From 267f6508f04e4f737620d1e041a0c77f60bb7052 Mon Sep 17 00:00:00 2001 From: dongshuilong Date: Tue, 25 Jan 2022 19:26:45 +0800 Subject: [PATCH] fix ppshitu_lite bugs and fix README.md --- deploy/lite_shitu/README.md | 153 +++++++++++----------- deploy/lite_shitu/generate_json_config.py | 115 ++++++++++------ deploy/lite_shitu/include/config_parser.h | 2 +- deploy/lite_shitu/include/recognition.h | 5 +- deploy/lite_shitu/include/utils.h | 20 +-- deploy/lite_shitu/src/main.cc | 71 +++------- deploy/lite_shitu/src/object_detector.cc | 5 +- deploy/lite_shitu/src/preprocess_op.cc | 10 +- deploy/lite_shitu/src/recognition.cc | 2 +- deploy/lite_shitu/src/utils.cc | 32 +++-- docs/images/ppshitu_lite_demo.png | Bin 0 -> 55001 bytes 11 files changed, 211 insertions(+), 204 deletions(-) create mode 100644 docs/images/ppshitu_lite_demo.png diff --git a/deploy/lite_shitu/README.md b/deploy/lite_shitu/README.md index e775b134a..5d6dde45d 100644 --- a/deploy/lite_shitu/README.md +++ b/deploy/lite_shitu/README.md @@ -1,4 +1,4 @@ -# Paddle-Lite端侧部署 +# PP-ShiTu在Paddle-Lite端侧部署 本教程将介绍基于[Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) 在移动端部署PaddleDetection模型的详细步骤。 @@ -125,30 +125,71 @@ Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括 #### 2.1.3 转换示例 -下面以PaddleDetection中的 `PicoDet` 模型为例，介绍使用`paddle_lite_opt`完成预训练模型到inference模型，再到Paddle-Lite优化模型的转换。 +下面介绍使用`paddle_lite_opt`完成主体检测模型和识别模型的预训练模型，转成inference模型，最终转换成Paddle-Lite的优化模型的过程。 + +##### 2.1.3.1 转换主体检测模型 ```shell +# 当前目录为 $PaddleClas/deploy/lite_shitu +# $code_path需替换成相应的运行目录,可以根据需要，将$code_path设置成需要的目录 +export $code_path=~ +cd $code_path +git clone https://github.com/PaddlePaddle/PaddleDetection.git # 进入PaddleDetection根目录 -cd PaddleDetection_root_path +cd PaddleDetection # 将预训练模型导出为inference模型 -python tools/export_model.py -c configs/picodet/picodet_s_320_coco.yml \ - -o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams --output_dir=output_inference +python tools/export_model.py -c configs/picodet/application/mainbody_detection/picodet_lcnet_x2_5_640_mainbody.yml -o weights=https://paddledet.bj.bcebos.com/models/picodet_lcnet_x2_5_640_mainbody.pdparams --output_dir=inference # 将inference模型转化为Paddle-Lite优化模型 -# FP32 -paddle_lite_opt --valid_targets=arm --model_file=output_inference/picodet_s_320_coco/model.pdmodel --param_file=output_inference/picodet_s_320_coco/model.pdiparams --optimize_out=output_inference/picodet_s_320_coco/model -# FP16 -paddle_lite_opt --valid_targets=arm --model_file=output_inference/picodet_s_320_coco/model.pdmodel --param_file=output_inference/picodet_s_320_coco/model.pdiparams --optimize_out=output_inference/picodet_s_320_coco/model --enable_fp16=true +paddle_lite_opt --model_file=inference/picodet_lcnet_x2_5_640_mainbody/model.pdmodel --param_file=inference/picodet_lcnet_x2_5_640_mainbody/model.pdiparams --optimize_out=inference/picodet_lcnet_x2_5_640_mainbody/mainbody_det -# 将inference模型配置转化为json格式 -python deploy/lite/convert_yml_to_json.py output_inference/picodet_s_320_coco/infer_cfg.yml +# 将转好的模型复制到lite_shitu目录下 +cd $PaddleClas/deploy/lite_shitu +mkdir models +cp $code_path/PaddleDetection/inference/picodet_lcnet_x2_5_640_mainbody/mainbody_det.nb $PaddleClas/deploy/lite_shitu/models ``` -最终在output_inference/picodet_s_320_coco/文件夹下生成`model.nb` 和 `infer_cfg.json`的文件。 +##### 2.1.3.2 转换识别模型 + +```shell +# 转换inference model +待补充,生成的inference model存储在PaddleClas/inference下，同时生成label.txt，也存在此文件夹下 + +# 转换为Paddle-Lite模型 +paddle_lite_opt --model_file=inference/inference.pdmodel --param_file=inference/inference.pdiparams --optimize_out=inference/rec + +# 将模型、label文件拷贝到lite_shitu下 +cp inference/rec.nb deploy/lite_shitu/models/ +cp inference/label.txt deploy/lite_shitu/models/ +cd deploy/lite_shitu +``` **注意**：`--optimize_out` 参数为优化后模型的保存路径，无需加后缀`.nb`；`--model_file` 参数为模型结构信息文件的路径，`--param_file` 参数为模型权重信息文件的路径，请注意文件名。 +##### 2.1.3.3 准备测试图像 + +```shell +mkdir images +# 根据需要准备测试图像，可以在images文件夹中存放多张图像 +cp ../images/wangzai.jpg images/ +``` + + + +##### 2.1.3.4 将yaml文件转换成json文件 + +```shell +# 如果测试单张图像 +python generate_json_config.py --det_model_path models/mainbody_det.nb --rec_model_path models/rec.nb --rec_label_path models/label.txt --img_path images/wangzai.jpg +# or +# 如果测试多张图像 +python generate_json_config.py --det_model_path models/mainbody_det.nb --rec_model_path models/rec.nb --rec_label_path models/label.txt --img_dir images + +# 执行完成后，会在lit_shitu下生成shitu_config.json配置文件 + +``` + ### 2.2 与手机联调 首先需要进行一些准备工作。 @@ -183,41 +224,28 @@ List of devices attached 4. 编译lite部署代码生成移动端可执行文件 ```shell -cd {PadddleDetection_Root} -cd deploy/lite/ +cd $PaddleClas/deploy/lite_shitu inference_lite_path=/{lite prediction library path}/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.with_cv/ -mkdir $inference_lite_path/demo/cxx/lite +mkdir $inference_lite_path/demo/cxx/ppshitu_lite -cp -r Makefile src/ include/ *runtime_config.json $inference_lite_path/demo/cxx/lite +cp -r Makefile src/ include/ *.json models/ images/ $inference_lite_path/demo/cxx/ppshitu_lite -cd $inference_lite_path/demo/cxx/lite +cd $inference_lite_path/demo/cxx/ppshitu_lite # 执行编译，等待完成后得到可执行文件main make ARM_ABI=arm8 #如果是arm7，则执行 make ARM_ABI = arm7 (或者在Makefile中修改该项) - ``` 5. 准备优化后的模型、预测库文件、测试图像。 ```shell mkdir deploy -cp main *runtime_config.json deploy/ +mv models deploy/ +mv images deploy/ +cp pp_shitu deploy/ cd deploy -mkdir model_det -mkdir model_keypoint - -# 将优化后的模型、预测库文件、测试图像放置在预测库中的demo/cxx/detection文件夹下 -cp {PadddleDetection_Root}/output_inference/picodet_s_320_coco/model.nb ./model_det/ -cp {PadddleDetection_Root}/output_inference/picodet_s_320_coco/infer_cfg.json ./model_det/ - -# 如果需要关键点模型，则只需操作： -cp {PadddleDetection_Root}/output_inference/hrnet_w32_256x192/model.nb ./model_keypoint/ -cp {PadddleDetection_Root}/output_inference/hrnet_w32_256x192/infer_cfg.json ./model_keypoint/ - -# 将测试图像复制到deploy文件夹中 -cp [your_test_img].jpg ./demo.jpg # 将C++预测动态库so文件复制到deploy文件夹中 cp ../../../cxx/lib/libpaddle_light_api_shared.so ./ @@ -227,45 +255,19 @@ cp ../../../cxx/lib/libpaddle_light_api_shared.so ./ ``` deploy/ -|-- model_det/ -| |--model.nb 优化后的检测模型文件 -| |--infer_cfg.json 检测器模型配置文件 -|-- model_keypoint/ -| |--model.nb 优化后的关键点模型文件 -| |--infer_cfg.json 关键点模型配置文件 -|-- main 生成的移动端执行文件 -|-- det_runtime_config.json 目标检测执行时参数配置文件 -|-- keypoint_runtime_config.json 关键点检测执行时参数配置文件 +|-- models/ +| |--mainbody_det.nb 优化后的主体检测模型文件 +| |--rec.nb 优化后的识别模型文件 +| |--label.txt 识别模型的label文件 +|-- images/ +| ... 