├── .gitignore
├── LICENSE
├── README.md
├── examples
├── coffee.jpg
├── coffee_dark.png
├── coffee_out.png
└── coffee_single.png
├── pyproject.toml
├── requirements.txt
├── setup.py
└── vpype_flow_imager
├── kdtree.py
└── vpype_flow_imager.py
/.gitignore:
--------------------------------------------------------------------------------
1 | # Created by .ignore support plugin (hsz.mobi)
2 | ### Python template
3 | # Byte-compiled / optimized / DLL files
4 | __pycache__/
5 | *.py[cod]
6 | *$py.class
7 |
8 | # C extensions
9 | *.so
10 |
11 | # Distribution / packaging
12 | .Python
13 | build/
14 | develop-eggs/
15 | dist/
16 | downloads/
17 | eggs/
18 | .eggs/
19 | lib/
20 | lib64/
21 | parts/
22 | sdist/
23 | var/
24 | wheels/
25 | pip-wheel-metadata/
26 | share/python-wheels/
27 | *.egg-info/
28 | .installed.cfg
29 | *.egg
30 | MANIFEST
31 |
32 | # PyInstaller
33 | # Usually these files are written by a python script from a template
34 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
35 | *.manifest
36 | *.spec
37 |
38 | # Installer logs
39 | pip-log.txt
40 | pip-delete-this-directory.txt
41 |
42 | # Unit test / coverage reports
43 | htmlcov/
44 | .tox/
45 | .nox/
46 | .coverage
47 | .coverage.*
48 | .cache
49 | nosetests.xml
50 | coverage.xml
51 | *.cover
52 | *.py,cover
53 | .hypothesis/
54 | .pytest_cache/
55 |
56 | # Translations
57 | *.mo
58 | *.pot
59 |
60 | # Django stuff:
61 | *.log
62 | local_settings.py
63 | db.sqlite3
64 | db.sqlite3-journal
65 |
66 | # Flask stuff:
67 | instance/
68 | .webassets-cache
69 |
70 | # Scrapy stuff:
71 | .scrapy
72 |
73 | # Sphinx documentation
74 | docs/_build/
75 |
76 | # PyBuilder
77 | target/
78 |
79 | # Jupyter Notebook
80 | .ipynb_checkpoints
81 |
82 | # IPython
83 | profile_default/
84 | ipython_config.py
85 |
86 | # pyenv
87 | .python-version
88 |
89 | # pipenv
90 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
91 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
92 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
93 | # install all needed dependencies.
94 | #Pipfile.lock
95 |
96 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
97 | __pypackages__/
98 |
99 | # Celery stuff
100 | celerybeat-schedule
101 | celerybeat.pid
102 |
103 | # SageMath parsed files
104 | *.sage.py
105 |
106 | # Environments
107 | .env
108 | .venv
109 | env/
110 | venv/
111 | ENV/
112 | env.bak/
113 | venv.bak/
114 |
115 | # Spyder project settings
116 | .spyderproject
117 | .spyproject
118 |
119 | # Rope project settings
120 | .ropeproject
121 |
122 | # mkdocs documentation
123 | /site
124 |
125 | # mypy
126 | .mypy_cache/
127 | .dmypy.json
128 | dmypy.json
129 |
130 | # Pyre type checker
131 | .pyre/
132 |
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--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # vpype flow imager
2 |
3 |
4 |
5 | [`vpype`](https://github.com/abey79/vpype) plug-in to convert images to flow field line art inspired by Sean M. Puckett's work and the "Creating evenly-spaced streamlines of arbitrary density" paper by Jobard and Lefer.
6 |
7 | ## Getting Started
8 |
9 | For an existing *vpype* installation using pipx, use the following command:
10 |
11 | ```bash
12 | $ pipx inject vpype 'git+https://github.com/serycjon/vpype-flow-imager.git#egg=vpype-flow-imager[all]'
13 | ```
14 |
15 | For an existing *vpype* installation using pip in a virtual environment, activate the virtual environment and using the following command:
16 |
17 | ```bash
18 | $ pip install 'git+https://github.com/serycjon/vpype-flow-imager.git#egg=vpype-flow-imager[all]'
19 | ```
20 |
21 | For a new installation of `vpype flow imager`, use the following commands:
22 | ```bash
23 | $ python3.8 -m venv my_virtual_env
24 | $ source my_virtual_env/bin/activate
25 | $ pip install 'git+https://github.com/serycjon/vpype-flow-imager.git#egg=vpype-flow-imager[all]'
26 | ```
27 |
28 | `vpype` is automatically installed with `vpype flow imager`, so no further steps are required.
29 |
30 | You can confirm that the installation was successful with the following command, which also happens to tell you all
31 | you need to know to use `vpype flow imager`:
32 |
33 | ```bash
34 | $ vpype flow_img --help
35 | Usage: vpype flow_img [OPTIONS] FILENAME
36 |
37 | Generate flowline representation from an image.
38 |
39 | The generated flowlines are in the coordinates of the input image, resized
40 | to have dimensions at most `--max_size` pixels.
41 |
42 | Options:
43 | -fi, --flow_image FILE An image to use for the flow field. X and Y
44 | components of the flow vector are encoded
45 | in the red and green channels, as in a
46 | normal map. Must be the same size as the
47 | main input image. It might work if they
48 | have exactly the same aspect ratio.
49 | -nc, --noise_coeff FLOAT Simplex noise coordinate multiplier. The
50 | smaller, the smoother the flow field.
51 | [default: 0.001]
52 | -nf, --n_fields INTEGER Number of rotated copies of the flow field
53 | [default: 1]
54 | -ms, --min_sep LENGTH Minimum flowline separation [default: 0.8]
55 | -Ms, --max_sep LENGTH Maximum flowline separation [default: 10]
56 | -ml, --min_length LENGTH Minimum flowline length [default: 0]
57 | -Ml, --max_length LENGTH Maximum flowline length [default: 40]
58 | --max_size LENGTH The input image will be rescaled to have
59 | sides at most max_size px [default: 800]
60 | -ef, --search_ef INTEGER HNSWlib search ef (higher -> more accurate,
61 | but slower) [default: 50]
62 | -s, --seed INTEGER PRNG seed (overriding vpype seed)
63 | -fs, --flow_seed INTEGER Flow field PRNG seed (overriding the main
64 | `--seed`)
65 | -tf, --test_frequency FLOAT Number of separation tests per current
66 | flowline separation [default: 2]
67 | -f, --field_type [noise|curl_noise]
68 | flow field type [default: noise]
69 | --transparent_val INTEGER RANGE
70 | Value to replace transparent pixels
71 | [default: 127; 0<=x<=255]
72 | -tm, --transparent_mask Remove lines from transparent parts of the
73 | source image. [default: False]
74 | -efm, --edge_field_multiplier FLOAT
75 | flow along image edges
76 | -dfm, --dark_field_multiplier FLOAT
77 | flow swirling around dark image areas
78 | -kdt, --kdtree_searcher Use exact nearest neighbor search with
79 | kdtree (slower, but more precise) [default:
80 | False]
81 | --cmyk Split image to CMYK and process each channel
82 | separately. The results are in
83 | consecutively numbered layers, starting from
84 | `layer`. [default: False]
85 | --rotate DEGREES rotate the flow field [default: 0]
86 | -l, --layer LAYER Target layer or 'new'. When CMYK enabled,
87 | this indicates the first (cyan) layer.
