{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a01fdc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "#| eval: false\n",
    "! [ -e /content ] && pip install -Uqq fastai  # upgrade fastai on colab"
   ]
  },
  {
   "cell_type": "raw",
   "id": "493f7e72",
   "metadata": {},
   "source": [
    "---\n",
    "skip_exec: true\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b62b0f6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| default_exp vision.utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4ba4284",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "import uuid\n",
    "from fastai.torch_basics import *\n",
    "from fastai.data.all import *\n",
    "from fastai.vision.core import *\n",
    "from fastdownload import download_url\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c6961ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "from nbdev.showdoc import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b41b88b",
   "metadata": {},
   "source": [
    "# Vision utils\n",
    "\n",
    "> Some utils function to quickly download a bunch of images, check them and pre-resize them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f534f4c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "def _get_downloaded_image_filename(dest, name, suffix):\n",
    "    start_index = 1\n",
    "    candidate_name = name\n",
    "\n",
    "    while (dest/f\"{candidate_name}{suffix}\").is_file():\n",
    "        candidate_name = f\"{candidate_name}{start_index}\"\n",
    "        start_index += 1\n",
    "\n",
    "    return candidate_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e94c0aa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "def _download_image_inner(dest, inp, timeout=4, preserve_filename=False):\n",
    "    i,url = inp\n",
    "    url = url.split(\"?\")[0]\n",
    "    url_path = Path(url)\n",
    "    suffix = url_path.suffix if url_path.suffix else '.jpg'\n",
    "    name = _get_downloaded_image_filename(dest, url_path.stem, suffix) if preserve_filename else str(uuid.uuid4())\n",
    "    try: download_url(url, dest/f\"{name}{suffix}\", show_progress=False, timeout=timeout)\n",
    "    except Exception as e: f\"Couldn't download {url}.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2889065",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "with tempfile.TemporaryDirectory() as d:\n",
    "    d = Path(d)\n",
    "    url = \"https://www.fast.ai/images/jh-head.jpg\"\n",
    "    _download_image_inner(d, (125,url))\n",
    "    test_eq(len(d.ls()), 1)\n",
    "\n",
    "with tempfile.TemporaryDirectory() as d:\n",
    "    d = Path(d)\n",
    "    url = \"https://www.fast.ai/images/jh-head.jpg\"\n",
    "\n",
    "    _download_image_inner(d, (125,url), preserve_filename=True)\n",
    "    assert (d/'jh-head.jpg').is_file()\n",
    "    assert not (d/'jh-head.jpg1').exists()\n",
    "\n",
    "    _download_image_inner(d, (125,url), preserve_filename=True)\n",
    "    assert (d/'jh-head.jpg').is_file()\n",
    "    assert (d/'jh-head1.jpg').is_file()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0593ffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "def download_images(dest, url_file=None, urls=None, max_pics=1000, n_workers=8, timeout=4, preserve_filename=False):\n",
    "    \"Download images listed in text file `url_file` to path `dest`, at most `max_pics`\"\n",
    "    if urls is None: urls = url_file.read_text().strip().split(\"\\n\")[:max_pics]\n",
    "    dest = Path(dest)\n",
    "    dest.mkdir(exist_ok=True)\n",
    "    parallel(partial(_download_image_inner, dest, timeout=timeout, preserve_filename=preserve_filename),\n",
    "             list(enumerate(urls)), n_workers=n_workers, threadpool=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4164341c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "with tempfile.TemporaryDirectory() as d:\n",
    "    d = Path(d)\n",
    "    url_file = d/'urls.txt'\n",
    "    url_file.write_text(\"\\n\".join([f\"https://www.fast.ai/images/{n}\" for n in \"jh-head.jpg headshot-small.jpg\".split()]))\n",
    "    \n",
    "    download_images(d, url_file, preserve_filename=True)\n",
    "    assert (d/'jh-head.jpg').is_file()\n",
    "    assert (d/'headshot-small.jpg').is_file()\n",
    "    assert not (d/'jh-head1.jpg').exists()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41c04937",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "def resize_to(img, targ_sz, use_min=False):\n",
    "    \"Size to resize to, to hit `targ_sz` at same aspect ratio, in PIL coords (i.e w*h)\"\n",
