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wgpu_2.ipynb
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{ | |
"nbformat": 4, | |
"nbformat_minor": 0, | |
"metadata": { | |
"colab": { | |
"provenance": [], | |
"collapsed_sections": [ | |
"U-NbA5tWTIyX", | |
"iouUH3v6TL37" | |
], | |
"machine_shape": "hm", | |
"authorship_tag": "ABX9TyNCr/bseupf7X3ljVItgV3t", | |
"include_colab_link": true | |
}, | |
"kernelspec": { | |
"name": "python3", | |
"display_name": "Python 3" | |
}, | |
"language_info": { | |
"name": "python" | |
} | |
}, | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "view-in-github", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"<a href=\"https://colab.research.google.com/gist/stwind/4f03174a3f6a70930ce31c0735cfee88/wgpu_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"source": [ | |
"## Setups" | |
], | |
"metadata": { | |
"id": "wD2vwswDTHhI" | |
} | |
}, | |
{ | |
"cell_type": "markdown", | |
"source": [ | |
"### Dependencies" | |
], | |
"metadata": { | |
"id": "U-NbA5tWTIyX" | |
} | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "CpZPwDIpTAe9", | |
"outputId": "5d884c76-bd8d-44e0-8568-3a62b0dc18df" | |
}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.0/62.0 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.0/62.0 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.4/16.4 MB\u001b[0m \u001b[31m298.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.6/8.6 MB\u001b[0m \u001b[31m242.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m37.6/37.6 MB\u001b[0m \u001b[31m287.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m319.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m709.3/709.3 kB\u001b[0m \u001b[31m325.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
"\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", | |
"tensorflow 2.18.0 requires numpy<2.1.0,>=1.26.0, but you have numpy 2.2.4 which is incompatible.\n", | |
"numba 0.60.0 requires numpy<2.1,>=1.22, but you have numpy 2.2.4 which is incompatible.\u001b[0m\u001b[31m\n", | |
"\u001b[0m" | |
] | |
} | |
], | |
"source": [ | |
"!pip install --no-cache-dir -Uq numpy matplotlib pillow scipy einops ffmpeg-python wgpu trimesh" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"source": [ | |
"### Commons" | |
], | |
"metadata": { | |
"id": "iouUH3v6TL37" | |
} | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"%matplotlib inline\n", | |
"%config InlineBackend.figure_format = 'retina'\n", | |
"\n", | |
"import os\n", | |
"import numpy as np\n", | |
"import matplotlib as mpl\n", | |
"import matplotlib.pyplot as plt\n", | |
"import matplotlib.font_manager as fm\n", | |
"import locale\n", | |
"from fastprogress import progress_bar\n", | |
"from einops import rearrange, reduce, repeat, einsum\n", | |
"\n", | |
"locale.getpreferredencoding = lambda: \"UTF-8\"\n", | |
"\n", | |
"COLORS = {\n", | |
" \"red\": np.array([0.79215686, 0.14901961, 0.14901961]),\n", | |
" \"blue\": np.array([0.08683021, 0.41940383, 0.71699529]),\n", | |
" **{f\"gray{k:02d}\": np.array([k,k,k])*.01 for k in np.arange(5,100,5)}\n", | |
"}\n", | |
"\n", | |
"def mpl_theme(gray=COLORS['gray50'], stroke_width=.1, fontsize=7,\n", | |
" facecolor=COLORS['gray10']):\n", | |
" ## category20: https://github.com/d3/d3-3.x-api-reference/blob/master/Ordinal-Scales.md#category20\n", | |
" cat20 = mpl.cycler(color=[\"1f77b4\",\"ff7f0e\",\"2ca02c\",\"d62728\",\"9467bd\",\"8c564b\",\"e377c2\",\"7f7f7f\",\"bcbd22\",\"17becf\",\n", | |
" \"aec7e8\",\"ffbb78\",\"98df8a\",\"ff9896\",\"c5b0d5\",\"c49c94\",\"f7b6d2\",\"c7c7c7\", \"dbdb8d\", \"9edae5\"])\n", | |
" return {\n", | |
" \"font.size\": fontsize,\n", | |
" \"text.color\": gray,\n", | |
"\n", | |
" \"figure.dpi\": 100,\n", | |
" \"figure.facecolor\": facecolor,\n", | |
" \"figure.frameon\": False,\n", | |
" \"figure.figsize\": (5, 3),\n", | |
" \"figure.titlesize\": \"large\",\n", | |
" \"figure.titleweight\": \"bold\",\n", | |
" \"figure.constrained_layout.use\": True,\n", | |
" \"figure.constrained_layout.w_pad\": 0.05,\n", | |
" \"figure.constrained_layout.h_pad\": 0.05,\n", | |
" \"figure.constrained_layout.wspace\": 0.03,\n", | |
" \"figure.constrained_layout.hspace\": 0.03,\n", | |
"\n", | |
" \"axes.labelcolor\": gray,\n", | |
" \"axes.labelpad\": 8,\n", | |
" \"axes.labelsize\": \"medium\",\n", | |
" \"axes.labelweight\": \"normal\",\n", | |
" \"axes.spines.left\": False,\n", | |
" \"axes.spines.bottom\": False,\n", | |
" \"axes.spines.top\": False,\n", | |
" \"axes.spines.right\": False,\n", | |
" \"axes.facecolor\": facecolor,\n", | |
" \"axes.edgecolor\": gray,\n", | |
" \"axes.linewidth\": stroke_width,\n", | |
" \"axes.axisbelow\": True,\n", | |
" \"axes.xmargin\": 0.02,\n", | |
" \"axes.ymargin\": 0.02,\n", | |
" \"axes.zmargin\": 0.02,\n", | |
" \"axes.prop_cycle\": cat20,\n", | |
" \"axes.titlepad\": 8,\n", | |
" \"axes.titlesize\": \"medium\",\n", | |
" \"axes.titleweight\": 500,\n", | |
" \"axes.grid\": True,\n", | |
" \"axes.grid.axis\": \"both\",\n", | |
"\n", | |
" \"axes3d.grid\": False,\n", | |
" \"axes3d.xaxis.panecolor\": COLORS['gray15'],\n", | |
" \"axes3d.yaxis.panecolor\": COLORS['gray20'],\n", | |
" \"axes3d.zaxis.panecolor\": COLORS['gray25'],\n", | |
"\n", | |
" \"ytick.right\": False,\n", | |
" \"ytick.color\": gray,\n", | |
" \"ytick.major.width\": stroke_width,\n", | |
" \"ytick.major.size\": 0,\n", | |
" \"ytick.minor.left\": False,\n", | |
" \"ytick.labelsize\": \"small\",\n", | |
"\n", | |
" \"xtick.labelsize\": \"small\",\n", | |
" \"xtick.minor.visible\": True,\n", | |
" \"xtick.minor.top\": False,\n", | |
" \"xtick.minor.bottom\": False,\n", | |
" \"xtick.color\": gray,\n", | |
" \"xtick.major.width\": stroke_width,\n", | |
" \"xtick.major.size\": 0,\n", | |
"\n", | |
" \"grid.color\": gray,\n", | |
" \"grid.linewidth\": stroke_width,\n", | |
" \"grid.linestyle\": \"-\",\n", | |
" \"legend.fancybox\": False,\n", | |
" \"legend.edgecolor\": '0.3',\n", | |
" \"legend.framealpha\": 0.7,\n", | |
" \"legend.handletextpad\": 0.8,\n", | |
"\n", | |
" \"lines.linewidth\": 0.7\n", | |
" }\n", | |
"\n", | |
"def mpl_add_font(fname):\n", | |
" if fname not in [fe.fname for fe in fm.fontManager.ttflist]:\n", | |
" fm.fontManager.addfont(fname)\n", | |
"\n", | |
"def setup_overpass(folder=\"fonts\"):\n", | |
" os.makedirs(folder, exist_ok=True)\n", | |
" for style in [\"Regular\", \"Italic\", \"SemiBold\", \"SemiBoldItalic\", \"Bold\", \"BoldItalic\"]:\n", | |
" ttf = f\"Overpass-{style}.ttf\"\n", | |
" !wget -qc \"https://github.com/RedHatOfficial/Overpass/raw/master/fonts/ttf/{ttf}\" -O \"{folder}/{ttf}\"\n", | |
" mpl_add_font(f\"{folder}/{ttf}\")\n", | |
" mpl.rcParams['font.sans-serif'].insert(0, \"Overpass\")\n", | |
"\n", | |
"def setup_quicksand(folder=\"fonts\"):\n", | |
" os.makedirs(folder, exist_ok=True)\n", | |
" for style in [\"Bold\", \"Light\", \"Medium\", \"Regular\"]:\n", | |
" ttf = f\"Quicksand-{style}.ttf\"\n", | |
" !wget -qc \"https://github.com/andrew-paglinawan/QuicksandFamily/raw/refs/heads/master/fonts/statics/{ttf}\" -O \"{folder}/{ttf}\"\n", | |
" mpl_add_font(f\"{folder}/{ttf}\")\n", | |
" mpl.rcParams['font.sans-serif'].insert(0, \"Quicksand\")\n", | |
"\n", | |
"setup_quicksand()\n", | |
"\n", | |
