utils.py

# 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

    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))