from processing_functions import misc
from processing_functions import general_funcs as gf
import os
import cv2
"""
initially i've trated grass and soil vegtation as "vegetation", but now i will only use not grass vegetation
"""
classifiedPath = "/home/kaue/data/extracted_images/samples/2019-07-11-16-21-46/already_classified"
toClassifyFolder = "/home/kaue/data/extracted_images/samples/2019-07-11-16-21-46/reclassification"
originalsPath = "/home/kaue/data/extracted_images/2019-07-11-16-21-46/ngr"
classifiedList = misc.orderedFileList(classifiedPath,'*.png')
for path in classifiedList:
# print(path)
filename = misc.filenameFromPath(path)
originalImgPath = os.path.join(originalsPath,misc.modFilenameExt(filename,'.jpg'))
outPath = os.path.join(toClassifyFolder,filename)
print(originalImgPath)
classifiedImg = cv2.imread(path)
originalImg = cv2.imread(originalImgPath)
for i,column in enumerate(classifiedImg):
for j,pixel in enumerate(column):
# as there are only black or white pixels, we can test only a channel
import os
import glob
import random
from processing_functions import misc as msc
from shutil import copyfile
# define the number of samples you want
n_samples = 50
imgsPath = os.environ['HOME'] + '/data/extracted_images/2019-07-11-16-21-46/ngr'
out_path = os.environ[
'HOME'] + '/data/extracted_images/samples/' + msc.find_datetime_String(
imgsPath)
# print(out_path)
msc.createDir(out_path)
imList = msc.orderedFileList(imgsPath)
n = len(imList)
print(n)
for i in range(n_samples):
imSample = imList[random.randint(0, n - 1)]
filename = msc.filenameFromPath(imSample)
# print(filename)
copyfile(imSample, os.path.join(out_path, filename))
print('fim')
if print_dimensions:
print(w, h, new_w, new_h)
new_size = (new_w, new_h)
new_image = Image.new("RGB", new_size)
new_image.paste(image, ((new_size[0] - image.size[0]) // 2,
(new_size[1] - image.size[1]) // 2))
new_image.save(imgpath.replace('.png', '_outline.png'))
#######
# naming for the local folder:
outlocalfolder = "phase3_reloaded"
pickles_list = msc.orderedFileList(vd.PICKLES_PATH, '*.pickle')
t1 = time.time()
#print(pickles_list)
dataDict = {}
for picklepath in pickles_list:
fname = msc.filenameFromPathWtithoutExt(picklepath)
#print(fname)
with open(picklepath, 'rb') as picklefile:
dataDict[fname] = pickle.load(picklefile)
#print(dataDict)
import os
from processing_functions import misc as msc
from processing_functions import color_stuff as cs
import glob
basedir = "/home/kaue/data/extracted_images/samples/2019-07-11-16-21-46"
onlyCheck = False
dirList = msc.getSubdirs(basedir)
for folder in dirList:
if msc.checkForImages(folder, '*.png'):
imList = msc.orderedFileList(folder, '*.png', True)
for filePath in imList:
print(filePath)
if cs.checkIfPixVal(filePath):
if onlyCheck:
print(filePath)
else:
cs.chg_specific_pix_val(filePath, filePath)
out_dirs = ['orig_resiz','overlay','masks','ndvi','only_veg']
msc.createDirStructure(out_basepath,out_dirs)
outpaths = {}
for name in out_dirs:
outpaths[name] = os.path.join(out_basepath,name)
# imgs_path = "/home/kaue/data/extracted_images/2019-07-11-16-21-46/ngr"
# imgs_path = "/home/kaue/data/extracted_images/red_band"
imgs_path = "/home/kaue/data/extracted_images/newextracted"
img_list = msc.orderedFileList(imgs_path,extension='*.png')
img_list = sorted(img_list)
# del img_list[0:225]
print(img_list[0:5])
for i,img_path in enumerate(img_list):
t1 = time.time()
