We present the scientific performance results of pynpoint, our Python-based software package that uses principal component analysis to detect and estimate the flux of exoplanets in two-dimensional imaging data. Recent advances in adaptive optics and imaging technology at visible and infrared wavelengths have opened the door to direct detections of planetary companions to nearby stars, but image processing techniques have yet to be optimized. We show that the performance of our approach gives a marked improvement over what is presently possible using existing methods such as loci. To test our approach, we use real angular differential imaging (ADI) data taken with the adaptive optics-assisted high resolution near-infrared camera NACO at the VLT. These data were taken during the commissioning of the apodizing phase plate (APP) coronagraph. By inserting simulated planets into these data, we test the performance of our method as a function of planet brightness for different positions on the image. We find that in all cases pynpoint has a detection threshold that is superior to that given by our loci analysis when assessed in a common statistical framework. We obtain our best improvements for smaller inner working angles (IWAs). For an IWA of ∼0.29 arcsec we find that we achieve a detection sensitivity that is a factor of 5 better than loci. We also investigate our ability to correctly measure the flux of planets. Again, we find improvements over loci, with pynpoint giving more stable results. Finally, we apply our package to a non-APP data set of the exoplanet β Pictoris b and reveal the planet with high signal-to-noise. This confirms that pynpoint can potentially be applied with high fidelity to a wide range of high-contrast imaging data sets.