    "    w,h = img.size\n",
    "    min_sz = (min if use_min else max)(w,h)\n",
    "    ratio = targ_sz/min_sz\n",
    "    return int(w*ratio),int(h*ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a6a4d9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class _FakeImg():\n",
    "    def __init__(self, size): self.size=size\n",
    "\n",
    "img = _FakeImg((200,500))\n",
    "test_eq(resize_to(img, 400), [160,400])\n",
    "test_eq(resize_to(img, 400, use_min=True), [400,1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bff393f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "def verify_image(fn):\n",
    "    \"Confirm that `fn` can be opened\"\n",
    "    try:\n",
    "        im = Image.open(fn)\n",
    "        im.draft(im.mode, (32,32))\n",
    "        im.load()\n",
    "        return True\n",
    "    except: return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f418bded",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "def verify_images(fns):\n",
    "    \"Find images in `fns` that can't be opened\"\n",
    "    return L(fns[i] for i,o in enumerate(parallel(verify_image, fns)) if not o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aad88d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "def resize_image(file, dest, src='.', max_size=None, n_channels=3, ext=None,\n",
    "                 img_format=None, resample=BILINEAR, resume=False, **kwargs ):\n",
    "    \"Resize file to dest to max_size\"\n",
    "    dest = Path(dest)\n",
    "    \n",
    "    dest_fname = dest/file\n",
    "    dest_fname.parent.mkdir(exist_ok=True, parents=True)\n",
    "    file = Path(src)/file\n",
    "    if resume and dest_fname.exists(): return\n",
    "    if not verify_image(file): return\n",
    "\n",
    "    img = Image.open(file)\n",
    "    imgarr = np.array(img)\n",
    "    img_channels = 1 if len(imgarr.shape) == 2 else imgarr.shape[2]\n",
    "    if ext is not None: dest_fname=dest_fname.with_suffix(ext)\n",
    "    if (max_size is not None and (img.height > max_size or img.width > max_size)) or img_channels != n_channels:\n",
    "        if max_size is not None:\n",
    "            new_sz = resize_to(img, max_size)\n",
    "            img = img.resize(new_sz, resample=resample)\n",
    "        if n_channels == 3: img = img.convert(\"RGB\")\n",
    "        img.save(dest_fname, img_format, **kwargs)\n",
    "    elif file != dest_fname : shutil.copy2(file, dest_fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "027d46db",
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'puppy.jpg'\n",
    "dest = Path('.')\n",
    "resize_image(file, dest, src='images', max_size=400)\n",
    "im = Image.open(dest/file)\n",
    "test_eq(im.shape[1],400)\n",
    "(dest/file).unlink()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ec6f281",
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'puppy.jpg'\n",
    "dest = Path('images')\n",
    "resize_image(file, dest, src=dest, max_size=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9103784",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "def resize_images(path, max_workers=defaults.cpus, max_size=None, recurse=False,\n",
    "                  dest=Path('.'), n_channels=3, ext=None, img_format=None, resample=BILINEAR,\n",
    "                  resume=None, **kwargs):\n",
    "    \"Resize files on path recursively to dest to max_size\"\n",
    "    path = Path(path)\n",
    "    if resume is None and dest != Path('.'): resume=False\n",
    "    os.makedirs(dest, exist_ok=True)\n",
    "    files = get_image_files(path, recurse=recurse)\n",
    "    files = [o.relative_to(path) for o in files]\n",
    "    parallel(resize_image, files, src=path, n_workers=max_workers, max_size=max_size, dest=dest, n_channels=n_channels, ext=ext,\n",
    "                   img_format=img_format, resample=resample, resume=resume, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04990b6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with tempfile.TemporaryDirectory() as d:\n",
    "    dest = Path(d)/'resized_images'\n",
    "    resize_images('images', max_size=100, dest=dest, max_workers=0, recurse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d519e6ff",
   "metadata": {},
   "source": [
    "# Export -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a675cf17",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "from nbdev import nbdev_export\n",
    "nbdev_export()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d585434",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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