图片文件 +|-- pp_shitu 生成的移动端执行文件 +|-- shitu_config.json 执行时参数配置文件 |-- libpaddle_light_api_shared.so Paddle-Lite库文件 ``` **注意：** -* `det_runtime_config.json` 包含了目标检测的超参数，请按需进行修改： - -```shell -{ - "model_dir_det": "./model_det/", #检测器模型路径 - "batch_size_det": 1, #检测预测时batchsize - "threshold_det": 0.5, #检测器输出阈值 - "image_file": "demo.jpg", #测试图片 - "image_dir": "", #测试图片文件夹 - "run_benchmark": true, #性能测试开关 - "cpu_threads": 4 #线程数 -} -``` - -* `keypoint_runtime_config.json` 包含了关键点检测的超参数，请按需进行修改： -```shell -{ - "model_dir_keypoint": "./model_keypoint/", #关键点模型路径（不使用需为空字符） - "batch_size_keypoint": 8, #关键点预测时batchsize - "threshold_keypoint": 0.5, #关键点输出阈值 - "image_file": "demo.jpg", #测试图片 - "image_dir": "", #测试图片文件夹 - "run_benchmark": true, #性能测试开关 - "cpu_threads": 4 #线程数 -} -``` +* `shitu_config.json` 包含了目标检测的超参数，请按需进行修改 6. 启动调试，上述步骤完成后就可以使用ADB将文件夹 `deploy/` push到手机上运行，步骤如下： @@ -278,23 +280,20 @@ cd /data/local/tmp/deploy export LD_LIBRARY_PATH=/data/local/tmp/deploy:$LD_LIBRARY_PATH # 修改权限为可执行 -chmod 777 main -# 以检测为例，执行程序 -./main det_runtime_config.json +chmod 777 pp_shitu +# 执行程序 +./pp_shitu shitu_config.json ``` 如果对代码做了修改，则需要重新编译并push到手机上。 运行效果如下： -
- -
- +![](../../docs/images/ppshitu_lite_demo.png) ## FAQ -Q1：如果想更换模型怎么办，需要重新按照流程走一遍吗？ +Q1：如果想更换模型怎么办，需要重新按照流程走一遍吗？ A1：如果已经走通了上述步骤，更换模型只需要替换 `.nb` 模型文件即可，同时要注意修改下配置文件中的 `.nb` 文件路径以及类别映射文件（如有必要）。 -Q2：换一个图测试怎么做？ -A2：替换 deploy 下的测试图像为你想要测试的图像，使用 ADB 再次 push 到手机上即可。 +Q2：换一个图测试怎么做？ +A2：替换 deploy 下的测试图像为你想要测试的图像，并重新生成json配置文件（或者直接修改图像路径），使用 ADB 再次 push 到手机上即可。 diff --git a/deploy/lite_shitu/generate_json_config.py b/deploy/lite_shitu/generate_json_config.py index fb54685db..1525cdab9 100644 --- a/deploy/lite_shitu/generate_json_config.py +++ b/deploy/lite_shitu/generate_json_config.py @@ -7,40 +7,63 @@ import yaml def parse_args(): parser = argparse.ArgumentParser() - parser.add_argument('--yaml_path', - type=str, - default='../configs/inference_drink.yaml') - parser.add_argument('--img_dir', type=str, default=None, - help='The dir path for inference images') - parser.add_argument('--det_model_path', - type=str, default='./det.nb', - help="The model path for mainbody detection") - parser.add_argument('--rec_model_path', type=str, default='./rec.nb', help="The rec model path") - parser.add_argument('--rec-label-path', type=str, default='./label.txt', help='The rec model label') - parser.add_argument('--arch', - type=str, - default='GFL', - help='The model structure for detection model') - parser.add_argument('--fpn-stride', - type=list, - default=[8, 16, 32, 64], - help="The fpn strid for detection model") - parser.add_argument('--keep_top_k', - type=int, - default=100, - help='The params for nms(postprocess for