minor changes, deleted some irrelevant comments, and blank lines
deleted blank line
removed part of the code that did only the first video from each set/trap. now does all files. also changed to move rather than copy to save space
deleted file. no need to save data to .txt, because can call python directly from r with rPython (?), and import directly
deleted file, no need to rite to .txt file. instead defined function Output_image, which turns data into jpg file by calling python code, and fed this pc data. this is withing 3_Apply_clustering.r
python code took directory full of .jpg files, read them in, and outputted .jpg of weighed some. now code defines a function that can be called from R, that reads video files from a given directory, takes the image at the 10 second mark for each of them, then outputes a weighted sum image .jpg file in the master firectory
modified this so that it can be called by R and output principal components. it was too slow, so switched to CCIPCA. also install the package for CCIPCA, so no need to edit the source code (CCIPCA.py, IPCA.py) like i did here. should probably delete this file
added mdb to ignore list
intial commit. function within 3_Apply_clustering called Output_image.r takes data file of length 3*W*H, with (b, g, r, b, g, r, ...), with by rows, moving left to right, and outputs it into matrices blue, green, red. python code called Output_image.py, takes this and outputs .jpg to given dest_dir
initial commit. r code to train arnn, nbayes, rndf, and mode models on training data, then test it on test data. goes from prcomps of video frame data, to classification of substrate, impact, subdominant substrate etc
python code to be called from R. takes RepFrames_new data set, which are the new frames to be added, and addes them to the current pca. the ccipca.RData file must be loaded into R first, and then after the pca is done, the info must be saved in a list and written to ccipca.RData. this file also contains a list of all videos in the current pca. this is all done in 3_Apply_clustering.r
takes all frames, from all videos for a given Set and Trap, and performs a pca on them. outputs list of video_file_frames, the raw video data, and the principal components
initial commit. takes imput as mp4 file, takes frame every 30 frames, and outputs data, and principal componenets
initial commit. takes video file and calculates total variance. used to select low variance video, which would hopefully have the most still view
initial commit, output from 3_Apply_clustering.r. shows current pca, has list of files
initial commit. python code that i occasionaly use to rename all files in a folder
changed a whooole bunch of stuff. stopped writing frames to .txt files and reading from them. instead called python code directly - inputting and outputting data from python. tidyied up code a lot more, and put a lot more stuff in python functions to be called. started using CCIPCA rather than IPCA since it is faster. also installed CCIPCA as a python library. r code now updates current pca, and writes it to RData file, ccipca.RData. anways, changed a whole bunch of stuff, and probably not going to go back, so boo to the old file!