3

I applied a raster processing that is already in a known coordinate system with this code,

import cv2
import numpy as np
from matplotlib import pyplot as src

img = cv2.imread(r'E:\2_PROJETS_DISK_E\threshold\621.tif',0)

# Otsu's thresholding after Gaussian filtering
blur = cv2.GaussianBlur(img,(5,5),0)
ret1,th1 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

for i in xrange(1):
 plt.imshow(images[i*3+2],'gray')
 plt.xticks([]), plt.yticks([])
plt.show()

cv2.imwrite( r'E:\2_PROJETS_DISK_E\threshold\621-1.tif',th1);

but when I save my raster at the end of this script, I get a non-georeferenced TIFF raster

How can I keep the same coordinate system of the initial raster (without tranforming the output raster into local coordinates)

Since I am new to Python, and I have no knowledge of Python, i would like someone to correct me my script please.

3
2

You should be able to set the projection and the geotransform of your new raster using the information of the previous raster.

Can you test the following code? (You can append it to your code or create a new script a run it after the code you provided).

import gdal

# open original raster and get projection and geotransform
original_ds = gdal.Open(r'E:\2_PROJETS_DISK_E\threshold\621.tif', 0)
sr = ds.GetProjection()
gt = ds.GetGeoTransform()
del original_ds

# open target raster (in editing mode) and set the projection and geotransform
ds = gdal.Open(r'E:\2_PROJETS_DISK_E\threshold\621-1.tif', 1)
ds.SetProjection(sr)
ds.SetGeoTransform(gt)

# save and close target raster
del ds
1

thank you for the answer, but it doesnt works for me

i tried this and it works, hope it will help another:

import cv2
import numpy as np
import gdal

in_imgpath = r'E:\2_PROJETS_DISK_E\Raster2.tif'


img = cv2.imread(in_imgpath ,0)

dataset1 = gdal.Open(in_imgpath)
projection = dataset1.GetProjection()
geotransform = dataset1.GetGeoTransform()

# Otsu's thresholding after Gaussian filtering
blur = cv2.GaussianBlur(img,(5,5),0)
ret1,th1 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
kernal = np.ones((3,3), np.uint8)
dilation = cv2.dilate(th1, kernal, iterations=2)
erosion = cv2.erode(dilation, kernal, iterations=1)
opening = cv2.morphologyEx(erosion, cv2.MORPH_OPEN, kernal, iterations=3)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernal, iterations=4)


out_imgpath = r'E:\2_PROJETS_DISK_E\Raster2.tif'

cv2.imwrite(out_imgpath ,closing)
dataset2 = gdal.Open(out_imgpath, gdal.GA_Update)
dataset2.SetGeoTransform( geotransform )
dataset2.SetProjection( projection )
0

You can read the image using gdal.ReadAsArray() and write the image with gdal as well.

import cv2
import numpy as np
import gdal

in_imgpath = r'E:\2_PROJETS_DISK_E\Raster2.tif'

dataset1 = gdal.Open(in_imgpath, gdal.GA_ReadOnly)
projection = dataset1.GetProjection()
geotransform = dataset1.GetGeoTransform()
img = dataset1.ReadAsArray()
rows, cols = img.shape # assuming it's a 2D array
dataset1 = None

# Otsu's thresholding after Gaussian filtering
blur = cv2.GaussianBlur(img,(5,5),0)
ret1,th1 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
kernal = np.ones((3,3), np.uint8)
dilation = cv2.dilate(th1, kernal, iterations=2)
erosion = cv2.erode(dilation, kernal, iterations=1)
opening = cv2.morphologyEx(erosion, cv2.MORPH_OPEN, kernal, iterations=3)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernal, iterations=4)


out_imgpath = r'E:\2_PROJETS_DISK_E\Raster2.tif'

driver = gdal.GetDriverByName('GTiff')
dataset = driver.Create(out_imgpath, cols, rows, n, gdal.GDT_Byte)
dataset.SetGeoTransform(geo_transform)
dataset.SetProjection(projection)
band.WriteArray(closing)
dataset = None
band = None
0

Just write the output array with GDAL along with the transform and projection information.

import cv2 
from osgeo import gdal

def writeArr2Tif(outFileName, arr_out, rows, cols, bandIndex, noData, geoTrans, proj):
    driver = gdal.GetDriverByName("GTiff")
    outDs = driver.Create(outFileName, rows, cols, bandIndex, gdal.GDT_Byte)
    outDs.SetGeoTransform(geoTrans)##sets same geotransform as input
    outDs.SetProjection(proj)##sets same projection as input
    outDs.GetRasterBand(bandIndex).WriteArray(arr_out)
    outDs.GetRasterBand(bandIndex).SetNoDataValue(noData)##if you want these values transparent
    outDs.FlushCache() ##saves to disk!!
    outDs = None
    print("done")

ds = gdal.Open(infile)
img_arr = ds.ReadAsArray()

width = ds.RasterXSize
height = ds.RasterYSize
nBand = ds.RasterCount 
trans = ds.GetGeoTransform()
proj = ds.GetProjection()

# Taking a matrix of size 5 as the kernel 
kernel = np.ones((3, 3), np.uint8) 
img_dilation = cv2.dilate(img_arr, kernel, iterations=1) 

writeArr2Tif(out_path_ero, img_erosion, height, width, 1, 255, trans, proj)

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