detection)') - parser.add_argument('--nms-name', - type=str, - default='MultiClassNMS', - help='The nms name for postprocess of detection model') - parser.add_argument('--nms_threshold', - type=float, - default=0.5, - help='The nms nms_threshold for detection postprocess') - parser.add_argument('--nms_top_k', - type=int, - default=1000, - help='The nms_top_k in postprocess of detection model') + parser.add_argument( + '--yaml_path', type=str, default='../configs/inference_drink.yaml') + parser.add_argument( + '--img_dir', + type=str, + default=None, + help='The dir path for inference images') + parser.add_argument( + '--img_path', + type=str, + default=None, + help='The dir path for inference images') + parser.add_argument( + '--det_model_path', + type=str, + default='./det.nb', + help="The model path for mainbody detection") + parser.add_argument( + '--rec_model_path', + type=str, + default='./rec.nb', + help="The rec model path") + parser.add_argument( + '--rec_label_path', + type=str, + default='./label.txt', + help='The rec model label') + parser.add_argument( + '--arch', + type=str, + default='PicoDet', + help='The model structure for detection model') + parser.add_argument( + '--fpn-stride', + type=list, + default=[8, 16, 32, 64], + help="The fpn strid for detection model") + parser.add_argument( + '--keep_top_k', + type=int, + default=100, + help='The params for nms(postprocess for detection)') + parser.add_argument( + '--nms-name', + type=str, + default='MultiClassNMS', + help='The nms name for postprocess of detection model') + parser.add_argument( + '--nms_threshold', + type=float, + default=0.5, + help='The nms nms_threshold for detection postprocess') + parser.add_argument( + '--nms_top_k', + type=int, + default=1000, + help='The nms_top_k in postprocess of detection model') parser.add_argument( '--score_threshold', type=float, @@ -55,18 +78,24 @@ def main(): config_yaml = yaml.safe_load(open(args.yaml_path)) config_json = {} config_json["Global"] = {} - config_json["Global"]["infer_imgs"] = config_yaml["Global"]["infer_imgs"] - config_json["Global"]["infer_imgs_dir"] = args.img_dir + config_json["Global"][ + "infer_imgs"] = args.img_path if args.img_path else config_yaml[ + "Global"]["infer_imgs"] + if args.img_dir is not None: + config_json["Global"]["infer_imgs_dir"] = args.img_dir + config_json["Global"]["infer_imgs"] = None + else: + config_json["Global"][ + "infer_imgs"] = args.img_path if args.img_path else config_yaml[ + "Global"]["infer_imgs"] config_json["Global"]["batch_size"] = config_yaml["Global"]["batch_size"] - config_json["Global"]["cpu_num_threads"] = config_yaml["Global"][ - "cpu_num_threads"] + config_json["Global"]["cpu_num_threads"] = min( + config_yaml["Global"]["cpu_num_threads"], 4) config_json["Global"]["image_shape"] = config_yaml["Global"]["image_shape"] - config_json["Global"][ - "det_model_path"] = args.det_model_path - config_json["Global"][ - "rec_model_path"] = args.rec_model_path + config_json["Global"]["det_model_path"] = args.det_model_path + config_json["Global"]["rec_model_path"] = args.rec_model_path