"plt.style.use([\"dark_background\", mpl_theme()])" | |
], | |
"metadata": { | |
"id": "boA9jQAOTLeg" | |
}, | |
"execution_count": 1, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"import math\n", | |
"import sys\n", | |
"import io\n", | |
"import bz2\n", | |
"import ffmpeg\n", | |
"import requests\n", | |
"import subprocess\n", | |
"import cv2\n", | |
"import PIL\n", | |
"import IPython.display as ipd\n", | |
"import ipywidgets as widgets\n", | |
"from scipy import linalg\n", | |
"from fastprogress import progress_bar\n", | |
"from einops import rearrange, reduce, repeat\n", | |
"from base64 import b64encode\n", | |
"from zipfile import ZipFile\n", | |
"from contextlib import contextmanager\n", | |
"from matplotlib.patches import Circle\n", | |
"from mpl_toolkits.mplot3d.art3d import Line3DCollection, Poly3DCollection\n", | |
"\n", | |
"class Output(object):\n", | |
" def __init__(self):\n", | |
" self.out = widgets.Output()\n", | |
"\n", | |
" def display(self):\n", | |
" display(self.out)\n", | |
" return self\n", | |
"\n", | |
" def clear(self):\n", | |
" self.out.clear_output()\n", | |
" return self.out\n", | |
"\n", | |
" def close(self):\n", | |
" return self.out.close()\n", | |
"\n", | |
"def to_single_rgb(img):\n", | |
" img = np.asarray(img)\n", | |
" if len(img.shape) == 4: # take first frame from animations\n", | |
" return img[0,:,:,:]\n", | |
" if len(img.shape) == 2: # convert gray to rgb\n", | |
" return img[:,:,np.newaxis].repeat(3, 2)\n", | |
" if img.shape[-1] == 4: # drop alpha\n", | |
" return img[:,:,:3]\n", | |
" else:\n", | |
" return img\n", | |
"\n", | |
"def imread(url, size=None, mode=None):\n", | |
" if url.startswith(('http:', 'https:')):\n", | |
" resp = requests.get(url)\n", | |
" if resp.status_code != 200:\n", | |
" return None\n", | |
"\n", | |
" f = io.BytesIO(resp.content)\n", | |
" else:\n", | |
" f = url\n", | |
" img = PIL.Image.open(f)\n", | |
" if size is not None:\n", | |
" img.thumbnail((size, size), PIL.Image.Resampling.LANCZOS)\n", | |
" if mode is not None:\n", | |
" img = img.convert(mode)\n", | |
" return img\n", | |
"\n", | |
"def imshow(img, fmt='png', retina=True, zoom=None):\n", | |
" if isinstance(img, str):\n", | |
" display(ipd.Image(filename=img, retina=retina))\n", | |
" return\n", | |
"\n", | |
" if len(img.shape) == 3 and img.shape[-1] == 1:\n", | |
" img = img.squeeze()\n", | |
" if img.dtype == np.float32:\n", | |
" img = img * 255.0\n", | |
" img = np.uint8(img.clip(0, 255))\n", | |
" if fmt in ('jpeg', 'jpg'):\n", | |
" img = to_single_rgb(img)\n", | |
"\n", | |
" image = PIL.Image.fromarray(img)\n", | |
" height, width = img.shape[:2]\n", | |
" if zoom is not None:\n", | |
" width *= zoom\n", | |
" height *= zoom\n", | |
" retina = zoom == 1\n", | |
" if zoom < 1:\n", | |
" image.resize((int(width), int(height)))\n", | |
"\n", | |
" data = io.BytesIO()\n", | |
" image.save(data, fmt)\n", | |
" display(ipd.Image(data=data.getvalue(),width=width, height=height,retina=retina))\n", | |
"\n", | |
"def find_rectangle(n, ratio=1):\n", | |
" ny = int((n / ratio) ** .5)\n", | |
" return ny, math.ceil(n / ny)\n", | |
"\n", | |
"def make_mosaic(imgs, nx=None, ny=None, gap=0):\n", | |
" n, h, w = imgs.shape[:3]\n", | |
" has_channels = len(imgs.shape) > 3\n", | |
"\n", | |
" if nx is None and ny is None:\n", | |
" ny, nx = find_rectangle(n)\n", | |
" elif ny is None:\n", | |
" ny = math.ceil(n / nx)\n", | |
" elif nx is None:\n", | |
" nx = math.ceil(n / ny)\n", | |
"\n", | |
" sh, sw = h + gap, w + gap\n", | |
" shape = (ny * sh - gap, nx * sw - gap)\n", | |
" if has_channels:\n", | |
" shape += (imgs.shape[-1],)\n", | |
"\n", | |
" canvas = np.zeros(shape, dtype=imgs.dtype)\n", | |
" for i, x in enumerate(imgs):\n", | |
" iy, ix = divmod(i, nx)\n", | |
" canvas[iy * sh:iy * sh + h, ix * sw:ix * sw + w] = x\n", | |
" return canvas\n", | |
"\n", | |
"def ffprobe_video(path):\n", | |
" probe = ffmpeg.probe(path)\n", | |
" return next(s for s in probe['streams'] if s['codec_type'] == 'video')\n", | |
"\n", | |
"def read_frame(path, frame_no):\n", | |
" cap = cv2.VideoCapture(path)\n", | |
" cap.set(cv2.CAP_PROP_POS_FRAMES, frame_no)\n", | |
" ret, frame = cap.read()\n", | |
" if not ret:\n", | |
" raise RuntimeError(f\"Faild reading frame {frame_no} from {path}\")\n", | |
" return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n", | |
"\n", | |
"def read_frames(path, start=0, num=None):\n", | |
" cap = cv2.VideoCapture(path)\n", | |
" n_frames = num or int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n", | |
" cap.set(cv2.CAP_PROP_POS_FRAMES, start)\n", | |
" for i in range(n_frames):\n", | |
" ret, frame = cap.read()\n", | |
" if not ret:\n", | |
" raise RuntimeError(f\"Faild reading frame {i} from {path}\")\n", | |
" yield cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n", | |
"\n", | |
"def read_video_frames(path):\n", | |
" info = ffprobe_video(path)\n", | |
" out, _ = ffmpeg.input(path).output('pipe:', format='rawvideo', pix_fmt='rgb24').run(capture_stdout=True)\n", | |
" return np.frombuffer(out, np.uint8).reshape([-1, info['height'], info['width'], 3])\n", | |
"\n", | |
"def show_video(path):\n", | |
" vcap = cv2.VideoCapture(path)\n", | |
" width = int(vcap.get(cv2.CAP_PROP_FRAME_WIDTH))\n", | |
" with open(path, \"r+b\") as f:\n", | |
" url = f\"data:video/mp4;base64,{b64encode(f.read()).decode()}\"\n", | |
" return ipd.HTML(f\"\"\"<video autoplay=\"autoplay\" width={width} controls loop><source src=\"{url}\"></video>\"\"\")\n", | |
"\n", | |
"def write_video(frames, size, path=\"__temp__.mp4\", fps=30,\n", | |
" preset=\"veryfast\", args=[]):\n", | |
" height, width = size\n", | |
" command = ['ffmpeg','-v','error','-f','rawvideo','-vcodec','rawvideo',\n", | |
" '-pix_fmt','rgb24','-s',f'{width}x{height}','-r', f'{fps}',\n", | |
" '-i', '-',\n", | |
" \"-movflags\", \"+faststart\", \"-preset\", preset,\n", | |
" \"-g\", \"30\", \"-bf\",\"2\",\"-c:v\", \"libx264\",\"-profile:v\", \"high\",\n", | |
" '-an', '-vcodec','h264','-pix_fmt','yuv420p', *args, '-y', path]\n", | |
" with subprocess.Popen(command, stdin=subprocess.PIPE, stderr=subprocess.PIPE) as proc:\n", | |
" with proc.stdin as stdin:\n", | |
" for image in frames:\n", | |
" data = image.tobytes()\n", | |
" if stdin.write(data) != len(data):\n", | |
" proc.wait()\n", | |
" stderr = proc.stderr\n", | |
" assert stderr is not None\n", | |
" s = stderr.read().decode()\n", | |
" raise RuntimeError(f\"Error writing '{path}': {s}\")\n", | |
" return path\n", | |
"\n", | |
"def read_video(path):\n", | |
" command = ['ffmpeg','-v','error','-nostdin','-i',path,'-vcodec','rawvideo',\n", | |
" '-f','image2pipe','-pix_fmt','rgb24','-vsync','vfr','-']\n", | |
"\n", | |
" info = ffprobe_video(path)\n", | |
" num_bytes = info['height'] * info['width'] * 3 * np.dtype(np.uint8).itemsize\n", | |
" with subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) as proc:\n", | |
" stdout = proc.stdout\n", | |
" assert stdout is not None\n", | |
" data = stdout.read(num_bytes)\n", | |
" while data is not None and len(data) == num_bytes:\n", | |
" image = np.frombuffer(data, dtype=np.uint8)\n", | |
" yield image.reshape(info['height'], info['width'], 3)\n", | |
" data = stdout.read(num_bytes)\n", | |
"\n", | |
