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6 Commits

Author SHA1 Message Date
Ruben van de Ven 185962ace5 try ultralytics 2023-10-20 18:50:16 +02:00
Ruben van de Ven 081e26a458 try ultralytics 2023-10-20 18:50:07 +02:00
Ruben van de Ven cc1e417db4 Try different trackers. Ultralytics & KeypointRCNN 2023-10-20 18:49:51 +02:00
Ruben van de Ven 2434470cdf bypass renderer reads tracker data 2023-10-20 18:48:59 +02:00
Ruben van de Ven 994e150d37 start function 2023-10-20 18:48:26 +02:00
Ruben van de Ven 343b5b4c4d remove deprecated script 2023-10-20 13:55:29 +02:00
7 changed files with 716 additions and 127 deletions

708
poetry.lock generated
View File

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python-versions = ">=3.7"
files = [
{file = "virtualenv-20.24.5-py3-none-any.whl", hash = "sha256:b80039f280f4919c77b30f1c23294ae357c4c8701042086e3fc005963e4e537b"},
{file = "virtualenv-20.24.5.tar.gz", hash = "sha256:e8361967f6da6fbdf1426483bfe9fca8287c242ac0bc30429905721cefbff752"},
]
[package.dependencies]
distlib = ">=0.3.7,<1"
filelock = ">=3.12.2,<4"
platformdirs = ">=3.9.1,<4"
[package.extras]
docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"]
[[package]]
name = "wcwidth"
version = "0.2.8"
@ -3365,20 +3868,35 @@ MarkupSafe = ">=2.1.1"
watchdog = ["watchdog (>=2.3)"]
[[package]]
name = "wheel"
version = "0.41.2"
description = "A built-package format for Python"
name = "win32-setctime"
version = "1.1.0"
description = "A small Python utility to set file creation time on Windows"
optional = false
python-versions = ">=3.7"
python-versions = ">=3.5"
files = [
{file = "wheel-0.41.2-py3-none-any.whl", hash = "sha256:75909db2664838d015e3d9139004ee16711748a52c8f336b52882266540215d8"},
{file = "wheel-0.41.2.tar.gz", hash = "sha256:0c5ac5ff2afb79ac23ab82bab027a0be7b5dbcf2e54dc50efe4bf507de1f7985"},
{file = "win32_setctime-1.1.0-py3-none-any.whl", hash = "sha256:231db239e959c2fe7eb1d7dc129f11172354f98361c4fa2d6d2d7e278baa8aad"},
{file = "win32_setctime-1.1.0.tar.gz", hash = "sha256:15cf5750465118d6929ae4de4eb46e8edae9a5634350c01ba582df868e932cb2"},
]
[package.extras]
test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"]
[[package]]
name = "yacs"
version = "0.1.8"
description = "Yet Another Configuration System"
optional = false
python-versions = "*"
files = [
{file = "yacs-0.1.8-py2-none-any.whl", hash = "sha256:d43d1854c1ffc4634c5b349d1c1120f86f05c3a294c9d141134f282961ab5d94"},
{file = "yacs-0.1.8-py3-none-any.whl", hash = "sha256:99f893e30497a4b66842821bac316386f7bd5c4f47ad35c9073ef089aa33af32"},
{file = "yacs-0.1.8.tar.gz", hash = "sha256:efc4c732942b3103bea904ee89af98bcd27d01f0ac12d8d4d369f1e7a2914384"},
]
[package.dependencies]
PyYAML = "*"
[metadata]
lock-version = "2.0"
python-versions = "^3.10,<3.12,"
content-hash = "54eabff3ca832efc7790a1044ace59b882e134b536d45c42df91701a7001a6c0"
content-hash = "d220aef575cc8223929a215a2a6094bb9bc2644a674ae1a2fca93f39b7fca9b4"

View File

@ -25,7 +25,7 @@ torchvision = [
{ url = "https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp310-cp310-linux_x86_64.whl", markers = "python_version ~= '3.10' and sys_platform == 'linux'" },
]
deep-sort-realtime = "^1.3.2"
ultralytics = "^8.0.200"
[build-system]
requires = ["poetry-core"]

View File

@ -1,5 +0,0 @@
from trap import plumber
if __name__ == "__main__":
plumber.start()

View File

@ -2,6 +2,8 @@ import argparse
from pathlib import Path
import types
from trap.tracker import DETECTORS
from pyparsing import Optional
class LambdaParser(argparse.ArgumentParser):
@ -208,6 +210,10 @@ tracker_parser.add_argument("--save-for-training",
help="Specify the path in which to save",
type=Path,
default=None)
tracker_parser.add_argument("--detector",
help="Specify the detector to use",
type=str,
choices=DETECTORS)
# Renderer

View File

@ -93,3 +93,6 @@ def start():
logger.info('Stop')
if __name__ == "__main__":
start()

View File

@ -22,13 +22,12 @@ class Renderer:
self.prediction_sock = context.socket(zmq.SUB)
self.prediction_sock.setsockopt(zmq.CONFLATE, 1) # only keep latest frame. NB. make sure this comes BEFORE connect, otherwise it's ignored!!
self.prediction_sock.setsockopt(zmq.SUBSCRIBE, b'')
self.prediction_sock.connect(config.zmq_prediction_addr)
self.prediction_sock.connect(config.zmq_prediction_addr if not self.config.bypass_prediction else config.zmq_trajectory_addr)
self.frame_sock = context.socket(zmq.SUB)
self.frame_sock.setsockopt(zmq.CONFLATE, 1) # only keep latest frame. NB. make sure this comes BEFORE connect, otherwise it's ignored!!
self.frame_sock.setsockopt(zmq.SUBSCRIBE, b'')
self.frame_sock.connect(config.zmq_frame_addr)
self.H = np.loadtxt(self.config.homography, delimiter=',')
@ -73,10 +72,12 @@ class Renderer:
for track_id, prediction in predictions.items():
if not 'history' in prediction or not len(prediction['history']):
continue
coords = cv2.perspectiveTransform(np.array([prediction['history']]), self.inv_H)[0]
# logger.warning(f"{coords=}")
center = [int(p) for p in coords[-1]]
cv2.circle(img, center, 5, (0,255,0))
cv2.putText(img, track_id, (center[0]+8, center[1]), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(0,255,0))
for ci in range(1, len(coords)):
start = [int(p) for p in coords[ci-1]]
@ -91,7 +92,7 @@ class Renderer:
for ci in range(1, len(pred_coords)):
start = [int(p) for p in pred_coords[ci-1]]
end = [int(p) for p in pred_coords[ci]]
cv2.line(img, start, end, (0,0,255), 2)
cv2.line(img, start, end, (0,0,255), 1)

View File

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