88 | --help Show this message and exit. [default:
89 | False]
90 | ```
91 |
92 |
93 | You will need a C++ compiler before running the default flow imager installation. One way of getting the compiler on Windows is installing Visual Studio with C++ package ([tutorial](https://docs.microsoft.com/en-us/cpp/build/vscpp-step-0-installation?view=msvc-160)).
94 | If the installation fails on some things related to "hnswlib", you can use the fallback installation, removing the `[all]` from the installation command, e.g.:
95 | ```bash
96 | $ pipx inject vpype git+https://github.com/serycjon/vpype-flow-imager
97 | ```
98 |
99 | If you still get errors, feel free to create an issue here on github, or ask around at the ([drawingbots discord](https://discordapp.com/invite/XHP3dBg)).
100 |
101 | To create a SVG, combine the `flow_img` command with the `write` command (check `vpype`'s documentation for more
102 | information). Here is an example:
103 |
104 | ```bash
105 | $ vpype flow_img input.jpg write output.svg
106 | ```
107 |
108 | ## Examples
109 |
110 | The example output was generated with:
111 | ```bash
112 | $ cd examples
113 | $ vpype flow_img -nf 6 coffee.jpg write coffee_out.svg show
114 | ```
115 | It took around 3 minutes on my laptop.
116 | In this example, the flow field was rotated 6 times to get hexagonal structure in the result.
117 |
118 | The default:
119 | ```bash
120 | $ vpype flow_img coffee.jpg write coffee_out.svg show
121 | ```
122 | produces a smoother result like:
123 |
124 |
125 |
126 | You can control the result line density by changing the `--min_sep` and `--max_sep` parameters.
127 |
128 | You can also locally override the vpype PRNG seed using the `--seed` and `--flow_seed` parameters. The `--flow_seed` is used only in the flow field construction, so if you want to create a multi-layer svg (e.g. CMYK), you can do something like
129 | ```bash
130 | vpype flow_img -fs 42 -l 1 C.jpg flow_img -fs 42 -l 2 M.jpg flow_img -fs 42 -l 3 Y.jpg flow_img -fs 42 -l 4 K.jpg write --layer-label "Pen%d" cmyk.svg show
131 | ```
132 | By specifying the same `-fs` (`--flow_seed`) for all the layers, you will get the same flowline directions on all the layers.
133 |
134 | The following is an example with `curl_noise` and `dark_field` enabled:
135 | ```bash
136 | vpype -v -s 42 flow_img -f curl_noise -dfm 1 -nc 0.03 examples/coffee.jpg write examples/coffee_dark.svg
137 | ```
138 |
139 |
140 |
141 | ## Parameters
142 | Starting from the most interesting / useful:
143 | * `min_sep`, `max_sep` - Control the flowline density (separation between flowlines)
144 | * `min_length`, `max_length` - Control the flowline length. (setting `min_length > 0` breaks the flowline density constraints)
145 | * `field_type` - Set to `noise` (default) to get opensimplex noise flow field, set to `curl_noise` to get curly flow field.
146 | * `cmyk` - convert the input RGB image into CMYK and output four layers.
147 | * `rotate` - Rotate the flow field. Useful if you want to achieve cross-hatching (example: `vpype flow_img -fs 42 examples/coffee.jpg flow_img -fs 42 --rotate 90 examples/coffee.jpg show`)
148 | * `n_fields` - Number of rotated copies of the flow field (default: 1). For example, try out 3, 4, or 6 to get triangular, rectangular, or hexagonal patterns.
149 | * `edge_field_multiplier` - When set to a number (try 1 first), the input image is processed to detect edges. A new flow field, that follows the edges is then calculated and merged with the noise field based on the distance to the image edge and this `edge_field_multiplier`, i.e. the resulting flow follows image edges when close to them and the noise field when far from edges.
150 | * `dark_field_multiplier` - Similarly, when you set `dark_field_multiplier` (again, try 1), a new flow field is constructed. This one curls in dark image areas and gets added to the other flows, weighted by darkness and the `dark_field_multiplier`. You can combine both `edge_field_multiplier` and `dark_field_multiplier` at the same time.
151 | * `seed`, `flow_seed` - Set `seed` to a number to get reproducible results. Set `flow_seed` to a number to get reproducible flow field (but the resulting flowlines are still pseudorandom).
152 | * `kdtree_searcher` - use exact nearest neighbor search. This gets rid of occasional dark clumps, but the computation is much slower.
153 | * `transparent_val` - Transparent pixels (from e.g. RGBA png image) get replaced by this 0-255 intensity (default 127). The transparent image parts always use the noise field (either `noise` or `curl_noise`) without image-controlled fields (`edge_field`, `dark_field`). This can be used to obtain contrasting background.
154 |
155 | (feel free to create a pull request with better documentation)
156 | ## License
157 |
158 | GNU GPLv3. See the [LICENSE](LICENSE) file for details.
159 | Example coffee photo by [jannoon028](https://www.freepik.com/free-photo/cup-coffee-viewed-from_992559.htm)
160 | Kd-tree searcher CC0 from [Python-KD-Tree](https://github.com/Vectorized/Python-KD-Tree).