img_name = msc.fileNumberFromPathAsStr(img_path)
out_orig = os.path.join(vd.PREDS_PATH,img_name+'.png')
out_pred = os.path.join(vd.PREDS_PATH,img_name+'_pred.png')
dest_orig = os.path.join(outpaths['orig_resiz'],img_name+'.png')
from processing_functions import misc as msc
from processing_functions import color_stuff as cs
import os
path = "/home/kaue/data/politecnico_v2/val"
destpath = "/home/kaue/data/politecnico_v2/downsized"
filelist = msc.orderedFileList(path)
for filepath in filelist:
filename = msc.fileNumberFromPathAsStr(filepath)+'.png'
print(filename)
cs.allBlackImg(filepath,os.path.join(destpath,filename),(512,512))
strToContain = 'color'
pathlist = msc.getSubdirs(basedir)
# sizes = np.empty(len(pathlist), dtype=int)
# print(sizes)
# for i,path in enumerate(pathlist):
# sizes[i] = path.count('/')
# print(sizes)
# max_lvl = np.max(sizes)
# print(max_lvl)
for path in pathlist:
if msc.checkForImages(path,'*.png'):
parent = msc.get_parent_dir(path)S
imgList = msc.orderedFileList(path,'*.png')
for imgPath in imgList:
if strToContain in imgPath:
filename = msc.filenameFromPath(imgPath)
newfilename = filename.replace(strToReplace,strToAdd)
print(newfilename)
move(imgPath,os.path.join(parent,newfilename))
# os.removedirs(path)
msc.telegram_bot_sendtext("copying terminated")
from processing_functions import misc as msc
from processing_functions import general_funcs as gf
import os
in_out_list = [("/home/kaue/Dropbox/data/gt/versions/only_trees",
"/home/kaue/Dropbox/data/gt_downsized/only_trees"),
("/home/kaue/Dropbox/data/gt/versions/with_terrain_veg",
"/home/kaue/Dropbox/data/gt_downsized/with_terrain_veg")]
for inoutpair in in_out_list:
try:
os.makedirs(inoutpair[1])
except:
pass
imgList = msc.orderedFileList(inoutpair[0], '*.png')
for imgpath in imgList:
imgname = msc.filenameFromPath(imgpath)
outpath = os.path.join(inoutpair[1], imgname)
# print(outpath)
gf.resize_and_save(imgpath, outpath)
from processing_functions import misc as msc
basepath = "/media/kauevestena/data/Semantic-Segmentation-Suite/Cityscapes"
ext = '*.png'
folderlist = msc.getSubdirs(basepath)
for folder in folderlist:
if msc.checkForImages(folder, ext):
filelist = msc.orderedFileList(folder, ext)
print(folder)
print(len(filelist))
#script to classify vegetation and no vegatation in a manualy classified in white for vegetation and anything to non-vegetation
from processing_functions import misc as msc
from processing_functions import color_stuff as cs
import datetime
import os
# import cv2
# inputpath = "/home/kaue/data/extracted_images/samples/2019-07-11-16-21-46/regular"
inputpath = "/home/kaue/data/extracted_images/samples/2019-07-11-16-21-46/reclassification"
# outpath = "/home/kaue/data/extracted_images/samples/2019-07-11-16-21-46/already_classified"
outpath = "/home/kaue/data/extracted_images/samples/2019-07-11-16-21-46/reclassification_blackwhite"
imagelist = msc.orderedFileList(inputpath, extension='*.png')
minDate = datetime.datetime(year=2019, day=17, month=7)
for imgpath in imagelist:
filedate = msc.getModTime(imgpath)
if filedate > minDate:
savepath = os.path.join(outpath, msc.filenameFromPath(imgpath))
# cs.remove_other_classes(imgpath,savepath,255,255,255)
cs.remove_other_classes(imgpath, savepath, 0, 0, 0, 255, 255, 255)
print(imgpath)
msc.telegram_bot_sendtext("GT classification ended")
created to open all images, if you want to manually annotate with kolourpaint
