# command lines To save the online youtube video of the land slide (need the pytube package)
# 'pip install pytube' on the cmd prompt
# You may run this script for any video link from youtube
yt = YouTube("https://www.youtube.com/watch?time_continue=2&v=5-nyAz484WA")
stre = yt.streams.first()
# stre.download(folder_main)
# %%
# if the video has been correctly downloaded you may run this commands to load the video name.
vidName = stre.default_filename
vidPath = folder_main + '/' + vidName
landSlide = opyf.videoAnalyzer(vidPath)
#%%
landSlide.set_vlim([0, 20])
landSlide.set_vecTime(Ntot=10, shift=2)
landSlide.set_filtersParams(maxDevInRadius=1)
landSlide.extractGoodFeaturesAndDisplacements()
#%% And now to draw a FIeld
landSlide.extractGoodFeaturesPositionsDisplacementsAndInterpolate()
#%% optional by loading the mask
landSlide = opyf.videoAnalyzer(vidPath, mask=folder_main + '/mask.png')
landSlide.extractGoodFeaturesPositionsDisplacementsAndInterpolate()
os.chdir("./")
# if opyf is not installed where is the opyf folder?
sys.path.append('../../')
import opyf
import matplotlib.pyplot as plt
#On ipython try the magic command "%matplotlib qt5" for external outputs or "%matplotlib inline" for inline outputs
plt.close('all')
#Path toward the video file
filePath='./2018.07.04_Station_fixe_30m_sample.mp4'
#set the object information
video=opyf.videoAnalyzer(filePath)
'''
this manipualtion create an object [video] that contains information deduced from the video file.
#if it is a frame sequence use: {opyf.frameSequenceAnalyzer(path)} and type the "path" where images are.
'''
#%% ######################
video.set_vecTime(Ntot=10,shift=1,step=2,starting_frame=20)
print(video.vec,'\n',video.prev)
"""
#Use .set_vecTime vector to define the processing plan
#This method define video.vec and video.prev, two vecotrs required for the image processing:
# (by default {.set_vecttTime(starting_frame=0, step=1, shift=1, Ntot=1)}
#%%
# %matplotlib qt5
import sys, os
os.chdir(os.path.dirname(os.path.abspath(__file__)))
import opyf
import cv2
import numpy as np
import matplotlib.pyplot as plt
plt.close('all')
#%%
video = opyf.videoAnalyzer('IMG_1139.MOV')
#extract a a picture to build a mask
# cv2.imwrite('for_mask_1142.png',video.vis)
video.set_vecTime(Ntot=25, starting_frame=200)
video.set_interpolationParams(Sharpness=2)
video.set_goodFeaturesToTrackParams(qualityLevel=0.01)
# video.set_filtersParams(CLAHE=True, maxDevInRadius=2, minNperRadius=5, wayBackGoodFlag=1,RadiusF=15)
mask = cv2.imread('mask_1139.png')
A = mask > 100
video.set_stabilization(mask=A[:, :, 0], mute=False)
image_points = np.array(
[