#!/usr/bin/python
""" !/home/yo/.virtualenvs/sacrebleu/bin/python """
from sacrecommon import post_video, post_to_wall
from moviecommon import push_video, render_video
from cloudsmovement import last_hour_plot
from sunsetplotter import brightness_plot
import time
import os
# Find newest dir (hopefuly with photos)
dirs = [f for f in os.listdir(os.getcwd()) if os.path.isdir(f)]
newest = max(dirs, key = os.path.getmtime)
# Upload to Youtube
render_video(newest)
video_id = push_video(newest)
# Publish with current date as a message
message = '_' + time.strftime('%d %B %Y, %H:%M')
post_video(video_id, message)
# Movement detection whole day plot
plotname = last_hour_plot(newest, hour_only = False)
post_to_wall(plotname, ' ')
os.remove(plotname)
# Brightness plot
suntrace = brightness_plot(newest)
post_to_wall(suntrace, ' ')
os.remove(suntrace)
f = np.fft.fft2(gray) fshift = np.fft.fftshift(f) rows, cols = gray.shape crow,ccol = rows/2 , cols/2 # remove the low frequencies by masking with a rectangular # window of size 2h x 2h h = 10 fshift[crow-h : crow+h, ccol-h : ccol+h] = 0 # shift back (we shifted the center before) f_ishift = np.fft.ifftshift(fshift) # inverse fft to get the image back filtered = np.fft.ifft2(f_ishift) filtered = np.abs(filtered) # Convert gray to rgb so you can .. #filtered_c = cv2.cvtColor(filtered, cv2.COLOR_GRAY2BGR) # Joint two images vis = np.concatenate((gray, filtered), axis=0) # Save on hdd savepath = 'dupa.png' cv2.imwrite(savepath, vis) post_to_wall(savepath, 'high pass filter and original image') os.remove(savepath)
