Ubuntu 미세먼지 팁들
sudo apt-get install lsb-release scrot wget http://github.com/downloads/djmelik/archey/archey-0.2.8.deb sudo dpkg -i archey-0.2.8.deb archey install
이 블로그의 인기 게시물
파이썬으로 Homomorphic Filtering 하기
import cv2 # For OpenCV modules (For Image I/O and Contour Finding) import numpy as np # For general purpose array manipulation import scipy . fftpack # For FFT2 #### imclearborder definition def imclearborder ( imgBW , radius ): # Given a black and white image, first find all of its contours imgBWcopy = imgBW . copy () contours , hierarchy = cv2 . findContours ( imgBWcopy . copy (), cv2 . RETR_LIST , cv2 . CHAIN_APPROX_SIMPLE ) # Get dimensions of image imgRows = imgBW . shape [ 0 ] imgCols = imgBW . shape [ 1 ] contourList = [] # ID list of contours that touch the border # For each contour... for idx in np . arange ( len ( contours )): # Get the i'th contour cnt = contours [ idx ] # Look at each point in the contour for pt in cnt : rowCnt = pt [ 0 ][ 1 ] colCnt = pt [ 0 ][ 0 ] # If this is within the radius of th...
파이썬으로 Histogram matching 하기.
import numpy as np def hist_match ( source , template ): """ Adjust the pixel values of a grayscale image such that its histogram matches that of a target image Arguments: ----------- source: np.ndarray Image to transform; the histogram is computed over the flattened array template: np.ndarray Template image; can have different dimensions to source Returns: ----------- matched: np.ndarray The transformed output image """ oldshape = source . shape source = source . ravel () template = template . ravel () # get the set of unique pixel values and their corresponding indices and # counts s_values , bin_idx , s_counts = np . unique ( source , return_inverse = True , return_counts = True ) t_values , t_counts = np . unique ( template , return_counts = True ) ...
sklearn tsne by ipynb, iris & mint
답글삭제https://github.com/danielfrg/tsne.git
ofxTSNE by XCode
답글삭제https://github.com/thomwolf/ofxHighDim.git
ImageNet TL ConvNet 으로 분류해서 티스니 해주는 소스 !
삭제Get ofxCcv
삭제run the setup_ccv script to download the trained convnet.
sh setup_ccv.sh
Then you need to populate a folder called 'images' inside your data folder. Be careful to use small-sized images because the entire directory will be loaded into memory. I've provided a script which downloads 20 images each from 31 categories in Caltech-256. If you'd like to download those, run:
python download_images.py