![]() ![]() ![]() ![]() The module is available as open source under the terms of the Apache License, Version 2. To get the best performance, use app.process(image) Contributingīug reports and/or pull requests are welcome License Using parameters ( scale, mode, output_format, delete_from_history) will take additional time to process single image. DxO has won and continues to win the most awards, but Im sure Topaz has some merits. STANDARD, delete_from_history = True, output_format = OutputFormat. For noise reduction DxO and Topaz are clear winners. print ( output_path )Īdditional parameters can be passed to process() method (Takes additional time): from gigapixel import Scale, Mode, OutputFormat output_path = app. image = Path ( 'path/to/image.jpg' ) output_path = app. app = Gigapixel ( exe_path, output_suffix ) # Process image. output_suffix = '-gigapixel' # Create Gigapixel instance. pic.jpg -> pic-gigapixel.jpg) # You should set same value inside Gigapixel (File -> Preferences -> Default filename suffix). exe_path = Path ( 'C:\Program Files\Topaz Labs LLC\Topaz Gigapixel AI\Topaz Gigapixel AI.exe' ) # Output file suffix. Install the current version with PyPI pip install -U gigapixelįrom gigapixel import Gigapixel, Scale, Mode, OutputFormat from pathlib import Path # Path to Gigapixel executable file. If there’s a specific image you’d like us to see, you can send it us at this dropbox link. Topaz Gigapixel AI v6.1.0 or newer required Installation We’ll be updating TPAI regularly to address those pieces of feedback and issue reports. ![]()
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