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SillyTavern-extras/talkinghead/tha3/app/postprocessor.py
Juha Jeronen 9cc091bc7b add framerate correction to most postproc filters
2024-01-18 02:46:18 +02:00

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"""Smoke and mirrors. Glitch artistry. Pixel-space postprocessing effects.
These effects work in linear intensity space, before gamma correction.
"""
__all__ = ["Postprocessor"]
import logging
import math
import time
from typing import Dict, List, Optional, Tuple, TypeVar, Union
import torch
import torchvision
from tha3.app.util import RunningAverage
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# # Default configuration for the postprocessor.
# # This documents the correct ordering of the filters.
# # Feel free to improvise, but make sure to understand why your filter chain makes sense.
# default_chain = [
# # physical input signal
# ("bloom", {}),
# # video camera
# ("chromatic_aberration", {}),
# ("vignetting", {}),
# # scifi hologram output
# ("translucency", {}),
# ("alphanoise", {"magnitude": 0.1, "sigma": 0.0}),
# # # lo-fi analog video
# # ("analog_lowres", {}),
# # ("alphanoise", {"magnitude": 0.2, "sigma": 2.0}),
# # ("analog_badhsync", {}),
# # # ("analog_vhsglitches", {}),
# # ("analog_vhstracking", {}),
# # CRT TV output
# ("banding", {}),
# ("scanlines", {})
# ]
default_chain = [] # Overridden by the animator, which sends us the chain.
T = TypeVar("T")
Atom = Union[str, bool, int, float]
MaybeContained = Union[T, List[T], Dict[str, T]]
class Postprocessor:
"""
`chain`: Postprocessor filter chain configuration.
Don't mind the complicated type signature; the format is just::
[(filter_name0, {param0: value0, ...}),
...]
The filter name must be a method of `Postprocessor`, taking in an image, and any number of named parameters.
To use a filter's default parameter values, supply an empty dictionary for the parameters.
The outer `Optional[List[Tuple[...]]]` just formalizes that `chain` may be omitted (to use the built-in
default chain, for testing), and the top-level format that it's an ordered list of filters. The filters
are applied in order, first to last.
The auxiliary type definitions are::
MaybeContained = Union[T, List[T], Dict[str: T]]
Atom = Union[str, bool, int, float]
The leaf value (atom) types are restricted so that filter chain configurations JSON easily.
The leaf values may actually be contained inside arbitrarily nested lists and dicts (with str keys),
which is currently not captured by the type signature (the definition should be recursive).
The chain is stored as `self.chain`. Any modifications to that attribute modify the chain,
taking effect immediately. It is recommended to update the chain atomically, by::
my_postprocessor.chain = my_new_chain
In filter descriptions:
[static] := depends only on input image, no explicit time dependence.
[dynamic] := beside input image, also depends on time. In other words,
produces animation even if the input image stays the same.
"""
def __init__(self, device: torch.device, chain: Optional[List[Tuple[str, Dict[str, MaybeContained[Atom]]]]] = None):
# We intentionally keep very little state in this class, for a more FP/REST approach with less bugs.
# Filters for static effects are stateless.
#
# We deviate from FP in that:
# - The filters MUTATE, i.e. they overwrite the image being processed.
# This is to allow optimizing their implementations for memory usage and speed.
# - The filter for a dynamic effect may store state, if needed for performing FPS correction.
self.device = device
if chain is None:
chain = default_chain
self.chain = chain
# Meshgrid cache for geometric position of each pixel
self._yy = None
self._xx = None
self._meshy = None
self._meshx = None
self._prev_h = None
self._prev_w = None
# FPS correction
self.CALIBRATION_FPS = 25 # design FPS for dynamic effects (for automatic FPS correction)
self.stream_start_timestamp = time.time_ns() # for updating frame counter reliably (no accumulation)
self.frame_no = -1 # float, frame counter for *normalized* frame number *at CALIBRATION_FPS*
self.last_frame_no = -1
# Performance measurement
self.render_duration_statistics = RunningAverage()
self.last_report_time = None
# Caches for individual dynamic effects
self.alphanoise_last_image = None
def render_into(self, image):
"""Apply current postprocess chain, modifying `image`."""