"def sdiv(a, b, nan=0, posinf=0, neginf=0):\n", | |
" return np.nan_to_num(a / b, nan=nan, posinf=posinf, neginf=neginf)\n", | |
"\n", | |
"def topk(x, n):\n", | |
" return np.argpartition(x, -n)[-n:]\n", | |
"\n", | |
"def norm(x, a, b, **kw):\n", | |
" return sdiv(x - a, b - a, **kw)\n", | |
"\n", | |
"def norm_v(x, axis=None, **kw):\n", | |
" return norm(x, x.min(axis, keepdims=True), x.max(axis, keepdims=True), **kw)\n", | |
"\n", | |
"def normalize(x, keepdims=True, axis=-1, **kw):\n", | |
" return sdiv(x, np.linalg.norm(x, keepdims=keepdims, axis=axis), **kw)\n", | |
"\n", | |
"def nudge(x, v=0, eps=1e-12):\n", | |
" return np.where(np.isclose(np.abs(x), v, atol=eps), np.where(x - v >= 0, eps, -eps), x)\n", | |
"\n", | |
"def linspace_m(start, stop, n):\n", | |
" return np.linspace(start, stop, n, endpoint=False) + (stop - start) * .5 / n\n", | |
"\n", | |
"def indices_m(dims, shape, dtype=\"u4\"):\n", | |
" return tuple(np.meshgrid(*[np.round(linspace_m(0, d, s)).astype(dtype)\n", | |
" for d, s in zip(dims, shape)],\n", | |
" indexing='ij'))\n", | |
"\n", | |
"def saturate(x):\n", | |
" return np.clip(x, 0, 1)\n", | |
"\n", | |
"def lerp(a, b, t):\n", | |
" return a * (1.0 - t) + b * t\n", | |
"\n", | |
"def step(v, x):\n", | |
" return np.where(x < v, 0, 1)\n", | |
"\n", | |
"def window(x, a, b):\n", | |
" return step(a, x) * step(x, b)\n", | |
"\n", | |
"def satnorm(x, a, b):\n", | |
" return saturate(norm(x, a, b))\n", | |
"\n", | |
"def smoothstep(x):\n", | |
" return x * x * (3 - 2 * x)\n", | |
"\n", | |
"def smootherstep(x):\n", | |
" return x * x * x * (x * (x * 6 - 15) + 10)\n", | |
"\n", | |
"def dot(a, b, axis=-1, **kw):\n", | |
" return (a * b).sum(axis, **kw)\n", | |
"\n", | |
"def cross(a, b, axis=-1):\n", | |
" return a.take(0, axis) * b.take(1, axis) - a.take(1, axis) * b.take(0, axis)\n", | |
"\n", | |
"def cubic(a, b, c, d, t):\n", | |
" \"\"\"https://www.desmos.com/calculator/waof4r6avv\"\"\"\n", | |
" s = 1. - t\n", | |
" return s * s * (s * a + 3 * t * b) + t * t * (3 * s * c + t * d)\n", | |
"\n", | |
"def plt_show(pin=mpl.rcParams['savefig.pad_inches']):\n", | |
" with plt.rc_context({'savefig.pad_inches': pin}):\n", | |
" plt.show()\n", | |
"\n", | |
"def fig_image(fig=None, transparent=False, bbox_inches=None,\n", | |
" dpi=mpl.rcParams[\"figure.dpi\"]*2):\n", | |
" fig = fig or plt.gcf()\n", | |
"\n", | |
" buf = io.BytesIO()\n", | |
" fig.savefig(buf, format=\"png\", pad_inches=0, bbox_inches=bbox_inches,\n", | |
" facecolor=fig.get_facecolor(), dpi=dpi,transparent=transparent)\n", | |
" buf.seek(0)\n", | |
" data = np.frombuffer(buf.getvalue(), dtype=np.uint8)\n", | |
" buf.close()\n", | |
" plt.close(fig)\n", | |
"\n", | |
" code = cv2.COLOR_BGRA2RGBA if transparent else cv2.COLOR_BGR2RGB\n", | |
" return cv2.cvtColor(cv2.imdecode(data, cv2.IMREAD_UNCHANGED), code)\n", | |
"\n", | |
"def plt_savefig(name, pad_inches=mpl.rcParams['savefig.pad_inches'],\n", | |
" bbox_inches=0,facecolor='auto',\n", | |
" dpi=mpl.rcParams[\"figure.dpi\"]*2,close=True,**kw):\n", | |
" plt.savefig(name,\n", | |
" pad_inches=pad_inches,\n", | |
" bbox_inches=bbox_inches,\n", | |
" facecolor=facecolor,\n", | |
" dpi=dpi,**kw)\n", | |
" if close:\n", | |
" plt.close()\n", | |
"\n", | |
"class Flex(object):\n", | |
" def __init__(self, ratios, gap, size=None):\n", | |
" n, s = len(ratios), sum(ratios)\n", | |
" self.ratios = ratios\n", | |
" self.gap = gap\n", | |
" space = gap * n / s if size is None else gap * n / (size - gap * (n - 1))\n", | |
" self.h = dict(nrows=1, ncols=n, width_ratios=ratios, wspace=space)\n", | |
" self.v = dict(nrows=n, ncols=1, height_ratios=ratios, hspace=space)\n", | |
" self.size = s + gap * (n - 1) if size is None else size\n", | |
"\n", | |
"def ax_lim(mn, mx, ax=None):\n", | |
" ax = ax or plt.gca()\n", | |
" ax.set_xlim(mn[0], mx[0])\n", | |
" ax.set_ylim(mn[1], mx[1])\n", | |
" if len(mn) > 2:\n", | |
" ax.set_zlim(mn[2], mx[2])\n", | |
"\n", | |
"def ax_spines(sides=[\"left\",\"right\",\"bottom\",\"top\"], ax=None, **kw):\n", | |
" ax = ax or plt.gca()\n", | |
" ax.spines[sides].set(**kw)\n", | |
"\n", | |
"def ax_lines(lines, ax=None, **kw):\n", | |
" ax = ax or plt.gca()\n", | |
" ax.add_collection(mpl.collections.LineCollection(lines,**kw))\n", | |
"\n", | |
"def ax_line3d(lines, ax=None, **kw):\n", | |
" ax = ax or plt.gca()\n", | |
" return ax.add_collection(Line3DCollection(lines, **kw))\n", | |
"\n", | |
"def ax_poly3d(verts, ax=None, **kw):\n", | |
" ax = ax or plt.gca()\n", | |
" return ax.add_collection(Poly3DCollection(verts, **kw))\n", | |
"\n", | |
"def ax_trisurf(v, f, ax=None, **kw):\n", | |
" ax = ax or plt.gca()\n", | |
" ax.plot_trisurf(v[:,0],v[:,1],v[:,2],triangles=f, **kw)\n", | |
"\n", | |
"def ax_box2(mn, mx, ax=None):\n", | |
" ax = ax or plt.gca()\n", | |
" ax.set(xlim=(mn[0],mx[0]),ylim=(mn[1],mx[1]),aspect='equal')\n", | |
"\n", | |
"def ax_box3(mn, mx, ax=None):\n", | |
" ax = ax or plt.gca()\n", | |
" ax.set(xlim=(mn[0],mx[0]),ylim=(mn[1],mx[1]),zlim=(mn[2],mx[2]),box_aspect=mx-mn)\n", | |
"\n", | |
"def ax_axis_lines(ax=None, **kw):\n", | |
" ax = ax or plt.gca()\n", | |
" ax.xaxis.line.set(**kw)\n", | |
" ax.yaxis.line.set(**kw)\n", | |
" ax.zaxis.line.set(**kw)\n", | |
"\n", | |
"def ax_scatter(pts, ax=None, **kw):\n", | |
" ax = ax or plt.gca()\n", | |
" return ax.scatter(*[pts[...,i] for i in range(pts.shape[-1])], **kw)\n", | |
"\n", | |
"def ax_swatch(cols, size=32, ax=None, **kw):\n", | |
" ax = ax or plt.gca()\n", | |
" ax.imshow(repeat(cols, \"n ...->h (n w) ...\",h=size,w=size), **kw)\n", | |
"\n", | |
"def ax_texts(pts, texts, ax=None, **kw):\n", | |
" ax = ax or plt.gca()\n", | |
" for p, t in zip(pts, texts):\n", | |
" ax.text(p[0], p[1], t, **kw)\n", | |
"\n", | |
"def ax_circle(c, r, ax=None, **kw):\n", | |
" ax = ax or plt.gca()\n", | |
" return ax.add_patch(Circle(c, r, **kw))\n", | |
"\n", | |
"def lowess(x, y, f=2. / 3., iter=3):\n", | |
" \"\"\"https://gist.github.com/agramfort/850437\n", | |
" lowess(x, y, f=2./3., iter=3) -> yest\n", | |
" Lowess smoother: Robust locally weighted regression.\n", | |
" The lowess function fits a nonparametric regression curve to a scatterplot.\n", | |
" The arrays x and y contain an equal number of elements; each pair\n", | |
" (x[i], y[i]) defines a data point in the scatterplot. The function returns\n", | |
" the estimated (smooth) values of y.\n", | |
" The smoothing span is given by f. A larger value for f will result in a\n", | |
" smoother curve. The number of robustifying iterations is given by iter. The\n", | |
" function will run faster with a smaller number of iterations.\n", | |
" \"\"\"\n", | |
" n = len(x)\n", | |
" r = int(math.ceil(f * n))\n", | |
" h = [np.sort(np.abs(x - x[i]))[r] for i in range(n)]\n", | |
" w = np.clip(np.abs((x[:, None] - x[None, :]) / h), 0.0, 1.0)\n", | |
" w = (1 - w ** 3) ** 3\n", | |
" yest = np.zeros(n)\n", | |
" delta = np.ones(n)\n", | |
" for iteration in range(iter):\n", | |
" for i in range(n):\n", | |
" weights = delta * w[:, i]\n", | |
" b = np.array([np.sum(weights * y), np.sum(weights * y * x)])\n", | |
" A = np.array([[np.sum(weights), np.sum(weights * x)],\n", | |
" [np.sum(weights * x), np.sum(weights * x * x)]])\n", | |