config_json["Global"]["rec_label_path"] = args.rec_label_path - config_json["Global"]["labe_list"] = config_yaml["Global"]["labe_list"] + config_json["Global"]["label_list"] = config_yaml["Global"]["labe_list"] config_json["Global"]["rec_nms_thresold"] = config_yaml["Global"][ "rec_nms_thresold"] config_json["Global"]["max_det_results"] = config_yaml["Global"][ diff --git a/deploy/lite_shitu/include/config_parser.h b/deploy/lite_shitu/include/config_parser.h index 1228d1980..dca0e5a68 100644 --- a/deploy/lite_shitu/include/config_parser.h +++ b/deploy/lite_shitu/include/config_parser.h @@ -60,7 +60,7 @@ class ConfigPaser { // Get label_list for visualization if (config["Global"].isMember("label_list")) { label_list_.clear(); - for (auto item : config["label_list"]) { + for (auto item : config["Global"]["label_list"]) { label_list_.emplace_back(item.as()); } } else { diff --git a/deploy/lite_shitu/include/recognition.h b/deploy/lite_shitu/include/recognition.h index b7d2a48c2..0c45e946e 100644 --- a/deploy/lite_shitu/include/recognition.h +++ b/deploy/lite_shitu/include/recognition.h @@ -12,6 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. +#pragma once #include "paddle_api.h" // NOLINT #include "json/json.h" #include @@ -54,8 +55,10 @@ public: } LoadLabel(config_file["Global"]["rec_label_path"].as()); SetPreProcessParam(config_file["RecPreProcess"]["transform_ops"]); - if (!config_file["Global"].isMember("return_k")) + if (!config_file["Global"].isMember("return_k")){ this->topk = config_file["Global"]["return_k"].as(); + } + printf("rec model create!\n"); } void LoadLabel(std::string path) { diff --git a/deploy/lite_shitu/include/utils.h b/deploy/lite_shitu/include/utils.h index cbda311ec..18a04cf34 100644 --- a/deploy/lite_shitu/include/utils.h +++ b/deploy/lite_shitu/include/utils.h @@ -14,13 +14,14 @@ #pragma once -#include -#include -#include -#include -#include -#include #include +#include +#include +#include +#include +#include +#include +#include namespace PPShiTu { @@ -32,8 +33,11 @@ struct ObjectResult { int class_id; // Confidence of detected object float confidence; + + // RecModel result + std::vector rec_result; }; -void nms(std::vector &input_boxes, float nms_threshold); +void nms(std::vector &input_boxes, float nms_threshold, bool rec_nms=false); -} // namespace PPShiTu +} // namespace PPShiTu diff --git a/deploy/lite_shitu/src/main.cc b/deploy/lite_shitu/src/main.cc index ae23e7c55..e9c05c72a 100644 --- a/deploy/lite_shitu/src/main.cc +++ b/deploy/lite_shitu/src/main.cc @@ -73,17 +73,10 @@ void DetPredictImage(const std::vector &batch_imgs, std::vector det_t = {0, 0, 0}; int steps = ceil(float(batch_imgs.size()) / batch_size_det); for (int idx = 0; idx < steps; idx++) { - std::vector batch_imgs; int left_image_cnt = batch_imgs.size() - idx * batch_size_det; if (left_image_cnt > batch_size_det) { left_image_cnt = batch_size_det; } - /* for (int bs = 0; bs < left_image_cnt; bs++) { */ - /* std::string image_file_path = all_img_paths.at(idx * batch_size_det + - * bs); */ - /* cv::Mat im = cv::imread(image_file_path, 1); */ - /* batch_imgs.insert(batch_imgs.end(), im); */ - /* } */ // Store all detected result std::vector result; std::vector bbox_num; @@ -108,32 +101,7 @@ void DetPredictImage(const std::vector &batch_imgs, } detect_num += 1; im_result.push_back(item); - /* if (item.rect.size() > 6) { */ - /* is_rbox = true; */ - /* printf("class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n", - */ - /* item.class_id, */ - /* item.confidence, */ - /* item.rect[0], */ - /* item.rect[1], */ - /* item.rect[2], */ - /* item.rect[3], */ - /* item.rect[4], */ - /* item.rect[5], */ - /* item.rect[6], */ - /* item.rect[7]); */ - /* } else { */ - /* printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n", */ - /* item.class_id, */ - /* item.confidence, */ - /* item.rect[0], */ - /* item.rect[1], */ - /* item.rect[2], */ - /* item.rect[3]); */ - /* } */ } - /* std::cout << all_img_paths.at(idx * batch_size_det + i) */ - /* << " The number of detected box: " << detect_num << std::endl; */ item_start_idx = item_start_idx + bbox_num[i]; } @@ -144,14 +112,13 @@ void DetPredictImage(const std::vector &batch_imgs, } void PrintResult(const std::string &image_path, - std::vector &det_result, - std::vector> &rec_results) { + std::vector &det_result) { printf("%s:\n", image_path.c_str()); for (int i = 0; i < det_result.size(); ++i) { printf("\tresult%d: bbox[%d, %d, %d, %d], score: %f, label: %s\n", i, det_result[i].rect[0], det_result[i].rect[1], det_result[i].rect[2], - det_result[i].rect[3], rec_results[i][0].score, - rec_results[i][0].class_name.c_str()); + det_result[i].rect[3], det_result[i].rec_result[0].score, + det_result[i].rec_result[0].class_name.c_str()); } } @@ -163,37 +130,33 @@ int main(int argc, char **argv) { return -1; } std::string config_path = argv[1]; - std::string img_path = ""; + std::string img_dir = ""; if (argc >= 3) { - img_path = argv[2]; + img_dir = argv[2]; } // Parsing command-line PPShiTu::load_jsonf(config_path, RT_Config); - if (RT_Config["Global"]["det_inference_model_dir"] - .as() - .empty()) { - std::cout << "Please set [det_inference_model_dir] in " << config_path - << std::endl; + if (RT_Config["Global"]["det_model_path"].as().empty()) { + std::cout << "Please set [det_model_path] in " << config_path << std::endl; return -1; } if (RT_Config["Global"]["infer_imgs"].as().empty() && - img_path.empty()) { + img_dir.empty()) { std::cout << "Please set [infer_imgs] in " << config_path << " Or use command: <" << argv[0] << " [shitu_config]" << " [image_dir]>" << std::endl; return -1; } - if (!img_path.empty()) { + if (!img_dir.empty()) { std::cout << "Use image_dir in command line overide the path in config file" << std::endl; - RT_Config["Global"]["infer_imgs_dir"] = img_path; + RT_Config["Global"]["infer_imgs_dir"] = img_dir; RT_Config["Global"]["infer_imgs"] = ""; } // Load model and create a object detector PPShiTu::ObjectDetector