| """ | |
| Light weight head pose estimation with SolvePnP | |
| Author: Yuanjun Xiong | |
| """ | |
| # parameters | |
| fx = 1 | |
| # model points |
| version="2.4.12" | |
| echo "Installing OpenCV" $version | |
| mkdir OpenCV | |
| cd OpenCV | |
| echo "Removing any pre-installed ffmpeg and x264" | |
| sudo apt-get -qq remove ffmpeg x264 libx264-dev | |
| echo "Installing Dependenices" |
To use OpenCV's VideoWriter class to write an H264 encoded video, one would encounter some error like
Could not open codec libx264: unspecified error
Here is the trick. Beforehand, one has to make sure
--enable-libx264 is switched on in ffmpeg
| __author__ = 'Yuanjun Xiong' | |
| """ | |
| This script will transform an image based Caffe model to its optic flow ready form | |
| The basic approach is to average the three channels of the first set of convolution filters. | |
| Averaged filters are then replicated K times to incorporate K input frames of optical flow maps. | |
| Refer to "Towards Good Practices for Very Deep Two-Stream ConvNets" for more details. | |
| ====================================================================== | |
| Usage: | |
| python build_flow_network.py <caffe root> <first layer name> <image model prototxt> <image model weights> <flow model prototxt> <flow model weights[out]> |
This gist holds the model spec for the baseline CNN model on the WIDER dataset.
The CNN structure is AlexNet. Network parameters are initialized using a model pretrained on ImageNet.
The weights can be downloaded at
cuhk_wider_baseline_cnn.caffemodel
Please refer to
This gist holds the Caffe style model spec for the CVPR'15 paper
Recognize Complex Events from Static Images by Fusing Deep Channels
The model has two channels, one for appearance analysis, the other one for detection bounding box analysis.
The appearcance analysis channel has the similar structure of the AlexNet and thus is initialized using a model pretrained on ImageNet.
| import cv2 | |
| import sys | |
| (major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.') | |
| assert minor_ver >= 2, "Must use opencv 3.2.x up" | |
| if __name__ == '__main__' : | |
| # Set up tracker. | |
| # Instead of MIL, you can also use |
| %% This part goes in preamble | |
| \newcommand{\dummyfig}[1]{ | |
| \centering | |
| \fbox{ | |
| \begin{minipage}[c][0.33\textheight][c]{0.5\textwidth} | |
| \centering{#1} | |
| \end{minipage} | |
| } | |
| } |
| youtube-dl -f 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/bestvideo+bestaudio' \ | |
| --merge-output-format mp4 \ | |
| "http://www.youtube.com/watch?v=P9pzm5b6FFY" | |
| # This command downloads the best available quality video together with the best audio. Then it combines them with the post-processor. |
| __author__ = 'alex' | |
| # from pyspark import SparkContext, SparkConf | |
| import nltk | |
| from nltk.corpus import stopwords | |
| sw = stopwords.words('english') | |
| tk = nltk.tokenize.WordPunctTokenizer() |