"""
defaultPath = "/home/kaue/data/extracted_images/samples/2019-07-11-16-21-46/regular"
basePath2 = "/home/kaue/data/extracted_images/2019-07-11-16-21-46/ngr"
if len(sys.argv) < 2:
# print("insert pathname")
# sys.exit()
path = defaultPath
else:
path = sys.argv[1]
filePathList = msc.orderedFileList(path,'*.png',1)
basePath = msc.pathWithoutFilename(sys.argv[0])
#create file to store the last opened image:
lastOpenedPath = os.path.join(basePath,'last_opened.txt')
if not os.path.exists(lastOpenedPath):
# storageFile = open(lastOpenedPath,'w')
# storageFile.close()
msc.writeToFile(lastOpenedPath,"")
if os.stat(lastOpenedPath).st_size == 0:
# storageFile = open(lastOpenedPath,'w')
# storageFile.write("-1")
# storageFile.close()]
import os
from processing_functions import misc as msc
from processing_functions import color_stuff as cs
from processing_functions import general_funcs as gf
import glob
basedir = "/home/kaue/data/extracted_images/samples/2019-07-11-16-21-46"
dirList = msc.getSubdirs(basedir)
for folder in dirList:
if msc.checkForImages(folder):
imList = msc.orderedFileList(folder)
for filePath in imList:
gf.saveJPG_as_PNG(filePath, True)
width = 1024
height = 1024
FPS = 15
seconds = 127
fourcc = VideoWriter_fourcc(*'MP42')
video = VideoWriter(videopath, fourcc, float(FPS), (width, height))
############3
for name in out_dirs:
outpaths[name] = os.path.join(out_basepath, name)
imgs_path = "/home/kaue/data/extracted_images/2019-07-11-16-21-46/ngr"
img_list = msc.orderedFileList(imgs_path)
for i, img_path in enumerate(img_list):
t1 = time.time()
img_name = msc.fileNumberFromPathAsStr(img_path)
dest_orig = os.path.join(outpaths['orig_resiz'], img_name + '.png')
dest_pred = os.path.join(outpaths['masks'], img_name + '.png')
dest_onlyveg = os.path.join(outpaths['only_veg'], img_name + '.png')
dest_ndvi = os.path.join(outpaths['ndvi'], img_name + '.png')
dest_overlay = os.path.join(outpaths['overlay'], img_name + '.png')
# allpaths = [dest_ndvi,dest_onlyveg,dest_orig,dest_overlay,dest_pred]
import cv2
import numpy as np
import time
outfolder = msc.joinToHome("data/cnn_output/ndvi_v2")
input_path = msc.joinToHome("data/cnn_output/only_veg")
stats_path = msc.joinToHome("data/cnn_output/ndvi_stats.csv")
if os.path.exists(stats_path):
os.remove(stats_path)
img_inputlist = msc.orderedFileList(input_path,'*.png')
for i,imgpath in enumerate(img_inputlist):
t1 = time.time()
imgname = msc.filenameFromPathWtithoutExt(imgpath)
outpath = os.path.join(outfolder,imgname+'.png')
print(outpath)
cs.gen_uint8_NDVI(imgpath,outpath,absolute_scale=True,print_stats=True)
if ckptnumber < 500:
if not i % 25 == 0:
del validCkptFolders[i]
elif ckptnumber < 900:
if not i % 10 == 0:
del validCkptFolders[i]
elif ckptnumber < 1000:
if not i % 5 == 0:
del validCkptFolders[i]
print(len(validCkptFolders))
print(validCkptFolders)
# Ground Truth Images
gtImages = msc.joinToHome("/Dropbox/data/gt/originals")
gtImagesList = msc.orderedFileList(gtImages, '*.png')
# print(gtImagesList)
# Ground Truth Masks (gtm)
gtMasksVersions = msc.joinToHome("/Dropbox/data/gt_downsized")
gtmDirs = msc.getSubdirs(gtMasksVersions)
print(gtmDirs)
listOfListsOnDirs = []
for dirPath in gtmDirs:
listOfListsOnDirs.append(msc.orderedFileList(dirPath, '*.png'))
# list of images and ground truth versions
imgs_and_gts = []
def getDBlist():
return msc.orderedFileList(TINYDBS_PATH,'*.json')