Mooney images are high-contrast, two-tone visual stimuli, created by thresholding photographic images. They allow researchers to separate image content from image understanding, making them valuable for studying visual perception. An ideal Mooney image for this purpose achieves a specific balance: it initially appears unrecognizable but becomes fully interpretable to the observer after seeing the original template. Researchers traditionally created these stimuli manually using subjective criteria, which is labor-intensive and can introduce inconsistencies across studies. Automated generation techniques now offer an alternative to this manual approach. Here, we present MooneyMaker, an open-source Python package that automates the generation of ambiguous Mooney images using several complementary approaches. Users can choose between various generation techniques that range from approaches based on image statistics to deep learning models. These models strategically alter edge information to increase initial ambiguity. The package lets users create two-tone images with multiple methods and directly compare the results visually. In an experiment, we validate MooneyMaker by generating Mooney images using different techniques and assess their recognizability for human observers before and after disambiguating them by presenting the template images. Our results reveal that techniques with lower initial recognizability are associated with higher post-template recognition (i.e. a larger disambiguation effect). To help vision scientists build effective databases of Mooney stimuli, we provide practical guidelines for technique selection. By standardizing the generation process, MooneyMaker supports more consistent and reproducible visual perception research.

翻译：穆尼图像是通过对摄影图像进行阈值处理生成的高对比度双色调视觉刺激。这类图像使研究者能够分离图像内容与图像理解过程，因而对视觉感知研究具有重要价值。理想的穆尼图像需达成一种特定平衡：初始状态下难以识别，但在观察者看到原始模板图像后变得完全可解读。传统上，研究者需依据主观标准手动创建这类刺激材料，该方法不仅耗时费力，且易导致不同研究间的不一致性。自动化生成技术为此类人工方法提供了替代方案。本文介绍MooneyMaker——一个基于多种互补方法自动生成模糊穆尼图像的开源Python工具包。用户可在基于图像统计学的传统方法与深度学习模型等多种生成技术间进行选择，这些模型通过策略性改变边缘信息以增强初始模糊性。该工具包支持用户使用多种方法创建双色调图像，并可直接进行可视化结果对比。我们通过实验验证了MooneyMaker的性能：采用不同技术生成穆尼图像，并通过呈现模板图像消除歧义前后评估人类观察者对图像的识别能力。实验结果表明，初始识别率较低的技术往往伴随更高的模板后识别率（即更强的消歧效应）。为帮助视觉科学家构建有效的穆尼刺激数据库，我们提供了技术选择的实用指南。通过标准化生成流程，MooneyMaker有助于开展更具一致性和可重复性的视觉感知研究。