diff --git a/configs/keypoint/README.md b/configs/keypoint/README.md index 9912ecc6a390b4bfc1bed2fe0b9c452fdbff6389..e31e4420a30d95094060feaf3b78d827899d04e2 100644 --- a/configs/keypoint/README.md +++ b/configs/keypoint/README.md @@ -86,3 +86,15 @@ python deploy/python/keypoint_infer.py --model_dir=output_inference/hrnet_w32_38 #keypoint top-down模型 + detector 检测联合部署推理（联合推理只支持top-down方式） python deploy/python/keypoint_det_unite_infer.py --det_model_dir=output_inference/ppyolo_r50vd_dcn_2x_coco/ --keypoint_model_dir=output_inference/hrnet_w32_384x288/ --video_file=../video/xxx.mp4 --device=gpu ``` + +​ **与多目标跟踪模型FairMOT联合部署预测：** + +```shell +#导出FairMOT跟踪模型 +python tools/export_model.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams + +#用导出的跟踪和关键点模型Python联合预测 +python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inference/fairmot_dla34_30e_1088x608/ --keypoint_model_dir=output_inference/higherhrnet_hrnet_w32_512/ --video_file={your video name}.mp4 --device=GPU +``` +**注意:** + 跟踪模型导出教程请参考`configs/mot/README.md`。 diff --git a/configs/mot/README.md b/configs/mot/README.md index e3f6ff2f31644ded4c32df39ceaa4971138c2234..59f35407e00a17c43b9e9a8cfc39a013f8c020ac 100644 --- a/configs/mot/README.md +++ b/configs/mot/README.md @@ -255,11 +255,18 @@ CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/fairmot/fairm ### 5. Using exported model for python inference ```bash -python deploy/python/mot_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --use_gpu=True --save_results +python deploy/python/mot_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --save_mot_txts ``` **Notes:** -The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_results` to save the txt result file, or `--save_images` to save the visualization images. +The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_mot_txts` to save the txt result file, or `--save_images` to save the visualization images. +### 6. Using exported MOT and keypoint model for unite python inference + +```bash +python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inference/fairmot_dla34_30e_1088x608/ --keypoint_model_dir=output_inference/higherhrnet_hrnet_w32_512/ --video_file={your video name}.mp4 --device=GPU +``` +**Notes:** + Keypoint model export tutorial: `configs/keypoint/README.md`. ## Citations ``` diff --git a/configs/mot/README_cn.md b/configs/mot/README_cn.md index 1fe4e4d879d34db086e7a37624a410a87ccaa8b9..891080507452a8247aaabb39845f133d186d5c3b 100644 --- a/configs/mot/README_cn.md +++ b/configs/mot/README_cn.md @@ -253,10 +253,18 @@ CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/fairmot/fairm ### 5. 用导出的模型基于Python去预测 ```bash -python deploy/python/mot_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --use_gpu=True --save_results +python deploy/python/mot_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --save_mot_txts ``` **注意:** - 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_results`表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。 + 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。 + +### 6. 用导出的跟踪和关键点模型Python联合预测 + +```bash +python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inference/fairmot_dla34_30e_1088x608/ --keypoint_model_dir=output_inference/higherhrnet_hrnet_w32_512/ --video_file={your video name}.mp4 --device=GPU +``` +**注意:** + 关键点模型导出教程请参考`configs/keypoint/README.md`。 ## 引用 ``` diff --git a/configs/mot/fairmot/README.md b/configs/mot/fairmot/README.md index ac4e5dcdedc7a69b0fc595a361b54a6bb272de37..b9b979f56f6b6dbab65e37cec4db67f645b7af79 100644 --- a/configs/mot/fairmot/README.md +++ b/configs/mot/fairmot/README.md @@ -86,10 +86,10 @@ CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/fairmot/fairm ### 5. Using exported model for python inference ```bash -python deploy/python/mot_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --use_gpu=True --save_results +python deploy/python/mot_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --save_mot_txts ``` **Notes:** -The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_results` to save the txt result file, or `--save_images` to save the visualization images. +The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_mot_txts` to save the txt result file, or `--save_images` to save the visualization images. ## Citations diff --git a/configs/mot/fairmot/README_cn.md b/configs/mot/fairmot/README_cn.md index 95035c702dccaac01a373c19225bdb939d9ddf20..7a670a30f6998c990e472d4e5e76fb765b4f18ba 100644 --- a/configs/mot/fairmot/README_cn.md +++ b/configs/mot/fairmot/README_cn.md @@ -84,10 +84,10 @@ CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/fairmot/fairm ### 5. 用导出的模型基于Python去预测 ```bash -python deploy/python/mot_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --use_gpu=True --save_results +python deploy/python/mot_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --save_mot_txts ``` **注意:** - 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_results`表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。 + 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。 ## 引用 ``` diff --git a/configs/mot/jde/README.md b/configs/mot/jde/README.md index a4b1a8be56a2480c712a4c22f3f0958d05496a29..62b655628221e17d09e178246ee90dd0df5ac588 100644 --- a/configs/mot/jde/README.md +++ b/configs/mot/jde/README.md @@ -92,10 +92,10 @@ CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/jde/jde_darkn ### 5. Using exported model for python inference ```bash -python deploy/python/mot_infer.py --model_dir=output_inference/jde_darknet53_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --use_gpu=True --save_results +python deploy/python/mot_infer.py --model_dir=output_inference/jde_darknet53_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --save_mot_txts ``` **Notes:** -The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_results` to save the txt result file, or `--save_images` to save the visualization images. +The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_mot_txts` to save the txt result file, or `--save_images` to save the visualization images. ## Citations diff --git a/configs/mot/jde/README_cn.md b/configs/mot/jde/README_cn.md index ff5cc1b8c1e27ec477277dcb4df6f523734eef41..c77ac7c7d20025bdba398643437e54c6af988da0 100644 --- a/configs/mot/jde/README_cn.md +++ b/configs/mot/jde/README_cn.md @@ -93,10 +93,10 @@ CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/jde/jde_darkn ### 5. 用导出的模型基于Python去预测 ```bash -python deploy/python/mot_infer.py --model_dir=output_inference/jde_darknet53_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --use_gpu=True --save_results +python deploy/python/mot_infer.py --model_dir=output_inference/jde_darknet53_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --save_mot_txts ``` **注意:** - 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_results`表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。。 + 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。。 ## 引用 ``` diff --git a/deploy/python/mot_infer.py b/deploy/python/mot_infer.py index 95d5f3500cf45307bab67e00f8a08554e71ee367..94e4e5f8beee0083eb02c141ec6bc00a95dc5459 100644 --- a/deploy/python/mot_infer.py +++ b/deploy/python/mot_infer.py @@ -43,9 +43,8 @@ class MOT_Detector(object): Args: pred_config (object): config of model, defined by `Config(model_dir)` model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml - use_gpu (bool): whether use gpu + device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU run_mode (str): mode of running(fluid/trt_fp32/trt_fp16) - batch_size (int): size of pre batch in inference trt_min_shape (int): min shape for dynamic shape in trt trt_max_shape (int): max shape for dynamic shape in trt trt_opt_shape (int): opt shape for dynamic shape in trt @@ -58,9 +57,8 @@ class MOT_Detector(object): def __init__(self, pred_config, model_dir, - use_gpu=False, + device='CPU', run_mode='fluid', - batch_size=1, trt_min_shape=1, trt_max_shape=1088, trt_opt_shape=608, @@ -71,9 +69,8 @@ class MOT_Detector(object): self.predictor, self.config = load_predictor( model_dir, run_mode=run_mode, - batch_size=batch_size, min_subgraph_size=self.pred_config.min_subgraph_size, - use_gpu=use_gpu, + device=device, use_dynamic_shape=self.pred_config.use_dynamic_shape, trt_min_shape=trt_min_shape, trt_max_shape=trt_max_shape, @@ -83,6 +80,7 @@ class MOT_Detector(object): enable_mkldnn=enable_mkldnn) self.det_times = Timer() self.cpu_mem, self.gpu_mem, self.gpu_util = 0, 0, 0 + self.tracker = JDETracker() def preprocess(self, im): @@ -208,7 +206,7 @@ class PredictConfig_MOT(): def load_predictor(model_dir, run_mode='fluid', batch_size=1, - use_gpu=False, + device='CPU', min_subgraph_size=3, use_dynamic_shape=False, trt_min_shape=1, @@ -222,7 +220,7 @@ def load_predictor(model_dir, model_dir (str): root path of __model__ and __params__ run_mode (str): mode of running(fluid/trt_fp32/trt_fp16/trt_int8) batch_size (int): size of pre batch in inference - use_gpu (bool): whether use gpu + device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU use_dynamic_shape (bool): use dynamic shape or not trt_min_shape (int): min shape for dynamic shape in trt trt_max_shape (int): max shape for dynamic shape in trt @@ -236,23 +234,20 @@ def load_predictor(model_dir, Raises: ValueError: predict by TensorRT need use_gpu == True. """ - if not use_gpu and not run_mode == 'fluid': + if device != 'GPU' and run_mode != 'fluid': raise ValueError( - "Predict by TensorRT mode: {}, expect use_gpu==True, but use_gpu == {}" - .format(run_mode, use_gpu)) + "Predict by TensorRT mode: {}, expect device=='GPU', but device == {}" + .format(run_mode, device)) config = Config( os.path.join(model_dir, 'model.pdmodel'), os.path.join(model_dir, 'model.pdiparams')) - precision_map = { - 'trt_int8': Config.Precision.Int8, - 'trt_fp32': Config.Precision.Float32, - 'trt_fp16': Config.Precision.Half - } - if use_gpu: + if device == 'GPU': # initial GPU memory(M), device ID config.enable_use_gpu(200, 0) # optimize graph and fuse op config.switch_ir_optim(True) + elif device == 'XPU': + config.enable_xpu(10 * 1024 * 1024) else: config.disable_gpu() config.set_cpu_math_library_num_threads(cpu_threads) @@ -267,6 +262,11 @@ def load_predictor(model_dir, ) pass + precision_map = { + 'trt_int8': Config.Precision.Int8, + 'trt_fp32': Config.Precision.Float32, + 'trt_fp16': Config.Precision.Half + } if run_mode in precision_map.keys(): config.enable_tensorrt_engine( workspace_size=1 << 10, @@ -391,7 +391,7 @@ def main(): detector = MOT_Detector( pred_config, FLAGS.model_dir, - use_gpu=FLAGS.use_gpu, + device=FLAGS.device, run_mode=FLAGS.run_mode, trt_min_shape=FLAGS.trt_min_shape, trt_max_shape=FLAGS.trt_max_shape, @@ -412,5 +412,8 @@ if __name__ == '__main__': parser = argsparser() FLAGS = parser.parse_args() print_arguments(FLAGS) + FLAGS.device = FLAGS.device.upper() + assert FLAGS.device in ['CPU', 'GPU', 'XPU' + ], "device should be CPU, GPU or XPU" main() diff --git a/deploy/python/mot_keypoint_unite_infer.py b/deploy/python/mot_keypoint_unite_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..2a2a6c7f51da6f197f894b848aaef33e50ee8015 --- /dev/null +++ b/deploy/python/mot_keypoint_unite_infer.py @@ -0,0 +1,164 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import cv2 +import math +import numpy as np +import paddle + +from mot_keypoint_unite_utils import argsparser +from keypoint_infer import KeyPoint_Detector, PredictConfig_KeyPoint +from keypoint_visualize import draw_pose +from benchmark_utils import PaddleInferBenchmark +from utils import Timer + +from tracker import JDETracker +from mot_preprocess import LetterBoxResize +from mot_infer import MOT_Detector, PredictConfig_MOT, write_mot_results +from infer import print_arguments +from ppdet.modeling.mot import visualization as mot_vis +from ppdet.modeling.mot.utils import Timer as FPSTimer + + +def mot_keypoint_unite_predict_video(mot_model, keypoint_model, camera_id): + if