| This is a note on how to cross compile opencv for pretty much any ARM device(HardFP supported in this case) and deploy. Native | |
| compiling in ARM devices can be painfully slow and they seem to hang often during build(mine got stuck at 43%). So if you happen | |
| to have a desktop/laptop/server running ubuntu or similar linux distro, u can build opencv in fractionth of the time taken for | |
| native compiling without any issues. | |
| Building opencv3 with TBB and NEON and VFP support can boost opencv performance. Thanks to Adrian at pyimagesearch for pointing that out. | |
| Both my PC and target machine aka orange pi zero are running ubuntu 16.04 with python2.7 and python 3.5. | |
| 1.Run the following commands in both machines to install the necessary libraries etc.(mine worked with them,so they should be enough | |
| hopefully) | |
| sudo apt-get update && sudo apt-get upgrade |
| libdir = /opt/opencv-4.0.0/lib | |
| includedir = /opt/opencv-4.0.0/include/opencv4 | |
| Name: OpenCV | |
| Description: OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. | |
| Version: 4.0.0 | |
| Libs: -L${libdir} -lopencv_aruco -lopencv_bgsegm -lopencv_bioinspired -lopencv_calib3d -lopencv_ccalib -lopencv_core -lopencv_datasets -lopencv_dnn_objdetect -lopencv_dnn -lopencv_dpm -lopencv_face -lopencv_features2d -lopencv_flann -lopencv_freetype -lopencv_fuzzy -lopencv_gapi -lopencv_hfs -lopencv_highgui -lopencv_imgcodecs -lopencv_img_hash -lopencv_imgproc -lopencv_line_descriptor -lopencv_ml -lopencv_objdetect -lopencv_optflow -lopencv_phase_unwrapping -lopencv_photo -lopencv_plot -lopencv_reg -lopencv_rgbd -lopencv_saliency -lopencv_shape -lopencv_stereo -lopencv_stitching -lopencv_structured_light -lopencv_superres -lopencv_surface_matching -lopencv_text -lopencv_tracking -lopencv_videoio -lopencv_video -lopencv_videostab -lopencv_xfeatures2d -lopencv_ximgproc -lopenc |
| #!/bin/bash | |
| # This gist provides several test cases to verify a host machine using NVIDIA graphics card if it's ready for tensorflow and pytorch. | |
| ### TC#1: Verify hardware information | |
| # Expectation: The OS shows hardware bare information | |
| lspci | grep -i nvidia | |
| sudo lshw -C display | |
| ### TC#2: Verify nvidia-driver-X |
| # Summary: We will create a virtual environment of Python-3.9 and install OpenCV-Python to it. | |
| # 1. Add 3rd-party OpenCV-3.9 repository | |
| sudo apt-key adv --refresh-keys --keyserver keyserver.ubuntu.com | |
| sudo add-apt-repository ppa:deadsnakes/ppa | |
| sudo apt-get update | |
| # 2. Install packages | |
| sudo apt-get install -y software-properties-common python3-pip python3.9 python3.9-dev python3.9-venv |
| import numpy as np | |
| import cv2 | |
| from IPython.display import display, Image | |
| import time | |
| video_path = "./HowDeepIsYourLove.mp4" | |
| video = cv2.VideoCapture(video_path) | |
| display_handle=display(None, display_id=True) | |
| kernel = np.ones((5,5),np.float32)/25 |
| sudo -s | |
| echo "source /etc/profile.d/pynq_venv.sh" >> ~/.bashrc | |
| echo "export XILINX_XRT=/usr" >> ~/.bashrc | |
| source ~/.bashrc | |
| python some_script.py |
OS: Ubuntu 18.04.4 LTS
Mainboads
├── iGPU