├── LICENSE ├── README.md ├── code ├── assembleImages.py ├── datatypes.py ├── generateBaseImages.py ├── georef.py ├── models │ └── seg3.h5 ├── segmentation.py ├── sonar.R ├── sonarMapperMain.py └── utilities.py ├── environment.yaml ├── example ├── config.ini └── testfile.sl2 ├── image1.png ├── image2.jpg └── image3.jpg /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # **SonarMapper** 2 | Python scripts to process and visualize sonar data from Lowrance Fishfinders 3 | 4 | We like kayaking and always wondered what is underneath the hull. Combined with a general interest in mapping we got 5 | a fishfinder from Lowrance. However looking at recorded data on the device afterwards is not a pleasure and Lowrance 6 | doesn't deliver any free software to process the files on a PC in high resolution. 7 | Therefore we created these scripts to process and visualize the data from the fishfinder created sl2 files. 8 | 9 | Currently supported functions: 10 | - read sl2 file into csv files (meta ink. position information and image information) 11 | - create down scan and primary scan images 12 | - create side scan images 13 | - create plot of the track with depth information 14 | - georeference the side scan data and export as Geotiff 15 | 16 | 17 | The first part of reading the sl2 file is based on a library from KennethTM (https://github.com/KennethTM/sonaR). 18 | However this library is set in R which means that an installation of R is required. 19 | 20 | ![Example output](image2.jpg) 21 | 22 | **If you need more information to get started check the videos:** 23 | Installation: https://www.youtube.com/watch?v=3kaIQv3jeYc 24 | How To: https://www.youtube.com/watch?v=RMYd-Z57woo 25 | 26 | # Installation information for Windows 27 | **install R and Rtools** 28 | https://cran.r-project.org/bin/windows/base/ 29 | https://cran.r-project.org/bin/windows/Rtools/ 30 | 31 | **open R and run in the R terminal** 32 | _install.packages("remotes")_ 33 | _install.packages("glue")_ 34 | 35 | _library(remotes)_ 36 | _remotes::install_github('KennethTM/sonaR')_ 37 | 38 | **check Rscript path** 39 | in code/sonarMapperMain.py check the path to Rscript, since it depends on your installed version 40 | _pathToR='"C:/Program Files/R/R-4.2.1/bin/x64/Rscript.exe"'_ 41 | 42 | **install conda** 43 | https://www.anaconda.com/products/distribution 44 | 45 | **create environment in conda from yaml** 46 | cd to the folder containing the yaml file 47 | 48 | _conda env create -f environment.yaml_ 49 | 50 | activate the new environment 51 | _conda activate sonar-env_ 52 | 53 | # Installation hint for Ubuntu 54 | I added some changes to the code as comments that need to be applied for Ubuntu. 55 | However I didn't invest much time in it and had issues with the GDAL library, which effects the track plotting and georeference. 56 | 57 | 58 | # How to run it # 59 | activate the python enviroment 60 | __conda activate sonar-env__ 61 | 62 | Create a folder for your trip files. (for example "Tag5") 63 | Now copy your .sl2 files to this folder. 64 | Adapt the config to your current project. 65 | - it needs to point to the sl2 file folder and the file name of all sl2 files you wish to process separated by commas (use / here not the windows backslash) 66 | _config.ini_ 67 | 68 | 69 | **run it with the created config :-) (example path)** 70 | _(sonar-env) C:\PATH\SonarMapper\code>python sonarMapperMain.py C:\PATH\Tag5\config.ini_ 71 | 72 | ![Explanation image](image1.png) 73 | 74 | **the area for georeference can be selected for example via openstreetmap** 75 | https://www.openstreetmap.org/export 76 | 77 | **Update to config** 78 | - new option was added: "combinedDownAndPrimeView" which creates a combined view of primary and down scan 79 | 80 | # Add Ons 81 | (1) Some of the processes take a lot of RAM. So try to make as much free as