opus-submitter/main.py
2025-10-28 23:10:46 +01:00

126 lines
4.5 KiB
Python

# HOW TO USE
# 1. Install Tesseract (https://github.com/tesseract-ocr/tesseract/releases/)
# 2. Install required libraries (see in the code below)
# 3. Put all your Opus Magnum-generated gifs in a folder
# 4. Facultative: if you are importing your gifs automatically from Discord using DiscordChatExporter, also save the CSV data file
# 5. Facultative: if you want to make an automatic correspondence between the usernames and desired display names, you can create a username.csv file with 2 columns: usename and name
# 6. Change the settings in the code below according to your needs
# 7. If everything goes well, the results should be output on the console as well as in a csv file.
# Import required packages
import cv2
import pytesseract
from PIL import Image
import os
import pandas as pd
### SETTINGS
# Mention the installed location of Tesseract-OCR in your system
# pytesseract.pytesseract.tesseract_cmd = 'C:/Program Files/Tesseract-OCR/tesseract.exe'
pytesseract.pytesseract.tesseract_cmd = '/usr/bin/tesseract'
# Insert the path of the folder containing the GIFs here
gifs_path = "gifs_opus_magnum"
# Do you want to include the Discord usernames in the output (requires Discord messages data file)? If False, will use name of file instead
use_discord_data = False
# Insert the path of the CSV file containing Discord messages data
discord_data_path = "EvLan - EvLan 2 - solutions-opus-magnum [1260715259514327070].csv"
# Do you want to include the real names of the participants (requires username correspondence file)?
use_real_names = False
# Insert the path of the CSV file containing username -> name correspondence
usernames_data_path = "usernames.csv"
# Insert here the desired path for the output CSV file
output_path = "results_opus_magnum.csv"
### END OF SETTINGS
results = pd.DataFrame(columns=['username','name','puzzle','cost','cycles','area','notes'])
filenames = os.listdir(gifs_path)
if use_discord_data:
discord_data_df = pd.read_csv(discord_data_path)
if use_real_names:
usernames_data_df = pd.read_csv(usernames_data_path)
for filename in filenames:
img_path = os.path.join(gifs_path, filename)
# Convert GIF to JPG
with Image.open(img_path) as img:
width, height = img.size
img.seek(0)
rgb_img = img.convert("RGB")
rgb_img.save("temp.jpg", "JPEG")
# Read image from which text needs to be extracted
img = cv2.imread("temp.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Optional: resize for better OCR
gray = cv2.resize(gray, None, fx=1, fy=1, interpolation=cv2.INTER_CUBIC)
# Manually crop regions based on known layout (x, y, w, h)
regions = [
(15, 600, 330, 28), # PUZZLE NAME
(412, 603, 65, 22), # COST
(577, 603, 65, 22), # CYCLES
(739, 603, 65, 22) # AREA
]
output = img.copy()
# "Username", "name" and "notes" fields are filled in in this section
username = filename
notes = ""
if use_discord_data:
for _, row in discord_data_df.iterrows():
attachments = row['Attachments'].split(',')
for attachment in attachments:
if filename == attachment.split('\\')[1]:
username = row['Author']
notes = row['Content']
if use_real_names:
name = usernames_data_df.loc[username == usernames_data_df['username']].iloc[0]['name']
else:
name = ""
def find_text(dims, gray, output, content):
x, y, w, h = dims
roi = gray[y:y+h, x:x+w]
roi = cv2.bitwise_not(roi)
if content == 'digits' or content == 'digits_with_6':
config = "--oem 3 --psm 7 -c tessedit_char_whitelist=0123456789"
else:
config = "--oem 3 --psm 7"
text = pytesseract.image_to_string(roi, config=config).strip()
# Remove the extra 6 (actually the G for Gold) for cost value
if content == 'digits_with_6':
text = text[:-1]
cv2.rectangle(output, (x, y), (x+w, y+h), (0, 255, 0), 2)
return text
puzzle = find_text(regions[0], gray, output, 'letters')
cost = find_text(regions[1], gray, output, 'digits_with_6')
cycles = find_text(regions[2], gray, output, 'digits')
area = find_text(regions[3], gray, output, 'digits')
results.loc[len(results)] = [username, name, puzzle, cost, cycles, area, notes]
# Save image with green rectangles around the considered zones, for debug purposes
#cv2.imwrite("output_debug.jpg", output)
os.remove("temp.jpg")
print("Done.")
print(results)
results.to_csv(output_path)