Track directly from rtsp
This commit is contained in:
Ruben van de Ven
parent
b6360c09a3
commit
dd10ce13af
4 changed files with 71 additions and 22 deletions
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@ -11,10 +11,11 @@
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These are roughly the steps to go from datagathering to training
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1. Make sure to have some recordings with a fixed camera.
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1. Make sure to have some recordings with a fixed camera. [UPDATE: not needed anymore, except for calibration & homography footage]
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* Recording can be done with `ffmpeg -rtsp_transport udp -i rtsp://USER:PASS@IP:554/Streaming/Channels/1.mp4 hof2-cam-$(date "+%Y%m%d-%H%M").mp4`
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2. Follow the steps in the auxilary [traptools](https://git.rubenvandeven.com/security_vision/traptools) repository to obtain (1) camera matrix, lens distortion, image dimensions, and (2+3) homography
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3. Run the tracker, e.g. `poetry run tracker --detector ultralytics --homography ../DATASETS/NAME/homography.json --video-src ../DATASETS/NAME/*.mp4 --calibration ../DATASETS/NAME/calibration.json --save-for-training EXPERIMENTS/raw/NAME/`
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* Note: You can run this right of the camera stream: `poetry run tracker --eval_device cuda:0 --detector ultralytics --video-src rtsp://USER:PW@ADDRESS/STREAM --homography ../DATASETS/NAME/homography.json --calibration ../DATASETS/NAME/calibration.json --save-for-training EXPERIMENTS/raw/NAME/`, each recording adding a new file to the `raw` folder.
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4. Parse tracker data to Trajectron format: `poetry run process_data --src-dir EXPERIMENTS/raw/NAME --dst-dir EXPERIMENTS/trajectron-data/ --name NAME`
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5. Train Trajectron model `poetry run trajectron_train --eval_every 10 --vis_every 1 --train_data_dict NAME_train.pkl --eval_data_dict NAME_val.pkl --offline_scene_graph no --preprocess_workers 8 --log_dir EXPERIMENTS/models --log_tag _NAME --train_epochs 100 --conf EXPERIMENTS/config.json --batch_size 256 --data_dir EXPERIMENTS/trajectron-data `
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6. The run!
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@ -109,10 +109,10 @@ class AnimationRenderer:
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self.batch_anim = pyglet.graphics.Batch()
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self.debug_lines = [
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pyglet.shapes.Line(1380, self.config.camera.h, 1380, 670, 2, (255,255,255,255), batch=self.batch_overlay),
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pyglet.shapes.Line(0, 660, 1380, 670, 2, (255,255,255,255), batch=self.batch_overlay),
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pyglet.shapes.Line(1140, 760, 1140, 675, 2, (255,255,255,255), batch=self.batch_overlay),
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pyglet.shapes.Line(0, 750, 1380, 760, 2, (255,255,255,255), batch=self.batch_overlay),
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pyglet.shapes.Line(1370, self.config.camera.h-360, 1380, 670-360, 2, (255,255,255,255), batch=self.batch_overlay),
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pyglet.shapes.Line(0, 660-360, 1380, 670-360, 2, (255,255,255,255), batch=self.batch_overlay),
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pyglet.shapes.Line(1140, 760-360, 1140, 675-360, 2, (255,255,255,255), batch=self.batch_overlay),
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pyglet.shapes.Line(0, 770-360, 1380, 770-360, 2, (255,255,255,255), batch=self.batch_overlay),
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]
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@ -113,6 +113,20 @@ def get_maps_for_input(input_dict, scene, hyperparams):
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return maps_dict
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# If homography is in cm, predictions can be terrible. Correct that here
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# TODO)) This should actually not be here, but we should use alternative homography
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# and then scale up in rendering
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def history_cm_to_m(history):
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return [(h[0]/100, h[1]/100) for h in history]
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def prediction_m_to_cm(source):
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# histories_dict[t][node]
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for t in source:
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for node in source[t]:
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source[t][node] *= 100
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# print(t,node, source[t][node])
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return source
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class PredictionServer:
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def __init__(self, config: Namespace, is_running: Event):
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self.config = config
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@ -270,6 +284,7 @@ class PredictionServer:
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# TODO: modify this into a mapping function between JS data an the expected Node format
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# node = FakeNode(online_env.NodeType.PEDESTRIAN)
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history = [[h['x'], h['y']] for h in track.get_projected_history_as_dict(frame.H, self.config.camera)]
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history = history_cm_to_m(history)
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history = np.array(history)
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x = history[:, 0]
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y = history[:, 1]
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@ -360,6 +375,10 @@ class PredictionServer:
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hyperparams['prediction_horizon']
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)
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prediction_dict, histories_dict, futures_dict = prediction_m_to_cm(prediction_dict), prediction_m_to_cm(histories_dict), prediction_m_to_cm(futures_dict)
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assert(len(prediction_dict.keys()) <= 1)