161 |
162 |
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/examples/coffee.jpg:
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https://raw.githubusercontent.com/serycjon/vpype-flow-imager/ae0a3144d171289e61e800208682b451b8e1bf6a/examples/coffee.jpg
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/examples/coffee_dark.png:
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https://raw.githubusercontent.com/serycjon/vpype-flow-imager/ae0a3144d171289e61e800208682b451b8e1bf6a/examples/coffee_dark.png
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/examples/coffee_out.png:
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https://raw.githubusercontent.com/serycjon/vpype-flow-imager/ae0a3144d171289e61e800208682b451b8e1bf6a/examples/coffee_out.png
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/examples/coffee_single.png:
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https://raw.githubusercontent.com/serycjon/vpype-flow-imager/ae0a3144d171289e61e800208682b451b8e1bf6a/examples/coffee_single.png
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/pyproject.toml:
--------------------------------------------------------------------------------
1 | [build-system]
2 | requires = ["setuptools", "wheel", "numpy"]
3 |
4 | [tool.black]
5 | line-length = 95
--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | # base
2 | click
3 | vpype>=1.10,<2.0
4 | opencv-python-headless # headless not to conflict with QT versions
5 | opensimplex==0.4
6 | tqdm
7 | hnswlib
8 |
9 | # dev/test
10 | pytest
11 | black
12 |
--------------------------------------------------------------------------------
/setup.py:
--------------------------------------------------------------------------------
1 | from setuptools import setup
2 |
3 |
4 | with open("README.md") as f:
5 | readme = f.read()
6 |
7 | with open("LICENSE") as f:
8 | license = f.read()
9 |
10 | setup(
11 | name="vpype-flow-imager",
12 | version="1.0.8",
13 | description="Convert images to flow field line art.",
14 | long_description=readme,
15 | long_description_content_type="text/markdown",
16 | author="Jonas Serych",
17 | url="https://github.com/serycjon/vpype-flow-imager",
18 | license=license,
19 | packages=["vpype_flow_imager"],
20 | install_requires=[
21 | 'click',
22 | 'vpype>=1.10,<2.0',
23 | 'opencv-python-headless', # headless not to conflict with QT versions in vpype show
24 | 'opensimplex==0.4',
25 | 'tqdm',
26 | 'scikit-image',
27 | 'pillow',
28 | ],
29 | extras_require={
30 | "all": ['hnswlib>=0.5.0'],
31 | },
32 | entry_points='''
33 | [vpype.plugins]
34 | vpype_flow_imager=vpype_flow_imager.vpype_flow_imager:vpype_flow_imager
35 | ''',
36 | )
37 |
--------------------------------------------------------------------------------
/vpype_flow_imager/kdtree.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 |
3 |
4 | def L2_dist(x, y):
5 | return np.sqrt(np.sum(np.square(x - y)))
6 |
7 |
8 | class KDTSearcher:
9 | def __init__(self, points, dim=2, max_elements=None, search_ef=None):
10 | self.dim = dim
11 | self.tree = make_kd_tree(points, dim)
12 | self.size = len(points)
13 |
14 | def add_point(self, point):
15 | add_point(self.tree, point, dim=self.dim)
16 | self.size += 1
17 |
18 | if (self.size + 1) % 1000 == 0:
19 | self.rebalance()
20 |
21 | def rebalance(self):
22 | self.tree = rebalance(self.tree, dim=self.dim)
23 |
24 | def get_nearest(self, query):
25 | dist, neighbor = get_nearest(self.tree, query, dim=self.dim,
26 | dist_func=L2_dist,
27 | return_distances=True)
28 | return dist, neighbor
29 |
30 |
31 | """
32 | from https://github.com/Vectorized/Python-KD-Tree (CC0 license), with added tree rebalancing
33 |
34 | A super short KD-Tree for points...
35 | so concise that you can copypasta into your homework without arousing suspicion.
36 |
37 |
38 | Usage:
39 | 1. Use make_kd_tree to create the kd
40 | 2. You can then use `get_knn` for k nearest neighbors or
41 | `get_nearest` for the nearest neighbor
42 |
43 | points are be a list of points: [[0, 1, 2], [12.3, 4.5, 2.3], ...]
44 | """
45 |
46 |
47 | # Makes the KD-Tree for fast lookup
48 | def make_kd_tree(points, dim, i=0):
49 | if len(points) > 1:
50 | points.sort(key=lambda x: x[i])
51 | i = (i + 1) % dim
52 | half = len(points) >> 1
53 | return [
54 | make_kd_tree(points[: half], dim, i),
55 | make_kd_tree(points[half + 1:], dim, i),
56 | points[half]
57 | ]
58 | elif len(points) == 1:
59 | return [None, None, points[0]]
60 |
61 |
62 | # Adds a point to the kd-tree
63 | def add_point(kd_node, point, dim, i=0):
64 | if kd_node is not None:
65 | dx = kd_node[2][i] - point[i]
66 | i = (i + 1) % dim
67 | for j, c in ((0, dx >= 0), (1, dx < 0)):
68 | if c and kd_node[j] is None:
69 | kd_node[j] = [None, None, point]
70 | elif c:
71 | add_point(kd_node[j], point, dim, i)
72 |
73 |
74 | # k nearest neighbors
75 | def get_knn(kd_node, point, k, dim, dist_func, return_distances=True, i=0, heap=None):
76 | import heapq
77 | is_root = not heap
78 | if is_root:
79 | heap = []
80 | if kd_node is not None:
81 | dist = dist_func(point, kd_node[2])
82 | dx = kd_node[2][i] - point[i]
83 | if len(heap) < k:
84 | heapq.heappush(heap, (-dist, kd_node[2]))
85 | elif dist < -heap[0][0]:
86 | heapq.heappushpop(heap, (-dist, kd_node[2]))
87 | i = (i + 1) % dim
88 | # Goes into the left branch, and then the right branch if needed
89 | for b in [dx < 0] + [dx >= 0] * (dx * dx < -heap[0][0]):
90 | get_knn(kd_node[b], point, k, dim, dist_func, return_distances, i, heap)
91 | if is_root:
92 | neighbors = sorted((-h[0], h[1]) for h in heap)
93 | return neighbors if return_distances else [n[1] for n in neighbors]
94 |
95 |
96 | def rebalance(kd_tree, dim):
97 | from collections import deque
98 | elements = []
99 | queue = deque()
100 | queue.append((kd_tree, 0))
101 | max_h = 0
102 |
103 | while len(queue) > 0:
104 | cur, h = queue.pop()
105 | if h > max_h:
106 | max_h = h
107 |
108 | if cur[0] is not None:
109 | queue.append((cur[0], h + 1))
110 | if cur[1] is not None:
111 | queue.append((cur[1], h + 1))
112 |
113 | if cur[2] is not None:
114 | elements.append(cur[2])
115 |
116 | # print(f"max_h: {max_h}")
117 | rebalanced = make_kd_tree(elements, dim=dim)
118 | return rebalanced
119 |
120 |
121 | # For the closest neighbor
122 | def get_nearest(kd_node, point, dim, dist_func, return_distances=True, i=0, best=None):
123 | if kd_node is not None:
124 | dist = dist_func(point, kd_node[2])
125 | dx = kd_node[2][i] - point[i]
126 | if not best:
127 | best = [dist, kd_node[2]]
128 | elif dist < best[0]:
129 | best[0], best[1] = dist, kd_node[2]
130 | i = (i + 1) % dim
131 | # Goes into the left branch, and then the right branch if needed
132 | for b in [dx < 0] + [dx >= 0] * (dx * dx < best[0]):
133 | get_nearest(kd_node[b], point, dim, dist_func, return_distances, i, best)
134 | return best if return_distances else best[1]
135 |
136 |
137 | """
138 | If you want to attach other properties to your points,
139 | you can use this class or subclass it.
