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feat(test): add sync prepare

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This commit is contained in:
flandre 2025-06-10 19:19:25 +08:00
parent 721708cb3a
commit 35d0cac7c6
4 changed files with 154 additions and 26 deletions
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5
pyproject.toml
View File

@ -33,3 +33,8 @@ package-data = { "flandre.assets" = ["*.svg", "*.png"], "flandre.pyqt" = ["*.ui"
[project.scripts]
flandre = "flandre.launcher:entrypoint"
[project.optional-dependencies]
to = [
"nvidia-nvimgcodec-cu12>=0.5.0.13",
]

106
test/sync_time.py
View File

@ -1,23 +1,41 @@
import matplotlib.pyplot as plt
import json
from pathlib import Path
import itertools
import numpy as np
from flandre.utils.RfFrame import b2t
rec_folder = Path(
'/run/media/lambda/b86dccdc-f134-464b-a310-6575ee9ae85c/record/')
'/run/media/lambda/b86dccdc-f134-464b-a310-6575ee9ae85c/record2/')
robot_folder = rec_folder / 'robot'
device_folder = rec_folder / 'device'
camera_folder = rec_folder / 'camera'
device_ts_map = {int(file.stem): file for file in device_folder.glob('*')}
camera_ts_map = {int(file.stem): file for file in camera_folder.glob('*')}
device_ts_map = {int(file.stem): file for file in device_folder.glob('*.bin')}
camera_ts_map = {int(file.stem): file for file in camera_folder.glob('*.jpg')}
js = itertools.chain(*[json.loads(file.read_text())
for file in robot_folder.glob('*')])
robot_ts_map = {v['host_ts']: v for v in js}
robot_ts_map = {int(k): v for k, v in json.loads(
(rec_folder/'robot.json').read_text()).items()}
device_host_arr = np.array(sorted(device_ts_map.keys()))
device_device_list: list[int] = []
for k in device_host_arr:
seq, encoder, host_ts, device_deivce, buffer = b2t(
device_ts_map[k].open('rb').read(100))
device_device_list.append(device_deivce)
device_device_arr = np.array(device_device_list)
robot_host_arr = sorted(robot_ts_map.keys())
robot_device_arr = np.array([robot_ts_map[k]['device_ts']
for k in robot_host_arr])
def func(a: int, b: list[int]):
@ -32,36 +50,72 @@ def func(a: int, b: list[int]):
The largest number in `b` <= `a`, or None if no such number exists.
"""
# Create a new list containing only the numbers from b that are <= a
candidates = [num for num in b if num <= a]
# If the candidates list is not empty, return the largest number from it
if candidates:
return max(candidates)
else:
# If no number in b was less than or equal to a, return None
return None
arr = np.array(b)
arr -= a
arr = np.abs(arr)
argmin = np.argmin(arr)
m = b[argmin]
return m, a-m
# print(device_ts_map.__len__())
# print(robot_ts_map.__len__())
ass = sorted(list(device_ts_map.keys()))[50:-100]
a0 = ass[0]
a1 = ass[-1]
ass = sorted(list(device_ts_map.keys()))[500:-1500]
device_host0 = ass[0]
device_host1 = ass[-1]
print((a1-a0)*10**-9)
r0 = robot_ts_map[func(a0, list(robot_ts_map.keys()))]['device_ts']
r1 = robot_ts_map[func(a1, list(robot_ts_map.keys()))]['device_ts']
m0, robot_d0 = func(device_host0, sorted(list(robot_ts_map.keys())))
robot_device0 = robot_ts_map[m0]['device_ts']
m1, robot_d1 = func(device_host1, sorted(list(robot_ts_map.keys())))
robot_device1 = robot_ts_map[m1]['device_ts']
seq, encoder, host_ts, device_ts0, buffer = b2t(device_ts_map[a0].read_bytes())
print(device_ts0)
seq, encoder, host_ts, device_ts1, buffer = b2t(device_ts_map[a1].read_bytes())
print(device_ts1)
camera_host0, camera_d0 = func(
device_host0, sorted(list(camera_ts_map.keys())))
camera_host1, camera_d1 = func(
device_host1, sorted(list(camera_ts_map.keys())))
print(a1-a0, (device_ts1-device_ts0)*10**3, int((r1-r0)*10**9))
print((a1-a0-(device_ts1-device_ts0)*10**3)*10**-9)
print((a1-a0-int((r1-r0)*10**9))*10**-9)
seq, encoder, host_ts, device_deivce0, buffer = b2t(
device_ts_map[device_host0].read_bytes())
device_deivce0 *= 10**3
seq, encoder, host_ts, device_deivce1, buffer = b2t(
device_ts_map[device_host1].read_bytes())
device_deivce1 *= 10**3
device_host_diff = device_host1-device_host0
device_deivce_diff = device_deivce1-device_deivce0
robot_device_diff = robot_device1-robot_device0
camera_host_diff = camera_host1 - camera_host0
print(robot_d0/10**9, robot_d1/10**9)
print(camera_d0/10**9, camera_d1/10**9)
print('device_host_diff', device_host_diff/10**9)
print('device_deivce_diff', device_deivce_diff/10**9)
print('robot_device_diff', robot_device_diff/10**9)
print('camera_host_diff', camera_host_diff/10**9)
print('device_host - device_deivce', (device_host_diff-device_deivce_diff)/10**9)
print('device_host - robot_device', (device_host_diff-robot_device_diff)/10**9)
print('device_host - camera_host', (device_host_diff-camera_host_diff)/10**9)
print('device_deivce - robot_device',
(device_deivce_diff-robot_device_diff)/10**9)
x = np.array(list(device_ts_map.keys()))
y = np.zeros_like(x)
plt.scatter(x, y)
x = np.array(list(robot_ts_map.keys()))
y = np.zeros_like(x)+1
plt.scatter(x, y)
x = np.array(list(camera_ts_map.keys()))
y = np.zeros_like(x)+2
plt.scatter(x, y)
plt.show()
# for i, a in enumerate(ass):

