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Enable X11 forward to load images from remote server on MacOS Mojave

Steps

1. Install Xquartz to get X11 support on MacOS. You can google Xquartz and download it from its official site, or install using HomeBrew.

   brew cask install xquartz

2. Launch Xquartz. Go to Preference -> Security, click the box Allow connections from clients. NOTE: You have to lauch Xquartz with Allow connections from clients enable everytime you want to ssh to remote server with X11 forwarding support.

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#!/bin/bash

# global vars
IND_START=100
IND_END=499
TAR_PATH="../tars"
CHECK_PASS=0
CHECK_FAIL=0
CHECK_FAIL_LIST=""

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Environment:

• uname -a to check OS: Linux NAME 4.9.0-11-amd64 #1 SMP Debian 4.9.189-3+deb9u1 (2019-09-20) x86_64 GNU/Linux
• Python version: Python 3.4.1 |Anaconda 2.1.0 (64-bit)| (default, Sep 10 2014, 17:10:18) (a 3rd party module requires py3.4)

Behaviour:

• When trying to write logs in each process, e.g. one log file for each process, files can be created but no content is written.

Debug:

• No expected errors in worker function
• Failed to write even in single-processing or just call the function without multiprocessing module

fengyuentau

This seems to be an easy problem to solve, but yet took me almost an afternoon to google and finally figure out the solution myself.

Behaviour:

1. A table with large cells
2. The cell would not split across pages, even if one follows the solutions from microsoft support (check on OrchardSix's answer)
3. The option of Allow row to break across pages is not available

Solution:

1. Check if you change the Text Direction of the cell or its neighbour cells from originally horizontal to vertical, for example, | XXX | multiple lines with a lot of content | change the Text Direction | X | multiple lines |

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## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!

USE_INDEX_64 := 1

# cuDNN acceleration switch (uncomment to build with cuDNN).
USE_CUDNN := 1

# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1

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# Following is an example compiling OpenCV with conda python in MacOS.
# To compile opencv with python2, change options related to python3 accordingly.
# Option PYTHON3_LIBRARY is the location to libpythonx.x.dylib or libpythonx.x.so.
# Option PYTHON3_INCLUDE_DIR is the location to a directory with `Python.h` etc. Run `python -c 'import distutils.sysconfig as s; print(s.get_python_inc())'` to get the path.
# Option PYTHON3_EXECUTABLE is the location to pythonx.x executable. Run `which python` to get the directory.
cmake -D CMAKE_BUILD_TYPE=RELEASE \
    -D CMAKE_INSTALL_PREFIX=/Users/fengyuentau/Documents/opencv \
    -D PYTHON3_LIBRARY=/usr/local/Caskroom/miniconda/base/lib \
    -D PYTHON3_INCLUDE_DIR=/usr/local/Caskroom/miniconda/base/include/python3.8 \
    -D PYTHON3_EXECUTABLE=/usr/local/Caskroom/miniconda/base/bin/python \

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Guide for insightface evaluation

The insightface face recognition algorithm is awesome, though there is no details about neither how to perform the evaluation on the algorithm nor how to prepare data for evaluation/training. Of course, one can find some details from issues, but it will take a lot of time to do that. Here, all the related details are collected for the sake of saving your time.

Preparing data

File you need for evaluation:

• your_dataset.bin

NOTE: If you just want to perform the evaluation with the LFW dataset, just head to the Dataset-zoo from insightface to download on of the provided dataset, which contains lfw.bin for evaluation.

In case you want to create your own .bin file from your own dataset, following section is an example creating lfw.bin from LFW.

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cmake -D CMAKE_BUILD_TYPE=RELEASE \
    -D CMAKE_INSTALL_PREFIX=/home/tau/software/opencv-4.5.1 \
    -D BUILD_opencv_python2=OFF \
    -D BUILD_opencv_python3=ON \
    -D PYTHON3_LIBRARY=/home/tau/anaconda3/envs/opencv_dnn_cuda/lib \
    -D PYTHON3_INCLUDE_DIR=/home/tau/anaconda3/envs/opencv_dnn_cuda/include/python3.8 \
    -D PYTHON3_EXECUTABLE=/home/tau/anaconda3/envs/opencv_dnn_cuda/bin/python3.8 \
    -D INSTALL_PYTHON_EXAMPLES=OFF \
    -D INSTALL_C_EXAMPLES=OFF \
    -D BUILD_EXAMPLES=OFF \

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cmake_minimum_required(VERSION 2.8.12)

project(libfacedetection_opencvdnn)

# OpenCV
find_package(OpenCV 4.5.4 REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})

add_executable(detect detect.cpp)
target_link_libraries(detect ${OpenCV_LIBS})

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Compiling OpenCV with TIM-VX Backend

We are thrilled to introduce you the TIM-VX backend integrated in OpenCV DNN, which allows OpenCV DNN runs quantized DL models in neural processing units (NPU) on edge devices, such as Khadas VIM3 etc. It achives up to 2X faster than ARM CPU backend for running face detection and recognition model from OpenCV Zoo. More details can be found in OpenCV Zoo Benchmarks.

TIM-VX is provided with x86_64 simulator. So you can try OpenCV with TIM-VX backend on your x86_64 machine following steps below, or if you happen to have a physical board equiped with the A311D chip (like the Khadas VIM3 mentioned above). In this guide, we provide two ways compiling OpenCV with TIM-VX backend:

• (Recommanded) Compile OpenCV together with TIM-VX.
• Compile OpenCV with TIM-VX library installed previously.

Requirem