det( - RT_Config, - RT_Config["Global"]["det_inference_model_dir"].as(), + RT_Config, RT_Config["Global"]["det_model_path"].as(), RT_Config["Global"]["cpu_num_threads"].as(), RT_Config["Global"]["batch_size"].as()); // create rec model @@ -202,7 +165,6 @@ int main(int argc, char **argv) { std::vector det_result; std::vector batch_imgs; - std::vector> rec_results; double rec_time; if (!RT_Config["Global"]["infer_imgs"].as().empty() || !RT_Config["Global"]["infer_imgs_dir"].as().empty()) { @@ -239,7 +201,7 @@ int main(int argc, char **argv) { // add the whole image for recognition to improve recall PPShiTu::ObjectResult result_whole_img = { - {0, 0, srcimg.cols - 1, srcimg.rows - 1}, 0, 1.0}; + {0, 0, srcimg.cols, srcimg.rows}, 0, 1.0}; det_result.push_back(result_whole_img); // get rec result @@ -250,13 +212,14 @@ int main(int argc, char **argv) { cv::Mat crop_img = srcimg(rect); std::vector result = rec.RunRecModel(crop_img, rec_time); - rec_results.push_back(result); + det_result[j].rec_result.assign(result.begin(), result.end()); } - PrintResult(img_path, det_result, rec_results); - + // rec nms + PPShiTu::nms(det_result, + RT_Config["Global"]["rec_nms_thresold"].as(), true); + PrintResult(img_path, det_result); batch_imgs.clear(); det_result.clear(); - rec_results.clear(); } } return 0; diff --git a/deploy/lite_shitu/src/object_detector.cc b/deploy/lite_shitu/src/object_detector.cc index 59ccba3af..ffea31bb9 100644 --- a/deploy/lite_shitu/src/object_detector.cc +++ b/deploy/lite_shitu/src/object_detector.cc @@ -95,7 +95,7 @@ cv::Mat VisualizeResult(const cv::Mat& img, void ObjectDetector::Preprocess(const cv::Mat& ori_im) { // Clone the image : keep the original mat for postprocess cv::Mat im = ori_im.clone(); - cv::cvtColor(im, im, cv::COLOR_BGR2RGB); + // cv::cvtColor(im, im, cv::COLOR_BGR2RGB); preprocessor_.Run(&im, &inputs_); } @@ -235,7 +235,7 @@ void ObjectDetector::Predict(const std::vector& imgs, auto postprocess_start = std::chrono::steady_clock::now(); // Get output tensor output_data_list_.clear(); - int num_class = 80; + int num_class = 1; int reg_max = 7; auto output_names = predictor_->GetOutputNames(); // TODO: Unified model output. @@ -281,6 +281,7 @@ void ObjectDetector::Predict(const std::vector& imgs, out_bbox_num_data_.data()); } // Postprocessing result + result->clear(); if (config_.arch_ == "PicoDet") { PPShiTu::PicoDetPostProcess( diff --git a/deploy/lite_shitu/src/preprocess_op.cc b/deploy/lite_shitu/src/preprocess_op.cc index 90a54a1c6..9c74d6ee7 100644 --- a/deploy/lite_shitu/src/preprocess_op.cc +++ b/deploy/lite_shitu/src/preprocess_op.cc @@ -127,11 +127,11 @@ void TopDownEvalAffine::Run(cv::Mat* im, ImageBlob* data) { // Preprocessor op running order const std::vector Preprocessor::RUN_ORDER = {"InitInfo", - "TopDownEvalAffine", - "Resize", - "NormalizeImage", - "PadStride", - "Permute"}; + "DetTopDownEvalAffine", + "DetResize", + "DetNormalizeImage", + "DetPadStride", + "DetPermute"}; void Preprocessor::Run(cv::Mat* im, ImageBlob* data) { for (const auto& name : RUN_ORDER) { diff --git a/deploy/lite_shitu/src/recognition.cc b/deploy/lite_shitu/src/recognition.cc index 