time_render_start = time.time_ns()
c, h, w = image.shape
if h != self._prev_h or w != self._prev_w:
# Compute base meshgrid for the geometric position of each pixel.
# This is needed by filters that either vary by geometric position (e.g. `vignetting`),
# or deform the image (e.g. `analog_badhsync`).
#
# This postprocessor is typically applied to a video stream. As long as
# the image dimensions stay constant, we can re-use the previous meshgrid.
#
# We don't strictly keep state here - we just cache. :P
# Seems the deformation geometry must be float32 no matter the image data type.
self._yy = torch.linspace(-1.0, 1.0, h, dtype=torch.float32, device=self.device)
self._xx = torch.linspace(-1.0, 1.0, w, dtype=torch.float32, device=self.device)
self._meshy, self._meshx = torch.meshgrid((self._yy, self._xx), indexing="ij")
self._prev_h = h
self._prev_w = w
# Update the frame counter.
#
# We consider the frame number to be a float, so that dynamic filters can decide what
# to do at fractional frame positions. For continuously animated effects (e.g. banding)
# it makes sense to interpolate continuously, whereas other effects (e.g. scanlines)
# can make their decisions based on the integer part.
#
# As always with floats, we must be careful. Note that we operate in a mindset of robust
# engineering. Since doing the Right Thing here does not cost significantly more engineering
# effort than doing the intuitive but Wrong Thing, it is preferable to go for the proper solution,
# regardless of whether it would take a centuries-long session to actually trigger a failure
# in the less robust approach.
#
# So, floating point accuracy considerations? First, we note that accumulation invites
# disaster in two ways:
#
# - Accumulating the result accumulates also representation error and roundoff error.
# - When accumulating small positive numbers to a sum total, the update eventually
# becomes too small to add, causing the counter to get stuck. (For floats, `x + ϵ = x`
# for sufficiently small ϵ dependent on the magnitude of `x`.)
#
# Fortunately, frame number is a linear function of time, and time diffs can be measured
# precisely. Thus, we can freshly compute the current frame number at each frame, completely
# bypassing the need for accumulation:
#
seconds_since_stream_start = (time_render_start - self.stream_start_timestamp) / 10**9
self.last_frame_no = self.frame_no
self.frame_no = self.CALIBRATION_FPS * seconds_since_stream_start # float!
