318 lines
10 KiB
Plaintext
318 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# About\n",
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"\n",
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"This is a notebook for finding and copying Textures form extracted game assets to named images for the item database. \n",
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"\n",
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"## Why not a script?\n",
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"\n",
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"because depending on what extractor you use and the whims of the developers all this could use some serious tweaking every run. The notebook lets you run things in stages and inspect what your working with."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"\n",
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"database = {}\n",
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"\n",
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"with open(\"data/database.json\", \"r\") as f:\n",
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" database = json.load(f)\n",
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"\n",
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"db = database[\"db\"]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Item Database Pulled in"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pathlib import Path \n",
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"\n",
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"# Location were https://github.com/SeriousCache/UABE has extracted all Texture2D assets\n",
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"# Change as necessary\n",
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"datapath = Path(r\"E:\\Games\\SteamLibrary\\steamapps\\common\\Stationeers\\Stationpedia\\exported_textures\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Change this Datapath to point to the extracted textures"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"# Pull in a list of all found textures\n",
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"images = list(datapath.glob(\"*.png\"))\n",
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"names = [image.name for image in images]\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Finding matches\n",
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"\n",
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"This next section loops through all the item names and collects all the candidate textures. Then, through a series of rules, attempts to narrow down the choices to 1 texture."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"image_candidates = {}\n",
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"\n",
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"def filter_candidates(candidates):\n",
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" max_match_len = 0\n",
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" filtered_matches = []\n",
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"\n",
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" # go for longest match\n",
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" for can in candidates:\n",
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" name, match, mapping = can\n",
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" match_len = len(match)\n",
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" if match_len > max_match_len:\n",
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" max_match_len = match_len\n",
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" filtered_matches = [(name, match, mapping)]\n",
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" elif match_len == max_match_len:\n",
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" filtered_matches.append((name, match, mapping))\n",
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"\n",
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" # choose better matches\n",
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" if len(filtered_matches) > 1:\n",
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" better_matches = []\n",
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" for can in filtered_matches:\n",
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" name, match, mapping = can\n",
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" if mapping.startswith(\"Item\") and mapping in name:\n",
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" better_matches.append((name, match, mapping))\n",
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" elif mapping.startswith(\"Structure\") and mapping in name:\n",
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" better_matches.append((name, match, mapping))\n",
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" if len(better_matches) > 0:\n",
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" filtered_matches = better_matches\n",
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"\n",
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" #exclude build states if we have non build states\n",
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" if len(filtered_matches) > 1:\n",
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" non_build_state = []\n",
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" for can in filtered_matches:\n",
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" name, match, mapping = can\n",
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" if \"BuildState\" not in name:\n",
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" non_build_state.append((name, match, mapping))\n",
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" if len(non_build_state) > 0:\n",
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" filtered_matches = non_build_state\n",
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"\n",
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" #prefer matches without extra tags\n",
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" if len(filtered_matches) > 1:\n",
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" direct = []\n",
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" for can in filtered_matches:\n",
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" name, match, mapping = can\n",
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" if f\"{match}-\" in name:\n",
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" direct.append((name, match, mapping))\n",
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" if len(direct) > 0:\n",
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" filtered_matches = direct\n",
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" \n",
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" #filter to unique filenames\n",
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" if len(filtered_matches) > 1:\n",
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" unique_names = []\n",
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" unique_matches = []\n",
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" for can in filtered_matches:\n",
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" name, match, mapping = can\n",
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" if name not in unique_names:\n",
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" unique_names.append(name)\n",
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" unique_matches.append((name, match, mapping))\n",
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" filtered_matches = unique_matches\n",
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"\n",
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" #prefer not worse matches\n",
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" if len(filtered_matches) > 1:\n",
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" not_worse = []\n",
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" for can in filtered_matches:\n",
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" name, match, mapping = can\n",