possible, depending on the size of your file. 82 | (2) The georeferencing takes quite some time to process 83 | (3) The georeferenced images can be loaded for example to QGIS to assemble them 84 | 85 | # Example for the usage of georeferenced images 86 | Several images that are georeferenced can be put together on a map by for example using QGIS 87 | ![Georeferenced image](image3.jpg) 88 | 89 | # AI Segmentation 90 | This feature does segment the images of down and primary scan in the following classes: ground, fish, vegetation big, vegetation small. This can not only be used for visualtization but as well for noise reduction. It is makes use of the [segmentation model libary](https://github.com/qubvel/segmentation_models) and was trained on a hand labled dataset. Is on an experimental level and therefore requires you to install some additional libaries: 91 | ``` 92 | pip install "tensorflow<2.11" 93 | pip install albumentations==1.3.0 94 | pip install segmentation-models==1.0.1 95 | ``` 96 | If you want to make use of tensorflows GPU acceleration follow the tensorflow installation [link](https://www.tensorflow.org/install/pip) 97 | 98 | Read more about the sonarMapper segmentation [here](https://www.boweye.de/segmentation/). 99 | -------------------------------------------------------------------------------- /code/assembleImages.py: -------------------------------------------------------------------------------- 1 | import os 2 | from PIL import Image 3 | # set max image pixel of PIL 4 | Image.MAX_IMAGE_PIXELS = None 5 | 6 | import datatypes as dt 7 | import utilities as utils 8 | import generateBaseImages as baseIm 9 | from removeUnderground import removeUnderground 10 | import pandas as pd 11 | 12 | import warnings 13 | from shapely.errors import ShapelyDeprecationWarning 14 | warnings.filterwarnings("ignore", category=ShapelyDeprecationWarning) 15 | 16 | ################################################## 17 | # This file stitches images of different zoom 18 | # levels together 19 | # Author: Tobias Ranft & Kimberly Mason 20 | # Date: 2022-08-27 21 | ################################################## 22 | 23 | # Sets max image sizes. If image is too big, rescaling will happen 24 | maxImageWidth=65000 25 | maxImageHeight=2000 26 | 27 | # Crops an image at a certain depth. This is important since sometimes the auto zoom of the 28 | # Fishfinder zooms to for example 80 m for one frame but the actual depth is just 10 m. 29 | # depth[in] depth where cut off should happen 30 | # image[in] image to crop 31 | # maxDepth[in] maximum depth of the file 32 | # returns cropped image 33 | def cropImageAtDept(depth,image,maxDepth): 34 | width, height = image.size 35 | deptPerPixel=maxDepth/height 36 | pixelToCut=int(depth/deptPerPixel) 37 | #print("crop at "+str(pixelToCut)) 38 | area = (0, 0, width-5, pixelToCut) 39 | #area = (0, 0, width, 800) 40 | imagecrop = image.crop(area) 41 | return imagecrop 42 | 43 | # Generates properties necessary to stitch the images of different zoom levels together 44 | # propertyList[in] list containing the properties of each base image 45 | # returns image assembly properties 46 | def generateAssembleProperties(propertyList): 47 | #find biggest depth 48 | assembleProp=dt.assembleProperties() 49 | for prop in propertyList: 50 | assembleProp.maxDept=max(assembleProp.maxDept,prop.maxRange) 51 | assembleProp.minDept=min(assembleProp.minDept,prop.maxRange) 52 | assembleProp.finestRes=min(assembleProp.finestRes,prop.maxRange/prop.pixelY) 53 | assembleProp.framesTotal=assembleProp.framesTotal+prop.frames 54 | #print("max dept "+str(assembleProp.maxDept)) 55 | #print("finest res "+str(assembleProp.finestRes)) 56 | #print("framesTotal "+str(assembleProp.framesTotal)) 57 | # create final image 58 | assembleProp.yRes=int(assembleProp.maxDept/assembleProp.finestRes) 59 | return