8d3a99476..0e711f386 100644 --- a/deploy/lite_shitu/src/recognition.cc +++ b/deploy/lite_shitu/src/recognition.cc @@ -38,7 +38,7 @@ std::vector Recognition::RunRecModel(const cv::Mat &img, // Get output and post process std::unique_ptr output_tensor( - std::move(this->predictor->GetOutput(0))); + std::move(this->predictor->GetOutput(1))); auto *output_data = output_tensor->data(); auto end = std::chrono::system_clock::now(); auto duration = diff --git a/deploy/lite_shitu/src/utils.cc b/deploy/lite_shitu/src/utils.cc index 88b2c563c..3bc461770 100644 --- a/deploy/lite_shitu/src/utils.cc +++ b/deploy/lite_shitu/src/utils.cc @@ -16,14 +16,23 @@ namespace PPShiTu { -void nms(std::vector &input_boxes, float nms_threshold) { - std::sort(input_boxes.begin(), - input_boxes.end(), - [](ObjectResult a, ObjectResult b) { return a.confidence > b.confidence; }); +void nms(std::vector &input_boxes, float nms_threshold, + bool rec_nms) { + if (!rec_nms) { + std::sort(input_boxes.begin(), input_boxes.end(), + [](ObjectResult a, ObjectResult b) { + return a.confidence > b.confidence; + }); + } else { + std::sort(input_boxes.begin(), input_boxes.end(), + [](ObjectResult a, ObjectResult b) { + return a.rec_result[0].score > b.rec_result[0].score; + }); + } std::vector vArea(input_boxes.size()); for (int i = 0; i < int(input_boxes.size()); ++i) { - vArea[i] = (input_boxes.at(i).rect[2] - input_boxes.at(i).rect[0] + 1) - * (input_boxes.at(i).rect[3] - input_boxes.at(i).rect[1] + 1); + vArea[i] = (input_boxes.at(i).rect[2] - input_boxes.at(i).rect[0] + 1) * + (input_boxes.at(i).rect[3] - input_boxes.at(i).rect[1] + 1); } for (int i = 0; i < int(input_boxes.size()); ++i) { for (int j = i + 1; j < int(input_boxes.size());) { @@ -36,14 +45,13 @@ void nms(std::vector &input_boxes, float nms_threshold) { float inter = w * h; float ovr = inter / (vArea[i] + vArea[j] - inter); if (ovr >= nms_threshold) { - input_boxes.erase(input_boxes.begin() + j); - vArea.erase(vArea.begin() + j); - } - else { - j++; + input_boxes.erase(input_boxes.begin() + j); + vArea.erase(vArea.begin() + j); + } else { + j++; } } } } -} // namespace PPShiTu +} // namespace PPShiTu diff --git a/docs/images/ppshitu_lite_demo.png b/docs/images/ppshitu_lite_demo.png new file mode 100644 index 0000000000000000000000000000000000000000..a9b48293d9f7da512c71d0458881476d1c9cc780 GIT binary patch literal 55001 zcmeFYWl$Yaw6;lbCund2BoKnTOK^gR;LgF_-Q7uoyGw8j?yd)Scjw?P)7(4vel=5f zW`525n%Z@!uBNK1yLa!k*Lt6Kg(=8Mpdk|?LqS2INlA+SfP#W42aa12;eo$3r5_ie zppa+HL_`#%L_|I+*xMMJSsFn>NruHIBK%ZZzzZI0K8ANDjLtEH>|8T@2N*=_V^?k$vwfBm2sQze=umnG+?J9lY;u||CM@BRut=G=8 zgD;6p-y`hvM*SRNP~W>3VEVJo_63H3{Z&AOIOldV(x zOG)?yykdf^?IBKk>iV`+-+L;D912gx*>}D|`Zc|Op%g|YxcEb2!)LQ$vn^@2kpsW% z($-+4%F@zvS^l+h8=!cinJd>aS^&~z)%M`ff!6&I*>42QVWXf~dBx8mp zMo|GykoP`YAtJQe1asJPNiMA8{+U9cu0szg@J}m^|zuC9|@B zw!2tu;1hgJPBV3D`?4g_mhgpAD`RXJF|PkJ_wS3wh5e1RWFiKi5VPa;Gc)*aABo5P z1S=&3(V+}4?Q5txHc~$iVWa%GUvQ*ypK^6f_yuB3wYIUa!RY8|P3J+jfyk^%NTxGk zYv#m|x?RbL_$RC3xzB-fZ95 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