Try different trackers. Ultralytics & KeypointRCNN
This commit is contained in:
Ruben van de Ven
parent
2434470cdf
commit
cc1e417db4
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@ -2,6 +2,8 @@ import argparse
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from pathlib import Path
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import types
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from trap.tracker import DETECTORS
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from pyparsing import Optional
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class LambdaParser(argparse.ArgumentParser):
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@ -208,6 +210,10 @@ tracker_parser.add_argument("--save-for-training",
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help="Specify the path in which to save",
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type=Path,
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default=None)
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tracker_parser.add_argument("--detector",
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help="Specify the detector to use",
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type=str,
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choices=DETECTORS)
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# Renderer
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100
trap/tracker.py
100
trap/tracker.py
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@ -1,5 +1,7 @@
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from argparse import Namespace
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from collections import defaultdict
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import csv
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from dataclasses import dataclass, field
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import json
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import logging
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from multiprocessing import Event
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@ -11,9 +13,12 @@ import torch
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import zmq
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import cv2
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from torchvision.models.detection import retinanet_resnet50_fpn_v2, RetinaNet_ResNet50_FPN_V2_Weights
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from torchvision.models.detection import retinanet_resnet50_fpn_v2, RetinaNet_ResNet50_FPN_V2_Weights, keypointrcnn_resnet50_fpn, KeypointRCNN_ResNet50_FPN_Weights
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from deep_sort_realtime.deepsort_tracker import DeepSort
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from deep_sort_realtime.deep_sort.track import Track
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from deep_sort_realtime.deep_sort.track import Track as DeepsortTrack
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from ultralytics import YOLO
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from ultralytics.engine.results import Results as YOLOResult
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from trap.frame_emitter import Frame
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@ -28,6 +33,33 @@ TARGET_DT = .1
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logger = logging.getLogger("trap.tracker")
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DETECTOR_RESNET = 'resnet'
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DETECTOR_YOLOv8 = 'ultralytics'
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DETECTORS = [DETECTOR_RESNET, DETECTOR_YOLOv8]
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@dataclass
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class Track:
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track_id: str = None
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history: [Detection]= field(default_factory=lambda: [])
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@dataclass
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class Detection:
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track_id: str
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l: int # left
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t: int # top
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w: int # width
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h: int # height
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def get_foot_coords(self):
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return [self.l + 0.5 * self.w, self.t+self.h]
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@classmethod
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def from_deepsort(cls, dstrack: DeepsortTrack):
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return cls(dstrack.track_id, *dstrack.to_ltwh())
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class Tracker:
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def __init__(self, config: Namespace, is_running: Event):
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self.config = config
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@ -46,20 +78,30 @@ class Tracker:
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# # TODO: config device
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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weights = RetinaNet_ResNet50_FPN_V2_Weights.DEFAULT
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self.model = retinanet_resnet50_fpn_v2(weights=weights, score_thresh=0.35)
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# TODO: support removal
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self.tracks = defaultdict(lambda: Track())
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if self.config.detector == DETECTOR_RESNET:
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# weights = RetinaNet_ResNet50_FPN_V2_Weights.DEFAULT
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# self.model = retinanet_resnet50_fpn_v2(weights=weights, score_thresh=0.2)
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weights = KeypointRCNN_ResNet50_FPN_Weights.DEFAULT
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self.model = keypointrcnn_resnet50_fpn(weights=weights, box_score_thresh=0.20)
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self.model.to(self.device)
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# Put the model in inference mode
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self.model.eval()
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# Get the transforms for the model's weights
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self.preprocess = weights.transforms().to(self.device)
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elif self.config.detector == DETECTOR_YOLOv8:
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self.model = YOLO('EXPERIMENTS/yolov8x.pt')
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else:
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raise RuntimeError("No valid detector specified. See --help")
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# homography = list(source.glob('*img2world.txt'))[0]
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self.H = np.loadtxt(self.config.homography, delimiter=',')
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self.mot_tracker = DeepSort(max_age=5)
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self.mot_tracker = DeepSort(max_age=30, nms_max_overlap=0.9)
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logger.debug("Set up tracker")
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@ -94,30 +136,46 @@ class Tracker:
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# logger.info(f"Frame delivery delay = {time.time()-frame.time}s")
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start_time = time.time()
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detections = self.detect_persons(frame.img)
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tracks: [Track] = self.mot_tracker.update_tracks(detections, frame=frame.img)
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TEMP_boxes = [t.to_ltwh() for t in tracks]
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TEMP_coords = np.array([[[det[0] + 0.5 * det[2], det[1]+det[3]]] for det in TEMP_boxes])
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if len(TEMP_coords):
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TEMP_proj_coords = cv2.perspectiveTransform(TEMP_coords,self.H)
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if self.config.detector == DETECTOR_YOLOv8:
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detections: [Detection] = self._yolov8_track(frame.img)
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else :
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TEMP_proj_coords = []
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detections: [Detection] = self._resnet_track(frame.img)
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# Store detections into tracklets
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for detection in detections:
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track = self.tracks[detection.track_id]
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track.track_id = detection.track_id # for new tracks
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track.history.append(detection)
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# if len(track.history) > 30: # retain 90 tracks for 90 frames
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# track.history.pop(0)
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foot_coordinates = np.array([[t.get_foot_coords()] for t in detections])
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if len(foot_coordinates):
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projected_coordinates = cv2.perspectiveTransform(foot_coordinates,self.H)
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else:
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projected_coordinates = []
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# print(TEMP_proj_coords)
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trajectories = {}
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for i, coords in enumerate(TEMP_proj_coords):
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tid = tracks[i].track_id
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for detection, coords in zip(detections, projected_coordinates):
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tid = str(detection.track_id)
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trajectories[tid] = {
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"id": tid,
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"history": [{"x":c[0], "y":c[1]} for c in coords] # already doubles nested, fine for test
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"history": [{"x":c[0], "y":c[1]} for c in coords] if not self.config.bypass_prediction else coords.tolist() # already doubles nested, fine for test
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}
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# logger.debug(f"{trajectories}")
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# logger.info(f"{trajectories}")
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frame.trajectories = trajectories
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current_time = time.time()
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logger.debug(f"Trajectories: {len(trajectories)}. Current frame delay = {current_time-frame.time}s (trajectories: {current_time - start_time}s)")
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if self.config.bypass_prediction:
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self.trajectory_socket.send_string(json.dumps(trajectories))
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else:
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self.trajectory_socket.send(pickle.dumps(frame))
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# self.trajectory_socket.send_string(json.dumps(trajectories))
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# provide a {ID: {id: ID, history: [[x,y],[x,y],...]}}
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# TODO: provide a track object that actually keeps history (unlike tracker)
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@ -154,8 +212,16 @@ class Tracker:
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logger.info('Stopping')
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def _yolov8_track(self, img) -> [Detection]:
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results: [YOLOResult] = self.model.track(img, persist=True)
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return [Detection(track_id, *bbox) for bbox, track_id in zip(results[0].boxes.xywh.cpu(), results[0].boxes.id.int().cpu().tolist())]
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def detect_persons(self, frame) -> Detections:
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def _resnet_track(self, img) -> [Detection]:
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detections = self._resnet_detect_persons(img)
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tracks: [DeepsortTrack] = self.mot_tracker.update_tracks(detections, frame=img)
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return [Detection.from_deepsort(t) for t in tracks]
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def _resnet_detect_persons(self, frame) -> Detections:
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t = torch.from_numpy(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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# change axes of image loaded image to be compatilbe with torch.io.read_image (which has C,W,H format instead of W,H,C)
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t = t.permute(2, 0, 1)
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