camera_id != -1: + capture = cv2.VideoCapture(camera_id) + video_name = 'output.mp4' + else: + capture = cv2.VideoCapture(FLAGS.video_file) + video_name = os.path.split(FLAGS.video_file)[-1] + fps = 30 + frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT)) + print('frame_count', frame_count) + width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) + # yapf: disable + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + # yapf: enable + if not os.path.exists(FLAGS.output_dir): + os.makedirs(FLAGS.output_dir) + out_path = os.path.join(FLAGS.output_dir, video_name) + writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height)) + frame_id = 0 + timer_mot = FPSTimer() + timer_kp = FPSTimer() + timer_mot_kp = FPSTimer() + mot_results = [] + while (1): + ret, frame = capture.read() + if not ret: + break + timer_mot_kp.tic() + timer_mot.tic() + online_tlwhs, online_scores, online_ids = mot_model.predict( + frame, FLAGS.mot_threshold) + timer_mot.toc() + + mot_results.append( + (frame_id + 1, online_tlwhs, online_scores, online_ids)) + mot_fps = 1. / timer_mot.average_time + + timer_kp.tic() + keypoint_results = keypoint_model.predict([frame], + FLAGS.keypoint_threshold) + timer_kp.toc() + timer_mot_kp.toc() + kp_fps = 1. / timer_kp.average_time + mot_kp_fps = 1. / timer_mot_kp.average_time + + im = draw_pose( + frame, + keypoint_results, + visual_thread=FLAGS.keypoint_threshold, + returnimg=True) + + online_im = mot_vis.plot_tracking( + im, + online_tlwhs, + online_ids, + online_scores, + frame_id=frame_id, + fps=mot_kp_fps) + + im = np.array(online_im) + + frame_id += 1 + print('detect frame:%d' % (frame_id)) + + if FLAGS.save_images: + save_dir = os.path.join(FLAGS.output_dir, video_name.split('.')[-2]) + if not os.path.exists(save_dir): + os.makedirs(save_dir) + cv2.imwrite( + os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), im) + + writer.write(im) + if camera_id != -1: + cv2.imshow('Tracking and keypoint results', im) + if cv2.waitKey(1) & 0xFF == ord('q'): + break + if FLAGS.save_mot_txts: + result_filename = os.path.join(FLAGS.output_dir, + video_name.split('.')[-2] + '.txt') + write_mot_results(result_filename, mot_results) + writer.release() + + +def main(): + pred_config = PredictConfig_MOT(FLAGS.mot_model_dir) + mot_model = MOT_Detector( + pred_config, + FLAGS.mot_model_dir, + device=FLAGS.device, + run_mode=FLAGS.run_mode, + trt_min_shape=FLAGS.trt_min_shape, + trt_max_shape=FLAGS.trt_max_shape, + trt_opt_shape=FLAGS.trt_opt_shape, + trt_calib_mode=FLAGS.trt_calib_mode, + cpu_threads=FLAGS.cpu_threads, + enable_mkldnn=FLAGS.enable_mkldnn) + + pred_config = PredictConfig_KeyPoint(FLAGS.keypoint_model_dir) + keypoint_model = KeyPoint_Detector( + pred_config, + FLAGS.keypoint_model_dir, + device=FLAGS.device, + run_mode=FLAGS.run_mode, + trt_min_shape=FLAGS.trt_min_shape, + trt_max_shape=FLAGS.trt_max_shape, + trt_opt_shape=FLAGS.trt_opt_shape, + trt_calib_mode=FLAGS.trt_calib_mode, + cpu_threads=FLAGS.cpu_threads, + enable_mkldnn=FLAGS.enable_mkldnn, + use_dark=FLAGS.use_dark) + + # predict from video file or camera video stream + if FLAGS.video_file is not None or FLAGS.camera_id != -1: + mot_keypoint_unite_predict_video(mot_model, keypoint_model, + FLAGS.camera_id) + else: + print('Do not support unite predict single image.') + + +if __name__ == '__main__': + paddle.enable_static() + parser = argsparser() + FLAGS = parser.parse_args() + print_arguments(FLAGS) + FLAGS.device = FLAGS.device.upper() + assert FLAGS.device in ['CPU', 'GPU', 'XPU' + ], "device should be CPU, GPU or XPU" + + main() diff --git a/deploy/python/mot_keypoint_unite_utils.py b/deploy/python/mot_keypoint_unite_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..783a272368256c8235c49528552b68cd660d8552 --- /dev/null +++ b/deploy/python/mot_keypoint_unite_utils.py @@ -0,0 +1,127 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import ast +import argparse + + +def