# That leaves just the questions of how accurate the calculation is, and for how long.
# As to the first question:
#
# - Timestamps are an integer number of nanoseconds, so they are exact.
# - Dividing by 10**9, we move the decimal point. But floats are base-2, so 0.1
# is not representable in IEEE-754. So there will be some small representation error,
# which for float64 likely appears in the ~15th significant digit.
# - Basic arithmetic, such as multiplication, is guaranteed by IEEE-754
# to be accurate to the ULP.
#
# Thus, as the result, we obtain the closest number that is representable in IEEE-754,
# and the strategy works for the whole range of float64.
#
# As for the second question, floats are logarithmically spaced. So if this is left running
# "for long enough" during the same session, accuracy will eventually suffer. Instead of the
# counter getting stuck, however, this will manifest as the frame number updating by more
# than `1.0` each time it updates (i.e. whenever the elapsed number of frames reaches the
# next representable float).
#
# This could be fixed by resetting `stream_start_timestamp` once the frame number
# becomes too large. But in practice, how long does it take for this issue to occur?
# The ULP becomes 1.0 at ~5e15. To reach frame number 5e15, at the reference 25 FPS,
# the time required is 2e14 seconds, i.e. 2.31e9 days, or 6.34 million years.
# While I can almost imagine the eventual bug report, I think it's safe to ignore this.
# Apply the current filter chain.
chain = self.chain # read just once; other threads might reassign it while we're rendering
for filter_name, settings in chain:
apply_filter = getattr(self, filter_name)
apply_filter(image, **settings)
time_now = time.time_ns()
render_elapsed_sec = (time_now - time_render_start) / 10**9
self.render_duration_statistics.add_datapoint(render_elapsed_sec)
# Log the FPS counter in 5-second intervals.
if (self.last_report_time is None or time_now - self.last_report_time > 5e9):
avg_render_sec = self.render_duration_statistics.average()
msec = round(1000 * avg_render_sec, 1)
fps = round(1 / avg_render_sec, 1) if avg_render_sec > 0.0 else 0.0
logger.info(f"postproc: {msec:.1f}ms [{fps} FPS available]")
self.last_report_time = time_now
# --------------------------------------------------------------------------------
# Physical input signal
def bloom(self, image: torch.tensor, *,
luma_threshold: float = 0.8,
hdr_exposure: float = 0.7) -> None:
"""[static] Bloom effect (fake HDR). Popular in early 2000s anime.
Makes bright parts of the image bleed light into their surroundings, enhancing perceived contrast.
Only makes sense when the talkinghead is rendered on a dark-ish background.
`luma_threshold`: How bright is bright. 0.0 is full black, 1.0 is full white.
`hdr_exposure`: Controls the overall brightness of the output. Like in photography,
higher exposure means brighter image (saturating toward white).
"""
# There are online tutorials for how to create this effect, see e.g.:
# https://learnopengl.com/Advanced-Lighting/Bloom
# Find the bright parts.
Y = 0.2126 * image[0, :, :] + 0.7152 * image[1, :, :] + 0.0722 * image[2, :, :] # HDTV luminance (ITU-R Rec. 709)
mask = torch.ge(Y, luma_threshold) # [h, w]
# Make a copy of the image with just the bright parts.
mask = torch.unsqueeze(mask, 0) # -> [1, h, w]
brights = image * mask # [c, h, w]
# Blur the bright parts. Two-pass blur to save compute, since we need a very large blur kernel.
# It seems that in Torch, one large 1D blur is faster than looping with a smaller one.
#
# Although everything else in Torch takes (height, width), kernel size is given as (size_x, size_y);
# see `gaussian_blur_image` in https://pytorch.org/vision/main/_modules/torchvision/transforms/v2/functional/_misc.html
# for a hint (the part where it computes the padding).
brights = torchvision.transforms.GaussianBlur((21, 1), sigma=7.0)(brights) # blur along x
brights = torchvision.transforms.GaussianBlur((1, 21), sigma=7.0)(brights) # blur along y
# Additively blend the images. Note we are working in linear intensity space, and we will now go over 1.0 intensity.
image.add_(brights)
# We now have a fake HDR image. Tonemap it back to LDR.
image[:3, :, :] = 1.0 - torch.exp(-image[:3, :, :] * hdr_exposure) # RGB: tonemap
image[3, :, :] = torch.maximum(image[3, :, :], brights[3, :, :]) # alpha: max-combine
torch.clamp_(image, min=0.0, max=1.0)
# --------------------------------------------------------------------------------
# Video camera
def chromatic_aberration(self, image: torch.tensor, *,
transverse_sigma: float = 0.5,
axial_scale: float = 0.005) -> None:
"""[static] Simulate the two types of chromatic aberration in a camera lens.
Like everything else here, this is of course made of smoke and mirrors. We simulate the axial effect
(index of refraction varying w.r.t. wavelength) by geometrically scaling the RGB channels individually,
and the transverse effect (focal distance varying w.r.t. wavelength) by a gaussian blur.
Note that in a real lens:
- Axial CA is typical at long focal lengths (e.g. tele/zoom lens)
- Axial CA increases at high F-stops (low depth of field, i.e. sharp focus at all distances)
- Transverse CA is typical at short focal lengths (e.g. macro lens)
However, in an RGB postproc effect, it is useful to apply both together, to help hide the clear-cut red/blue bands
resulting from the different geometric scalings of just three wavelengths (instead of a continuous spectrum, like
a scene lit with natural light would have).
