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enable smoother

This commit is contained in:
Ruben van de Ven 2024-04-29 16:13:42 +02:00
parent c9f573fcdd
commit af2c943673
5 changed files with 28 additions and 14 deletions
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4
trap/config.py
View File

@ -195,6 +195,10 @@ frame_emitter_parser.add_argument("--video-src",
type=Path,
nargs='+',
default=lambda: list(Path('../DATASETS/VIRAT_subset_0102x/').glob('*.mp4')))
frame_emitter_parser.add_argument("--video-offset",
help="Start playback from given frame. Note that when src is an array, this applies to all videos individually.",
default=None,
type=int)
#TODO: camera as source
frame_emitter_parser.add_argument("--video-loop",

6
trap/frame_emitter.py
View File

@ -150,6 +150,12 @@ class FrameEmitter:
target_frame_duration = 1./fps
logger.info(f"Emit frames at {fps} fps")
if self.config.video_offset:
logger.info(f"Start at frame {self.config.video_offset}")
video.set(cv2.CAP_PROP_POS_FRAMES, self.config.video_offset)
i = self.config.video_offset
if '-' in video_path.stem:
path_stem = video_path.stem[:video_path.stem.rfind('-')]
else:

2
trap/prediction_server.py
View File

@ -122,7 +122,7 @@ class PredictionServer:
logger.warning("Running on CPU. Specifying --eval_device cuda:0 should dramatically speed up prediction")
if self.config.smooth_predictions:
self.smoother = Smoother()
self.smoother = Smoother(window_len=4)
context = zmq.Context()
self.trajectory_socket: zmq.Socket = context.socket(zmq.SUB)

24
trap/renderer.py
View File

@ -132,9 +132,10 @@ def decorate_frame(frame: Frame, prediction_frame: Frame, first_time) -> np.arra
# new_H = S * self.H * np.linalg.inv(S)
# warpedFrame = cv2.warpPerspective(img, new_H, (1000,1000))
# cv2.imwrite(str(self.config.output_dir / "orig.png"), warpedFrame)
cv2.rectangle(img, (0,0), (img.shape[1],25), (0,0,0), -1)
if not prediction_frame:
cv2.putText(img, f"Waiting for prediction...", (20,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
cv2.putText(img, f"Waiting for prediction...", (20,20), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
# continue
else:
inv_H = np.linalg.pinv(prediction_frame.H)
@ -151,7 +152,8 @@ def decorate_frame(frame: Frame, prediction_frame: Frame, first_time) -> np.arra
for ci in range(1, len(coords)):
start = [int(p) for p in coords[ci-1]]
end = [int(p) for p in coords[ci]]
color = (255,255,255) if confirmations[ci] else (100,100,100)
# color = (255,255,255) if confirmations[ci] else (100,100,100)
color = [100+155*ci/len(coords)]*3
cv2.line(img, start, end, color, 2, lineType=cv2.LINE_AA)
if not track.predictions or not len(track.predictions):
@ -180,17 +182,17 @@ def decorate_frame(frame: Frame, prediction_frame: Frame, first_time) -> np.arra
cv2.putText(img, f"{track_id} ({(track.history[-1].conf or 0):.2f})", (center[0]+8, center[1]), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.7, thickness=2, color=(0,255,0), lineType=cv2.LINE_AA)
cv2.putText(img, f"{frame.index:06d}", (20,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
cv2.putText(img, f"{frame.time - first_time:.3f}s", (120,50), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
cv2.putText(img, f"{frame.index:06d}", (20,20), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
cv2.putText(img, f"{frame.time - first_time:.3f}s", (120,20), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
if prediction_frame:
# render Δt and Δ frames
cv2.putText(img, f"{prediction_frame.index - frame.index}", (90,50), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
cv2.putText(img, f"{prediction_frame.time - time.time():.2f}s", (200,50), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
cv2.putText(img, f"{len(prediction_frame.tracks)} tracks", (500,50), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
cv2.putText(img, f"h: {np.average([len(t.history or []) for t in prediction_frame.tracks.values()])}", (580, 50), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
cv2.putText(img, f"ph: {np.average([len(t.predictor_history or []) for t in prediction_frame.tracks.values()])}", (660, 50), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
cv2.putText(img, f"p: {np.average([len(t.predictions or []) for t in prediction_frame.tracks.values()])}", (740, 50), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
cv2.putText(img, f"{prediction_frame.index - frame.index}", (90,20), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
cv2.putText(img, f"{prediction_frame.time - time.time():.2f}s", (200,20), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
cv2.putText(img, f"{len(prediction_frame.tracks)} tracks", (500,20), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
cv2.putText(img, f"h: {np.average([len(t.history or []) for t in prediction_frame.tracks.values()])}", (580,20), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
cv2.putText(img, f"ph: {np.average([len(t.predictor_history or []) for t in prediction_frame.tracks.values()])}", (660,20), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
cv2.putText(img, f"p: {np.average([len(t.predictions or []) for t in prediction_frame.tracks.values()])}", (740,20), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,255), 1)
return img

6
trap/tracker.py
View File

@ -26,6 +26,7 @@ from trap.frame_emitter import DetectionState, Frame, Detection, Track
from tsmoothie.smoother import KalmanSmoother, ConvolutionSmoother
import tsmoothie.smoother
# Detection = [int, int, int, int, float, int]
# Detections = [Detection]
@ -322,8 +323,9 @@ def run_tracker(config: Namespace, is_running: Event):
class Smoother:
def __init__(self):
self.smoother = ConvolutionSmoother(window_len=20, window_type='ones', copy=None)
def __init__(self, window_len=2):
self.smoother = ConvolutionSmoother(window_len=window_len, window_type='ones', copy=None)
def smooth_frame_tracks(self, frame: Frame) -> Frame:
new_tracks = []