argsparser(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--mot_model_dir", + type=str, + default=None, + help=("Directory include:'model.pdiparams', 'model.pdmodel', " + "'infer_cfg.yml', created by tools/export_model.py."), + required=True) + parser.add_argument( + "--keypoint_model_dir", + type=str, + default=None, + help=("Directory include:'model.pdiparams', 'model.pdmodel', " + "'infer_cfg.yml', created by tools/export_model.py."), + required=True) + parser.add_argument( + "--image_file", type=str, default=None, help="Path of image file.") + parser.add_argument( + "--image_dir", + type=str, + default=None, + help="Dir of image file, `image_file` has a higher priority.") + parser.add_argument( + "--keypoint_batch_size", + type=int, + default=1, + help=("batch_size for keypoint inference. In detection-keypoint unit" + "inference, the batch size in detection is 1. Then collate det " + "result in batch for keypoint inference.")) + parser.add_argument( + "--video_file", + type=str, + default=None, + help="Path of video file, `video_file` or `camera_id` has a highest priority." + ) + parser.add_argument( + "--camera_id", + type=int, + default=-1, + help="device id of camera to predict.") + parser.add_argument( + "--mot_threshold", type=float, default=0.5, help="Threshold of score.") + parser.add_argument( + "--keypoint_threshold", + type=float, + default=0.5, + help="Threshold of score.") + parser.add_argument( + "--output_dir", + type=str, + default="output", + help="Directory of output visualization files.") + parser.add_argument( + "--run_mode", + type=str, + default='fluid', + help="mode of running(fluid/trt_fp32/trt_fp16/trt_int8)") + parser.add_argument( + "--device", + type=str, + default='cpu', + help="Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU." + ) + parser.add_argument( + "--run_benchmark", + type=ast.literal_eval, + default=False, + help="Whether to predict a image_file repeatedly for benchmark") + parser.add_argument( + "--enable_mkldnn", + type=ast.literal_eval, + default=False, + help="Whether use mkldnn with CPU.") + parser.add_argument( + "--cpu_threads", type=int, default=1, help="Num of threads with CPU.") + parser.add_argument( + "--trt_min_shape", type=int, default=1, help="min_shape for TensorRT.") + parser.add_argument( + "--trt_max_shape", + type=int, + default=1088, + help="max_shape for TensorRT.") + parser.add_argument( + "--trt_opt_shape", + type=int, + default=608, + help="opt_shape for TensorRT.") + parser.add_argument( + "--trt_calib_mode", + type=bool, + default=False, + help="If the model is produced by TRT offline quantitative " + "calibration, trt_calib_mode need to set True.") + parser.add_argument( + '--save_images', + action='store_true', + help='Save visualization image results.') + parser.add_argument( + '--save_mot_txts', + action='store_true', + help='Save tracking results (txt).') + parser.add_argument( + '--use_dark', + type=bool, + default=True, + help='whether to use darkpose to get better keypoint position predict ') + + return parser diff --git a/deploy/python/tracker/jde_tracker.py b/deploy/python/tracker/jde_tracker.py index 907906f0e322fddeed4b482fcc4ad9e50ebb996c..b44e8700dbc9101704d4ecc84484d312ea165aaa 100644 --- a/deploy/python/tracker/jde_tracker.py +++ b/deploy/python/tracker/jde_tracker.py @@ -24,7 +24,6 @@ __all__ = ['JDETracker'] class JDETracker(object): - __inject__ = ['motion'] """ JDE tracker diff --git a/deploy/python/utils.py b/deploy/python/utils.py index a9f48cf28e8a8882519039d9e335782698874325..7179d43ee3a1ed45ef0cb2157727d7254df1b0a7 100644 --- a/deploy/python/utils.py +++ b/deploy/python/utils.py @@ -103,9 +103,9 @@ def argsparser(): parser.add_argument( '--save_images', action='store_true', - help='Save tracking results (image).') + help='Save visualization image results.') parser.add_argument( - '--save_results', + '--save_mot_txts', action='store_true', help='Save tracking results (txt).') parser.add_argument(