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2ade58549f
5 changed files with 4027 additions and 2403 deletions
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@ -3,7 +3,7 @@
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## Install
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* Run `bash build_opencv_with_gstreamer.sh` to build opencv with gstreamer support
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* Use `uv` to install
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* Use pyenv + poetry to install
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## How to
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3981
poetry.lock
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3981
poetry.lock
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@ -1,36 +1,11 @@
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[project]
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[tool.poetry]
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name = "trap"
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version = "0.1.0"
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description = "Art installation with trajectory prediction"
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authors = [{ name = "Ruben van de Ven", email = "git@rubenvandeven.com" }]
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requires-python = "~=3.10.4"
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authors = ["Ruben van de Ven <git@rubenvandeven.com>"]
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readme = "README.md"
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dependencies = [
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"trajectron-plus-plus",
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"torch==1.12.1",
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"torchvision==0.13.1",
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"deep-sort-realtime>=1.3.2,<2",
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"ultralytics~=8.3",
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"ffmpeg-python>=0.2.0,<0.3",
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"torchreid>=0.2.5,<0.3",
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"gdown>=4.7.1,<5",
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"pandas-helper-calc",
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"tsmoothie>=1.0.5,<2",
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"pyglet>=2.0.15,<3",
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"pyglet-cornerpin>=0.3.0,<0.4",
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"opencv-python",
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"setproctitle>=1.3.3,<2",
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"bytetracker",
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"jsonlines>=4.0.0,<5",
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"tensorboardx>=2.6.2.2,<3",
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"shapely>=1,<2",
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"baumer-neoapi",
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"qrcode~=8.0",
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"pyusb>=1.3.1,<2",
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"ipywidgets>=8.1.5,<9",
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]
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[project.scripts]
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[tool.poetry.scripts]
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trapserv = "trap.plumber:start"
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tracker = "trap.tools:tracker_preprocess"
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compare = "trap.tools:tracker_compare"
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@ -38,16 +13,42 @@ process_data = "trap.process_data:main"
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blacklist = "trap.tools:blacklist_tracks"
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rewrite_tracks = "trap.tools:rewrite_raw_track_files"
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[tool.uv]
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[tool.poetry.dependencies]
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python = "^3.10,<3.12,"
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[tool.uv.sources]
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trajectron-plus-plus = { path = "../Trajectron-plus-plus/", editable = true }
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torch = [{ url = "https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp310-cp310-linux_x86_64.whl", marker = "python_version ~= '3.10' and sys_platform == 'linux'" }]
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torchvision = [{ url = "https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp310-cp310-linux_x86_64.whl", marker = "python_version ~= '3.10' and sys_platform == 'linux'" }]
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pandas-helper-calc = { git = "https://github.com/scls19fr/pandas-helper-calc" }
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bytetracker = { git = "https://github.com/rubenvandeven/bytetrack-pip" }
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baumer-neoapi = { path = "../../Downloads/Baumer_neoAPI_1.4.1_lin_x86_64_python/wheel/baumer_neoapi-1.4.1-cp34.cp35.cp36.cp37.cp38.cp39.cp310.cp311.cp312-none-linux_x86_64.whl" }
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trajectron-plus-plus = { path = "../Trajectron-plus-plus/", develop = true }
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#trajectron-plus-plus = { git = "https://git.rubenvandeven.com/security_vision/Trajectron-plus-plus/" }
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torch = [
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{ version="1.12.1" },
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# { url = "https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp38-cp38-linux_x86_64.whl", markers = "python_version ~= '3.8' and sys_platform == 'linux'" },
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{ url = "https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp310-cp310-linux_x86_64.whl", markers = "python_version ~= '3.10' and sys_platform == 'linux'" },
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]
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torchvision = [
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{ version="0.13.1" },
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# { url = "https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp38-cp38-linux_x86_64.whl", markers = "python_version ~= '3.8' and sys_platform == 'linux'" },
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{ 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'" },
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]
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deep-sort-realtime = "^1.3.2"
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ultralytics = "^8.3"
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ffmpeg-python = "^0.2.0"
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torchreid = "^0.2.5"
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gdown = "^4.7.1"
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pandas-helper-calc = {git = "https://github.com/scls19fr/pandas-helper-calc"}
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tsmoothie = "^1.0.5"
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pyglet = "^2.0.15"
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pyglet-cornerpin = "^0.3.0"
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opencv-python = {file="./opencv_python-4.10.0.84-cp310-cp310-linux_x86_64.whl"}
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setproctitle = "^1.3.3"
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bytetracker = { git = "https://github.com/rubenvandeven/bytetrack-pip" }
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jsonlines = "^4.0.0"
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tensorboardx = "^2.6.2.2"
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shapely = "^1"
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baumer-neoapi = {path = "../../Downloads/Baumer_neoAPI_1.4.1_lin_x86_64_python/wheel/baumer_neoapi-1.4.1-cp34.cp35.cp36.cp37.cp38.cp39.cp310.cp311.cp312-none-linux_x86_64.whl"}
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qrcode = "^8.0"
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pyusb = "^1.3.1"
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ipywidgets = "^8.1.5"
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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requires = ["poetry-core"]
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build-backend = "poetry.core.masonry.api"
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@ -70,7 +70,6 @@ class LaserFrame():
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steps = int(distance // point_interval)
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for step in range(steps+1): # have both 0 and 1 in the lerp for empty points
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t = step/(steps+1)
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t = 1 # just asap to starting point of next shape
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x = int(lerp(a.x, b.x, t))
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y = int(lerp(a.y, b.y, t))
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points.append(LaserPoint(x,y, (0,0,0), 0, True))