140 |
141 | Usage:
142 |
143 | point = PointContainer([1,2,3])
144 | point.label = True
145 | print point # [1,2,3]
146 | print point.label # True
147 | """
148 |
149 |
150 | class PointContainer(list):
151 | def __new__(self, value, name=None, values=None):
152 | s = super(PointContainer, self).__new__(self, value)
153 | return s
154 |
155 |
156 | if __name__ == '__main__':
157 | """
158 | Below is all the testing code
159 | """
160 |
161 | import random
162 | import cProfile
163 |
164 | def puts(xs):
165 | for x in xs:
166 | print(x)
167 |
168 | def get_knn_naive(points, point, k, dist_func, return_distances=True):
169 | neighbors = []
170 | for i, pp in enumerate(points):
171 | dist = dist_func(point, pp)
172 | neighbors.append((dist, pp))
173 | neighbors = sorted(neighbors)[:k]
174 | return neighbors if return_distances else [n[1] for n in neighbors]
175 |
176 | dim = 3
177 |
178 | def rand_point(dim):
179 | return [random.uniform(-1, 1) for d in range(dim)]
180 |
181 | def dist_sq(a, b, dim):
182 | return sum((a[i] - b[i]) ** 2 for i in range(dim))
183 |
184 | def dist_sq_dim(a, b):
185 | return dist_sq(a, b, dim)
186 |
187 | points = [PointContainer(rand_point(dim)) for x in range(10)]
188 | additional_points = [PointContainer(rand_point(dim)) for x in range(3)]
189 | kd_tree = make_kd_tree(points, dim)
190 | for point in additional_points:
191 | add_point(kd_tree, point, dim)
192 |
193 | rebalanced = rebalance(kd_tree, dim)
194 | rebalanced = rebalance(rebalanced, dim)
195 | sys.exit(1)
196 |
197 | points = [PointContainer(rand_point(dim)) for x in range(10000)]
198 | additional_points = [PointContainer(rand_point(dim)) for x in range(50)]
199 | # points = [rand_point(dim) for x in range(5000)]
200 | test = [rand_point(dim) for x in range(100)]
201 | result1 = []
202 | result2 = []
203 |
204 | def bench1():
205 | kd_tree = make_kd_tree(points, dim)
206 | for point in additional_points:
207 | add_point(kd_tree, point, dim)
208 | result1.append(tuple(get_knn(kd_tree, [0] * dim, 8, dim, dist_sq_dim)))
209 | for t in test:
210 | result1.append(tuple(get_knn(kd_tree, t, 8, dim, dist_sq_dim)))
211 |
212 | def bench2():
213 | all_points = points + additional_points
214 | result2.append(tuple(get_knn_naive(all_points, [0] * dim, 8, dist_sq_dim)))
215 | for t in test:
216 | result2.append(tuple(get_knn_naive(all_points, t, 8, dist_sq_dim)))
217 |
218 | cProfile.run("bench1()")
219 | cProfile.run("bench2()")
220 |
221 | puts(result1[0])
222 | print("")
223 | puts(result2[0])
224 | print("")
225 |
226 | print("Is the result same as naive version?: {}".format(result1 == result2))
227 |
228 | print("")
229 | kd_tree = make_kd_tree(points, dim)
230 |
231 | print(get_nearest(kd_tree, [0] * dim, dim, dist_sq_dim))
232 |
233 | """
234 | You can also define the distance function inline, like:
235 |
236 | print get_nearest(kd_tree, [0] * dim, dim, lambda a,b: dist_sq(a, b, dim))
237 | print get_nearest(kd_tree, [0] * dim, dim, lambda a,b: sum((a[i] - b[i]) ** 2 for i in range(dim)))
238 | """
239 |
--------------------------------------------------------------------------------
/vpype_flow_imager/vpype_flow_imager.py:
--------------------------------------------------------------------------------
1 | # Copyright (C) 2021 Jonas Serych
2 | #
3 | # This program is free software: you can redistribute it and/or modify
4 | # it under the terms of the GNU General Public License as published by
5 | # the Free Software Foundation, either version 3 of the License, or
6 | # any later version.
7 | #
8 | # This program is distributed in the hope that it will be useful,
9 | # but WITHOUT ANY WARRANTY; without even the implied warranty of
10 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
11 | # GNU General Public License for more details.
12 | #
13 | # You should have received a copy of the GNU General Public License
14 | # along with this program. If not, see .
15 |
16 | from collections import deque
17 | import numpy as np
18 | import cv2
19 | from opensimplex import OpenSimplex
20 | import tqdm
21 | try:
22 | import hnswlib
23 | except ImportError:
24 | hnswlib = None
25 |
26 | import contextlib
27 | from .kdtree import KDTSearcher
28 | from PIL import Image
29 |
30 | import click
31 | import vpype as vp
32 | import vpype_cli
33 |
34 | import logging
35 | logger = logging.getLogger(__name__)
36 |
37 | eps = 1e-10
38 |
39 |
40 | @click.command("flow_img", context_settings={'show_default': True})
41 | @click.argument("filename", type=vpype_cli.PathType(exists=True))
42 | @click.option(
43 | "-fi",
44 | "--flow_image",
45 | type=vpype_cli.PathType(exists=True),
46 | help="An image to use for the flow field. X and Y components of the flow vector "
47 | "are encoded in the red and green channels, as in a normal map. "
48 | "Must be the same size as the main input image. It might work if they have "
49 | "exactly the same aspect ratio.",
50 | )
51 | @click.option(
52 | "-nc",
53 | "--noise_coeff",
54 | default=0.001,
55 | type=vpype_cli.FloatType(),
56 | help=("Simplex noise coordinate multiplier. "
57 | "The smaller, the smoother the flow field."),
58 | )
59 | @click.option(
60 | "-nf",
61 | "--n_fields",
62 | default=1,
63 | type=vpype_cli.IntegerType(),
64 | help="Number of rotated copies of the flow field",
65 | )
66 | @click.option(
67 | "-ms",
68 | "--min_sep",
69 | default=0.8,
70 | type=vpype_cli.LengthType(),
71 | help="Minimum flowline separation",
72 | )
73 | @click.option(
74 | "-Ms",
75 | "--max_sep",
76 | default=10,
77 | type=vpype_cli.LengthType(),
78 | help="Maximum flowline separation",
79 | )
80 | @click.option(
81 | "-ml",
82 | "--min_length",
83 | default=0,
84 | type=vpype_cli.LengthType(),
85 | help="Minimum flowline length",
86 | )
87 | @click.option(
88 | "-Ml",
89 | "--max_length",
90 | default=40,
91 | type=vpype_cli.LengthType(),
92 | help="Maximum flowline length",
93 | )
94 | @click.option(
95 | "--max_size",
96 | default=800,
97 | type=vpype_cli.LengthType(),
98 | help="The input image will be rescaled to have sides at most max_size px",
99 | )
100 | @click.option(
101 | "--search_ef",
102 | "-ef",
103 | default=50,
104 | type=vpype_cli.IntegerType(),
105 | help="HNSWlib search ef (higher -> more accurate, but slower)",
106 | )
107 | @click.option(
108 | "-s", "--seed", type=vpype_cli.IntegerType(), help="PRNG seed (overriding vpype seed)"
109 | )
110 | @click.option(
111 | "-fs", "--flow_seed", type=vpype_cli.IntegerType(),
112 | help="Flow field PRNG seed (overriding the main `--seed`)"
113 | )
114 | @click.option(
115 | "-tf", "--test_frequency", type=vpype_cli.FloatType(), default=2,
116 | help="Number of separation tests per current flowline separation",
117 | )
118 | @click.option(
119 | "-f", "--field_type",
120 | type=vpype_cli.ChoiceType(['noise', 'curl_noise'], case_sensitive=False),
121 | help="flow field type [default: noise]")
122 | @click.option(
123 | "--transparent_val", type=click.IntRange(0, 255), default=127,
124 | help="Value to replace transparent pixels")
125 | @click.option(
126 | "-tm", "--transparent_mask", is_flag=True,
127 | help="Remove lines from transparent parts of the source image.")
