  Use of edge computing in application of Computer Vision (CV) is an active
field of research. Today, most CV applications make use of Convolutional Neural
Networks (CNNs) to inference on and interpret video data. These edge devices
are responsible for several CV related tasks, such as gathering, processing and
enhancing, inferencing on, and displaying video data. Due to ease of
reconfiguration, computation on FPGA fabric is used to achieve such complex
computation tasks. Xilinx provides the PYNQ environment as a user-friendly
interface that facilitates in Hardware/Software system integration. However, to
the best of authors' knowledge there is no end-to-end framework available for
the PYNQ environment that allows Hardware/Software system integration and
deployment of CNNs for real-time input feed from High Definition Multimedia
Interface (HDMI) input to HDMI output, along with insertion of customized
hardware IPs. In this work we propose an integration of rea\textbf{L}-time
image \textbf{E}nancement IP with \textbf{A}I inferencing engine in the
\textbf{P}ynq environment (\textbf{LEAP}), that integrates HDMI, AI
acceleration, image enhancement in the PYNQ environment for Xilinx's
Microprocessor on Chip (MPSoC) platform. We evaluate our methodology with two
well know CNN models, Resnet50 and YOLOv3. To validate our proposed
methodology, LEAP, a simple image enhancement algorithm, histogram
equalization, is designed and integrated in the FPGA fabric along with Xilinx's
Deep Processing Unit (DPU). Our results show successful implementation of
end-to-end integration using completely open source information.
