.emplace_back(std::move(img));
flattened_interest_data.emplace_back(std::move(data));
flattened_idx_to_batch_idx[flattened_idx++] = n;
}
}
++taskId_iter;
}
/* 2. inference. */
int n_input_image = flattened_imgs.size();
fight_det_result model_results[n_input_image];
{
int steps = (n_input_image + MAX_BATCH - 1) / MAX_BATCH;
for (int step = 0; step < steps; ++step)
{
int offset = step * MAX_BATCH;
int batch_size = (step == steps - 1) ? n_input_image - offset : MAX_BATCH;
fight_det_process_batch(tools_, flattened_imgs.data() + offset, batch_size, model_results + offset);
}
}
/* 3. postprocess. */
{
/* a. review to 2d format. */
std::unordered_map<taskid_t, std::vector<fight_det_result>> taskid_to_sdk_result;
{
for (int n = 0; n < n_input_image; ++n)
taskid_to_sdk_result[flattened_interest_data[n].taskId].emplace_back(std::move(model_results[n]));
}
/* b. post process */
int n = 0;
for (auto iter = taskIds.begin(); iter != taskIds.end(); ++iter)
{
result_data_t result_data;
auto &taskId = *iter;
auto fall_algor_param_wrap = task_param_manager_->get_task_other_param(taskId, this->fall_algor_type_);
auto fall_algor_param = (fall_algor_param_wrap != nullptr) ? ((fall_algor_param_type)fall_algor_param_wrap->algor_param) : nullptr;
auto fight_algor_param_wrap = task_param_manager_->get_task_other_param(taskId, this->fight_algor_type_);
auto fight_algor_param = (fight_algor_param_wrap != nullptr) ? ((fight_algor_param_type)fight_algor_param_wrap->algor_param) : nullptr;
auto &model_result = taskid_to_sdk_result[taskId];
const unsigned model_result_size = model_result.size();
bool has_fight_pair[model_result_size];
memset(has_fight_pair, false, model_result_size * sizeof(bool));
for (int i = 0; i < model_result_size; ++i, ++n)
{
const sy_img& src_img = det_input_images[flattened_idx_to_batch_idx[n]];
auto &cropped_img = flattened_imgs[n]; // croped image.
auto &preprocessed_data = flattened_interest_data[n];
auto &fight_result_iter = model_result[i];
for (int j = 0; j < FIGHT_MODEL_NUM; ++j)
{
auto &res = fight_result_iter.fight_infos[j];
if (fight_algor_param != nullptr)
if (!has_fight_pair[i])
for (int z = i + 1; z < model_result_size; ++z)
if (!has_fight_pair[z] && (res.fight_score > fight_algor_param->threshold ||
model_result[z].fight_infos[j].fight_score > fight_algor_param->threshold))
if (iou(preprocessed_data.box, flattened_interest_data[n + (z - i)].box) > fight_algor_param->iou_threshold)
{
// std::printf("[Debug] fight Pair ");
has_fight_pair[i] = has_fight_pair[z] = true;
auto &preprocessed_data2 = flattened_interest_data[n + (z - i)];
result_fight_data_t data;
{
data.taskid = taskId;
data.objectids.insert(preprocessed_data.objId);
data.objectids.insert(preprocessed_data2.objId);
data.box = union_box(preprocessed_data.box, preprocessed_data2.box);
// data.box.score = (res.fight_score + model_result[z].fight_infos[j].fight_score) * 0.5;
data.box.score = std::max(res.fight_score, model_result[z].fight_infos[j].fight_score);
// std::printf("\tltrb is [%d %d %d %d] wh of src img is [%d %d]", data.box.left, data.box.top, data.box.right, data.box.bottom, src_img.w_, src_img.h_);
sy_img img;
{
img.c_ = src_img.c_;
img.w_ = data.box.width();
img.h_ = data.box.height();
}
cudaError_t cuda_status;
const unsigned nbytes = img.c_ * img.h_ * img.w_ * sizeof(unsigned char);
if (CUDA_SUCCESS != (cuda_status = cudaMalloc((void**)&img.data_, nbytes)))
{
LOG_ERROR("cudaMalloc failed: {} malloc nbytes is {} mb is {} ", cudaGetErrorString(cuda_status), nbytes, nbytes / (1024 * 1024));
continue;
}
if (CUDA_SUCCESS != ( cuda_status = cudacommon::CropImgGpu(src_img.data_, src_img.w_, src_img.h_, img.data_, data.box.left, data.box.top, img.w_, img.h_)))
{
LOG_ERROR("Crop image GPU failed error is {} wh is [{} {}] ltrb is [{} {} {} {}]",
cudaGetErrorString(cuda_status), src_img.w_, src_img.h_, data.box.left, data.box.top, data.box.right, data.box.bottom);
CHECK(cudaFree(img.data_));
continue;
}
data.roi_img = img;
data.ori_img = src_img;
data.id = gid_++;
}
result_data.fight_data.emplace_back(std::move(data));
}
if (fall_algor_param != nullptr)
if (res.fall_score > fall_algor_param->threshold)
{
if ((float)(src_img.h_ - preprocessed_data.box.bottom) > (float)(src_img.h_ * 0.05))
{
result_fall_data_t data;
{
data.box = preprocessed_data.box;
data.objectid = preprocessed_data.objId;
data.taskid = taskId;
data.roi_img = cropped_img;
data.ori_img = src_img;
data.id = gid_++;
}
result_data.fall_data.emplace_back(std::move(data));
goto _continue;
}
}
}
cudaFree(cropped_img.data_);
_continue:
{
}
}
results.emplace_back(std::move(result_data));
}
}
return true;
} // func end
} // namespace fight_fall_cls
} // namespace ai_engine_module