Physically grounded PSF-based image degradation is necessary for studying optics-related object-detector robustness, but existing workflows typically rely on offline dataset generation and integrate poorly with GPU-resident frameworks such as MMDetection. We present CIDPL, a CUDA-accelerated Python library that adapts our standalone Image Degradation Application (IDA) into a real-time, framework-integrated pipeline for MMDetection. CIDPL couples Python and C++ via PyBind11, performs degradation directly on GPU tensors in the DataPreprocessor, and organizes multiple optical variants in a traceable Super-Batch format. Numerical validation shows exact agreement with IDA for TIFF inputs, while optical validation reproduces KrakenOS-based SFR trends. In throughput tests, CIDPL improves mean degradation speed over IDA by 4.7x on a single GPU and 7.6x on two GPUs, enabling real-time processing at 117 FPS with negligible overhead during both training and inference. KITTI experiments further show that the integration enables practical detector-level robustness studies under varying defocus conditions.