PyCinemetricsV2: Interactive computational film software based on transformers and PySide6

Based on feedback from film scholars, we developed PyCinemetricsV2, an upgraded film analytics software. Built on pre-trained AIGC models and PySide6, it offers features such as removing false positives and adding false negatives after TransNetV2 Shot-Boundary Detection (SBD), as well as generating configurable shot mosaic. Using the multimodal Git-base model, it provides descriptions of shot frames and enables open-domain object recognition. Using Faster-Whisper, it delivers more accurate dialog recognition, while PaddleOCR is employed to identify film metadata and intertitles. Based on InsightFace, facial recognition is implemented for the shot-first frames or for fine-grained frames, generating a list of credits. The case study demonstrates its accuracy, efficiency, and improved user experience in film analytics. • PyCinemetricsV2 introduces five upgrades: interactive modification after Shot-Boundary Detection (SBD), open-classes object recognition, dialog detection, credits detection, and face detection. • The software, built upon PySide6, delivers an enhanced user experience with multithreading capabilities and intercomponent communication. • The software excels at analyzing pacing, visual symbolism, and credits.