" beta = linalg.solve(A, b)\n", | |
" yest[i] = beta[0] + beta[1] * x[i]\n", | |
"\n", | |
" residuals = y - yest\n", | |
" s = np.median(np.abs(residuals))\n", | |
" delta = np.clip(residuals / (6.0 * s), -1, 1)\n", | |
" delta = (1 - delta ** 2) ** 2\n", | |
"\n", | |
" return yest\n", | |
"\n", | |
"def plot_metrics(metrics, groups=None, title=\"Metrics\", lowess=False):\n", | |
" groups = groups or [list(metrics.keys())]\n", | |
" n = len(groups)\n", | |
" ny = math.ceil(n / 2)\n", | |
" fig = plt.figure(figsize=(8 if n > 1 else 4, 2 * ny))\n", | |
"\n", | |
" for i, group in enumerate(groups, 1):\n", | |
" ax = fig.add_subplot(ny, 2 if n > 1 else 1, i)\n", | |
" for k in group:\n", | |
" x, y = np.arange(len(metrics[k])), metrics[k]\n", | |
" alpha = max(0.3, min(1, (1000 - len(x)) / 1000))\n", | |
" ax.plot(x, y, alpha=alpha, label=k, marker='.', markeredgewidth=0,lw=.5,ms=5)\n", | |
" if np.any(np.min(y) - y[0] > (np.max(y) - np.min(y)) * 0.01):\n", | |
" ax.set_ylim(np.min(y), y[0])\n", | |
" if lowess and len(y) >= 9:\n", | |
" ax.plot(x, lowess(x, y, f=0.25, iter=3), linestyle='-', alpha=0.8, label=k + \".lowess\", lw=2)\n", | |
" ax.legend(loc='lower left')\n", | |
" ax.grid(axis='x')\n", | |
"\n", | |
" fig.suptitle(title)\n", | |
" plt.show()\n", | |
"\n", | |
"def sph2cart(sph):\n", | |
" az, el, r = rearrange(sph, \"... d -> d ...\")\n", | |
" c = np.cos(el)\n", | |
" return rearrange(np.stack((c * np.cos(az), c * np.sin(az), np.sin(el)) * r), \"d ... -> ... d\")\n", | |
"\n", | |
"def cart2sph(cart, axis=-1):\n", | |
" x, y, z = cart.take(0,axis), cart.take(1,axis), cart.take(2,axis)\n", | |
" az, el = np.arctan2(y, x), np.arctan2(z, np.hypot(x, y))\n", | |
" r = np.sqrt(x ** 2 + y ** 2 + z ** 2)\n", | |
" return np.stack((az, el, r), axis)\n", | |
"\n", | |
"def iter_batch(xs, bs, drop_last=True):\n", | |
" n = len(xs) // bs\n", | |
" for i in range(n):\n", | |
" yield xs[i*bs:(i+1)*bs]\n", | |
" if not drop_last:\n", | |
" yield xs[n*bs:]\n", | |
"\n", | |
"def unpack_bz2(src_path):\n", | |
" data = bz2.BZ2File(src_path).read()\n", | |
" dst_path = src_path[:-4]\n", | |
" with open(dst_path, 'wb') as fp:\n", | |
" fp.write(data)\n", | |
" return dst_path\n", | |
"\n", | |
"def make_zip(files, target, filename=os.path.basename):\n", | |
" with ZipFile(target, 'w') as f:\n", | |
" for p in files:\n", | |
" f.write(p, filename(p))\n", | |
" return target" | |
], | |
"metadata": { | |
"id": "CsVuWjNOTQDl" | |
}, | |
"execution_count": 2, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"source": [ | |
"## Contents" | |
], | |
"metadata": { | |
"id": "4yn3XxeaTRO9" | |
} | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"def orthogonal(v, m=.5, n=.5):\n", | |
" x, y, z = rearrange(v, \"... d -> d ...\")\n", | |
" res = np.stack((m * -y + n * -z, m * x, n * x))\n", | |
" return normalize(rearrange(res, \"d ... -> ... d\"))\n", | |
"\n", | |
"def quat_axis_angle(a, r):\n", | |
" r = (np.asarray(r) * .5)[...,None]\n", | |
" return np.concatenate((a * np.sin(r), np.cos(r)),-1)\n", | |
"\n", | |
"def quat_mul(a, b):\n", | |
" ax, ay, az, aw = rearrange(a, \"... d -> d ...\")\n", | |
" bx, by, bz, bw = rearrange(b, \"... d -> d ...\")\n", | |
" res = np.stack((\n", | |
" ax * bw + aw * bx + ay * bz - az * by,\n", | |
" ay * bw + aw * by + az * bx - ax * bz,\n", | |
" az * bw + aw * bz + ax * by - ay * bx,\n", | |
" aw * bw - ax * bx - ay * by - az * bz))\n", | |
" return rearrange(res, \"d ... -> ... d\")\n", | |
"\n", | |
"def quat_between(a, b):\n", | |
" w, q = dot(a, b), np.cross(a, b)\n", | |
" qw = w + np.sqrt(q[...,0] ** 2 + q[...,1] ** 2 + q[...,2] ** 2 + w ** 2)\n", | |
" with np.errstate(invalid='ignore'):\n", | |
" qa = normalize(np.concatenate((q, qw[...,None]),-1))\n", | |
" qb = quat_axis_angle(orthogonal(a), np.full(a.shape[:-1],np.pi))\n", | |
" return np.where(w[...,None] != -1, qa, qb)\n", | |
"\n", | |
"def quat_mul_v(q, v):\n", | |
" x, y, z = rearrange(v, \"... d -> d ...\")\n", | |
" qx, qy, qz, qw = rearrange(q, \"... d -> d ...\")\n", | |
" ix = qw * x + qy * z - qz * y\n", | |
" iy = qw * y + qz * x - qx * z\n", | |
" iz = qw * z + qx * y - qy * x\n", | |
" iw = qx * x + qy * y + qz * z\n", | |
" res = np.stack((ix * qw + iw * qx - iy * qz + iz * qy,\n", | |
" iy * qw + iw * qy - iz * qx + ix * qz,\n", | |
" iz * qw + iw * qz - ix * qy + iy * qx))\n", | |
" return rearrange(res, \"d ... -> ... d\")\n", | |
"\n", | |
"def quat_lookat(vdir):\n", | |
" YZ = np.array([[0,1,0],[0,0,1]], dtype=vdir.dtype)\n", | |
" x = np.cross(vdir, YZ[:1])\n", | |
" x = np.where(np.linalg.norm(x, axis=-1, keepdims=True) == 0,\n", | |
" normalize(np.cross(normalize(vdir + np.array([0,0,1e-6])), YZ[:1])), x)\n", | |
" y = np.cross(x, vdir)\n", | |
" q0 = np.where(dot(YZ[:1], y, keepdims=True) == -1,\n", | |
" quat_axis_angle(YZ[1:], [np.pi]), quat_between(YZ[:1], y))\n", | |
" z = quat_mul_v(q0, YZ[1:])\n", | |
" q1 = np.where(dot(z, vdir, keepdims=True) == -1,\n", | |
" quat_axis_angle(quat_mul_v(q0, YZ[:1]), [np.pi]),\n", | |
" quat_between(z, vdir))\n", | |
" return quat_mul(q1, q0)\n", | |
"\n", | |
"def m44_perspective(fovy=np.radians(45), aspect=1, near=.01, far=100, dtype=\"f4\"):\n", | |
" f, nf = np.tan((np.pi - fovy) * .5), 1 / (near - far)\n", | |
" return np.array([[f / aspect, 0, 0, 0],\n", | |
" [0, f, 0, 0],\n", | |
" [0, 0, far * nf, far * near * nf],\n", | |
" [0, 0, -1, 0]],\n", | |
" dtype=dtype)\n", | |
"\n", | |
"def m44_lookat(eye, q, dtype=\"f4\"):\n", | |
" mat = np.eye(4, dtype=dtype)[None].repeat(eye.shape[0], 0)\n", | |
" mat[:,:3,:3] = quat_mul_v(q[:,None], np.array([[-1,0,0],[0,1,0],[0,0,-1]],dtype=dtype)[None])\n", | |
" mat[:,:3, 3] = -einsum(mat[:,:3,:3], eye, \"... i j,... j -> ... i\")\n", | |
" return mat\n", | |
"\n", | |
"def m44_rot_axis(idx, theta, dtype=\"f4\"):\n", | |
" c, s = np.cos(theta), np.sin(theta)\n", | |
" a, b = (idx + 1) % 3, (idx + 2) % 3\n", | |
"\n", | |
" mat = np.eye(4, dtype=dtype)\n", | |
" mat[a, a], mat[b, b] = c, c\n", | |
" mat[a, b], mat[b, a] = -s, s\n", | |
" return mat\n", | |
"\n", | |
"def m44_scaling(scale, dtype=\"f4\"):\n", | |
" return np.diagflat([scale[0], scale[1], scale[2], 1.0]).astype(dtype)" | |
], | |
"metadata": { | |
"id": "MdOEiHXYTSKD" | |
}, | |
"execution_count": 3, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"import wgpu\n", | |
"from contextlib import contextmanager\n", | |
"\n", | |
"def vertex_data(attribs):\n", | |
" dtype = [(k, f\"({v.shape[-1] if v.ndim > 1 else 1},){v.dtype.descr[0][1]}\")\n", | |
" for k, v in attribs]\n", | |
" return np.column_stack([v for _, v in attribs]).ravel().view(dtype)\n", | |
"\n", | |
"def prim_quad(s=1):\n", | |
" return vertex_data([\n", | |
" [\"position\", np.array([[-s,s],[-s,-s],[s,s],[s,-s]], dtype=\"f4\")],\n", | |
" [\"texcoord\", np.array([[0,1],[0,0],[1,1],[1,0]], dtype=\"f4\")]])\n", | |
"\n", | |
"def prim_cube(s=.5):\n", | |
" positions = np.array([s, s, -s, s, s, s, s, -s, s, s, -s, -s, -s, s, s, -s, s, -s, -s, -s, -s, -s, -s, s, -s, s, s, s, s, s, s, s, -s, -s, s, -s, -s, -s, -s, s, -s, -s, s, -s, s, -s, -s, s, s, s, s, -s, s, s, -s, -s, s, s, -s, s, -s, s, -s, s, s, -s, s, -s, -s, -s, -s, -s],dtype=\"f4\")\n", | |