assembleProp 60 | 61 | # Function to assemble single images with fixed depth to one big image 62 | # new_im[in,out] assembled image to be generated from the function 63 | # propertyList[in] list with properties of all sub images 64 | # assembleProp[in] information to assemble single images 65 | # side[in] is it a side scan? Otherwise it is a Downscan or primary 66 | # pathForImages[in] path to where the singe images are stored 67 | def assembleScan(new_im,propertyList,assembleProp,side:bool,pathForImages): 68 | new_width, new_height = new_im.size 69 | new_half_height=new_height/2 70 | currentFrame=0 71 | for oneProp in propertyList: 72 | #print("maxRange "+str(oneProp.maxRange)) 73 | #print("pixy "+str(oneProp.pixelY)) 74 | imgPath=pathForImages+"/fig_"+oneProp.name+".png" 75 | img=Image.open(imgPath) 76 | 77 | #img=img.transpose(Image.FLIP_TOP_BOTTOM) 78 | img=img.transpose(Image.Transpose.FLIP_TOP_BOTTOM) 79 | 80 | resizeFactor=oneProp.maxRange/assembleProp.minDept 81 | #print("resizefaktor "+str(resizeFactor)) 82 | newsize=(oneProp.frames,int(oneProp.pixelY*resizeFactor)) 83 | #img.thumbnail(size) 84 | img=img.resize(newsize) 85 | width, height = img.size 86 | height_half=height/2 87 | #print("current frame "+str(currentFrame)) 88 | yShift=int(new_half_height-height_half)*side 89 | new_im.paste(img, (currentFrame,yShift)) 90 | currentFrame=currentFrame+oneProp.frames 91 | 92 | 93 | # pathToData[in] path to image csv files 94 | # side[in] is it a side scan? Otherwise its a Downscan or primary 95 | # legend[in] should a legend be added 96 | # filename[in] part of the name of the image to save 97 | def printLegendAndSave(img, assembleProp,pathToData,side:bool, legend,filename="finalImage", pathToMeta="", rmUnderground=False): 98 | if(rmUnderground): 99 | # will set areas under the ground to zero 100 | img=removeUnderground(pathToMeta,img,assembleProp.maxDept) 101 | if(legend): 102 | if(side): 103 | baseIm.drawOnSideImage(img,assembleProp.maxDept) 104 | else: 105 | baseIm.drawOnImage(img,assembleProp.maxDept) 106 | 107 | #print("I will save now") 108 | #typeOfImg=os.path.basename(os.path.normpath(pathToData)) 109 | savepath=os.path.dirname(pathToData) 110 | #print(savepath) 111 | LegendLabel="L" if legend == True else "NL" 112 | img.save(savepath+"/" + filename + LegendLabel + ".jpg", "JPEG" , quality = 90) 113 | 114 | # Process one view of the sonar with all its zoom levels. (like a full Downscan or full Sidescan) 115 | # The final image will be saved one folder above the path to data 116 | # pathToData[in] path to image csv files 117 | # pathToImg[in] path to where the singe zoom level images should be stored 118 | # side[in] is it a side scan? Otherwise its a Downscan or primary 119 | # cmap[in] colour map 120 | # cutOffDept[in] maximum depth in meters to cut the image off 121 | def processOneView(pathToData,pathToImg,side:bool,cmap:str,cutOffDept): 122 | 123 | allFiles=utils.sorted_alphanumeric(os.listdir(pathToData)) 124 | 125 | #create images 126 | propertyList=baseIm.generateImagesAndPropertyList(allFiles,pathToData,pathToImg,cmap) 127 | 128 | # get assemble properties 129 | assembleProp=generateAssembleProperties(propertyList) 130 | 131 | # generate empty image 132 | new_im = Image.new('RGB', (assembleProp.framesTotal,assembleProp.yRes)) 133 | 134 | # assemble image 135 | assembleScan(new_im,propertyList,assembleProp,side,pathToImg) 136 | 137 | if(assembleProp.maxDept>cutOffDept): 138 | new_im=cropImageAtDept(cutOffDept,new_im,assembleProp.maxDept) 139 | assembleProp.maxDept=cutOffDept 140 | 141 | size=min(assembleProp.framesTotal,maxImageWidth),min(assembleProp.yRes,maxImageHeight) 142 | resizedImg=new_im.resize(size) 143 | 144 | return resizedImg, assembleProp 145 | 146 | if(legend): 147 | if(side): 148 | baseIm.drawOnSideImage(resizedImg,assembleProp.maxDept) 149 | else: 150 | baseIm.drawOnImage(resizedImg,assembleProp.maxDept) 151 | 152 | #print("I will save now") 153 | typeOfImg=os.path.basename(os.path.normpath(pathToData)) 154 | savepath=os.path.dirname(pathToData) 155 | #print(savepath) 156 | LegendLabel="L" if legend == True else "NL" 157 | resizedImg.save(savepath+"/"+typeOfImg+"finalIamge"+LegendLabel+".jpg", "JPEG",quality=90) 158 | # resizedImg.save(savepath+"/"+typeOfImg+"finalIamge.png", "PNG") 159 | 160 | def getSmallerDepth(pathDown,pathPrime): 161 | 162 | maxDeptDown=0 163 | allFiles=utils.sorted_alphanumeric(os.listdir(pathDown)) 164 | for file in allFiles: 165 | properties=dt.dataProperties() 166 | data = pd.read_csv(pathDown+"/"+file,header=0, dtype=str) 167 | baseIm.fillProperties(data,properties) 168 | maxDeptDown=max(maxDeptDown,properties.maxRange) 169 | 170 | maxDeptPrime=0 171 | allFiles=utils.sorted_alphanumeric(os.listdir(pathPrime)) 172 | for file in allFiles: 173 | properties=dt.dataProperties() 174 | data = pd.read_csv(pathPrime+"/"+file,header=0, dtype=str) 175 | baseIm.fillProperties(data,properties) 176 | maxDeptPrime=max(maxDeptPrime,properties.maxRange) 177 | 178 | 179 | return min(maxDeptDown,maxDeptPrime) 180 | 181 | -------------------------------------------------------------------------------- /code/datatypes.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import configparser 3 | import os 4 | 5 | ################################################## 6 | # This file contains the used datatype classes 7 | # Author: Tobias Ranft & Kimberly Mason 8 | # Date: 2022-08-27 9 | ################################################## 10 | 11 | # Property of one image segment 12 | # segment is a part of an image with constant height 13 | class dataProperties: 14 | def __init__(self): 15 | self.minRange=0 16 | self.maxRange=0 17 | self.pixelY=0 18 | self.frames=0 19 | self.name="" 20 | self.startIndex=0 21 | 22 | # properties to assemble several zoom segments 23 | # to one image 24 | class assembleProperties: 25 | def __init__(self): 26 | self.maxDept=0 27 | self.minDept=100000 28 | self.finestRes=1000 29 | self.framesTotal=0 30 | self.yMax=0 31 | 32 | # Selection of properties to control the settings 33 | # of the script. 34 | class processingSelection: 35 | def __init__(self): 36 | # process down scan 37 | self.down=False 38 | # process side scan 39 | self.side=False 40 | # process primary scan 41 | self.prime=False 42 | # process secondary scan 43 | self.second=False 44 | # generate georeference 45 | self.georef=False 46 | # generate map of the track 47 | self.track=False 48 | # correct the coordinates 49 | self.coordCorr=False 50 | # call R 51 | self.callR=False 52 | # process combined Down and Prime image 53 | self.combinedDownPrime=False 54 | 55 | self.cmapDown="self" 56 | self.cmapSide="self" 57 | # colour map for the primary chanel 58 | # https://matplotlib.org/stable/tutorials/colors/colormaps.html 59 | self.cmapPrime="jet" 60 | # dept where the image gets cut of 61 | self.cutOffDept=1000 62 | 63 | self.basepath="" 64 | self.files=[] 65 | 66 | self.segmentation=False 67 | 68 | # Class to hold meta information to the image 69 | class MetaInformation: 70 | def __init__(self): 71 | self.dataPrim=[] 72 | self.dataDown=[] 73 | self.dataSide=[] 74 | self.sideLon=[] 75 | self.sideLat=[] 76 | self.sideHeading=[] 77 | 78 | # class to hold information about the requested georeference area 79 | class Geocord: 80 | def __init__(self,north,south,east,west): 81 | if(north faster access 101 | dataList=data.values.tolist() 102 | 103 | #max value of data list 104 | maxYValue=int(max(max(dataList))) 105 | #print("max "+str(maxYValue)) 106 | 107 | # create an empty image 108 | new_im = Image.new('RGB', (properties.frames,properties.pixelY)) 109 | #print("image size "+str(properties.frames)+" "+str(properties.pixelY)) 110 | pixels = new_im.load() 111 | 112 | #create value dict for all colour to speed up 113 | valDict={} 114 | for i in range(maxYValue+10): 115 | pixval=int(255/maxYValue*i) 116 | r=int(pixval/255*78) 117 | g=int(pixval/255*255) 118 | b=int(pixval/255*174) 119 | valDict[i]=(r,g,b) 120 | #print(valDict) 121 | 122 | # loop over the image and transfer the intensities to colour values 123 | for row in range (0,properties.pixelY,1): 124 | for col in range (0,properties.frames,1): 125 | pixval=valDict[dataList[row][col]] 126 | pixels[col,row]=pixval#(pixval,pixval,pixval) 127 | new_im=new_im.transpose(Image.Transpose.FLIP_TOP_BOTTOM) 128 | new_im.save(image_path, "PNG") 129 | return properties 130 | 131 | 132 | # This function will loop over all image csv files and create png image files out of them. 133 | # Moreover it will create a list with the properties of this files 134 | # Already created images will not be recreated 135 | # allFiles[in] list with the names to all files to process 136 | # path[in] base path of where the image csv files are located 137 | # imgPath[in] path to where the png image files should be saved 138 | # cmap[in] colour map that should be used. use "self" for detailed processing 139 | # return: returns a list with the properties of for each image. This list will be as well created if 140 | # the image are all already present 141 | def generateImagesAndPropertyList(allFiles,path,imgPath,cmap): 142 | propertyList=[] 143 | fameCounter=0 144 | numberImages=len(allFiles) 145 | imageCounter=1 146 | for file in allFiles: 147 | res1=file.find("_") 148 | res2=file.find(".") 149 | nr=file[res1+1:res2] 150 | #print(nr) 151 | if(cmap=="self"): 152 | oneProperty=processOneFrameSelf(path+"/"+file,nr,imgPath) 153 | else: 154 | oneProperty=processOneFrame(path+"/"+file,nr,imgPath,cmap) 155 | #set frame counting from start 156 | oneProperty.startIndex=fameCounter 157 | fameCounter+=oneProperty.frames 158 | propertyList.append(oneProperty) 159 | 160 | #print progress 161 | print("processed image "+str(imageCounter) + " out of "+str(numberImages)) 162 | imageCounter=imageCounter+1 163 | return propertyList 164 | 165 | # Labels the image with dept distance lines and numbers 166 | # For Primary,Secondary and Downscan 167 | # image[in,out] image to be labeled 168 | # maxDepth[in] max dept in meters that the image represents 169 | def drawOnImage(image,maxDepth): 170 | width, height = image.size 171 | draw = ImageDraw.Draw(image) 172 | font = ImageFont.truetype("arial.ttf", 24) 173 | # font = ImageFont.truetype("Ubuntu-R.ttf", 24) use this line for ubuntu instead of arial 174 | 175 | #draw depth 176 | deptPerPixel=maxDepth/height 177 | _10mPixDiff=int(10/deptPerPixel) 178 | _1mPixDiff=int(1/deptPerPixel) 179 | #draw depth lines 180 | PixDiff=_1mPixDiff 181 | diffInMeters=1 182 | if(maxDepth>20): 183 | PixDiff=_10mPixDiff 184 | diffInMeters=10 185 | 186 | currentdepth=0 187 | for i in range(0,height,PixDiff): 188 | deptToPrint='%.1f' % currentdepth 189 | draw.text((0, i),deptToPrint,(255,255,255),font=font) 190 | draw.line((50, i, width,i), fill=255) 191 | currentdepth=currentdepth+diffInMeters 192 | 193 | 194 | #draw frames 195 | for i in range(100,width,100): 196 | draw.text((i, 0),str(i),(255,255,255),font=font) 197 | 198 | # Labels the image with side distance lines and numbers 199 | # For Sidescan 200 | # image[in,out] image to be labeled 201 | # maxDepth[in] max distance in meters to one side that the image represents 202 | def drawOnSideImage(image,maxDepth): 203 | width, height = image.size 204 | draw = ImageDraw.Draw(image) 205 | font = ImageFont.truetype("arial.ttf", 24) 206 | # font = ImageFont.truetype("Ubuntu-R.ttf", 24) use this line for ubuntu instead of arial 207 | 208 | #draw dept 209 | deptPerPixel=maxDepth*2/height 210 | _10mPixDiff=int(10/deptPerPixel) 211 | _1mPixDiff=int(1/deptPerPixel) 212 | #draw dept