diff --git a/configs/mot/deepsort/README.md b/configs/mot/deepsort/README.md index 96cdcc4cfb3d25198859a2a84720c693cec6c7a9..b479849387094fb24052d3ac08ac09f4013ec0fa 100644 --- a/configs/mot/deepsort/README.md +++ b/configs/mot/deepsort/README.md @@ -95,7 +95,7 @@ CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/deep ### 4. Using exported model for python inference ```bash -python deploy/python/mot_reid_infer.py --model_dir=output_inference/jde_yolov3_darknet53_30e_1088x608/ --reid_model_dir=output_inference/deepsort_yolov3_pcb_pyramid_r101/ --video_file={your video name}.mp4 --device=GPU --save_mot_txts +python deploy/python/mot_sde_infer.py --model_dir=output_inference/jde_yolov3_darknet53_30e_1088x608/ --reid_model_dir=output_inference/deepsort_yolov3_pcb_pyramid_r101/ --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_mot_txts` to save the txt result file, or `--save_images` to save the visualization images. diff --git a/configs/mot/deepsort/README_cn.md b/configs/mot/deepsort/README_cn.md index 3c82a83b75440d0dc19af561587c7e06fad955ca..82e128471f2df7355232c00136e05d9868545e05 100644 --- a/configs/mot/deepsort/README_cn.md +++ b/configs/mot/deepsort/README_cn.md @@ -98,7 +98,7 @@ CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/deep ### 4. 用导出的模型基于Python去预测 ```bash -python deploy/python/mot_reid_infer.py --model_dir=output_inference/jde_yolov3_darknet53_30e_1088x608/ --reid_model_dir=output_inference/deepsort_yolov3_pcb_pyramid_r101/ --video_file={your video name}.mp4 --device=GPU --save_mot_txts +python deploy/python/mot_sde_infer.py --model_dir=output_inference/jde_yolov3_darknet53_30e_1088x608/ --reid_model_dir=output_inference/deepsort_yolov3_pcb_pyramid_r101/ --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_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。 diff --git a/deploy/python/infer.py b/deploy/python/infer.py index ba8706f215e6e64eb45137be5538a90e6e0c938d..d9c0efba3b2194932ac2e42a3fa47313671622b8 100644 --- a/deploy/python/infer.py +++ b/deploy/python/infer.py @@ -345,6 +345,9 @@ class PredictConfig(): self.use_dynamic_shape = yml_conf['use_dynamic_shape'] if 'mask' in yml_conf: self.mask = yml_conf['mask'] + self.tracker = None + if 'tracker' in yml_conf: + self.tracker = yml_conf['tracker'] self.print_config() def check_model(self, yml_conf): diff --git a/deploy/python/mot_jde_infer.py b/deploy/python/mot_jde_infer.py index 97cceb31f883c4c221569744eb44eb3ea24d788a..651fac58b64daf76398a9b87eee07192619c3fc2 100644 --- a/deploy/python/mot_jde_infer.py +++ b/deploy/python/mot_jde_infer.py @@ -79,7 +79,12 @@ class JDE_Detector(Detector): cpu_threads=cpu_threads, enable_mkldnn=enable_mkldnn) assert batch_size == 1, "The JDE Detector only supports batch size=1 now" - self.tracker = JDETracker() + assert pred_config.tracker, "Tracking model should have tracker" + tp = pred_config.tracker + conf_thres = tp['conf_thres'] if 'conf_thres' in tp else 0. + tracked_thresh = tp['tracked_thresh'] if 'tracked_thresh' in tp else 0.7 + metric_type = tp['metric_type'] if 'metric_type' in tp else 'euclidean' + self.tracker = JDETracker(conf_thres=conf_thres, tracked_thresh=tracked_thresh, metric_type=metric_type) def postprocess(self, pred_dets, pred_embs, threshold): online_targets = self.tracker.update(pred_dets, pred_embs) diff --git a/deploy/python/mot_sde_infer.py b/deploy/python/mot_sde_infer.py index 6e3a58dc41326951dac339eaa22be9393325c603..efed68fdf0bfea2389598491380a0c10d2ee29c1 100644 --- a/deploy/python/mot_sde_infer.py +++ b/deploy/python/mot_sde_infer.py @@ -214,7 +214,7 @@ class SDE_ReID(object): enable_mkldnn=enable_mkldnn) self.det_times = Timer() self.cpu_mem, self.gpu_mem, self.gpu_util = 0, 0, 0 - + assert pred_config.tracker, "Tracking model should have tracker" self.tracker = DeepSORTTracker() def preprocess(self, crops): @@ -246,7 +246,6 @@ class SDE_ReID(object): inputs = self.preprocess(crops) self.det_times.preprocess_time_s.end() - pred_dets, pred_embs = None, None input_names = self.predictor.get_input_names() for i in range(len(input_names)): input_tensor = self.predictor.get_input_handle(input_names[i]) diff --git a/ppdet/engine/export_utils.py b/ppdet/engine/export_utils.py index c1282e0359fddfe0ab55de1dd571714162d99afb..4f12b554fdb9df5631e5d14c92032d0899a0c359 100644 --- a/ppdet/engine/export_utils.py +++ b/ppdet/engine/export_utils.py @@ -82,6 +82,11 @@ def _parse_reader(reader_cfg, dataset_cfg, metric, arch, image_shape): return preprocess_list, label_list +def _parse_tracker(tracker_cfg): + tracker_params = {} + for k, v in tracker_cfg.items(): + tracker_params.update({k: v}) + return tracker_params def _dump_infer_config(config, path, image_shape, model): arch_state = False @@ -96,6 +101,13 @@ def _dump_infer_config(config, path, image_shape, model): }) infer_arch = config['architecture'] + if infer_arch in MOT_ARCH: + if infer_arch == 'DeepSORT': + tracker_cfg = config['DeepSORTTracker'] + else: + tracker_cfg = config['JDETracker'] + infer_cfg['tracker'] = _parse_tracker(tracker_cfg) + for arch, min_subgraph_size in TRT_MIN_SUBGRAPH.items(): if arch in infer_arch: infer_cfg['arch'] = arch