See:
https://en.wikipedia.org/wiki/Chromatic_aberration
"""
# Axial: Shrink R (deflected less), pass G through (lens reference wavelength), enlarge B (deflected more).
grid_R = torch.stack((self._meshx * (1.0 + axial_scale), self._meshy * (1.0 + axial_scale)), 2)
grid_R = grid_R.unsqueeze(0)
grid_B = torch.stack((self._meshx * (1.0 - axial_scale), self._meshy * (1.0 - axial_scale)), 2)
grid_B = grid_B.unsqueeze(0)
image_batch_R = image[0, :, :].unsqueeze(0).unsqueeze(0) # [h, w] -> [c, h, w] -> [n, c, h, w]
warped_R = torch.nn.functional.grid_sample(image_batch_R, grid_R, mode="bilinear", padding_mode="border", align_corners=False)
warped_R = warped_R.squeeze(0) # [1, c, h, w] -> [c, h, w]
image_batch_B = image[2, :, :].unsqueeze(0).unsqueeze(0)
warped_B = torch.nn.functional.grid_sample(image_batch_B, grid_B, mode="bilinear", padding_mode="border", align_corners=False)
warped_B = warped_B.squeeze(0) # [1, c, h, w] -> [c, h, w]
# Transverse (blur to simulate wrong focal distance for R and B)
warped_R[:, :, :] = torchvision.transforms.GaussianBlur((5, 5), sigma=transverse_sigma)(warped_R)
warped_B[:, :, :] = torchvision.transforms.GaussianBlur((5, 5), sigma=transverse_sigma)(warped_B)
# Alpha channel: treat similarly to each of R,G,B and average the three resulting alpha channels
image_batch_A = image[3, :, :].unsqueeze(0).unsqueeze(0)
warped_A1 = torch.nn.functional.grid_sample(image_batch_A, grid_R, mode="bilinear", padding_mode="border", align_corners=False)
warped_A1[:, :, :] = torchvision.transforms.GaussianBlur((5, 5), sigma=transverse_sigma)(warped_A1)
warped_A2 = torch.nn.functional.grid_sample(image_batch_A, grid_B, mode="bilinear", padding_mode="border", align_corners=False)
warped_A2[:, :, :] = torchvision.transforms.GaussianBlur((5, 5), sigma=transverse_sigma)(warped_A2)
averaged_alpha = (warped_A1 + image[3, :, :] + warped_A2) / 3.0
image[0, :, :] = warped_R
# image[1, :, :] passed through as-is
image[2, :, :] = warped_B
image[3, :, :] = averaged_alpha
def vignetting(self, image: torch.tensor, *,
strength: float = 0.42) -> None:
"""[static] Simulate vignetting (less light hitting the corners of a film frame or CCD sensor).
The profile used here is [cos(strength * d * pi)]**2, where `d` is the distance
from the center, scaled such that `d = 1.0` is reached at the corners.
Thus, at the midpoints of the frame edges, `d = 1 / sqrt(2) ~ 0.707`.
"""
euclidean_distance_from_center = (self._meshy**2 + self._meshx**2)**0.5 / 2**0.5 # [h, w]
brightness = torch.cos(strength * euclidean_distance_from_center * math.pi)**2 # [h, w]
brightness = torch.unsqueeze(brightness, 0) # -> [1, h, w]
image[:3, :, :] *= brightness
# --------------------------------------------------------------------------------
# Scifi hologram
def translucency(self, image: torch.tensor, *,
alpha: float = 0.9) -> None:
"""[static] A simple translucency filter for a hologram look.
Multiplicatively adjusts the alpha channel.