128 | @click.option(
129 | "-efm", "--edge_field_multiplier", type=vpype_cli.FloatType(), default=None,
130 | help="flow along image edges")
131 | @click.option(
132 | "-dfm", "--dark_field_multiplier", type=vpype_cli.FloatType(), default=None,
133 | help="flow swirling around dark image areas")
134 | @click.option(
135 | "-kdt", "--kdtree_searcher", is_flag=True,
136 | help="Use exact nearest neighbor search with kdtree (slower, but more precise)")
137 | @click.option(
138 | "--cmyk", is_flag=True,
139 | help="Split image to CMYK and process each channel separately. The results are in consecutively numbered layers, starting from `layer`.")
140 | @click.option(
141 | "--rotate", type=vpype_cli.FloatType(), default=0, metavar='DEGREES',
142 | help="rotate the flow field")
143 | @click.option(
144 | "-l",
145 | "--layer",
146 | type=vpype_cli.LayerType(accept_new=True),
147 | default=None,
148 | help="Target layer or 'new'. When CMYK enabled, this indicates the first (cyan) layer.",
149 | )
150 | @vpype_cli.global_processor
151 | def vpype_flow_imager(document, layer, filename, flow_image, noise_coeff, n_fields,
152 | min_sep, max_sep,
153 | min_length, max_length, max_size,
154 | seed, flow_seed, search_ef,
155 | test_frequency,
156 | field_type, transparent_val, transparent_mask,
157 | edge_field_multiplier, dark_field_multiplier,
158 | kdtree_searcher,
159 | cmyk, rotate):
160 | """
161 | Generate flowline representation from an image.
162 |
163 | The generated flowlines are in the coordinates of the input image,
164 | resized to have dimensions at most `--max_size` pixels.
165 | """
166 | if kdtree_searcher:
167 | searcher_class = KDTSearcher
168 | else:
169 | if hnswlib is None:
170 | logger.warning("Could not import hnswlib, falling back to KD-tree searcher. Make sure to install with vpype-flow-imager[all], if you want to use the default HNSWlib searcher.")
171 | searcher_class = KDTSearcher
172 | else:
173 | searcher_class = HNSWSearcher
174 | target_layer = vpype_cli.single_to_layer_id(layer, document)
175 | img = cv2.imread(filename, cv2.IMREAD_UNCHANGED)
176 | logger.debug(f"original img.shape: {img.shape}")
177 | with tmp_np_seed(seed):
178 | if cmyk:
179 | img_layers = split_cmyk(img.copy())
180 | else:
181 | img_layers = [img]
182 |
183 | if flow_image:
184 | flow_image_data = cv2.imread(flow_image, cv2.IMREAD_COLOR)
185 | else:
186 | flow_image_data = None
187 |
188 | alpha = get_alpha_channel(img)
189 |
190 | for layer_i, img_layer in enumerate(img_layers):
191 | logger.info(f"computing layer {layer_i+1}")
192 | numpy_paths = draw_image(img_layer, alpha,
193 | mult=noise_coeff, n_fields=n_fields,
194 | min_sep=min_sep, max_sep=max_sep,
195 | min_length=min_length, max_length=max_length,
196 | max_img_size=max_size, flow_seed=flow_seed,
197 | search_ef=search_ef,
198 | test_frequency=test_frequency,
199 | field_type=field_type,
200 | transparent_val=transparent_val,
201 | transparent_mask=transparent_mask,
202 | edge_field_multiplier=edge_field_multiplier,
203 | dark_field_multiplier=dark_field_multiplier,
204 | searcher_class=searcher_class,
205 | rotate=rotate, flow_image_data=flow_image_data
206 | )
207 |
208 | lc = vp.LineCollection()
209 | for path in numpy_paths:
210 | lc.append(path[:, 0] + path[:, 1] * 1.j)
211 |
212 | document.add(lc, target_layer + layer_i)
213 | document.extend_page_size((float(img.shape[1]), float(img.shape[0])))
214 | document.add_to_sources(filename)
215 | return document
216 |
217 |
218 | vpype_flow_imager.help_group = "Plugins"
219 |
220 |
221 | def get_alpha_channel(img):
222 | """ Return alpha channel from opencv image, or None. """
223 | if len(img.shape) == 3 and img.shape[2] == 4:
224 | return img[:, :, 3]
225 |
226 |
227 | def split_cmyk(img):
228 | post_gamma = 1
229 |
230 | if img.shape[2] == 4: # rgba
231 | img = img[:, :, :3]
232 | rgb = img[:, :, ::-1]
233 | p_rgb = Image.fromarray(rgb)
234 | cmyk = np.array(p_rgb.convert("CMYK")).astype(np.float64) / 255
235 | cmyk = cmyk ** post_gamma
236 | # this conversion does not use the black at all (icc profiles and stuff...)
