439 lines
16 KiB
Python
439 lines
16 KiB
Python
# Libraries
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from PIL import Image, ImageDraw, ImageFont
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import json
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import os
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import re
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import threading
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import time
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import traceback
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import tiktoken
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from pathlib import Path
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from colorama import Fore
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from dotenv import load_dotenv
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from retry import retry
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from tqdm import tqdm
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from util.translation import TranslationConfig, translateAI as sharedtranslateAI, getPricingConfig, calculateCost
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# Globals
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MODEL = os.getenv("model")
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TIMEOUT = int(os.getenv("timeout"))
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LANGUAGE = os.getenv("language").capitalize()
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PROMPT = Path("prompt.txt").read_text(encoding="utf-8")
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VOCAB = Path("vocab.txt").read_text(encoding="utf-8")
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LOCK = threading.Lock()
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PBAR = None
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WIDTH = int(os.getenv("width"))
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LISTWIDTH = int(os.getenv("listWidth"))
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NOTEWIDTH = int(os.getenv("noteWidth"))
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MAXHISTORY = 10
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ESTIMATE = ""
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TOKENS = [0, 0]
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NAMESLIST = []
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MISMATCH = [] # Lists files that throw a mismatch error (Length of GPT list response is wrong)
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FILENAME = None
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# Regex - Need to change this if you want to translate from/to other languages. Default is Japanese Regex
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LANGREGEX = r"[一-龠ぁ-ゔァ-ヴーａ-ｚＡ-Ｚ０-９\uFF61-\uFF9F]+"
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# Get pricing configuration based on the model
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PRICING_CONFIG = getPricingConfig(MODEL)
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INPUTAPICOST = PRICING_CONFIG["inputAPICost"]
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OUTPUTAPICOST = PRICING_CONFIG["outputAPICost"]
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BATCHSIZE = PRICING_CONFIG["batchSize"]
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FREQUENCY_PENALTY = PRICING_CONFIG["frequencyPenalty"]
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# Initialize Translation Config
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TRANSLATION_CONFIG = TranslationConfig(
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model=MODEL,
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language=LANGUAGE,
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prompt=PROMPT,
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vocab=VOCAB,
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langRegex=LANGREGEX,
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batchSize=BATCHSIZE,
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maxHistory=MAXHISTORY,
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estimateMode=False # Will be set dynamically based on ESTIMATE
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)
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LEAVE = False
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# tqdm Globals
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BAR_FORMAT = "{l_bar}{bar:10}{r_bar}{bar:-10b}"
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POSITION = 0
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LEAVE = False
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def handleImages(folderName, estimate):
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global ESTIMATE, TOKENS, FILENAME
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ESTIMATE = estimate
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FILENAME = folderName
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start = time.time()
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# Translate Strings
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translatedData = openFiles(f"files/{folderName}")
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# Custom Names
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# customList = [[], []]
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# customList = processImagesDir("Custom", customList)
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# Write TL To Images
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try:
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translatedList, originalList, dimensionsList = translatedData[0]
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for i in range(len(translatedList)):
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try:
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# Create image from string
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image = stringToImageOutline(translatedList[i], dimensionsList[i][0], dimensionsList[i][1])
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# Save image using the corresponding original filename
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image.save(rf"translated/{folderName}/{originalList[i]}.png", quality=100)
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except Exception as e:
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# Log error if image saving fails
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PBAR.write(f"Error processing {translatedList[i]}: {str(e)}")
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except IndexError:
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PBAR.write("Translated data is incomplete. Please check your input.")
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# Print File
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end = time.time()
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tqdm.write(getResultString(translatedData, end - start, folderName))
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with LOCK:
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TOKENS[0] += translatedData[1][0]
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TOKENS[1] += translatedData[1][1]
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# Print Total
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totalString = getResultString(["", TOKENS, None], end - start, "TOTAL")
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# Print any errors on maps
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if len(MISMATCH) > 0:
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return totalString + Fore.RED + f"\nMismatch Errors: {MISMATCH}" + Fore.RESET
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else:
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return totalString
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def openFiles(folderName):
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global PBAR
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if os.path.isdir(folderName):
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imageList = [[], [], []]
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imageList = processImagesDir(folderName, imageList)
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# Start Translation
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with tqdm(
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bar_format=BAR_FORMAT,
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position=POSITION,
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leave=LEAVE,
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desc=folderName,
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total=len(imageList[0]),
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) as PBAR:
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translatedData = translateImages(imageList)
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translatedData = [
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[translatedData[0], imageList[2], imageList[1]],
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translatedData[1],
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translatedData[2],
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]
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return translatedData
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else:
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print("The provided directory path does not exist.")