lines 213 | PixDiff=_1mPixDiff 214 | diffInMeters=1 215 | if(maxDepth>20): 216 | PixDiff=_10mPixDiff 217 | diffInMeters=10 218 | 219 | currentdepth=-maxDepth 220 | #halfHeight=int(height/2) 221 | for i in range(0,height,PixDiff): 222 | deptToPrint='%.1f' % currentdepth 223 | draw.text((0, i),deptToPrint,(255,255,255),font=font) 224 | draw.line((50, i, width,i), fill=255) 225 | currentdepth=currentdepth+diffInMeters 226 | 227 | 228 | #draw frames 229 | for i in range(100,width,100): 230 | draw.text((i, 0),str(i),(255,255,255),font=font) 231 | -------------------------------------------------------------------------------- /code/georef.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import matplotlib.ticker as tk 3 | from pandas.plotting import table 4 | import numpy as np 5 | import pandas as pd 6 | import os 7 | import math 8 | from PIL import Image 9 | from pyproj import Transformer 10 | import cv2 11 | import sys 12 | 13 | import datatypes as dt 14 | import utilities as utils 15 | import generateBaseImages as baseIm 16 | 17 | import warnings 18 | from shapely.errors import ShapelyDeprecationWarning 19 | warnings.filterwarnings("ignore", category=ShapelyDeprecationWarning) 20 | 21 | 22 | ################################################## 23 | # Collection of functions for geo data processing 24 | # Author: Tobias Ranft & Kimberly Mason 25 | # Date: 2022-08-27 26 | ################################################## 27 | 28 | """ def setPixelValue(pixpos,numberPixel,pixels,value): 29 | if(pixpos[0]>0 and pixpos[1]>0 and pixpos[0]0 or colour[1]>0 or colour[2]>0): 52 | returnColour=colour 53 | counter=counter+1 54 | if(counter<3): 55 | returnColour=(0,0,0,0) 56 | return returnColour 57 | 58 | # When the images get georeferenced there are sometimes small holes. 59 | # I don't know where they are coming from. Since actually with 60 | # the drawing function the should not be holes. 61 | # However this functions closes them with the colour values around 62 | # img[in,out] image to process 63 | def closeHoles(img): 64 | width = img.shape[1] 65 | height = img.shape[0] 66 | 67 | 68 | grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 69 | (thresh, bwImg) = cv2.threshold(grayImage, 0, 255, cv2.THRESH_BINARY) 70 | bwImg=(bwImg/255).astype(np.byte) 71 | 72 | print("closing holes") 73 | numberOfRows=width/10 74 | rowCounter=0 75 | rowPrintProgress=0 76 | 77 | #imgOut=img.copy() 78 | for j in range(1,width-1,1): 79 | for i in range(1,height-1,1): 80 | if(bwImg[i,j]==0): 81 | counter=bwImg[i+1,j]+bwImg[i-1,j]+bwImg[i,j+1]+bwImg[i,j-1] 82 | #if(counter>0): 83 | # print(str(counter)+" "+str(bwImg[i+1,j])+" "+str(bwImg[i-1,j])+" "+str(bwImg[i,j+1])+" "+str(bwImg[i,j-1])) 84 | if(counter>2): 85 | #print("close hole") 86 | pos1=img[i+1,j] 87 | pos2=img[i-1,j] 88 | pos3=img[i,j+1] 89 | pos4=img[i,j-1] 90 | outColour=returnNonZeroColour([pos1,pos2,pos3,pos4]) 91 | img[i,j]=outColour 92 | if(j>rowCounter): 93 | rowCounter=rowCounter+numberOfRows 94 | print(str(rowPrintProgress)+"%") 95 | rowPrintProgress+=10 96 | return img 97 | 98 | 99 | # Georeference side scan sonar data. So called mosaic. 100 | # coord[in] Coordinate window which should be processed 101 | # pathToData[in] Path to image csv files 102 | # pathToImg[in] Path to single depth level folder 103 | # pathToMeta[in] Path to meta data 104 | # cmap[in] Colour map 105 | def mosaic(coord,pathToData,pathToImg,pathToMeta,cmap): 106 | print("####### mosaic processing #######") 107 | 108 | # get the meta information 109 | metaInfo=utils.readMetaInformation(pathToMeta) 110 | # we want to transfer from global coordinate system WGS84 = epsg4326 111 | # to a local one that needs to be adapted based on where you are 112 | # for hamburg that is epsg25832 113 | # get the local geo coordinate system based on one point in the data 114 | print(max(metaInfo.sideLat)) 