This paper proposes and verifies an intelligent photon counting single pixel micro-imaging system. An object is imaged in a digital micromirror device (DMD) under a microscope. After a series of masks are loaded to the DMD for light modulation, modulated light intensity is detected by a space-distinguished single photon detector, and the original image is reconstructed using a deep learning network. The DMD flip and photon counting are implemented based on the exact multi-channel timing control performance of a programmable logic device. Simultaneously, an embedded Arm processor deploys deep learning rebuild networks in real time. Finally, DMD deflection control, photon counting, and a neural network reconstruction function are integrated in a software and hardware collaborative platform to take advantage of the platform's high integration, lightweight and low cost. Experimental results show that the image reconstruction quality of DFC-Net on Zynq is superior to that of the classic TVAL3 reconstruction algorithm at low sampling rates. Although increasing the number of measurements can obtain high quality reconstruction images, each measurement time should be sufficient to inhibit Poisson sculpture noise.