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def getResultString(translatedData, translationTime, filename):
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# File Print String
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cost = calculateCost(translatedData[1][0], translatedData[1][1], MODEL)
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totalTokenstring = (
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Fore.YELLOW + "[Input: " + str(translatedData[1][0]) + "]"
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"[Output: "
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+ str(translatedData[1][1])
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+ "]" "[Cost: ${:,.4f}".format(cost)
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+ "]"
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)
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timeString = Fore.BLUE + "[" + str(round(translationTime, 1)) + "s]"
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if translatedData[2] is None:
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# Success
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return filename + ": " + totalTokenstring + timeString + Fore.GREEN + " \u2713 " + Fore.RESET
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else:
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# Fail
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try:
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raise translatedData[2]
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except Exception as e:
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traceback.print_exc()
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errorString = str(e) + Fore.RED
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return filename + ": " + totalTokenstring + timeString + Fore.RED + " \u2717 " + errorString + Fore.RESET
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def getFontSize(text, image_width, image_height, font_path):
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# Start with a high font size and keep reducing it until the text fits within the image bounds
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font_size = min(image_width, image_height)
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while font_size > 0:
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font = ImageFont.truetype(font_path, font_size)
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text_bbox = ImageDraw.Draw(Image.new("RGB", (1, 1))).textbbox((0, 0), text, font=font)
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text_width = text_bbox[2] - text_bbox[0]
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text_height = text_bbox[3] - text_bbox[1]
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if text_width <= image_width and text_height <= image_height:
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return font_size
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font_size -= 1
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return font_size
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def stringToImage(text, width, height, font_path="fonts/TsunagiGothic.ttf", scale_factor=4):
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# Increase the resolution
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scaled_width = int(width * scale_factor)
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scaled_height = int(height * scale_factor)
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# Find the appropriate font size for the scaled up image
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font_size = getFontSize(text, scaled_width, scaled_height, font_path)
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if font_size == 0:
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raise ValueError("Text is too long to fit in the supplied dimensions.")
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# Create a new image with the scaled width and height and a transparent background
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image = Image.new("RGBA", (scaled_width, scaled_height), (255, 255, 255, 0))
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# Create a drawing context
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draw = ImageDraw.Draw(image)
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# Load the appropriate font
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font = ImageFont.truetype(font_path, font_size)
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# Calculate the size of the text to center it
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text_bbox = draw.textbbox((0, 0), text, font=font)
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text_width = text_bbox[2] - text_bbox[0]
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text_height = text_bbox[3] - text_bbox[1] + 20
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x = 0
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x = (scaled_width - text_width) // 2
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y = (scaled_height - text_height) // 2
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# Draw the text on the image
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draw.text((x, y), text, font=font, fill=(255, 255, 255, 255))
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# Resize back to the original dimensions to get a clearer text rendering
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image = image.resize(
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(width, height),
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Image.LANCZOS,
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)
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return image
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from PIL import Image, ImageDraw, ImageFont
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def stringToImageOutline(text, width, height, font_path="fonts/TsunagiGothic.ttf", scale_factor=4):
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# Outline
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outline_color = (255, 255, 255, 255)
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text_color = (0, 0, 0, 255)
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outline_thickness = 4
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# Increase the resolution
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scaled_width = int(width * scale_factor)
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scaled_height = int(height * scale_factor)
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# Find the appropriate font size for the scaled up image
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font_size = getFontSize(text, scaled_width, scaled_height, font_path)
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if font_size == 0:
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raise ValueError("Text is too long to fit in the supplied dimensions.")
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# Create a new image with the scaled width and height and a transparent background
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image = Image.new("RGBA", (scaled_width, scaled_height), (255, 255, 255, 0))
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# Create a drawing context
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draw = ImageDraw.Draw(image)
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# Load the appropriate font
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font = ImageFont.truetype(font_path, font_size)
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# Calculate the size of the text to center it
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text_bbox = draw.textbbox((0, 0), text, font=font)
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text_width = text_bbox[2] - text_bbox[0]
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text_height = text_bbox[3] - text_bbox[1] + 20
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x = (scaled_width - text_width) // 2
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y = (scaled_height - text_height) // 2
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# Draw the text outline by applying the text multiple times with small offsets
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for dx in range(-outline_thickness, outline_thickness + 1):
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for dy in range(-outline_thickness, outline_thickness + 1):
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if dx != 0 or dy != 0:
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draw.text((x + dx, y + dy), text, font=font, fill=outline_color)
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# Draw the main text
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draw.text((x, y), text, font=font, fill=text_color)
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# Resize back to the original dimensions to get a clearer text rendering
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image = image.resize((width, height), Image.LANCZOS)
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return image
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def stringToImageBox(text, width, height, font_path="fonts/TsunagiGothic.ttf", scale_factor=4):
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# Increase the resolution
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scaled_width = int(width * scale_factor)
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scaled_height = int(height * scale_factor)
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# Padding around the text
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padding = 10
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# Calculate the dimensions available for text placement
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available_width = scaled_width - 2 * padding
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available_height = scaled_height - 2 * padding
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# Determine the best font size to fit within the available dimensions
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font_size = getFontSize(text, available_width, available_height, font_path)
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if font_size <= 0:
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raise ValueError("Text is too long to fit in the supplied dimensions.")
