263 lines
9.1 KiB
Python
263 lines
9.1 KiB
Python
# Copyright 2022 Lunar Ring. All rights reserved.
|
|
# Written by Johannes Stelzer, email stelzer@lunar-ring.ai twitter @j_stelzer
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import torch
|
|
torch.backends.cudnn.benchmark = False
|
|
import numpy as np
|
|
import warnings
|
|
warnings.filterwarnings('ignore')
|
|
import time
|
|
import warnings
|
|
import datetime
|
|
from typing import List, Union
|
|
torch.set_grad_enabled(False)
|
|
import yaml
|
|
|
|
|
|
@torch.no_grad()
|
|
def interpolate_spherical(p0, p1, fract_mixing: float):
|
|
r"""
|
|
Helper function to correctly mix two random variables using spherical interpolation.
|
|
See https://en.wikipedia.org/wiki/Slerp
|
|
The function will always cast up to float64 for sake of extra 4.
|
|
Args:
|
|
p0:
|
|
First tensor for interpolation
|
|
p1:
|
|
Second tensor for interpolation
|
|
fract_mixing: float
|
|
Mixing coefficient of interval [0, 1].
|
|
0 will return in p0
|
|
1 will return in p1
|
|
0.x will return a mix between both preserving angular velocity.
|
|
"""
|
|
|
|
if p0.dtype == torch.float16:
|
|
recast_to = 'fp16'
|
|
else:
|
|
recast_to = 'fp32'
|
|
|
|
p0 = p0.double()
|
|
p1 = p1.double()
|
|
norm = torch.linalg.norm(p0) * torch.linalg.norm(p1)
|
|
epsilon = 1e-7
|
|
dot = torch.sum(p0 * p1) / norm
|
|
dot = dot.clamp(-1 + epsilon, 1 - epsilon)
|
|
|
|
theta_0 = torch.arccos(dot)
|
|
sin_theta_0 = torch.sin(theta_0)
|
|
theta_t = theta_0 * fract_mixing
|
|
s0 = torch.sin(theta_0 - theta_t) / sin_theta_0
|
|
s1 = torch.sin(theta_t) / sin_theta_0
|
|
interp = p0 * s0 + p1 * s1
|
|
|
|
if recast_to == 'fp16':
|
|
interp = interp.half()
|
|
elif recast_to == 'fp32':
|
|
interp = interp.float()
|
|
|
|
return interp
|
|
|
|
|
|
def interpolate_linear(p0, p1, fract_mixing):
|
|
r"""
|
|
Helper function to mix two variables using standard linear interpolation.
|
|
Args:
|
|
p0:
|
|
First tensor / np.ndarray for interpolation
|
|
p1:
|
|
Second tensor / np.ndarray for interpolation
|
|
fract_mixing: float
|
|
Mixing coefficient of interval [0, 1].
|
|
0 will return in p0
|
|
1 will return in p1
|
|
0.x will return a linear mix between both.
|
|
"""
|
|
reconvert_uint8 = False
|
|
if type(p0) is np.ndarray and p0.dtype == 'uint8':
|
|
reconvert_uint8 = True
|
|
p0 = p0.astype(np.float64)
|
|
|
|
if type(p1) is np.ndarray and p1.dtype == 'uint8':
|
|
reconvert_uint8 = True
|
|
p1 = p1.astype(np.float64)
|
|
|
|
interp = (1 - fract_mixing) * p0 + fract_mixing * p1
|
|
|
|
if reconvert_uint8:
|
|
interp = np.clip(interp, 0, 255).astype(np.uint8)
|
|
|
|
return interp
|
|
|
|
|
|
def add_frames_linear_interp(
|
|
list_imgs: List[np.ndarray],
|
|
fps_target: Union[float, int] = None,
|
|
duration_target: Union[float, int] = None,
|
|
nmb_frames_target: int = None):
|
|
r"""
|
|
Helper function to cheaply increase the number of frames given a list of images,
|
|
by virtue of standard linear interpolation.
|
|
The number of inserted frames will be automatically adjusted so that the total of number
|
|
of frames can be fixed precisely, using a random shuffling technique.
|
|
The function allows 1:1 comparisons between transitions as videos.
|
|
|
|
Args:
|
|
list_imgs: List[np.ndarray)
|
|
List of images, between each image new frames will be inserted via linear interpolation.
|
|
fps_target:
|
|
OptionA: specify here the desired frames per second.
|
|
duration_target:
|
|
OptionA: specify here the desired duration of the transition in seconds.
|
|
nmb_frames_target:
|
|
OptionB: directly fix the total number of frames of the output.