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@ -98,58 +97,6 @@ class LaserPath():
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def first(self):
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return self.points[0]
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def as_array(self):
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np.array([[p.x, p.y] for p in self.points])
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def simplyfied_path(self, start_v= 10., max_v= 20., a = 2):
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"""walk over the path with specific velocity,
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continuously accelerate (a) until max_v is reached
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place point at each step
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(see also tools.transition_path_points() )
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"""
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if len(self.points) < 1:
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return self.points
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path = self.as_array()
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# new_path = np.array([])
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lengths = np.sqrt(np.sum(np.diff(path, axis=0)**2, axis=1))
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cum_lenghts = np.cumsum(lengths)
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# distance = cum_lenghts[-1] * t
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# ts = np.concatenate((np.array([0.]), cum_lenghts / cum_lenghts[-1]))
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# print(cum_lenghts[-1])
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# DRAW_SPEED = 35 # fixed speed (independent of lenght) TODO)) make variable
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# ts = np.concatenate((np.array([0.]), cum_lenghts / DRAW_SPEED))
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new_path = [path[0]]
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position = 0
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next_pos = position + v
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for a, b, pos in zip(path[:-1], path[1:], cum_lenghts):
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# TODO))
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if pos < (next_pos):
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continue
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v = min(v+a, max_v)
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next_pos = position + v
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relative_t = inv_lerp(t_a, t_b, t)
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pass
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# for a, b, t_a, t_b in zip(path[:-1], path[1:], ts[:-1], ts[1:]):
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# if t_b < t:
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# new_path.append(b)
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# continue
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# # interpolate
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# relative_t = inv_lerp(t_a, t_b, t)
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# x = lerp(a[0], b[0], relative_t)
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# y = lerp(a[1], b[1], relative_t)
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# new_path.append([x,y])
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# break
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# return np.array(new_path)
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class LaserPoint():
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def __init__(self,x,y,c: Color = (255,0,0),i= 255,blank=False):
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self.x = x
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@ -337,18 +284,14 @@ class LaserRenderer:
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first_time = tracker_frame.time
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# print('-------')
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paths = render_frame_to_pathlist( tracker_frame, prediction_frame, first_time, self.config, self.tracks, self.predictions, self.config.render_clusters)
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print(f"Paths: {len(paths)} ... points {sum([len(p.points) for p in paths])}")
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paths = render_frame_to_pointlist( tracker_frame, prediction_frame, first_time, self.config, self.tracks, self.predictions, self.config.render_clusters)
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laserframe = LaserFrame(paths)
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# pointlist=pointlist_test
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# print([(p.x, p.y) for p in pointlist])
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# pointlist.extend(pointlist_test)
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pointlist = laserframe.get_points_interpolated_by_distance(30, last_laser_point)
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print(len(pointlist))
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# pointlist = pointlist[::2]
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# print('decimated', len(pointlist))
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pointlist = laserframe.get_points_interpolated_by_distance(2, last_laser_point)
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# print(len(pointlist))
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if len(pointlist):
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last_laser_point = pointlist[-1]
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@ -356,7 +299,7 @@ class LaserRenderer:
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frameType = CHeliosPoint * len(pointlist)
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frame = frameType()
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# print(len(pointlist)) #, last_laser_point.x, last_laser_point.y)
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# print(len(pointlist), last_laser_point.x, last_laser_point.y)
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for j, point in enumerate(pointlist):
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frame[j] = CHeliosPoint(int(point.x), int(point.y), point.color[0],point.color[1], point.color[2], point.i)
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@ -420,7 +363,7 @@ def get_animation_position(track: Track, current_frame: Frame) -> float:
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def circle_points(cx, cy, r, c: Color):
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r = 100
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steps = 30
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steps = 100
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pointlist: list[LaserPoint] = []
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for i in range(steps):
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x = int(cx + math.cos(i * (2*math.pi)/steps) * r)
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@ -441,7 +384,7 @@ def world_points_to_laser_points(points):
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return cv2.perspectiveTransform(np.array([points]), laser_H)
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# Deprecated
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def render_frame_to_pathlist(tracker_frame: Optional[Frame], prediction_frame: Optional[Frame], first_time: Optional[float], config: Namespace, tracks: Dict[str, Track], predictions: Dict[str, Track], as_clusters = True):
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def render_frame_to_pointlist(tracker_frame: Optional[Frame], prediction_frame: Optional[Frame], first_time: Optional[float], config: Namespace, tracks: Dict[str, Track], predictions: Dict[str, Track], as_clusters = True):
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# TODO: replace opencv with QPainter to support alpha? https://doc.qt.io/qtforpython-5/PySide2/QtGui/QPainter.html#PySide2.QtGui.PySide2.QtGui.QPainter.drawImage
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# or https://github.com/pygobject/pycairo?tab=readme-ov-file
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# or https://pyglet.readthedocs.io/en/latest/programming_guide/shapes.html
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@ -501,11 +444,6 @@ def render_frame_to_pathlist(tracker_frame: Optional[Frame], prediction_frame: O
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LaserPath(circle_points(0xFFF/2+2*test_r, 0xFFF/2, test_r, track_c))
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)
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else:
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# if not len(tracks):
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# paths.append(
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# LaserPath(circle_points(0xFFF/2+4*test_r, 0xFFF/2, test_r/2, pred_c))
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# )
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for track_id, track in tracks.items():
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inv_H = np.linalg.pinv(tracker_frame.H)
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history = track.get_projected_history(camera=config.camera)
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