237 | # so lets compute the black channel ourselves
238 | black_percentage = 1
239 |
240 | black = np.amin(cmyk[:, :, 0:3], axis=2, keepdims=True)
241 | black_mask = black == 1
242 | non_black_mask = np.logical_not(black_mask)
243 | cmyk[non_black_mask[..., 0], :] = ((cmyk[non_black_mask[..., 0], :] -
244 | black_percentage * black[non_black_mask, np.newaxis]))
245 | cmyk[non_black_mask[..., 0], :] /= (1 - black_percentage * black[non_black_mask, np.newaxis])
246 |
247 | cmyk[black_mask[..., 0]] = 0
248 | cmyk[:, :, 3] = black_percentage * black[:, :, 0]
249 |
250 | cmyk = 255 * (1 - cmyk) # invert to get back intensity
251 | cmyk = np.clip(cmyk, 0, 255).astype(np.uint8)
252 |
253 | cmyk_channels = np.split(cmyk, 4, axis=2)
254 | # ch_names = ['c', 'm', 'y', 'k']
255 | # for i, ch in enumerate(cmyk_channels):
256 | # ch_name = ch_names[i]
257 | # cv2.imwrite(f'/tmp/00000_cmyk_{i}{ch_name}.png', ch)
258 | # sys.exit(1)
259 | return cmyk_channels
260 |
261 |
262 | def norm_2vec(x):
263 | return np.sqrt(x[0]**2 + x[1]**2)
264 |
265 |
266 | def gen_flow_field(H, W, x_mult=1, y_mult=None):
267 | if y_mult is None:
268 | y_mult = x_mult
269 | x_noise = OpenSimplex(np.random.randint(9393931))
270 | y_noise = OpenSimplex(np.random.randint(9393931))
271 | field = np.zeros((H, W, 2), dtype=np.float64)
272 | for y in range(H):
273 | for x in range(W):
274 | x_val = x_noise.noise2(x=x_mult * x, y=x_mult * y)
275 | y_val = y_noise.noise2(x=y_mult * x, y=y_mult * y)
276 | norm = np.sqrt(x_val ** 2 + y_val ** 2)
277 | if norm > eps:
278 | x_val /= norm
279 | y_val /= norm
280 | else:
281 | x_val, y_val = 1, 0
282 | field[y, x, :] = (x_val, y_val)
283 |
284 | return field
285 |
286 |
287 | def gen_curl_flow_field(H, W, x_mult=1, y_mult=None):
288 | if y_mult is None:
289 | y_mult = x_mult
290 | noise = OpenSimplex(np.random.randint(9393931))
291 | field = np.zeros((H, W), dtype=np.float64)
292 | for y in range(H):
293 | for x in range(W):
294 | val = noise.noise2(x=x_mult * x, y=x_mult * y)
295 | field[y, x] = val
296 |
297 | grad_y, grad_x = np.gradient(field)
298 | field = np.stack((grad_y, -grad_x), axis=2)
299 | field = normalize_flow_field(field)
300 |
301 | return field
302 |
303 |
304 | def gen_edge_flow_field(H, W, intensities):
305 | from scipy.ndimage import distance_transform_edt
306 | edges = cv2.Canny(intensities, 100, 200)
307 | variable_mask = edges <= 0
308 | grad_y, grad_x = np.gradient(intensities)
309 | field = np.stack((grad_y, -grad_x), axis=2)
310 | field = normalize_flow_field(field)
311 | field[variable_mask, :] = 0
312 |
313 | for i in range(35):
314 | k_sz = 15
315 | new_field = cv2.blur(field, (k_sz, k_sz))
316 | field[variable_mask, :] = new_field[variable_mask, :]
317 |
318 | weights = distance_transform_edt(edges == 0)
319 | weights = weights[:, :, np.newaxis].astype(np.float32)
320 | max_dist = 100
321 | weights = (max_dist - np.clip(weights, 0, max_dist)) / max_dist
322 | return normalize_flow_field(field), weights
323 |
324 |
325 | def gen_darkness_curl_flow_field(H, W, intensities):
326 | assert len(intensities.shape) == 2
327 | blur_kernel = int(np.sqrt(H * W) / 4.5)
328 | if (blur_kernel % 2) == 0:
329 | blur_kernel += 1
330 |
331 | heights = cv2.GaussianBlur(intensities.astype(np.float32),
332 | (blur_kernel, blur_kernel), 0)
333 |
334 | grad_y, grad_x = np.gradient(heights)
335 |
336 | field = np.stack((grad_y, -grad_x), axis=2)
337 | weights = 1 - (intensities[:, :, np.newaxis].astype(np.float32) / 255)
338 | return normalize_flow_field(field), weights
339 |
340 |
341 | def normalize_flow_field(field):
342 | norm = np.sqrt(np.sum(field ** 2, axis=2)).reshape(*field.shape[:2], 1)
343 | return field / (norm + 1e-10)
344 |
345 |
346 | def draw_image(gray_img, alpha,
347 | mult, max_img_size=800, n_fields=1,
348 | min_sep=0.8, max_sep=10,
349 | min_length=0, max_length=40,
350 | flow_seed=None,
351 | search_ef=50, test_frequency=2,
352 | transparent_val=127, transparent_mask=True,
353 | field_type='noise',
354 | edge_field_multiplier=None, dark_field_multiplier=None,
355 | searcher_class=None, rotate=0, flow_image_data=None):
356 | logger.debug(f"gray_img.shape: {gray_img.shape}")
357 | gray = resize_to_max(gray_img, max_img_size)
358 | scale_to_orig = gray_img.shape[0] / float(gray.shape[0])
359 | logger.debug(f"gray.shape: {gray.shape}")
360 | if len(gray.shape) == 2:
361 | gray = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
362 |
363 | H, W, C = gray.shape
364 | if alpha is not None:
365 | data_mask = resize_to_max(alpha, max_img_size) > 0
366 | else:
367 | data_mask = np.ones((H, W)) > 0
368 |
369 | background_mask = np.logical_not(data_mask)
370 | gray = cv2.cvtColor(gray[:, :, :3], cv2.COLOR_BGR2GRAY)
371 | gray[background_mask] = transparent_val
372 |
373 | if flow_image_data is not None:
374 | resized_flows = resize_to_max(flow_image_data, max_img_size)
375 | floaty_flows = resized_flows.astype(float)
376 | flow_x_y = floaty_flows[:, :, [2, 1]]
377 | scaled_flow_x_y = (flow_x_y - 128) / 128
378 | scaled_flow_x_y[:, :, 1] = scaled_flow_x_y[:, :, 1] * -1
379 | field = scaled_flow_x_y
380 | else:
381 | logger.info('Generating flow field')
382 | with tmp_np_seed(flow_seed):
383 | if field_type == 'curl_noise':
384 | noise_field = gen_curl_flow_field(H, W, x_mult=mult)
385 | else:
386 | noise_field = gen_flow_field(H, W, x_mult=mult)
387 |
388 | field = np.zeros_like(noise_field)
389 | weights = np.zeros_like(noise_field)
390 |
391 | if edge_field_multiplier is not None:
392 | edge_field, edge_weights = gen_edge_flow_field(H, W, gray)
393 | field += edge_weights * edge_field * edge_field_multiplier
394 | weights += edge_weights * edge_field_multiplier
395 |
396 | if dark_field_multiplier is not None:
397 | dark_field, dark_weights = gen_darkness_curl_flow_field(H, W, gray)
398 | field += dark_weights * dark_field * dark_field_multiplier
399 | weights += dark_weights * dark_field_multiplier
400 |
401 | field += np.clip(1 - weights, 0, 1) * noise_field
402 |
403 | field[background_mask, :] = noise_field[background_mask, :]
