third_party/ASpanFormer/src/utils/misc.py

import os
import contextlib
import joblib
from typing import Union
from loguru import _Logger, logger
from itertools import chain

import torch
from yacs.config import CfgNode as CN
from pytorch_lightning.utilities import rank_zero_only
import cv2
import numpy as np


def lower_config(yacs_cfg):
    if not isinstance(yacs_cfg, CN):
        return yacs_cfg
    return {k.lower(): lower_config(v) for k, v in yacs_cfg.items()}


def upper_config(dict_cfg):
    if not isinstance(dict_cfg, dict):
        return dict_cfg
    return {k.upper(): upper_config(v) for k, v in dict_cfg.items()}


def log_on(condition, message, level):
    if condition:
        assert level in ["INFO", "DEBUG", "WARNING", "ERROR", "CRITICAL"]
        logger.log(level, message)


def get_rank_zero_only_logger(logger: _Logger):
    if rank_zero_only.rank == 0:
        return logger
    else:
        for _level in logger._core.levels.keys():
            level = _level.lower()
            setattr(logger, level, lambda x: None)
        logger._log = lambda x: None
    return logger


def setup_gpus(gpus: Union[str, int]) -> int:
    """A temporary fix for pytorch-lighting 1.3.x"""
    gpus = str(gpus)
    gpu_ids = []

    if "," not in gpus:
        n_gpus = int(gpus)
        return n_gpus if n_gpus != -1 else torch.cuda.device_count()
    else:
        gpu_ids = [i.strip() for i in gpus.split(",") if i != ""]

    # setup environment variables
    visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
    if visible_devices is None:
        os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
        os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(str(i) for i in gpu_ids)
        visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
        logger.warning(
            f"[Temporary Fix] manually set CUDA_VISIBLE_DEVICES when specifying gpus to use: {visible_devices}"
        )
    else:
        logger.warning(
            "[Temporary Fix] CUDA_VISIBLE_DEVICES already set by user or the main process."
        )
    return len(gpu_ids)


def flattenList(x):
    return list(chain(*x))


@contextlib.contextmanager
def tqdm_joblib(tqdm_object):
    """Context manager to patch joblib to report into tqdm progress bar given as argument

    Usage:
        with tqdm_joblib(tqdm(desc="My calculation", total=10)) as progress_bar:
            Parallel(n_jobs=16)(delayed(sqrt)(i**2) for i in range(10))

    When iterating over a generator, directly use of tqdm is also a solutin (but monitor the task queuing, instead of finishing)
        ret_vals = Parallel(n_jobs=args.world_size)(
                    delayed(lambda x: _compute_cov_score(pid, *x))(param)
                        for param in tqdm(combinations(image_ids, 2),
                                          desc=f'Computing cov_score of [{pid}]',
                                          total=len(image_ids)*(len(image_ids)-1)/2))
    Src: https://stackoverflow.com/a/58936697
    """

    class TqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack):
        def __init__(self, *args, **kwargs):
            super().__init__(*args, **kwargs)

        def __call__(self, *args, **kwargs):
            tqdm_object.update(n=self.batch_size)
            return super().__call__(*args, **kwargs)

    old_batch_callback = joblib.parallel.BatchCompletionCallBack
    joblib.parallel.BatchCompletionCallBack = TqdmBatchCompletionCallback
    try:
        yield tqdm_object
    finally:
        joblib.parallel.BatchCompletionCallBack = old_batch_callback
        tqdm_object.close()


def draw_points(img, points, color=(0, 255, 0), radius=3):
    dp = [(int(points[i, 0]), int(points[i, 1])) for i in range(points.shape[0])]
    for i in range(points.shape[0]):
        cv2.circle(img, dp[i], radius=radius, color=color)
    return img


def draw_match(
    img1,
    img2,
    corr1,
    corr2,
    inlier=[True],
    color=None,
    radius1=1,
    radius2=1,
    resize=None,
):
    if resize is not None:
        scale1, scale2 = [img1.shape[1] / resize[0], img1.shape[0] / resize[1]], [
            img2.shape[1] / resize[0],
            img2.shape[0] / resize[1],
        ]
        img1, img2 = cv2.resize(img1, resize, interpolation=cv2.INTER_AREA), cv2.resize(
            img2, resize, interpolation=cv2.INTER_AREA
        )
        corr1, corr2 = (
            corr1 / np.asarray(scale1)[np.newaxis],
            corr2 / np.asarray(scale2)[np.newaxis],
        )
    corr1_key = [
        cv2.KeyPoint(corr1[i, 0], corr1[i, 1], radius1) for i in range(corr1.shape[0])
    ]
    corr2_key = [
        cv2.KeyPoint(corr2[i, 0], corr2[i, 1], radius2) for i in range(corr2.shape[0])
    ]

    assert len(corr1) == len(corr2)

    draw_matches = [cv2.DMatch(i, i, 0) for i in range(len(corr1))]
    if color is None:
        color = [(0, 255, 0) if cur_inlier else (0, 0, 255) for cur_inlier in inlier]
    if len(color) == 1:
        display = cv2.drawMatches(
            img1,
            corr1_key,
            img2,
            corr2_key,
            draw_matches,
            None,
            matchColor=color[0],
            singlePointColor=color[0],
            flags=4,
        )
    else:
        height, width = max(img1.shape[0], img2.shape[0]), img1.shape[1] + img2.shape[1]
        display = np.zeros([height, width, 3], np.uint8)
        display[: img1.shape[0], : img1.shape[1]] = img1
        display[: img2.shape[0], img1.shape[1] :] = img2
        for i in range(len(corr1)):
            left_x, left_y, right_x, right_y = (
                int(corr1[i][0]),
                int(corr1[i][1]),
                int(corr2[i][0] + img1.shape[1]),
                int(corr2[i][1]),
            )
            cur_color = (int(color[i][0]), int(color[i][1]), int(color[i][2]))
            cv2.line(
                display,
                (left_x, left_y),
                (right_x, right_y),
                cur_color,
                1,
                lineType=cv2.LINE_AA,
            )
    return display