" texcoords = np.array([1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1],dtype=\"f4\")\n", | |
" indices = np.array([0, 1, 2, 0, 2, 3, 4, 5, 6, 4, 6, 7, 8, 9, 10, 8, 10, 11, 12, 13, 14, 12, 14, 15, 16, 17, 18, 16, 18, 19, 20, 21, 22, 20, 22, 23],dtype=\"u2\")\n", | |
" attribs = vertex_data([[\"position\",positions.reshape(-1,3)],[\"texcoord\",texcoords.reshape(-1,2)]])\n", | |
" return attribs, indices\n", | |
"\n", | |
"def wgpu_request_device():\n", | |
" adapter = wgpu.gpu.request_adapter_sync(power_preference=wgpu.PowerPreference.high_performance)\n", | |
" return adapter.request_device_sync()\n", | |
"\n", | |
"def wgpu_read_texture(texture):\n", | |
" size = texture.size\n", | |
" data = texture._device.queue.read_texture(\n", | |
" {\"texture\": texture,\"mip_level\": 0,\"origin\": (0, 0, 0)},\n", | |
" {\"offset\": 0,\"bytes_per_row\": 4 * size[0],\"rows_per_image\": size[1]},\n", | |
" size)\n", | |
" shape = (size[1], size[0], 4)\n", | |
" return np.frombuffer(data.cast(\"B\", shape), np.uint8).reshape(shape)\n", | |
"\n", | |
"def wgpu_bind_group_entries(buffers):\n", | |
" return [{\"binding\": i, \"resource\": {\"buffer\": buf, \"size\": buf.size}}\n", | |
" for i, buf in enumerate(buffers)]\n", | |
"\n", | |
"class WebGPU(object):\n", | |
" def __init__(self, size, use_depth=True, format=wgpu.TextureFormat.rgba8unorm_srgb):\n", | |
" self.device = wgpu_request_device()\n", | |
" self.size = size\n", | |
" self.texture = self.device.create_texture(\n", | |
" size=(*size, 1),\n", | |
" format=format,\n", | |
" usage=wgpu.TextureUsage.RENDER_ATTACHMENT | wgpu.TextureUsage.COPY_SRC)\n", | |
" self.use_depth = use_depth\n", | |
" self.depth = self.device.create_texture(\n", | |
" size=(*size, 1),\n", | |
" format=wgpu.TextureFormat.depth24plus,\n", | |
" usage=wgpu.TextureUsage.RENDER_ATTACHMENT,\n", | |
" sample_count=4)\n", | |
" self.msaa = self.device.create_texture(\n", | |
" size=self.texture.size,\n", | |
" format=self.texture.format,\n", | |
" usage=wgpu.TextureUsage.RENDER_ATTACHMENT,\n", | |
" sample_count=4)\n", | |
"\n", | |
" @contextmanager\n", | |
" def render_pass(self, clear_value=(0,0,0,1)):\n", | |
" encoder = self.device.create_command_encoder()\n", | |
"\n", | |
" render_pass = encoder.begin_render_pass(\n", | |
" color_attachments=[{\"view\": self.msaa.create_view(),\n", | |
" \"resolve_target\": self.texture.create_view(),\n", | |
" \"clear_value\": clear_value,\n", | |
" \"load_op\": wgpu.LoadOp.clear,\n", | |
" \"store_op\": wgpu.StoreOp.store}],\n", | |
" depth_stencil_attachment={\"view\":self.depth.create_view(),\n", | |
" \"depth_clear_value\": 1,\n", | |
" \"depth_load_op\": wgpu.LoadOp.clear,\n", | |
" \"depth_store_op\": wgpu.StoreOp.store} if self.use_depth else None)\n", | |
" yield render_pass\n", | |
" render_pass.end()\n", | |
" self.device.queue.submit([encoder.finish()])\n", | |
"\n", | |
" def uniform_buffer(self, data):\n", | |
" buffer = self.device.create_buffer(size=len(data),usage=wgpu.BufferUsage.UNIFORM | wgpu.BufferUsage.COPY_DST)\n", | |
" self.device.queue.write_buffer(buffer, 0, data)\n", | |
" return buffer\n", | |
"\n", | |
" def read(self):\n", | |
" return wgpu_read_texture(self.texture)\n", | |
"\n", | |
"class RenderBuffers(object):\n", | |
" def __init__(self, webgpu, attribs):\n", | |
" self.vertex_buffers = [\n", | |
" webgpu.device.create_buffer_with_data(\n", | |
" data=attrib.tobytes(),\n", | |
" usage=wgpu.BufferUsage.VERTEX | wgpu.BufferUsage.COPY_DST)\n", | |
" for attrib in attribs]\n", | |
" self.count = len(attribs[0])\n", | |
"\n", | |
" def __call__(self, render_pass):\n", | |
" for i, vertex_buffer in enumerate(self.vertex_buffers):\n", | |
" render_pass.set_vertex_buffer(i, vertex_buffer)\n", | |
" render_pass.draw(self.count)\n", | |
"\n", | |
"class RenderIndexedBuffers(object):\n", | |
" def __init__(self, webgpu, attribs, indices):\n", | |
" self.vertex_buffers = [\n", | |
" webgpu.device.create_buffer_with_data(\n", | |
" data=attrib.tobytes(),\n", | |
" usage=wgpu.BufferUsage.VERTEX | wgpu.BufferUsage.COPY_DST)\n", | |
" for attrib in attribs]\n", | |
" self.index_buffer = webgpu.device.create_buffer_with_data(\n", | |
" data=indices.tobytes(),\n", | |
" usage=wgpu.BufferUsage.INDEX | wgpu.BufferUsage.COPY_DST)\n", | |
" self.index_format = wgpu.IndexFormat.uint16 if indices.dtype == np.uint16 else wgpu.IndexFormat.uint32\n", | |
" self.count = len(indices)\n", | |
"\n", | |
" def __call__(self, render_pass):\n", | |
" for i, vertex_buffer in enumerate(self.vertex_buffers):\n", | |
" render_pass.set_vertex_buffer(i, vertex_buffer)\n", | |
" render_pass.set_index_buffer(self.index_buffer, self.index_format)\n", | |
" render_pass.draw_indexed(self.count)\n", | |
"\n", | |
"class Pipeline(object):\n", | |
" def __init__(self, pipeline, bind_groups=[]):\n", | |
" self.pipeline = pipeline\n", | |
" self.bind_groups = bind_groups\n", | |
"\n", | |
" def __call__(self, render_pass):\n", | |
" render_pass.set_pipeline(self.pipeline)\n", | |
" for i, bind_group in enumerate(self.bind_groups):\n", | |
" render_pass.set_bind_group(i, bind_group)" | |
], | |
"metadata": { | |
"id": "5G3Ec7kVT4yo" | |
}, | |
"execution_count": 4, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"webgpu = WebGPU((512,512))" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "4Dmm1AJcT7tF", | |
"outputId": "d425426c-bd15-4e48-ca99-b20f5200919b" | |
}, | |
"execution_count": 5, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"WARNING:wgpu:No windowing system present. Using surfaceless platform\n", | |
"WARNING:wgpu:No config found!\n", | |
"WARNING:wgpu:No config found!\n" | |
] | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"class RenderGnomon(Pipeline):\n", | |
" def __init__(self, webgpu, buffers):\n", | |
" shader = webgpu.device.create_shader_module(code=\"\"\"\n", | |
" override SIZE = 100.;\n", | |
"\n", | |
" @group(0) @binding(0) var<uniform> mat_vp : mat4x4f;\n", | |
"\n", | |
" struct VSOutput {\n", | |
" @builtin(position) position: vec4f,\n", | |
" @location(0) color: vec3f,\n", | |
" };\n", | |
"\n", | |
" @vertex fn vs(@builtin(vertex_index) index: u32) -> VSOutput {\n", | |
" let positions = array<vec3f,6>(vec3f(0),vec3f(1,0,0),vec3f(0),vec3f(0,1,0),vec3f(0),vec3f(0,0,1));\n", | |
" let colors = array<vec3f,6>(vec3f(1,0,0),vec3f(1,0,0),vec3f(0,1,0),vec3f(0,1,0),vec3f(0,0,1),vec3f(0,0,1));\n", | |
"\n", | |
" return VSOutput(mat_vp * vec4f(positions[index] * SIZE,1), colors[index]);\n", | |
" }\n", | |
"\n", | |
" @fragment fn fs(@location(0) color: vec3f) -> @location(0) vec4f {\n", | |
" return vec4f(pow(color, vec3f(2.2)), 1);\n", | |
" }\n", | |
" \"\"\")\n", | |
"\n", | |
" pipeline = webgpu.device.create_render_pipeline(\n", | |
" layout=webgpu.device.create_pipeline_layout(bind_group_layouts=[\n", | |
" webgpu.device.create_bind_group_layout(\n", | |
" entries=[{\"binding\":0,\n", | |
" \"visibility\":wgpu.ShaderStage.VERTEX,\n", | |
" \"buffer\":{\"type\":wgpu.BufferBindingType.uniform}}])\n", | |
" ]),\n", | |
" primitive={\"topology\": wgpu.PrimitiveTopology.line_list},\n", | |
" vertex={\"module\": shader,\"entry_point\": \"vs\"},\n", | |
" fragment={\"module\": shader,\"entry_point\": \"fs\",\n", | |
" \"targets\": [{\"format\": webgpu.texture.format}]},\n", | |
