add MSC nbs and cli cmds

2 files changed, 357 insertions, 0 deletions

diff --git a/megapixels/notebooks/datasets/adience/flickr_metadata_cleanup.ipynb b/megapixels/notebooks/datasets/adience/flickr_metadata_cleanup.ipynb
new file mode 100644
index 00000000..69aaced4
--- /dev/null
+++ b/megapixels/notebooks/datasets/adience/flickr_metadata_cleanup.ipynb
@@ -0,0 +1,169 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adience Flickr Metadata Cleanup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%reload_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "import os\n",
+    "from os.path import join\n",
+    "from glob import glob\n",
+    "from pathlib import Path\n",
+    "\n",
+    "from tqdm import tqdm_notebook as tqdm\n",
+    "import pandas as pd\n",
+    "\n",
+    "import sys\n",
+    "sys.path.append('/work/megapixels_dev/megapixels')\n",
+    "from app.utils import file_utils"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fp_in_files = '/data_store_hdd/datasets/people/adience/research/adience_flickr_api_queries.csv'\n",
+    "fp_in_nsid_urls = '/data_store_hdd/datasets/people/adience/research/adience_flickr_nsid_url.csv'\n",
+    "fp_in_nsid_profiles = '/data_store_hdd/datasets/people/adience/research/adience_flickr_nsid_profile.csv'\n",
+    "fp_out = '/data_store_hdd/datasets/people/adience/research/adience_flickr_meta.csv'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create metadata csv output\n",
+    "\n",
+    "|nsid|path_alias|count|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# files\n",
+    "df_files = pd.read_csv(fp_in_files)\n",
+    "df_files.fillna('', inplace=True)\n",
+    "files = df_files.to_dict('records')\n",
+    "\n",
+    "# nsid urls\n",
+    "df_urls = pd.read_csv(fp_in_nsid_urls)\n",
+    "df_urls.fillna('', inplace=True)\n",
+    "urls = df_urls.to_dict('records')\n",
+    "\n",
+    "# nsid profiles\n",
+    "df_profiles = pd.read_csv(fp_in_nsid_profiles)\n",
+    "df_profiles.fillna('', inplace=True)\n",
+    "profiles = df_profiles.to_dict('records')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create nsid lookup table\n",
+    "alias_lookup = {}\n",
+    "for url_meta in urls:\n",
+    "  alias_lookup[url_meta['nsid']] = str(Path(url_meta['url']).stem)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nsid_groups = df_files.groupby('nsid')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skipping: 10693681@N00\n",
+      "Skipping: 10743505@N04\n",
+      "Skipping: 113728563@N05\n",
+      "Skipping: 7648211@N03\n"
+     ]
+    }
+   ],
+   "source": [
+    "items = []\n",
+    "for nsid, nsid_group in nsid_groups:\n",
+    "  if nsid not in alias_lookup.keys():\n",
+    "    print(f'Skipping: {nsid}')\n",
+    "    continue\n",
+    "  path_alias = alias_lookup[nsid]\n",
+    "  obj = {'nsid': nsid, 'path_alias': path_alias, 'count': len(nsid_group)}\n",
+    "  items.append(obj)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_out = pd.DataFrame.from_dict(items)\n",
+    "df_out.to_csv(fp_out, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "megapixels",
+   "language": "python",
+   "name": "megapixels"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/megapixels/notebooks/datasets/adience/prepare_flickr_api.ipynb b/megapixels/notebooks/datasets/adience/prepare_flickr_api.ipynb
new file mode 100644
index 00000000..a35c3b24
--- /dev/null
+++ b/megapixels/notebooks/datasets/adience/prepare_flickr_api.ipynb
@@ -0,0 +1,188 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prepare Flickr API Batch CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%reload_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "import os\n",
+    "from os.path import join\n",
+    "from glob import glob, iglob\n",
+    "from pathlib import Path\n",
+    "from tqdm import tqdm_notebook as tqdm\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create CSV for API"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "| filepath | query | count |\n",
+    "|:---|:---|:---|\n",
+    "| 12234 | 12234@123| 10 |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fp_in_dir = '/data_store/datasets/people/adience/dataset/'\n",
+    "fp_out_queries = '/data_store/datasets/people/adience/research/adience_flickr_api_queries.csv'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9\n"
+     ]
+    }
+   ],
+   "source": [
+    "fp_files = glob(join(fp_in_dir, '*.txt'))\n",
+    "print(len(fp_files))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_images = pd.DataFrame()\n",
+    "for fp_file in fp_files:\n",
+    "  df = pd.read_csv(fp_file, delimiter='\\t')\n",
+    "  if 'user_id' in df.keys():\n",
+    "    df = df[['user_id', 'original_image']]\n",
+    "    df_images = dfs.append(df, ignore_index=True)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_images.drop_duplicates(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "images = df_images.to_dict('records')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for image in images:\n",
+    "  image['photo_id'] = image['original_image'].split('_')[0]\n",
+    "  image['filename'] = f'{image[\"photo_id\"]}.json'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_images = pd.DataFrame.from_dict(images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_images.drop(columns=['original_image'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10804\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(df_images))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_images.to_csv(fp_out_queries, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "megapixels",
+   "language": "python",
+   "name": "megapixels"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}