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# Create a new image with increased resolution
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image = Image.new("RGBA", (scaled_width, scaled_height), (255, 255, 255, 0))
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draw = ImageDraw.Draw(image)
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# Load the calculated font
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font = ImageFont.truetype(font_path, font_size)
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# Calculate the size and bounding box of the text
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text_bbox = draw.textbbox((0, 0), text, font=font)
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text_width = text_bbox[2] - text_bbox[0]
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text_height = text_bbox[3] - text_bbox[1] + 20
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# Determine centered position for the text while considering padding
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# Additional adjustment ensures text appears centrally aligned
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x = (scaled_width - text_width) // 2
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y = (scaled_height - text_height) // 2
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# Draw a black box with a white outline that fits the image dimensions precisely
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draw.rectangle([0, 0, scaled_width - 1, scaled_height - 1], outline=(255, 255, 255, 255), width=1)
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# Fill the inside box with black color
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draw.rectangle([1, 1, scaled_width - 2, scaled_height - 2], fill=(0, 0, 0, 255))
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# Render the text within the image
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draw.text((x, y), text, font=font, fill=(255, 255, 255, 255))
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# Shrink the image back to original dimensions with high-quality interpolation
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image = image.resize(
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(width, height),
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Image.LANCZOS,
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)
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return image
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def getImageDimensions(file_path):
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try:
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with Image.open(file_path) as img:
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width, height = img.size
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return width, height
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except Exception as e:
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print(f"Error reading {file_path}: {e}")
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return None, None
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def processImagesDir(directory_path, imageList):
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for file_name in os.listdir(directory_path):
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# .png and Japanese
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if ".png" in file_name:
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file_path = os.path.join(directory_path, file_name)
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if os.path.isfile(file_path):
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# Check if the file is an image
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try:
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width, height = getImageDimensions(file_path)
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if width is not None and height is not None:
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placeholders = {
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".png": "",
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}
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for target, replacement in placeholders.items():
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file_name = file_name.replace(target, replacement)
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match = re.search(r"[\[【].+?[\]】](.*)", file_name)
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if match:
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text = match.group(1)
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else:
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text = file_name
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imageList[0].append(text)
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imageList[1].append([width, height])
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imageList[2].append(file_name)
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except Exception as e:
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print(f"Error processing {file_name}: {e}")
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if ".txt" in file_name:
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try:
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with open(f"{directory_path}/{file_name}", "r", encoding="utf8") as file:
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for line in file:
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line = line.strip()
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line = line.replace(":", "：")
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line = line.replace("/", "／")
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line = line.replace("?", "？")
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imageList[0].append(line) # Using strip() to remove any extra newlines or spaces
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imageList[1].append([104, 15])
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except FileNotFoundError:
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print(f"The file at {file_path} was not found.")
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except IOError:
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print(f"An error occurred while reading the file at {file_path}.")
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return imageList
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def translateImages(imageList):
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totalTokens = [0, 0]
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# Translate GPT
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response = translateAI(imageList[0], "Keep the Translation as brief as possible")
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translatedList = response[0]
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totalTokens[0] += response[1][0]
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totalTokens[1] += response[1][1]
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return [translatedList, totalTokens, None]
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# Save some money and enter the character before translation
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def getSpeaker(speaker):
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match speaker:
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case "ファイン":
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return ["Fine", [0, 0]]
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case "":
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return ["", [0, 0]]
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case _:
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# Find Speaker
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for i in range(len(NAMESLIST)):
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if speaker == NAMESLIST[i][0]:
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return [NAMESLIST[i][1], [0, 0]]
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# Translate and Store Speaker
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response = translateAI(
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f"{speaker}",
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"Reply with the " + LANGUAGE + " translation of the NPC name.",
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False,
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)
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response[0] = response[0].title()
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response[0] = response[0].replace("'S", "'s")
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response[0] = response[0].replace("Speaker: ", "")
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# Retry if name doesn't translate for some reason
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if re.search(r"([a-zA-Z？?])", response[0]) == None:
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response = translateAI(
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f"{speaker}",
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"Reply with the " + LANGUAGE + " translation of the NPC name.",
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False,
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)
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response[0] = response[0].title()
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response[0] = response[0].replace("'S", "'s")
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speakerList = [speaker, response[0]]
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NAMESLIST.append(speakerList)
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return response
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return [speaker, [0, 0]]
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def translateAI(text, history, history_ctx=None):
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"""
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Legacy wrapper function for the new shared translation utility.
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This maintains compatibility with existing code while using the new shared implementation.
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"""
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global PBAR, MISMATCH, FILENAME
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# Update config estimate mode based on global ESTIMATE
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TRANSLATION_CONFIG.estimateMode = bool(ESTIMATE)
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# Call the new shared translation function
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return sharedtranslateAI(
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text=text,
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history=history,
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config=TRANSLATION_CONFIG,
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filename=FILENAME,
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pbar=PBAR,
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lock=LOCK,
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mismatchList=MISMATCH
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)
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