" depth_stencil={\"format\": webgpu.depth.format,\n", | |
" \"depth_write_enabled\": True,\n", | |
" \"depth_compare\": wgpu.CompareFunction.less},\n", | |
" multisample={\"count\": 4})\n", | |
" bind_group = webgpu.device.create_bind_group(\n", | |
" layout=pipeline.get_bind_group_layout(0),\n", | |
" entries=wgpu_bind_group_entries(buffers))\n", | |
" super().__init__(pipeline, [bind_group])\n", | |
"\n", | |
" def __call__(self, render_pass):\n", | |
" super().__call__(render_pass)\n", | |
" render_pass.draw(6)\n", | |
"\n", | |
"class RenderTexcoord(Pipeline):\n", | |
" def __init__(self, webgpu, buffers):\n", | |
" shader = webgpu.device.create_shader_module(code=\"\"\"\n", | |
" struct VSOutput {\n", | |
" @builtin(position) position: vec4f,\n", | |
" @location(0) texcoord: vec2f,\n", | |
" };\n", | |
"\n", | |
" @group(0) @binding(0) var<uniform> mat_m : mat4x4f;\n", | |
" @group(0) @binding(1) var<uniform> mat_vp : mat4x4f;\n", | |
"\n", | |
" @vertex fn vs(@location(0) position: vec3f,\n", | |
" @location(1) texcoord: vec2f) -> VSOutput {\n", | |
" return VSOutput(mat_vp * mat_m * vec4f(position, 1), texcoord);\n", | |
" }\n", | |
"\n", | |
" @fragment\n", | |
" fn fs(@location(0) texcoord: vec2f) -> @location(0) vec4f {\n", | |
" return vec4f(pow(vec3(texcoord, 1), vec3f(2.2)), 1);\n", | |
" }\n", | |
" \"\"\")\n", | |
" pipeline = webgpu.device.create_render_pipeline(\n", | |
" layout=webgpu.device.create_pipeline_layout(bind_group_layouts=[\n", | |
" webgpu.device.create_bind_group_layout(\n", | |
" entries=[{\"binding\":0,\"visibility\":wgpu.ShaderStage.VERTEX,\n", | |
" \"buffer\":{\"type\":wgpu.BufferBindingType.uniform}},\n", | |
" {\"binding\":1,\"visibility\":wgpu.ShaderStage.VERTEX,\n", | |
" \"buffer\":{\"type\":wgpu.BufferBindingType.uniform}}])\n", | |
" ]),\n", | |
" primitive={\"topology\": wgpu.PrimitiveTopology.triangle_list},\n", | |
" vertex={\"module\": shader,\"entry_point\": \"vs\",\n", | |
" \"buffers\": [{\"array_stride\": (3 + 2) * 4,\n", | |
" \"attributes\": [{\"shader_location\":0,\"offset\": 0,\n", | |
" \"format\": wgpu.VertexFormat.float32x3},\n", | |
" {\"shader_location\":1,\"offset\": 3 * 4,\n", | |
" \"format\": wgpu.VertexFormat.float32x2}]}]},\n", | |
" fragment={\"module\": shader,\"entry_point\": \"fs\",\n", | |
" \"targets\": [{\"format\": webgpu.texture.format}]},\n", | |
" depth_stencil={\"format\": webgpu.depth.format,\n", | |
" \"depth_write_enabled\": True,\n", | |
" \"depth_compare\": wgpu.CompareFunction.less},\n", | |
" multisample={\"count\": 4})\n", | |
" bind_group = webgpu.device.create_bind_group(\n", | |
" layout=pipeline.get_bind_group_layout(0),\n", | |
" entries=wgpu_bind_group_entries(buffers))\n", | |
" super().__init__(pipeline, [bind_group])" | |
], | |
"metadata": { | |
"id": "LRJaFcIHT8w6" | |
}, | |
"execution_count": 6, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"attribs, indices = prim_cube()\n", | |
"render_cube = RenderIndexedBuffers(webgpu, [attribs], indices)\n", | |
"\n", | |
"eye = np.array([[2,1,2]])\n", | |
"view = m44_lookat(eye, quat_lookat(-normalize(eye)))\n", | |
"u_mat_vp = webgpu.uniform_buffer((m44_perspective() @ view).T.tobytes())\n", | |
"u_mat_m = webgpu.uniform_buffer(np.eye(4,dtype=\"f4\").tobytes())\n", | |
"\n", | |
"render_gnomon = RenderGnomon(webgpu, [u_mat_vp])\n", | |
"render_texcoord = RenderTexcoord(webgpu, [u_mat_m, u_mat_vp])" | |
], | |
"metadata": { | |
"id": "EgHj6JFr45sK" | |
}, | |
"execution_count": 7, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"with webgpu.render_pass() as rp:\n", | |
" render_gnomon(rp)\n", | |
" render_texcoord(rp)\n", | |
" render_cube(rp)\n", | |
"\n", | |
"imshow(webgpu.read())" | |
], | |
"metadata": { | |
"id": "CucMmrQB49Vu", | |
"outputId": "3747a17a-e4a0-49c4-c323-39bb43a6fa49", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 273 | |
} | |
}, | |
"execution_count": 8, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<IPython.core.display.Image object>" | |
] | |
}, | |
"metadata": { | |
"image/png": { | |
"width": 256, | |
"height": 256 | |
} | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"class RenderComplex(Pipeline):\n", | |
" def __init__(self, webgpu, buffers):\n", | |
" shader = webgpu.device.create_shader_module(code=\"\"\"\n", | |
" struct VSOutput {\n", | |
" @builtin(position) position: vec4f,\n", | |
" @location(0) texcoord: vec2f,\n", | |
" };\n", | |
"\n", | |
" @group(0) @binding(0) var<uniform> mat_m : mat4x4f;\n", | |
" @group(0) @binding(1) var<uniform> mat_vp : mat4x4f;\n", | |
"\n", | |
" @vertex fn vs(@location(0) position: vec3f,\n", | |
" @location(1) texcoord: vec2f) -> VSOutput {\n", | |
" return VSOutput(mat_vp * mat_m * vec4f(position, 1), texcoord);\n", | |
" }\n", | |
"\n", | |
" fn grad(v:f32) -> f32 { return length(vec2(dpdx(v),dpdy(v))); }\n", | |
" fn norm(x:f32,a:f32,b:f32) -> f32 { return (x-a)/(b-a); }\n", | |
" fn satnorm(x:f32,a:f32,b:f32) -> f32 { return saturate(norm(x,a,b)); }\n", | |
" fn triangle(x:f32) -> f32 { return .5 - abs(fract(x) - .5); }\n", | |
" fn logn(x:f32, b:f32) -> f32 { return log(x) / log(b); }\n", | |
" fn ramp(x: f32, p: f32) -> f32 {\n", | |
" let h = x * 2. - 1.;\n", | |
" return .5 + .5 * sign(h) * pow(abs(h), p);\n", | |
" }\n", | |
"\n", | |
" fn contour_adaptive(v:f32, w:f32, f:f32, b:f32, m:f32) -> f32 {\n", | |
" let g = grad(v);\n", | |
" let s = -logn(m * g, b);\n", | |
"\n", | |
" let fy = f * pow(b, floor(s));\n", | |
" let vfy = v * fy;\n", | |
" let gfy = g * fy;\n", | |
" let wa = w + 1.;\n", | |
" let wb = w - 1.;\n", | |
" let c0 = satnorm(2.*triangle(vfy) / gfy, wa, wb);\n", | |
" let c1 = satnorm(2.*triangle(vfy / b) / (gfy / b), wa, wb);\n", | |
" let c2 = satnorm(2.*triangle(vfy * b) / (gfy * b), wa, wb);\n", | |
"\n", | |
" let t = (pow(b, fract(s)) - 1.) / (b - 1.);\n", | |
" return (mix(c1, c2, t) + c0) * .5;\n", | |
" }\n", | |
"\n", | |
" fn cmul(a: vec2f, b: vec2f) -> vec2f {\n", | |
" return vec2f(a.x * b.x - a.y * b.y,a.y * b.x + a.x * b.y);\n", | |
" }\n", | |
"\n", | |
" fn cdiv(a: vec2f, b: vec2f) -> vec2f {\n", | |
" var e: f32;\n", | |
" var f: f32;\n", | |
" var g = 1.0;\n", | |
" var h = 1.0;\n", | |
"\n", | |
" if(abs(b.x) >= abs(b.y)) {\n", | |
" e = b.y / b.x;\n", | |
" f = b.x + b.y * e;\n", | |
" h = e;\n", | |
" } else {\n", | |
" e = b.x / b.y;\n", | |
" f = b.x * e + b.y;\n", | |
" g = e;\n", | |
" }\n", | |
"\n", | |
" return (a * g + h * vec2f(a.y, -a.x)) / f;\n", | |
" }\n", | |
"\n", | |
" @fragment\n", | |
" fn fs(@location(0) texcoord: vec2f) -> @location(0) vec4f {\n", | |
" let p = (texcoord * 2. - 1.) * 2.;\n", | |
" let v = log(length(cmul(cdiv(p - vec2f(1, 0), p + vec2f(1, 0)), p)));\n", | |
"\n", | |
" var color = vec3f(.8);\n", | |
" let c = contour_adaptive(v, 1, 1, 2., 10.);\n", | |
" color = mix(color, vec3(.8,.1,.1), c);\n", | |
"\n", | |
" return vec4f(pow(color, vec3f(2.2)), 1);\n", | |
" }\n", | |
" \"\"\")\n", | |
" pipeline = webgpu.device.create_render_pipeline(\n", | |
" layout=webgpu.device.create_pipeline_layout(bind_group_layouts=[\n", | |
" webgpu.device.create_bind_group_layout(\n", | |
" entries=[{\"binding\":0,\"visibility\":wgpu.ShaderStage.VERTEX,\n", | |
" \"buffer\":{\"type\":wgpu.BufferBindingType.uniform}},\n", | |