|
|
"""
|
|
|
|
# Sanity
|
|
if nmb_frames_target is not None and fps_target is not None:
|
|
raise ValueError("You cannot specify both fps_target and nmb_frames_target")
|
|
if fps_target is None:
|
|
assert nmb_frames_target is not None, "Either specify nmb_frames_target or nmb_frames_target"
|
|
if nmb_frames_target is None:
|
|
assert fps_target is not None, "Either specify duration_target and fps_target OR nmb_frames_target"
|
|
assert duration_target is not None, "Either specify duration_target and fps_target OR nmb_frames_target"
|
|
nmb_frames_target = fps_target * duration_target
|
|
|
|
# Get number of frames that are missing
|
|
nmb_frames_diff = len(list_imgs) - 1
|
|
nmb_frames_missing = nmb_frames_target - nmb_frames_diff - 1
|
|
|
|
if nmb_frames_missing < 1:
|
|
return list_imgs
|
|
|
|
if type(list_imgs[0]) == PIL.Image.Image:
|
|
list_imgs = [np.asarray(l) for l in list_imgs]
|
|
list_imgs_float = [img.astype(np.float32) for img in list_imgs]
|
|
# Distribute missing frames, append nmb_frames_to_insert(i) frames for each frame
|
|
mean_nmb_frames_insert = nmb_frames_missing / nmb_frames_diff
|
|
constfact = np.floor(mean_nmb_frames_insert)
|
|
remainder_x = 1 - (mean_nmb_frames_insert - constfact)
|
|
nmb_iter = 0
|
|
while True:
|
|
nmb_frames_to_insert = np.random.rand(nmb_frames_diff)
|
|
nmb_frames_to_insert[nmb_frames_to_insert <= remainder_x] = 0
|
|
nmb_frames_to_insert[nmb_frames_to_insert > remainder_x] = 1
|
|
nmb_frames_to_insert += constfact
|
|
if np.sum(nmb_frames_to_insert) == nmb_frames_missing:
|
|
break
|
|
nmb_iter += 1
|
|
if nmb_iter > 100000:
|
|
print("add_frames_linear_interp: issue with inserting the right number of frames")
|
|
break
|
|
|
|
nmb_frames_to_insert = nmb_frames_to_insert.astype(np.int32)
|
|
list_imgs_interp = []
|
|
for i in range(len(list_imgs_float) - 1):
|
|
img0 = list_imgs_float[i]
|
|
img1 = list_imgs_float[i + 1]
|
|
list_imgs_interp.append(img0.astype(np.uint8))
|
|
list_fracts_linblend = np.linspace(0, 1, nmb_frames_to_insert[i] + 2)[1:-1]
|
|
for fract_linblend in list_fracts_linblend:
|
|
img_blend = interpolate_linear(img0, img1, fract_linblend).astype(np.uint8)
|
|
list_imgs_interp.append(img_blend.astype(np.uint8))
|
|
if i == len(list_imgs_float) - 2:
|
|
list_imgs_interp.append(img1.astype(np.uint8))
|
|
|
|
return list_imgs_interp
|
|
|
|
|
|
def get_spacing(nmb_points: int, scaling: float):
|
|
"""
|
|
Helper function for getting nonlinear spacing between 0 and 1, symmetric around 0.5
|
|
Args:
|
|
nmb_points: int
|
|
Number of points between [0, 1]
|
|
scaling: float
|
|
Higher values will return higher sampling density around 0.5
|
|
"""
|
|
if scaling < 1.7:
|
|
return np.linspace(0, 1, nmb_points)
|
|
nmb_points_per_side = nmb_points // 2 + 1
|
|
if np.mod(nmb_points, 2) != 0: # Uneven case
|
|
left_side = np.abs(np.linspace(1, 0, nmb_points_per_side)**scaling / 2 - 0.5)
|
|
right_side = 1 - left_side[::-1][1:]
|
|
else:
|
|
left_side = np.abs(np.linspace(1, 0, nmb_points_per_side)**scaling / 2 - 0.5)[0:-1]
|
|
right_side = 1 - left_side[::-1]
|
|
all_fracts = np.hstack([left_side, right_side])
|
|
return all_fracts
|
|
|
|
|
|
def get_time(resolution=None):
|
|
"""
|
|
Helper function returning an nicely formatted time string, e.g. 221117_1620
|
|
"""
|
|
if resolution is None:
|
|
resolution = "second"
|
|
if resolution == "day":
|
|
t = time.strftime('%y%m%d', time.localtime())
|
|
elif resolution == "minute":
|
|
t = time.strftime('%y%m%d_%H%M', time.localtime())
|
|
elif resolution == "second":
|
|
t = time.strftime('%y%m%d_%H%M%S', time.localtime())
|
|
elif resolution == "millisecond":
|
|
t = time.strftime('%y%m%d_%H%M%S', time.localtime())
|
|
t += "_"
|
|
t += str("{:03d}".format(int(int(datetime.utcnow().strftime('%f')) / 1000)))
|
|
else:
|
|
raise ValueError("bad resolution provided: %s" % resolution)
|
|
return t
|
|
|
|
|
|
def compare_dicts(a, b):
|
|
"""
|
|
Compares two dictionaries a and b and returns a dictionary c, with all
|
|
keys,values that have shared keys in a and b but same values in a and b.
|
|
The values of a and b are stacked together in the output.
|
|
Example:
|
|
a = {}; a['bobo'] = 4
|
|
b = {}; b['bobo'] = 5
|
|
c = dict_compare(a,b)
|
|
c = {"bobo",[4,5]}
|
|
"""
|
|
c = {}
|
|
for key in a.keys():
|
|
if key in b.keys():
|
|
val_a = a[key]
|
|
val_b = b[key]
|
|
if val_a != val_b:
|
|
c[key] = [val_a, val_b]
|
|
return c
|
|
|
|
|
|
def yml_load(fp_yml, print_fields=False):
|
|
"""
|
|
Helper function for loading yaml files
|
|
"""
|
|
with open(fp_yml) as f:
|
|
data = yaml.load(f, Loader=yaml.loader.SafeLoader)
|
|
dict_data = dict(data)
|
|
print("load: loaded {}".format(fp_yml))
|
|
return dict_data
|
|
|
|
|
|
def yml_save(fp_yml, dict_stuff):
|
|
"""
|
|
Helper function for saving yaml files
|
|
"""
|
|
with open(fp_yml, 'w') as f:
|
|
yaml.dump(dict_stuff, f, sort_keys=False, default_flow_style=False)
|
|
print("yml_save: saved {}".format(fp_yml))
|