115 | print(min(metaInfo.sideLat)) 116 | print(max(metaInfo.sideLon)) 117 | print(min(metaInfo.sideLon)) 118 | Geocord=dt.Geocord(max(metaInfo.sideLat),min(metaInfo.sideLat),max(metaInfo.sideLon),min(metaInfo.sideLon)) 119 | localCoordSystem=utils.getLocalGeoCoordinateSystem(Geocord) 120 | 121 | print("Use local geo coordinate system: "+str(localCoordSystem)) 122 | transformer = Transformer.from_crs("epsg:4326", localCoordSystem) 123 | #image border 124 | bottomleft=transformer.transform(coord.south,coord.west) 125 | topright=transformer.transform(coord.north,coord.east) 126 | distance=np.subtract(topright,bottomleft) 127 | print("distance "+ str(distance)) 128 | pixelSize=(coord.pixelSizeMeters,coord.pixelSizeMeters) 129 | numberPixel=np.divide(distance,pixelSize) 130 | numberPixel=(int(numberPixel[0]),int(numberPixel[1])) 131 | print("number pixels "+str(numberPixel)) 132 | maxPixel=pow(2,16) 133 | if(numberPixel[0]>maxPixel or numberPixel[1]>maxPixel): 134 | sys.exit("Error too many pixels. Reduce area or resolution") 135 | 136 | #get all files to process 137 | allFiles=utils.sorted_alphanumeric(os.listdir(pathToData)) 138 | 139 | #create an empty image 140 | cvImage = np.zeros((numberPixel[1],numberPixel[0],4), np.uint8) 141 | 142 | 143 | #create images and generate property list 144 | 145 | propertyList=baseIm.generateImagesAndPropertyList(allFiles,pathToData,pathToImg,cmap) 146 | for oneProp in propertyList: 147 | indices=range(oneProp.startIndex,oneProp.startIndex+oneProp.frames,1) 148 | pixInWidth=int(oneProp.maxRange/pixelSize[1]) 149 | #open image 150 | imgPath=pathToImg+"/fig_"+oneProp.name+".png" 151 | img=Image.open(imgPath) 152 | newsize=(oneProp.frames,pixInWidth*2) 153 | img=img.resize(newsize) 154 | pixelsInputImg = img.load() 155 | 156 | prevPixPosList=[] 157 | firstRound=True 158 | 159 | for index in indices: 160 | #print("index "+str(index)) 161 | globalCoord=transformer.transform(metaInfo.sideLat[index],metaInfo.sideLon[index]) 162 | localCoord=np.subtract(globalCoord,bottomleft) 163 | #print("local cord "+str(localCoord)) 164 | pixpos=(int(localCoord[0]/pixelSize[0]),int(localCoord[1]/pixelSize[1])) 165 | heading=metaInfo.sideHeading[index] 166 | imgInX=index-oneProp.startIndex 167 | #print("heading " +str(heading)) 168 | pixPosList=[] 169 | pixValList=[] 170 | 171 | for onePixel in range(-pixInWidth,pixInWidth,1): 172 | pixInImage=sweepToImage(pixpos,heading,onePixel) 173 | pixPosList.append(pixInImage) 174 | 175 | imgInY=onePixel+pixInWidth 176 | #print("img in coord "+str(imgInX)+" "+str(imgInY)) 177 | value=pixelsInputImg[imgInX,imgInY] 178 | pixValList.append(value) 179 | if(not firstRound): 180 | for i in range(0,(pixInWidth-1),1): 181 | pos1=pixPosList[i] 182 | pos2=pixPosList[i+1] 183 | pos3=prevPixPosList[i] 184 | pos4=prevPixPosList[i+1] 185 | area = np.array( [pos1, pos2, pos3, pos4] ) 186 | pixval=pixValList[i+1] 187 | pixvalWithAlpha=(pixval[0],pixval[1],pixval[2],255) 188 | cv2.drawContours(cvImage, [area], 0,pixvalWithAlpha , -1) 189 | 190 | for i in range((pixInWidth-1),2*(pixInWidth-1),1): 191 | pos1=pixPosList[i] 192 | #print("pos1 " +str(pos1)) 193 | pos2=pixPosList[i+1] 194 | pos3=prevPixPosList[i] 195 | pos4=prevPixPosList[i+1] 196 | area = np.array( [pos1, pos2, pos3, pos4] ) 197 | pixval=pixValList[i+1] 198 | pixvalWithAlpha=(pixval[0],pixval[1],pixval[2],255) 199 | cv2.drawContours(cvImage, [area], 0, pixvalWithAlpha, -1) 200 | 201 | firstRound=False 202 | prevPixPosList=pixPosList.copy() 203 | # close holes in the image 204 | cvImage=closeHoles(cvImage) 205 | # flip image 206 | cvImage=cv2.flip(cvImage, 0) 207 | # save image 208 | outPath=os.path.dirname(pathToData)+"/MosaicCV.png" 209 | cv2.imwrite(outPath, cvImage) 210 | # convert image to geotif 211 | utils.convertToGeoTif(outPath,coord) -------------------------------------------------------------------------------- /code/models/seg3.