"""
image[3, :, :].mul_(alpha)
# --------------------------------------------------------------------------------
# General use
def alphanoise(self, image: torch.tensor, *,
magnitude: float = 0.1,
sigma: float = 0.0) -> None:
"""[dynamic] Dynamic noise to alpha channel. A cheap alternative to luma noise.
`magnitude`: How much noise to apply. 0 is off, 1 is as much noise as possible.
`sigma`: If nonzero, apply a Gaussian blur to the noise, thus reducing its spatial frequency
(i.e. making larger and smoother "noise blobs").
The blur kernel size is fixed to 5, so `sigma = 1.0` is the largest that will be
somewhat accurate. Nevertheless, `sigma = 2.0` looks acceptable, too, producing
square blobs.
Suggested settings:
Scifi hologram: magnitude=0.1, sigma=0.0
Analog VHS tape: magnitude=0.2, sigma=2.0
"""
# Re-randomize the noise image whenever the normalized frame changes
if self.alphanoise_last_image is None or int(self.frame_no) > int(self.last_frame_no):
c, h, w = image.shape
noise_image = torch.rand(h, w, device=self.device, dtype=image.dtype)
if sigma > 0.0:
noise_image = noise_image.unsqueeze(0) # [h, w] -> [c, h, w] (where c=1)
noise_image = torchvision.transforms.GaussianBlur((5, 5), sigma=sigma)(noise_image)
noise_image = noise_image.squeeze(0) # -> [h, w]
self.alphanoise_last_image = noise_image
else:
noise_image = self.alphanoise_last_image
base_magnitude = 1.0 - magnitude
image[3, :, :].mul_(base_magnitude + magnitude * noise_image)
# --------------------------------------------------------------------------------
# Lo-fi analog video
def analog_lowres(self, image: torch.tensor, *,
kernel_size: int = 5,
sigma: float = 0.75) -> None:
"""[static] Low-resolution analog video signal, simulated by blurring.
`kernel_size`: size of the Gaussian blur kernel, in pixels.
`sigma`: standard deviation of the Gaussian blur kernel, in pixels.
Ideally, `kernel_size` should be `2 * (3 * sigma) + 1`, so that the kernel
reaches its "3 sigma" (99.7% mass) point where the finitely sized kernel
cuts the tail. "2 sigma" (95% mass) is also acceptable, to save some compute.
The default settings create a slight blur without destroying much detail.
"""
image[:, :, :] = torchvision.transforms.GaussianBlur((kernel_size, kernel_size), sigma=sigma)(image)
def analog_badhsync(self, image: torch.tensor, *,
speed: float = 8.0,
amplitude1: float = 0.001, density1: float = 4.0,
amplitude2: Optional[float] = 0.001, density2: Optional[float] = 13.0,
amplitude3: Optional[float] = 0.001, density3: Optional[float] = 27.0) -> None:
"""[dynamic] Analog video signal with fluctuating hsync.
We superpose three waves with different densities (1 / cycle length)
to make the pattern look more irregular.
E.g. density of 2.0 means that two full waves fit into the image height.
Amplitudes are given in units where the height and width of the image
are both 2.0.
"""
c, h, w = image.shape
# Animation
# FPS correction happens automatically, because `frame_no` is normalized to CALIBRATION_FPS.
cycle_pos = (self.frame_no / h) * speed
cycle_pos = cycle_pos - float(int(cycle_pos)) # fractional part
cycle_pos = 1.0 - cycle_pos # -> motion from top toward bottom
cycle_pos *= 2.0 # full cycle = 2 units
# Deformation
meshy = self._meshy
meshx = self._meshx + amplitude1 * torch.sin((density1 * (self._meshy + cycle_pos)) * math.pi)
if amplitude2 and density2:
meshx = self._meshx + amplitude2 * torch.sin((density2 * (self._meshy + cycle_pos)) * math.pi)
if amplitude3 and density3:
meshx = self._meshx + amplitude3 * torch.sin((density3 * (self._meshy + cycle_pos)) * math.pi)
grid = torch.stack((meshx, meshy), 2)
grid = grid.unsqueeze(0) # batch of one
image_batch = image.unsqueeze(0) # batch of one -> [1, c, h, w]
warped = torch.nn.functional.grid_sample(image_batch, grid, mode="bilinear", padding_mode="border", align_corners=False)
warped = warped.squeeze(0) # [1, c, h, w] -> [c, h, w]
image[:, :, :] = warped
def _vhs_noise(self, image: torch.tensor, *,
height: int) -> torch.tensor:
"""Generate a horizontal band of noise that looks as if it came from a blank VHS tape.