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if len(prediction_dict.keys()) == 0:
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return
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@ -27,6 +27,7 @@ from trap.frame_emitter import DetectionState, Frame, Detection, Track
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from tsmoothie.smoother import KalmanSmoother, ConvolutionSmoother
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import tsmoothie.smoother
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from datetime import datetime
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# Detection = [int, int, int, int, float, int]
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# Detections = [Detection]
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@ -56,6 +57,24 @@ def _yolov8_track(frame: Frame, model: YOLO, **kwargs) -> List[Detection]:
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return [Detection(track_id, bbox[0]-.5*bbox[2], bbox[1]-.5*bbox[3], bbox[2], bbox[3], 1, DetectionState.Confirmed, frame.index) for bbox, track_id in zip(results[0].boxes.xywh.cpu(), results[0].boxes.id.int().cpu().tolist())]
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class Multifile():
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def __init__(self, srcs: List[Path]):
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self.srcs = srcs
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self.g = self.__iter__()
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@property
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def name(self):
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return ", ".join([s.name for s in self.srcs])
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def __iter__(self):
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for path in self.srcs:
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with path.open('r') as fp:
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for l in fp:
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yield l
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def readline(self):
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return self.g.__next__()
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class TrainingDataWriter:
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def __init__(self, training_path = Optional[Path]):
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@ -77,7 +96,8 @@ class TrainingDataWriter:
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def __enter__(self):
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if self.path:
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self.training_fp = open(self.path / 'all.txt', 'w')
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d = datetime.now().isoformat(timespec="minutes")
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self.training_fp = open(self.path / f'all-{d}.txt', 'w')
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# following https://github.com/StanfordASL/Trajectron-plus-plus/blob/master/experiments/pedestrians/process_data.py
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self.csv = csv.DictWriter(self.training_fp, fieldnames=['frame_id', 'track_id', 'l', 't', 'w', 'h', 'x', 'y', 'state'], delimiter='\t', quoting=csv.QUOTE_NONE)
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self.count = 0
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@ -110,22 +130,31 @@ class TrainingDataWriter:
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return
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self.training_fp.close()
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lines = {
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'train': int(self.count * .8),
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'val': int(self.count * .12),
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'test': int(self.count * .08),
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}
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logger.info(f"Splitting gathered data from {self.training_fp.name}")
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with open(self.training_fp.name, 'r') as source_fp:
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for name, line_nrs in lines.items():
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dir_path = self.path / name
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dir_path.mkdir(exist_ok=True)
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file = dir_path / 'tracked.txt'
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logger.debug(f"- Write {line_nrs} lines to {file}")
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with file.open('w') as target_fp:
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for i in range(line_nrs):
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target_fp.write(source_fp.readline())
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source_files = list(self.path.glob("*.txt")) # we loop twice, so need a list instead of generator
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total = 0
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sources = Multifile(source_files)
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for line in sources:
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if len(line) > 3: # make sure not to count empty lines
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total += 1
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lines = {
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'train': int(total * .8),
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'val': int(total * .12),
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'test': int(total * .08),
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}
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logger.info(f"Splitting gathered data from {sources.name}")
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# for source_file in source_files:
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for name, line_nrs in lines.items():
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dir_path = self.path / name
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dir_path.mkdir(exist_ok=True)
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file = dir_path / 'tracked.txt'
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logger.debug(f"- Write {line_nrs} lines to {file}")
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with file.open('w') as target_fp:
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for i in range(line_nrs):
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target_fp.write(sources.readline())
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