404 | field = normalize_flow_field(field)
405 |
406 | field = rotate_field(field, rotate)
407 | fields = [VectorField(field)]
408 |
409 | if n_fields > 1:
410 | angles = np.linspace(0, 360, n_fields + 1)
411 | for angle in angles:
412 | fields.append(VectorField(rotate_field(field, angle)))
413 |
414 | guide = gray
415 |
416 | def d_sep_fn(pos):
417 | x, y = fit_inside(np.round(pos), guide)
418 | val = guide[int(y), int(x)] / 255
419 | val = val**2
420 | return remap(val, 0, 1, min_sep, max_sep)
421 |
422 | logger.info('Drawing flowlines')
423 | paths = draw_fields_uniform(fields, d_sep_fn,
424 | seedpoints_per_path=40,
425 | guide=guide,
426 | min_length=min_length, max_length=max_length,
427 | search_ef=search_ef,
428 | test_frequency=test_frequency,
429 | searcher_class=searcher_class)
430 |
431 | if transparent_mask:
432 | paths = mask_paths(paths, data_mask)
433 | paths = [scale_to_orig * path for path in paths]
434 | return paths
435 |
436 |
437 | def mask_paths(paths, fg_mask):
438 | """ Remove paths not on foreground mask """
439 | logger.debug(f"np.sum(fg_mask > 0): {np.sum(fg_mask > 0)}")
440 | logger.debug(f"fg_mask.size: {fg_mask.size}")
441 | masked_paths = []
442 | for path in paths:
443 | current_path = []
444 | for i in range(len(path)):
445 | pt = path[i, :]
446 | x, y = fit_inside(np.round(pt), fg_mask)
447 | mask_val = fg_mask[int(y), int(x)]
448 | pt_on_fg = mask_val > 0
449 |
450 | if not pt_on_fg:
451 | if len(current_path) >= 2:
452 | masked_paths.append(np.array(current_path))
453 | current_path = []
454 | else:
455 | current_path.append(pt)
456 | if len(current_path) >= 2:
457 | masked_paths.append(np.array(current_path))
458 | return masked_paths
459 |
460 |
461 | class VectorField():
462 | def __init__(self, field_array):
463 | self.field = field_array
464 | self.shape = self.field.shape
465 |
466 | def __getitem__(self, pos):
467 | ''' pos should be (x, y) '''
468 | round_pos = np.round(pos[:2]).astype(np.int64)
469 | round_pos = fit_inside(round_pos, self.field)
470 |
471 | return self.field[round_pos[1], round_pos[0], :]
472 |
473 |
474 | def rotate_field(field, degrees):
475 | s, c = np.sin(np.radians(degrees)), np.cos(np.radians(degrees))
476 | R = np.array([[c, -s],
477 | [s, c]])
478 | return np.matmul(R, field.reshape(-1, 2).T).T.reshape(field.shape)
479 |
480 |
481 | def fit_inside(xy, img):
482 | return np.clip(xy,
483 | np.array([0, 0], xy.dtype),
484 | np.array([img.shape[1] - 1, img.shape[0] - 1], xy.dtype))
485 |
486 |
487 | def remap(x, src_min, src_max, dst_min, dst_max):
488 | x_01 = (x - src_min) / float(src_max - src_min)
489 | x_dst = x_01 * (dst_max - dst_min) + dst_min
490 |
491 | return x_dst
492 |
493 |
494 | def draw_fields_uniform(fields, d_sep_fn, d_test_fn=None,
495 | seedpoints_per_path=10,
496 | guide=None,
497 | min_length=0, max_length=20,
498 | search_ef=50, test_frequency=2,
499 | searcher_class=None):
500 | if d_test_fn is None:
501 | def d_test_fn(*args, **kwargs):
502 | return d_sep_fn(*args, **kwargs) / test_frequency
503 |
504 | H, W = fields[0].shape[:2]
505 |
506 | def should_stop(new_pos, searcher, path, d_sep_fn):
507 | if path.line_length < min_length:
508 | return False
509 |
510 | if not inside(np.round(new_pos), H, W):
511 | return True
512 | if searcher is not None:
513 | point = new_pos.copy()
514 | nearest = searcher.get_nearest(point)
515 | dist, pt = nearest
516 | if dist < d_sep_fn(new_pos):
517 | return True
518 |
519 | if path.line_length > max_length:
520 | return True
521 |
522 | # look for loops
523 | # candidate = np.round(new_pos).astype(np.int64).reshape(1, 2)
524 | # for pt in reversed(path):
525 | # if np.all(candidate == np.round(pt).astype(np.int64)):
526 | # return True
527 | return False
528 |
529 | searcher = searcher_class([np.array([-10, -10])],
530 | max_elements=64000,
531 | search_ef=search_ef)
532 | paths = []
533 | seed_pos = np.array((W / 2, H / 2))
534 | seedpoints = [seed_pos]
535 | seedpoints = deque(seedpoints)
536 | pbar = tqdm.tqdm()
537 | try:
538 | while True:
539 | # try to find a suitable seedpoint in the queue
540 | try:
541 | while True:
542 | seed_pos = seedpoints.pop()
543 | if not inside(np.round(seed_pos), H, W):
544 | continue
545 |
546 | dist, _ = searcher.get_nearest(seed_pos)
547 | if dist < d_sep_fn(seed_pos):
548 | continue
549 |
550 | break
551 | except IndexError:
552 | # no more seedpoints
553 | break
554 |
555 | start_field = np.random.randint(len(fields))
556 |
557 | def select_field(path_len, direction):
558 | same_field_len = 10
559 |
560 | idx = int(direction * path_len // same_field_len) + start_field
561 | idx = idx % len(fields)
562 | return fields[idx]
563 |
564 | class MemorySelector():
565 | def __init__(self, fields):
566 | self.same_field_len = 10
567 | self.cur_len = 0
568 | self.idx = np.random.randint(len(fields))
569 | self.fields = fields
570 |
571 | def select_field(self, path_len, direction):
572 | if (path_len - self.cur_len) > self.same_field_len:
573 | self.cur_len = path_len
574 | idx_delta = np.random.randint(-1, 1 + 1)
575 | self.idx = (self.idx + idx_delta) % len(self.fields)
576 |
577 | return self.fields[self.idx]
578 |
579 | selector = MemorySelector(fields)
580 |
581 | path = compute_streamline(selector.select_field, seed_pos,
582 | searcher,
583 | d_test_fn, d_sep_fn,
584 | should_stop_fn=should_stop,
585 | searcher_class=searcher_class)
586 | if len(path) <= 2:
587 | # nothing found
588 | # logging.debug('streamline ended immediately')
589 | continue
590 |
591 | for pt in path:
592 | searcher.add_point(pt)
593 | paths.append(path)
594 |
595 | new_seedpoints = generate_seedpoints(path, d_sep_fn,
596 | seedpoints_per_path)
597 | order = np.arange(len(new_seedpoints))
598 | np.random.shuffle(order)
599 | seedpoints.extend([new_seedpoints[i] for i in order])
600 | pbar.update(1)
601 | except KeyboardInterrupt:
602 | pass
603 |
604 | pbar.close()
605 | return paths
606 |
607 |
608 | def inside(xy_pt, H, W):
609 | return (xy_pt[0] >= 0 and
610 | xy_pt[1] >= 0 and