" {\"binding\":1,\"visibility\":wgpu.ShaderStage.VERTEX,\n", | |
" \"buffer\":{\"type\":wgpu.BufferBindingType.uniform}}])\n", | |
" ]),\n", | |
" primitive={\"topology\": wgpu.PrimitiveTopology.triangle_list},\n", | |
" vertex={\"module\": shader,\"entry_point\": \"vs\",\n", | |
" \"buffers\": [{\"array_stride\": (3 + 2) * 4,\n", | |
" \"attributes\": [{\"shader_location\":0,\"offset\": 0,\n", | |
" \"format\": wgpu.VertexFormat.float32x3},\n", | |
" {\"shader_location\":1,\"offset\": 3 * 4,\n", | |
" \"format\": wgpu.VertexFormat.float32x2}]}]},\n", | |
" fragment={\"module\": shader,\"entry_point\": \"fs\",\n", | |
" \"targets\": [{\"format\": webgpu.texture.format}]},\n", | |
" depth_stencil={\"format\": webgpu.depth.format,\n", | |
" \"depth_write_enabled\": True,\n", | |
" \"depth_compare\": wgpu.CompareFunction.less},\n", | |
" multisample={\"count\": 4})\n", | |
" bind_group = webgpu.device.create_bind_group(\n", | |
" layout=pipeline.get_bind_group_layout(0),\n", | |
" entries=wgpu_bind_group_entries(buffers))\n", | |
" super().__init__(pipeline, [bind_group])\n", | |
"\n", | |
"render_complex = RenderComplex(webgpu, [u_mat_m,u_mat_vp])" | |
], | |
"metadata": { | |
"id": "aS_Tz_745Ih_" | |
}, | |
"execution_count": 9, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"with webgpu.render_pass() as rp:\n", | |
" render_gnomon(rp)\n", | |
" render_complex(rp)\n", | |
" render_cube(rp)\n", | |
"\n", | |
"imshow(webgpu.read())" | |
], | |
"metadata": { | |
"id": "YO2SaPsD5IY7", | |
"outputId": "10788fd2-176f-4c99-9c0d-f36587e9a605", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 273 | |
} | |
}, | |
"execution_count": 10, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<IPython.core.display.Image object>" | |
] | |
}, | |
"metadata": { | |
"image/png": { | |
"width": 256, | |
"height": 256 | |
} | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"!wget -qc --show-progress https://github.com/alecjacobson/common-3d-test-models/raw/master/data/spot.obj" | |
], | |
"metadata": { | |
"id": "58EbHtoF5hlP", | |
"outputId": "03f72f37-0175-4a01-becb-ff6a4765e688", | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
} | |
}, | |
"execution_count": 11, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"\rspot.obj 0%[ ] 0 --.-KB/s \rspot.obj 100%[===================>] 322.88K --.-KB/s in 0.04s \n" | |
] | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"import trimesh\n", | |
"\n", | |
"with open(\"spot.obj\",\"rb\") as f:\n", | |
" mesh = trimesh.exchange.obj.load_obj(f)['geometry']['spot.obj']\n", | |
"\n", | |
"verts = mesh['vertices'].astype(\"f4\")\n", | |
"\n", | |
"attribs = vertex_data([[\"position\",verts],[\"color\",normalize(verts) * .5 + .5]])\n", | |
"indices = mesh['faces'].ravel().astype(\"u2\")\n", | |
"\n", | |
"render_mesh = RenderIndexedBuffers(webgpu, [attribs], indices)" | |
], | |
"metadata": { | |
"id": "kBa3e7ji5VdT" | |
}, | |
"execution_count": 11, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"class RenderColor(Pipeline):\n", | |
" def __init__(self, webgpu, buffers):\n", | |
" shader = webgpu.device.create_shader_module(code=\"\"\"\n", | |
" struct VSOutput {\n", | |
" @builtin(position) position: vec4f,\n", | |
" @location(0) color: vec3f,\n", | |
" };\n", | |
"\n", | |
" @group(0) @binding(0) var<uniform> mat_m : mat4x4f;\n", | |
" @group(0) @binding(1) var<uniform> mat_vp : mat4x4f;\n", | |
"\n", | |
" @vertex fn vs(@location(0) position: vec3f,\n", | |
" @location(1) color: vec3f) -> VSOutput {\n", | |
" return VSOutput(mat_vp * mat_m * vec4f(position, 1), color);\n", | |
" }\n", | |
"\n", | |
" @fragment\n", | |
" fn fs(@location(0) color: vec3f) -> @location(0) vec4f {\n", | |
" return vec4f(pow(color,vec3f(2.2)), 1);\n", | |
" }\n", | |
" \"\"\")\n", | |
" pipeline = webgpu.device.create_render_pipeline(\n", | |
" layout=webgpu.device.create_pipeline_layout(bind_group_layouts=[\n", | |
" webgpu.device.create_bind_group_layout(\n", | |
" entries=[{\"binding\":0,\"visibility\":wgpu.ShaderStage.VERTEX,\n", | |
" \"buffer\":{\"type\":wgpu.BufferBindingType.uniform}},\n", | |
" {\"binding\":1,\"visibility\":wgpu.ShaderStage.VERTEX,\n", | |
" \"buffer\":{\"type\":wgpu.BufferBindingType.uniform}}])\n", | |
" ]),\n", | |
" primitive={\"topology\": wgpu.PrimitiveTopology.triangle_list},\n", | |
" vertex={\"module\": shader,\"entry_point\": \"vs\",\n", | |
" \"buffers\": [{\"array_stride\": (3 + 3) * 4,\n", | |
" \"attributes\": [{\"shader_location\":0,\"offset\": 0,\n", | |
" \"format\": wgpu.VertexFormat.float32x3},\n", | |
" {\"shader_location\":1,\"offset\": 3 * 4,\n", | |
" \"format\": wgpu.VertexFormat.float32x3}]}]},\n", | |
" fragment={\"module\": shader,\"entry_point\": \"fs\",\n", | |
" \"targets\": [{\"format\": webgpu.texture.format}]},\n", | |
" depth_stencil={\"format\": webgpu.depth.format,\n", | |
" \"depth_write_enabled\": True,\n", | |
" \"depth_compare\": wgpu.CompareFunction.less},\n", | |
" multisample={\"count\": 4})\n", | |
" bind_group = webgpu.device.create_bind_group(\n", | |
" layout=pipeline.get_bind_group_layout(0),\n", | |
" entries=wgpu_bind_group_entries(buffers))\n", | |
" super().__init__(pipeline, [bind_group])\n", | |
"\n", | |
"u_mat_m = webgpu.uniform_buffer((m44_rot_axis(1, np.pi)).T.tobytes())\n", | |
"render_color = RenderColor(webgpu, [u_mat_m, u_mat_vp])" | |
], | |
"metadata": { | |
"id": "ddYtwzav5vI8" | |
}, | |
"execution_count": 12, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"with webgpu.render_pass() as rp:\n", | |
" render_gnomon(rp)\n", | |
" render_color(rp)\n", | |
" render_mesh(rp)\n", | |
"\n", | |
"imshow(webgpu.read())" | |
], | |
"metadata": { | |
"id": "cTW8agFN5VU5", | |
"outputId": "a8a1c6a7-b2c4-478b-94d1-1b79ab0ac77c", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 273 | |
} | |
}, | |
"execution_count": 13, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<IPython.core.display.Image object>" | |
] | |
}, | |
"metadata": { | |
"image/png": { | |
"width": 256, | |
"height": 256 | |
} | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"def face_normals(v, f):\n", | |
" tri = v[f]\n", | |
" a, b, c = tri[:,0], tri[:,1], tri[:,2]\n", | |
" return normalize(np.cross(b - a, c - a))\n", | |
"\n", | |
"N = face_normals(mesh['vertices'],mesh['faces'])\n", | |
"\n", | |
"attribs = vertex_data([\n", | |
" [\"position\", mesh['vertices'][mesh['faces']].reshape(-1,3).astype(\"f4\")],\n", | |
" [\"normal\", repeat(N, \"n ...->(n 3) ...\").astype(\"f4\")]\n", | |
"])\n", | |
"\n", | |
"render_buf = RenderBuffers(webgpu, [attribs])" | |
], | |
"metadata": { | |
"id": "SO-Tb4UP6HSc" | |
}, | |
"execution_count": 14, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"class RenderNormal(Pipeline):\n", | |
" def __init__(self, webgpu, buffers):\n", | |
" shader = webgpu.device.create_shader_module(code=\"\"\"\n", | |
" struct VSOutput {\n", | |
" @builtin(position) position: vec4f,\n", | |
" @location(0) normal: vec3f,\n", | |
" };\n", | |
"\n", | |
" @group(0) @binding(0) var<uniform> mat_m : mat4x4f;\n", | |
" @group(0) @binding(1) var<uniform> mat_vp : mat4x4f;\n", | |
"\n", | |
" @vertex fn vs(@location(0) position: vec3f,\n", | |
" @location(1) normal: vec3f) -> VSOutput {\n", | |
