Occlusions of granular particles in images significantly affect the accuracy of evaluating particle morphology for granular materials. In this study, a novel framework of SOLO-PCNet is proposed, which can automatically segment all the particles and predict the complete contours of the occluded particles in densely packed materials. Firstly, the instance segmentation model SOLOv2 is trained for the prediction of all the detectable particles. Then a self-supervised learning algorithm PCNetM is introduced for the inference of the complete contours of the occluded particles so that the prediction of SOLOv2 can be directly input to PCNet-M for the subsequent completion. Thereafter, the particle morphology characteristics including elongation, equivalent mean size, convexity, and circularity are automatically calculated. Then, the evaluation metrics of the segmentation model and morphology characteristics are validated, and the results exhibit the strong generalization ability of the segmentation and completion tasks. Finally, the uncertainty of the completed contours with morphology properties is explored for reliable analysis. This study successfully acquires the complete contours for each particle and provides the foundation for evaluating the mechanical properties of the packed granular materials from individual particles.