52
test/sync_time_prepare.py Normal file
View File

@ -0,0 +1,52 @@
from flandre.utils.RfFrame import b2t
import json
from pathlib import Path
import itertools
import cv2
import numpy as np
from nvidia import nvimgcodec
decoder = nvimgcodec.Decoder()
encoder = nvimgcodec.Encoder()
rec_folder = Path(
'/run/media/lambda/b86dccdc-f134-464b-a310-6575ee9ae85c/record2/')
robot_folder = rec_folder / 'robot'
robot_folder = rec_folder / 'robot'
d: dict[int, dict] = dict()
for file in robot_folder.glob('*'):
for line in file.read_text().splitlines():
s = line.split(',')
v = dict(
host_ts=int(s[0]),
device_ts=int(float(s[1])*10**9),
x=float(s[2]),
y=float(s[3]),
z=float(s[4]),
rx=float(s[5]),
ry=float(s[6]),
rz=float(s[7]),
fx=float(s[8]),
fy=float(s[9]),
fz=float(s[10]),
frx=float(s[11]),
fry=float(s[12]),
frz=float(s[13]),
)
d[v['host_ts']] = v
(rec_folder/'robot.json').write_text(json.dumps(d))
for file in Path('/run/media/lambda/b86dccdc-f134-464b-a310-6575ee9ae85c/record2/camera_raw').glob('*.bin'):
fb = Path(file).read_bytes()
b = np.frombuffer(fb, dtype=np.uint8).reshape(
(1080, 1920, 3))
b = cv2.cvtColor(b, cv2.COLOR_BGR2RGB)
# cv2.imshow('',)
# cv2.waitKey(0)
# enc_params = nvimgcodec.EncodeParams(quality=5, chroma_subsampling=nvimgcodec.ChromaSubsampling.CSS_GRAY)
# encoder.write('a.jpg', b, params=enc_params)
encoder.write((file.parent.parent/'camera' /
file.with_suffix('.jpg').name).__str__(), b)

17
uv.lock
View File

@ -501,6 +501,11 @@ dependencies = [
{ name = "zstd" },
]
[package.optional-dependencies]
to = [
{ name = "nvidia-nvimgcodec-cu12" },
]
[package.metadata]
requires-dist = [
{ name = "click", specifier = ">=8.1.8" },
@ -509,6 +514,7 @@ requires-dist = [
{ name = "jupyter", specifier = ">=1.1.1" },
{ name = "matplotlib", specifier = ">=3.10.1" },
{ name = "mido", extras = ["ports-rtmidi"], specifier = ">=1.3.3" },
{ name = "nvidia-nvimgcodec-cu12", marker = "extra == 'to'", specifier = ">=0.5.0.13" },
{ name = "opencv-python", specifier = ">=4.10.0.84" },
{ name = "platformdirs", specifier = ">=4.3.6" },
{ name = "pyjoystick", specifier = ">=1.2.4" },
@ -520,6 +526,7 @@ requires-dist = [
{ name = "websockets", specifier = ">=15.0.1" },
{ name = "zstd", specifier = ">=1.5.6.7" },
]
provides-extras = ["to"]
[[package]]
name = "fonttools"
@ -1222,6 +1229,16 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/89/ea/00537f599eb230771157bc509f6ea5b2dddf05d4b09f9d2f1d7096a18781/numpy-2.2.0-cp312-cp312-win_amd64.whl", hash = "sha256:3a4199f519e57d517ebd48cb76b36c82da0360781c6a0353e64c0cac30ecaad3", size = 12613227, upload-time = "2024-12-08T15:32:34.792Z" },
]
[[package]]
name = "nvidia-nvimgcodec-cu12"
version = "0.5.0.13"
source = { registry = "https://pypi.org/simple" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/28/9a/f6c9105cb045f52af2096417ce92e7e8fba4d24ffe24d2cc82eb9bbe5534/nvidia_nvimgcodec_cu12-0.5.0.13-py3-none-manylinux2014_aarch64.whl", hash = "sha256:d76fadc2ed0f9075871627e45f2592c7807a0e944a0505afc21f87ccceb75caa", size = 17967646, upload-time = "2025-03-18T10:40:26.76Z" },
{ url = "https://files.pythonhosted.org/packages/f0/6d/1c9919912ee97a4f52674f1c2deec7ab80df8fdd9a8b76f8ed4d75ebf799/nvidia_nvimgcodec_cu12-0.5.0.13-py3-none-manylinux2014_x86_64.whl", hash = "sha256:24cf0a759b1b02a6c3c0aedf8bf6602643f74c4c6df68c4b1c3c4ec1d48d71b0", size = 23304465, upload-time = "2025-03-18T10:40:54.19Z" },
{ url = "https://files.pythonhosted.org/packages/3b/a8/58243c8f855b29116b1da9ee222b1afeb6a18a2120e66a0af2f85bee13ac/nvidia_nvimgcodec_cu12-0.5.0.13-py3-none-win_amd64.whl", hash = "sha256:331fc77cc844b982ab0d5c0e9d660b2e777e82c61471657c18f40ca3d15640c2", size = 27621245, upload-time = "2025-03-18T10:38:26.251Z" },
]
[[package]]
name = "opencv-python"
version = "4.10.0.84"