" return VSOutput(mat_vp * mat_m * vec4f(position, 1), normal);\n", | |
" }\n", | |
"\n", | |
" @fragment\n", | |
" fn fs(@location(0) normal: vec3f) -> @location(0) vec4f {\n", | |
" return vec4f(pow(normal * .5 + .5,vec3f(2.2)), 1);\n", | |
" }\n", | |
" \"\"\")\n", | |
" pipeline = webgpu.device.create_render_pipeline(\n", | |
" layout=webgpu.device.create_pipeline_layout(bind_group_layouts=[\n", | |
" webgpu.device.create_bind_group_layout(\n", | |
" entries=[{\"binding\":0,\"visibility\":wgpu.ShaderStage.VERTEX,\n", | |
" \"buffer\":{\"type\":wgpu.BufferBindingType.uniform}},\n", | |
" {\"binding\":1,\"visibility\":wgpu.ShaderStage.VERTEX,\n", | |
" \"buffer\":{\"type\":wgpu.BufferBindingType.uniform}}])\n", | |
" ]),\n", | |
" primitive={\"topology\": wgpu.PrimitiveTopology.triangle_list},\n", | |
" vertex={\"module\": shader,\"entry_point\": \"vs\",\n", | |
" \"buffers\": [{\"array_stride\": (3 + 3) * 4,\n", | |
" \"attributes\": [{\"shader_location\":0,\"offset\": 0,\n", | |
" \"format\": wgpu.VertexFormat.float32x3},\n", | |
" {\"shader_location\":1,\"offset\": 3 * 4,\n", | |
" \"format\": wgpu.VertexFormat.float32x3}]}]},\n", | |
" fragment={\"module\": shader,\"entry_point\": \"fs\",\n", | |
" \"targets\": [{\"format\": webgpu.texture.format}]},\n", | |
" depth_stencil={\"format\": webgpu.depth.format,\n", | |
" \"depth_write_enabled\": True,\n", | |
" \"depth_compare\": wgpu.CompareFunction.less},\n", | |
" multisample={\"count\": 4})\n", | |
" bind_group = webgpu.device.create_bind_group(\n", | |
" layout=pipeline.get_bind_group_layout(0),\n", | |
" entries=wgpu_bind_group_entries(buffers))\n", | |
" super().__init__(pipeline, [bind_group])\n", | |
"\n", | |
"render_normal = RenderNormal(webgpu, [u_mat_m, u_mat_vp])" | |
], | |
"metadata": { | |
"id": "jw1z04E_6KhT" | |
}, | |
"execution_count": 15, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"with webgpu.render_pass() as rp:\n", | |
" render_gnomon(rp)\n", | |
" render_normal(rp)\n", | |
" render_buf(rp)\n", | |
"\n", | |
"imshow(webgpu.read())" | |
], | |
"metadata": { | |
"id": "HPHoOD_y6HFj", | |
"outputId": "cda842cc-9dd4-4e62-d033-04ba6fc4d068", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 273 | |
} | |
}, | |
"execution_count": 16, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<IPython.core.display.Image object>" | |
] | |
}, | |
"metadata": { | |
"image/png": { | |
"width": 256, | |
"height": 256 | |
} | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"class RenderContour(Pipeline):\n", | |
" def __init__(self, webgpu, buffers):\n", | |
" shader = webgpu.device.create_shader_module(code=\"\"\"\n", | |
" struct VSOutput {\n", | |
" @builtin(position) position: vec4f,\n", | |
" @location(0) value: f32,\n", | |
" };\n", | |
"\n", | |
" @group(0) @binding(0) var<uniform> mat_m : mat4x4f;\n", | |
" @group(0) @binding(1) var<uniform> mat_vp : mat4x4f;\n", | |
"\n", | |
" @vertex fn vs(@location(0) position: vec3f,\n", | |
" @location(1) value: f32) -> VSOutput {\n", | |
" return VSOutput(mat_vp * mat_m * vec4f(position, 1), value);\n", | |
" }\n", | |
"\n", | |
" fn grad(v:f32) -> f32 { return length(vec2(dpdx(v),dpdy(v))); }\n", | |
" fn norm(x:f32,a:f32,b:f32) -> f32 { return (x-a)/(b-a); }\n", | |
" fn satnorm(x:f32,a:f32,b:f32) -> f32 { return saturate(norm(x,a,b)); }\n", | |
" fn triangle(x:f32) -> f32 { return .5 - abs(fract(x) - .5); }\n", | |
" fn logn(x:f32, b:f32) -> f32 { return log(x) / log(b); }\n", | |
" fn ramp(x: f32, p: f32) -> f32 {\n", | |
" let h = x * 2. - 1.;\n", | |
" return .5 + .5 * sign(h) * pow(abs(h), p);\n", | |
" }\n", | |
"\n", | |
" fn contour_adaptive(v:f32, w:f32, f:f32, b:f32, m:f32) -> f32 {\n", | |
" let g = grad(v);\n", | |
" let s = -logn(m * g, b);\n", | |
"\n", | |
" let fy = f * pow(b, floor(s));\n", | |
" let vfy = v * fy;\n", | |
" let gfy = g * fy;\n", | |
" let wa = w + 1.;\n", | |
" let wb = w - 1.;\n", | |
" let c0 = satnorm(2.*triangle(vfy) / gfy, wa, wb);\n", | |
" let c1 = satnorm(2.*triangle(vfy / b) / (gfy / b), wa, wb);\n", | |
" let c2 = satnorm(2.*triangle(vfy * b) / (gfy * b), wa, wb);\n", | |
"\n", | |
" let t = (pow(b, fract(s)) - 1.) / (b - 1.);\n", | |
" return (mix(c1, c2, t) + c0) * .5;\n", | |
" }\n", | |
"\n", | |
" @fragment\n", | |
" fn fs(@location(0) value: f32) -> @location(0) vec4f {\n", | |
" var color = vec3f(.8);\n", | |
" let c = contour_adaptive(value, 1, 1, 2., 10.);\n", | |
" color = mix(color, vec3(.8,.1,.1), c);\n", | |
"\n", | |
" return vec4f(pow(color,vec3f(2.2)), 1);\n", | |
" }\n", | |
" \"\"\")\n", | |
" pipeline = webgpu.device.create_render_pipeline(\n", | |
" layout=webgpu.device.create_pipeline_layout(bind_group_layouts=[\n", | |
" webgpu.device.create_bind_group_layout(\n", | |
" entries=[{\"binding\":0,\"visibility\":wgpu.ShaderStage.VERTEX,\n", | |
" \"buffer\":{\"type\":wgpu.BufferBindingType.uniform}},\n", | |
" {\"binding\":1,\"visibility\":wgpu.ShaderStage.VERTEX,\n", | |
" \"buffer\":{\"type\":wgpu.BufferBindingType.uniform}}])\n", | |
" ]),\n", | |
" primitive={\"topology\": wgpu.PrimitiveTopology.triangle_list},\n", | |
" vertex={\"module\": shader,\"entry_point\": \"vs\",\n", | |
" \"buffers\": [{\"array_stride\": (3 + 1) * 4,\n", | |
" \"attributes\": [{\"shader_location\":0,\"offset\": 0,\n", | |
" \"format\": wgpu.VertexFormat.float32x3},\n", | |
" {\"shader_location\":1,\"offset\": 3 * 4,\n", | |
" \"format\": wgpu.VertexFormat.float32}]}]},\n", | |
" fragment={\"module\": shader,\"entry_point\": \"fs\",\n", | |
" \"targets\": [{\"format\": webgpu.texture.format}]},\n", | |
" depth_stencil={\"format\": webgpu.depth.format,\n", | |
" \"depth_write_enabled\": True,\n", | |
" \"depth_compare\": wgpu.CompareFunction.less},\n", | |
" multisample={\"count\": 4})\n", | |
" bind_group = webgpu.device.create_bind_group(\n", | |
" layout=pipeline.get_bind_group_layout(0),\n", | |
" entries=wgpu_bind_group_entries(buffers))\n", | |
" super().__init__(pipeline, [bind_group])\n", | |
"\n", | |
"render_contour = RenderContour(webgpu, [u_mat_m, u_mat_vp])" | |
], | |
"metadata": { | |
"id": "vL7NTF94US24" | |
}, | |
"execution_count": 17, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"attribs = vertex_data([[\"position\",verts],[\"value\",verts[:,1]]])\n", | |
"\n", | |
"render_mesh = RenderIndexedBuffers(webgpu, [attribs], indices)" | |
], | |
"metadata": { | |
"id": "QODoE8_VMdc0" | |
}, | |
"execution_count": 18, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"with webgpu.render_pass() as rp:\n", | |
" render_gnomon(rp)\n", | |
" render_contour(rp)\n", | |
" render_mesh(rp)\n", | |
"\n", | |
"imshow(webgpu.read())" | |
], | |
"metadata": { | |
"id": "va4qeaJ1Mp0y", | |
"outputId": "b06c344f-b2d3-4315-c114-83c5e4cfbd5e", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 273 | |
} | |
}, | |
"execution_count": 19, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<IPython.core.display.Image object>" | |
] | |
}, | |
"metadata": { | |
"image/png": { | |
"width": 256, | |
"height": 256 | |
} | |
} | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [], | |
"metadata": { | |
"id": "09fdipdoMuIr" | |
}, | |
"execution_count": null, | |
"outputs": [] | |
} | |
] | |
} |
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