`height`: desired height of noise band, in pixels.
Output is a tensor of shape `[1, height, w]`, where `w` is the width of `image`.
"""
c, h, w = image.shape
# This looks best if we randomize the alpha channel, too.
noise_image = torch.rand(height, w, device=self.device, dtype=image.dtype).unsqueeze(0) # [1, h, w]
# Real VHS noise has horizontal runs of the same color, and the transitions between black and white are smooth.
noise_image = torchvision.transforms.GaussianBlur((5, 1), sigma=2.0)(noise_image)
return noise_image
def analog_vhsglitches(self, image: torch.tensor, *,
strength: float = 0.1,
unboost: float = 4.0,
max_glitches: int = 3,
min_glitch_height: int = 3, max_glitch_height: int = 6) -> None:
"""[dynamic] Damaged 1980s VHS video tape, with transient (per-frame) glitching lines.
This leaves the alpha channel alone, so the effect only affects parts that already show something.
This is an artistic interpretation that makes the effect less distracting when used with RGBA data.
`strength`: How much to blend in noise.
`unboost`: Use this to adjust the probability profile for the appearance of glitches.
The higher `unboost` is, the less probable it is for glitches to appear at all,
and there will be fewer of them (in the same video frame) when they do appear.
`max_glitches`: Maximum number of glitches in the video frame.
`min_glitch_height`, `max_glitch_height`: in pixels. The height is randomized separately for each glitch.
"""
# TODO: FPS correction for `analog_vhsglitches` (need to store glitching line metadata and noise images)
c, h, w = image.shape
n_glitches = torch.rand(1, device="cpu")**unboost # higher probability of having none or few glitching lines
n_glitches = int(max_glitches * n_glitches[0])
if not n_glitches:
return
glitch_start_lines = torch.rand(n_glitches, device="cpu")
glitch_start_lines = [int((h - (max_glitch_height - 1)) * x) for x in glitch_start_lines]
for line in glitch_start_lines:
glitch_height = torch.rand(1, device="cpu")
glitch_height = int(min_glitch_height + (max_glitch_height - min_glitch_height) * glitch_height[0])
noise_image = self._vhs_noise(image, height=glitch_height)
# Apply glitch to RGB only, so fully transparent parts stay transparent (important to make the effect less distracting).
image[:3, line:(line + glitch_height), :] = (1.0 - strength) * image[:3, line:(line + glitch_height), :] + strength * noise_image
def analog_vhstracking(self, image: torch.tensor, *,
base_offset: float = 0.03,
max_dynamic_offset: float = 0.01,
speed: float = 2.5) -> None:
"""[dynamic] 1980s VHS tape with bad tracking.
Image floats up and down, and a band of black and white noise appears at the bottom.
Units like in `analog_badhsync`.