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" if name.startswith(\"Item\") and not mapping.startswith(\"Item\"):\n",
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" continue\n",
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" elif name.startswith(\"Structure\") and not mapping.startswith(\"Structure\"):\n",
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" continue\n",
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" elif name.startswith(\"Kit\") and not mapping.startswith(\"Kit\"):\n",
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" continue\n",
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" elif not name.startswith(match):\n",
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" continue\n",
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" not_worse.append((name, match, mapping))\n",
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" if len(not_worse) > 0:\n",
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" filtered_matches = not_worse\n",
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"\n",
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" #if we have colored variants take White\n",
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" if len(filtered_matches) > 1:\n",
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" for can in filtered_matches:\n",
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" name, match, mapping = can\n",
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" if f\"_White\" in name:\n",
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" return [name]\n",
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"\n",
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" return [name for name, _, _ in filtered_matches]\n",
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"\n",
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"for entry in db.values():\n",
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" candidates = []\n",
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" for name in names:\n",
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" if entry[\"name\"] in name:\n",
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" candidates.append((name, entry[\"name\"], entry[\"name\"]))\n",
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" if entry[\"name\"].removeprefix(\"Item\") in name:\n",
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" candidates.append((name, entry[\"name\"].removeprefix(\"Item\"), entry[\"name\"]))\n",
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" if entry[\"name\"].removeprefix(\"Structure\") in name:\n",
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" candidates.append((name, entry[\"name\"].removeprefix(\"Structure\"), entry[\"name\"]))\n",
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" image_candidates[entry[\"name\"]] = filter_candidates(candidates)\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Some Items end up with no match but these items are often subtypes of an item that will have a match"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# rematch items to super structure?\n",
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"for name in image_candidates.keys():\n",
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" for other in image_candidates.keys():\n",
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" if name != other and name in other:\n",
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" if len(image_candidates[name]) > 0 and len(image_candidates[other]) == 0:\n",
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" image_candidates[other] = image_candidates[name]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Prepare out List of file copies. at this point a few items will never have a match. and one or two will have two choices but those choices will be arbitrary."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ItemBiomass []\n",
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"StructureBlocker []\n",
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"CartridgePlantAnalyser []\n",
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"StructureElevatorLevelIndustrial []\n",
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"ItemPlantEndothermic_Creative []\n",
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"Flag_ODA_10m []\n",
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"Flag_ODA_4m []\n",
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"Flag_ODA_6m []\n",
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"Flag_ODA_8m []\n",
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"ItemHorticultureBelt []\n",
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"ItemKitLiquidRegulator []\n",
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"ItemKitPortablesConnector []\n",
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"Landingpad_GasConnectorInwardPiece []\n",
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"Landingpad_LiquidConnectorInwardPiece []\n",
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"ItemMushroom ['ItemMushroom-resources.assets-3022.png', 'ItemMushroom-resources.assets-9304.png']\n",
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"StructurePlinth []\n",
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"ItemPlantThermogenic_Creative []\n"
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]
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}
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],
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"source": [
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"to_copy = []\n",
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"for name, candidates in image_candidates.items():\n",
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" if len(candidates) != 1:\n",
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" print(name, candidates)\n",
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" if len(candidates) > 1:\n",
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" #take first as fallback\n",
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" to_copy.append((name, candidates[0]))\n",
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" else:\n",
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" # print(name, candidates)\n",
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" to_copy.append((name, candidates[0]))\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1223 of 1223 | 100.00% \n",
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"Done\n"
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]
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}
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],
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"source": [
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"import shutil\n",
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"\n",
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"destpath = Path(\"img/stationpedia\")\n",
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"total_files = len(to_copy)\n",
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"\n",
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"count = 0\n",
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"print ( f\"{count} of {total_files} | { count / total_files * 100}\", end=\"\\r\")\n",
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"for name, file in to_copy:\n",
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" source = datapath / file\n",
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" dest = destpath / f\"{name}.png\"\n",
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" shutil.copy(source, dest)\n",
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" count += 1\n",
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" print ( f\"{count} of {total_files} | { (count / total_files) * 100 :.2f}% \", end=\"\\r\")\n",
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"print()\n",
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"print(\"Done\")\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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