import os import numpy as np import urllib import tarfile, zipfile from pathlib import Path from tqdm import tqdm def unpack(path): if path.endswith("tar.gz"): with tarfile.open(path, "r:gz") as tar: tar.extractall(path=os.path.split(path)[0]) elif path.endswith("tar"): with tarfile.open(path, "r:") as tar: tar.extractall(path=os.path.split(path)[0]) elif path.endswith("zip"): with zipfile.ZipFile(path, "r") as f: f.extractall(path=os.path.split(path)[0]) else: raise NotImplementedError( "Unknown file extension: {}".format(os.path.splitext(path)[1]) ) def reporthook(bar): """tqdm progress bar for downloads.""" def hook(b=1, bsize=1, tsize=None): if tsize is not None: bar.total = tsize bar.update(b * bsize - bar.n) return hook def get_root(name): base = "data/" root = os.path.join(base, name) os.makedirs(root, exist_ok=True) return root def is_prepared(root): return Path(root).joinpath(".ready").exists() def mark_prepared(root): Path(root).joinpath(".ready").touch() def prompt_download(file_, source, target_dir, content_dir=None): targetpath = os.path.join(target_dir, file_) while not os.path.exists(targetpath): if content_dir is not None and os.path.exists( os.path.join(target_dir, content_dir) ): break print( "Please download '{}' from '{}' to '{}'.".format(file_, source, targetpath) ) if content_dir is not None: print( "Or place its content into '{}'.".format( os.path.join(target_dir, content_dir) ) ) input("Press Enter when done...") return targetpath def download_url(file_, url, target_dir): targetpath = os.path.join(target_dir, file_) os.makedirs(target_dir, exist_ok=True) with tqdm( unit="B", unit_scale=True, unit_divisor=1024, miniters=1, desc=file_ ) as bar: urllib.request.urlretrieve(url, targetpath, reporthook=reporthook(bar)) return targetpath def download_urls(urls, target_dir): paths = dict() for fname, url in urls.items(): outpath = download_url(fname, url, target_dir) paths[fname] = outpath return paths def quadratic_crop(x, bbox, alpha=1.0): """bbox is xmin, ymin, xmax, ymax""" im_h, im_w = x.shape[:2] bbox = np.array(bbox, dtype=np.float32) bbox = np.clip(bbox, 0, max(im_h, im_w)) center = 0.5 * (bbox[0] + bbox[2]), 0.5 * (bbox[1] + bbox[3]) w = bbox[2] - bbox[0] h = bbox[3] - bbox[1] l = int(alpha * max(w, h)) l = max(l, 2) required_padding = -1 * min( center[0] - l, center[1] - l, im_w - (center[0] + l), im_h - (center[1] + l) ) required_padding = int(np.ceil(required_padding)) if required_padding > 0: padding = [ [required_padding, required_padding], [required_padding, required_padding], ] padding += [[0, 0]] * (len(x.shape) - 2) x = np.pad(x, padding, "reflect") center = center[0] + required_padding, center[1] + required_padding xmin = int(center[0] - l / 2) ymin = int(center[1] - l / 2) return np.array(x[ymin : ymin + l, xmin : xmin + l, ...])