"""
c, h, w = image.shape
# Animation
# FPS correction happens automatically, because `frame_no` is normalized to CALIBRATION_FPS.
cycle_pos = (self.frame_no / h) * speed
cycle_pos = cycle_pos - float(int(cycle_pos)) # fractional part
cycle_pos *= 2.0 # full cycle = 2 units
# Deformation - move image up/down
yoffs = max_dynamic_offset * math.sin(cycle_pos * math.pi)
meshy = self._meshy + yoffs
meshx = self._meshx
grid = torch.stack((meshx, meshy), 2)
grid = grid.unsqueeze(0) # batch of one
image_batch = image.unsqueeze(0) # batch of one -> [1, c, h, w]
warped = torch.nn.functional.grid_sample(image_batch, grid, mode="bilinear", padding_mode="border", align_corners=False)
warped = warped.squeeze(0) # [1, c, h, w] -> [c, h, w]
image[:, :, :] = warped
# Noise from bad VHS tracking at bottom
yoffs_pixels = int((yoffs / 2.0) * 512.0)
base_offset_pixels = int((base_offset / 2.0) * 512.0)
noise_pixels = yoffs_pixels + base_offset_pixels
if noise_pixels > 0:
image[:, -noise_pixels:, :] = self._vhs_noise(image, height=noise_pixels)
# # Fade out toward left/right, since the character does not take up the full width.
# # Works, but fails at reaching the iconic VHS look.
# xx = torch.linspace(0, math.pi, w, dtype=image.dtype, device=self.device)
# fade = torch.sin(xx)**2 # [w]
# fade = fade.unsqueeze(0) # [1, w]
# image[3, -noise_pixels:, :] = fade
# --------------------------------------------------------------------------------
# CRT TV output
def _rgb_to_hue(rgb: List[float]) -> float:
"""Convert an RGB color to an HSL hue, for use as `bandpass_hue` in `desaturate`.
This uses a cartesian-to-polar approximation of the HSL representation,
which is fine for hue detection, but should not be taken as an authoritative
H component of an accurate RGB->HSL conversion.
"""
R, G, B = rgb
alpha = 0.5 * (2.0 * R - G - B)
beta = 3.0**0.5 / 2.0 * (G - B)
hue = math.atan2(beta, alpha) / (2.0 * math.pi) # note atan2(0, 0) := 0
return hue
# This filter is adapted from an old GLSL code I made for Panda3D 1.8 back in 2014.
def desaturate(self, image: torch.tensor, *,
strength: float = 1.0,
tint_rgb: List[float] = [1.0, 1.0, 1.0],
bandpass_reference_rgb: List[float] = [1.0, 0.0, 0.0], bandpass_q: float = 0.0) -> None:
"""[static] Desaturation with bells and whistles.
Does not touch the alpha channel.
`strength`: Overall blending strength of the filter (0 is off, 1 is fully applied).
`tint_rgb`: Color to multiplicatively tint the image with. Applied after desaturation.
Some example tint values:
Green monochrome computer monitor: [0.5, 1.0, 0.5]
Amber monochrome computer monitor: [1.0, 0.5, 0.2]
Sepia effect: [0.8039, 0.6588, 0.5098]
No tint (off; default): [1.0, 1.0, 1.0]
`bandpass_reference_rgb`: Reference color for hue to let through the bandpass.
Use this to let e.g. red things bypass the desaturation.
The hue is extracted automatically from the given color.
`bandpass_q`: Hue bandpass band half-width, in (0, 1]. Hues farther away from `bandpass_hue`
than `bandpass_q` will be fully desaturated. The opposite colors on the color
circle are defined as having the largest possible hue difference, 1.0.
The shape of the filter is a quadratic spike centered on the reference hue,
and smoothly decaying to zero at `bandpass_q` away from the center.
The special value 0 (default) switches the hue bandpass code off,
saving some compute.
"""
R = image[0, :, :]
G = image[1, :, :]
B = image[2, :, :]
if bandpass_q > 0.0: # hue bandpass enabled?
