aredden

export CPLUS_INCLUDE_PATH=$CONDA_PREFIX/lib/python3.8 
export NVIDIA_ARCH_CAPABILITY=8.6 #Change this to your gpu's compute capability

# This script requires a directory structure which looks like the following, 
# with opencv-master from git, opencv_contrib-master from git, and a build directory
# with only this script inside. You must execute this shell script from inside the build
# directory, with your desired conda environment activated.
# The initial setup is copied from here: https://docs.opencv.org/3.4.15/d7/d9f/tutorial_linux_install.html
# With steps 2-5 replaced with this shell script.
# ├── build

aredden

import cv2
import numpy as np
import mediapipe as mp
image = cv2.imread("./input.png")
mask = np.zeros_like(image)
segm = mp.solutions.mediapipe.python.solutions.face_mesh.FaceMesh(
    True,
    min_detection_confidence=0.6
)
landmarks = segm.process(image).multi_face_landmarks

aredden

from typing import List
import numpy as np
def image_grid(images: List[np.ndarray], nrows: int):
    """
    Creates a grid of images with 'nrows' number of image rows, images must all be the same shape.
    """
    assert len(images) > nrows, "Must have more images than rows"
    ncols = int(np.ceil(len(images) / nrows))
    images_size = images[0].shape[:2]
    grid_img = np.zeros((nrows * images_size[0], ncols * images_size[1], 3), dtype=np.uint8)

aredden

from traceback import format_exception


def format_error(e: Exception, limit: int = 3):
    """Format exception as a string.

    Args:
        e (Exception): Raised exception object.
        limit (int): limit of error call stack lines to be included in the formatted string.
    """

aredden

const { ExifTool, Tags } = require('exiftool-vendored');
/**
 * Function to write makernote and exif data to a PNG file.
 * @param {string} filePath - Path to the PNG file.
 * @param {string} makerNote - Makernote exif key data to write.
 * @param {string} comment - Comment exif key data to write.
 * @return {Promise<boolean>} - True if successful, false otherwise.
 */
export const writeMetadata = async (filePath, makerNote, comment) => {
	const tool = new ExifTool();

aredden

aredden

from typing import AsyncGenerator, List, Coroutine, Any, Callable, Union
import asyncio
from loguru import logger


async def async_limit_iterator(
    limit=8,
    jobs: List[Any] = [],
    coroutine_fn: Callable[[Any], Coroutine[Any, None, None]] = None,
    throw_exceptions: bool = False,

aredden

import argparse
from typing import List, Union
import cv2, os, pathlib
from PIL import Image
from loguru import logger
from more_itertools import flatten
from mediapipe.python.solutions.face_detection import FaceDetection
import numpy as np
from traceback import format_exception

aredden

from pathlib import Path
from argparse import ArgumentParser
from typing import Optional
from pydantic import BaseModel

class Args(BaseModel):
    input_path: Path
    recursive: bool = False
    output_file: Optional[Path]
    extensions: list[str] = ['jpeg','jpg','png','webp']

aredden

import torch
from einops import rearrange

_magma_table = [
    (0.001462, 0.000466, 0.013866),
    (0.002258, 0.001295, 0.018331),
    (0.003279, 0.002305, 0.023708),
    (0.004512, 0.003490, 0.029965),
    (0.005950, 0.004843, 0.037130),
    (0.007588, 0.006356, 0.044973),