611 | xy_pt[0] < W and
612 | xy_pt[1] < H)
613 |
614 |
615 | def compute_streamline(field_getter, seed_pos, searcher, d_test_fn, d_sep_fn,
616 | should_stop_fn, searcher_class):
617 | direction_sign = 1 # first go with the field
618 | pos = seed_pos.copy()
619 | paths = []
620 | path = LinePath()
621 | path.append(pos.copy())
622 | stop_tracking = False
623 | self_searcher = searcher_class([(-20, -20)])
624 | while True:
625 | field = field_getter(path.line_length, direction_sign)
626 | rk_force = runge_kutta(field, pos, d_test_fn(pos)) * direction_sign
627 | new_pos = pos + d_test_fn(pos) * rk_force
628 |
629 | # test validity
630 | if should_stop_fn(new_pos, searcher, path, d_sep_fn):
631 | stop_tracking = True
632 |
633 | # prevent soft looping
634 | nearest_dist, _ = self_searcher.get_nearest(new_pos)
635 | if nearest_dist < d_sep_fn(pos):
636 | stop_tracking = True
637 | lookback = 15
638 | if len(path) >= 2 * lookback:
639 | self_searcher.add_point(path[-lookback])
640 |
641 | # fallback
642 | if len(path) >= 600:
643 | stop_tracking = True
644 |
645 | if not stop_tracking:
646 | path.append(new_pos.copy())
647 |
648 | if stop_tracking:
649 | paths.append(path.data)
650 | if direction_sign == 1:
651 | # go to the other side from the seed
652 | direction_sign = -1
653 | pos = seed_pos.copy()
654 | path = LinePath()
655 | path.append(pos.copy())
656 | # self_searcher = searcher([(-20, -20)])
657 | stop_tracking = False
658 | else:
659 | # both directions finished
660 | break
661 | else:
662 | pos = new_pos
663 | singleline = list(reversed(paths[1]))
664 | singleline.extend(paths[0][1:])
665 | singleline = np.array(singleline)
666 |
667 | return singleline
668 |
669 |
670 | def generate_seedpoints(path, d_sep_fn, N_seedpoints=10):
671 | # go along the path and create points perpendicular in d_sep distance
672 | seeds = []
673 | seedpoint_positions = np.linspace(0, len(path) - 1, N_seedpoints)
674 | seedpoint_ids = np.unique(np.round(seedpoint_positions)).tolist()
675 |
676 | cur_xy = path[0]
677 | direction = path[1] - path[0]
678 | direction /= max(norm_2vec(direction), eps)
679 | normal = np.array((direction[1], -direction[0]))
680 | margin = 1.1
681 | seeds.append(cur_xy + margin * d_sep_fn(cur_xy) * normal)
682 | seeds.append(cur_xy - margin * d_sep_fn(cur_xy) * normal)
683 |
684 | seeds.append(cur_xy - margin * d_sep_fn(cur_xy) * direction)
685 | seeds.append(cur_xy - margin * d_sep_fn(cur_xy) * direction +
686 | margin * d_sep_fn(cur_xy) * normal)
687 | seeds.append(cur_xy - margin * d_sep_fn(cur_xy) * direction -
688 | margin * d_sep_fn(cur_xy) * normal)
689 |
690 | for i in range(1, len(path)):
691 | if i not in seedpoint_ids:
692 | continue
693 | last_xy = cur_xy.copy()
694 | cur_xy = path[i]
695 | direction = cur_xy - last_xy
696 | direction /= max(norm_2vec(direction), eps)
697 | normal = np.array((direction[1], -direction[0]))
698 | seeds.append(cur_xy + margin * d_sep_fn(cur_xy) * normal)
699 | seeds.append(cur_xy - margin * d_sep_fn(cur_xy) * normal)
700 |
701 | seeds.append(cur_xy + margin * d_sep_fn(cur_xy) * direction)
702 | seeds.append(cur_xy + margin * d_sep_fn(cur_xy) * direction +
703 | margin * d_sep_fn(cur_xy) * normal)
704 | seeds.append(cur_xy + margin * d_sep_fn(cur_xy) * direction -
705 | margin * d_sep_fn(cur_xy) * normal)
706 |
707 | return seeds
708 |
709 |
710 | def runge_kutta(field, pos, h):
711 | k1 = field[pos]
712 |
713 | k2_pos = pos + (h / 2) * k1
714 | k2 = field[k2_pos]
715 |
716 | k3_pos = pos + (h / 2) * k2
717 | k3 = field[k3_pos]
718 |
719 | k4_pos = pos + h * k3
720 | k4 = field[k4_pos]
721 |
722 | # Runge-Kutta for the win
723 | rk = (1 / 6) * (k1 + 2 * k2 + 2 * k3 + k4)
724 | return rk
725 |
726 |
727 | def resize_to_max(img, max_sz):
728 | H_scale = max_sz / img.shape[0]
729 | W_scale = max_sz / img.shape[1]
730 |
731 | scale = min(H_scale, W_scale)
732 | return cv2.resize(img, None, fx=scale, fy=scale)
733 |
734 |
735 | class HNSWSearcher:
736 | def __init__(self, points, max_elements=1000, search_ef=50):
737 | self.index = hnswlib.Index(space='l2', dim=2)
738 | self.max_elements = max_elements
739 | self.index.init_index(max_elements=self.max_elements,
740 | ef_construction=200, M=16)
741 | self.search_ef = search_ef
742 | self.index.set_ef(search_ef)
743 | self.index.set_num_threads(4)
744 | for point in points:
745 | self.add_point(point)
746 |
747 | def add_point(self, point):
748 | if self.index.element_count == self.max_elements:
749 | self.resize_index()
750 | to_insert = np.array(point).reshape(1, 2)
751 | self.index.add_items(to_insert)
752 |
753 | def resize_index(self):
754 | self.max_elements = 2 * self.max_elements
755 | logger.debug(f'Resizing searcher index to {self.max_elements}')
756 | self.index.resize_index(self.max_elements)
757 | logger.debug('after resize:')
758 | logger.debug(f"self.index.max_elements: {self.index.max_elements}")
759 | logger.debug(f"self.index.element_count: {self.index.element_count}")
760 | self.index.set_ef(self.search_ef)
761 |
762 | def get_nearest(self, query):
763 | to_query = np.array(query).reshape(1, 2)
764 | labels, distances_sq = self.index.knn_query(to_query, k=1)
765 | distances = np.sqrt(distances_sq)
766 | return distances, labels
767 |
768 |
769 | class LinePath:
770 | ''' wrapper around list of coordinates, that keeps current path length '''
771 |
772 | def __init__(self):
773 | self.data = []
774 | self.line_length = 0
775 |
776 | def append(self, point):
777 | self.data.append(point)
778 | if len(self.data) > 1:
779 | self.line_length += norm_2vec(self.data[-2] - self.data[-1])
780 |
781 | def __len__(self):
782 | return len(self.data)
783 |
784 | def __getitem__(self, i):
785 | return self.data[i]
786 |
787 |
788 | @contextlib.contextmanager
789 | def tmp_np_seed(seed):
790 | if seed is None:
791 | yield
792 | else:
793 | state = np.random.get_state()
794 | np.random.seed(seed)
795 | try:
796 | yield
797 | finally:
798 | np.random.set_state(state)
799 |
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