# Calculate hue of each pixel, using a cartesian-to-polar approximation of the HSL representation.
# An approximation is fine here, because we only use this for a hue detector.
# This is faster and requires less branching than the exact hexagonal representation.
desat_alpha = 0.5 * (2.0 * R - G - B)
desat_beta = 3.0**0.5 / 2.0 * (G - B)
desat_hue = torch.atan2(desat_beta, desat_alpha) / (2.0 * math.pi) # note atan2(0, 0) := 0
desat_hue = desat_hue + torch.where(torch.lt(desat_hue, 0.0), 0.5, 0.0) # convert from `[-0.5, 0.5)` to `[0, 1)`
# -> [h, w]
# Determine whether to keep this pixel or desaturate (and by how much).
#
# Calculate distance of each pixel from reference hue, accounting for wrap-around.
bandpass_hue = self._rgb_to_hue(bandpass_reference_rgb)
desat_temp1 = torch.abs(desat_hue - bandpass_hue)
desat_temp2 = torch.abs((desat_hue + 1.0) - bandpass_hue)
desat_temp3 = torch.abs(desat_hue - (bandpass_hue + 1.0))
desat_hue_distance = 2.0 * torch.minimum(torch.minimum(desat_temp1, desat_temp2),
desat_temp3) # [0, 0.5] -> [0, 1]
# -> [h, w]
# - Pixels with their hue at least `bandpass_q` away from `bandpass_hue` are fully desaturated.
# - As distance falls below `bandpass_q`, a blend starts very gradually.
# - As the hue difference approaches zero, the pixel is fully passed through.
# - The 1.0 - ... together with the square makes a sharp spike at the reference hue.
desat_diff2 = (1.0 - torch.clamp(desat_hue_distance / bandpass_q, max=1.0))**2
strength_field = strength * (1.0 - desat_diff2) # [h, w]
else:
strength_field = strength # just a scalar!
# Desaturate, then apply tint
Y = 0.2126 * R + 0.7152 * G + 0.0722 * B # HDTV luminance (ITU-R Rec. 709) -> [h, w]
Y = Y.unsqueeze(0) # -> [1, h, w]
tint_color = torch.tensor(tint_rgb, device=self.device, dtype=image.dtype).unsqueeze(1).unsqueeze(2) # [c, 1, 1]
tinted_desat_image = Y * tint_color # -> [c, h, w]
# Final blend
image[:3, :, :] = (1.0 - strength_field) * image[:3, :, :] + strength_field * tinted_desat_image
def banding(self, image: torch.tensor, *,
strength: float = 0.4,
density: float = 2.0,
speed: float = 16.0) -> None:
"""[dynamic] Bad analog video signal, with traveling brighter and darker bands.
This simulates a CRT display as it looks when filmed on video without syncing.
`strength`: maximum brightness factor
`density`: how many banding cycles per full image height
`speed`: band movement, in pixels per frame
"""
c, h, w = image.shape
yy = torch.linspace(0, math.pi, h, dtype=image.dtype, device=self.device)
# Animation
# FPS correction happens automatically, because `frame_no` is normalized to CALIBRATION_FPS.
cycle_pos = (self.frame_no / h) * speed
cycle_pos = cycle_pos - float(int(cycle_pos)) # fractional part
cycle_pos = 1.0 - cycle_pos # -> motion from top toward bottom
band_effect = torch.sin(density * yy + cycle_pos * math.pi)**2 # [h]
band_effect = torch.unsqueeze(band_effect, 0) # -> [1, h] = [c, h]
band_effect = torch.unsqueeze(band_effect, 2) # -> [1, h, 1] = [c, h, w]
image[:3, :, :].mul_(1.0 + strength * band_effect)
torch.clamp_(image, min=0.0, max=1.0)
def scanlines(self, image: torch.tensor, *,
field: int = 0,
dynamic: bool = True) -> None:
"""[dynamic] CRT TV like scanlines.
`field`: Which CRT field is dimmed at the first frame. 0 = top, 1 = bottom.
`dynamic`: If `True`, the dimmed field will alternate each frame (top, bottom, top, bottom, ...)
for a more authentic CRT look (like Phosphor deinterlacer in VLC).
"""
if dynamic:
start = (field + int(self.frame_no)) % 2
else:
start = field
# We should ideally modify just the Y channel in YUV space, but modifying the alpha instead looks alright.
image[3, start::2, :].mul_(0.5)
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