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SquidProjects/SonarMapper/92dcf126afab28ba9d7cdc0c1952d6d070445b93/code/models/seg3.h5 -------------------------------------------------------------------------------- /code/segmentation.py: -------------------------------------------------------------------------------- 1 | import os 2 | import cv2 3 | import keras 4 | import numpy as np 5 | import matplotlib.pyplot as plt 6 | import albumentations as A 7 | from tensorflow.keras.callbacks import TensorBoard 8 | import tensorflow as tf 9 | import segmentation_models as sm 10 | import math 11 | 12 | # define colours for the different classes 13 | fishCol=(0,255,0) 14 | vegbigCol=(255,100,0) 15 | vegsmallCol= (0,255,255) 16 | groundCol= (0,0,255) 17 | backgroundCol=(100,2,2) 18 | 19 | # image patch size: size of the patches which get analyzed 20 | patch_size = (500,500) 21 | # the patch size the model is trained on 22 | model_patch_size = (320, 320) 23 | 24 | 25 | def generateColourOverlayThreshold(mask): 26 | """ 27 | Generates an overlay mask where the classes are coloured 28 | A threshold is used to determine colouration 29 | """ 30 | # split the mask into its layes, representing the different classes 31 | background=mask[..., 0].squeeze() 32 | ground=mask[..., 1].squeeze() 33 | vegsmall=mask[..., 2].squeeze() 34 | vegbig=mask[..., 3].squeeze() 35 | fish=mask[..., 4].squeeze() 36 | 37 | threshold=0.2 38 | 39 | mask_dim = mask.shape 40 | overlay = np.zeros((model_patch_size[0],model_patch_size[1],3), np.uint8) 41 | overlay.fill(0) 42 | overlay[np.where(background>0.5)] = backgroundCol 43 | overlay[np.where(ground>threshold)] = groundCol 44 | overlay[np.where(vegsmall>threshold)] = vegsmallCol 45 | overlay[np.where(vegbig>threshold)] = vegbigCol 46 | overlay[np.where(fish>threshold)] = fishCol 47 | overlay= cv2.resize(overlay, patch_size, interpolation = cv2.INTER_AREA) 48 | return overlay 49 | 50 | 51 | 52 | def generateColourOverlayMax(mask): 53 | """ 54 | Generates an overlay mask where the classes are coloured 55 | The maximal class value is used to determine colouration 56 | """ 57 | # split the mask into its layes, representing the different classes 58 | background=mask[..., 0].squeeze() 59 | ground=mask[..., 1].squeeze() 60 | vegsmall=mask[..., 2].squeeze() 61 | vegbig=mask[..., 3].squeeze() 62 | fish=mask[..., 4].squeeze() 63 | 64 | cv2.imwrite("background.jpg", background) 65 | cv2.imwrite("ground.jpg", ground) 66 | cv2.imwrite("vegsmall.jpg", vegsmall) 67 | cv2.imwrite("vegbig.jpg", vegbig) 68 | cv2.imwrite("fish.jpg", fish) 69 | 70 | threshold=0.2 71 | 72 | mask_dim = mask.shape 73 | overlay = np.zeros((model_patch_size[0],model_patch_size[1],3), np.uint8) 74 | overlay.fill(0) 75 | 76 | # for each class create a list array where it has the highes value 77 | listOfMaks=[background,ground,vegsmall,vegbig,fish] 78 | def compareMasks(currentMask, pos): 79 | if(posprintCounter): 143 | printCounter=printCounter+tenPercent 144 | print(str(int(printCounter/tenPercent)*10)+"%") 145 | 146 | 147 | newData=pd.DataFrame(Type) 148 | newData["Latitude"]=Lat 149 | newData["Longitude"]=Lon 150 | newData["GNSSHeading"]=Heading 151 | newData["WaterDepth"]=data["WaterDepth"] 152 | 153 | newData.to_csv(outFile) 154 | print("end of fix position") 155 | 156 | # Function to read the meta information 157 | # input 158 | # path path to meta information 159 | def readMetaInformation(path): 160 | print("start read meta information") 161 | data = pd.read_csv(path) 162 | metaInfo = dt.MetaInformation() 163 | metaInfo.dataPrim = data[data['SurveyTypeLabel'] == 'Primary'] 164 | metaInfo.dataDown = data[data['SurveyTypeLabel'] == 'Downscan'] 165 | metaInfo.dataSide = data[data['SurveyTypeLabel'] == 'Sidescan'] 166 | metaInfo.sideLat=metaInfo.dataSide.loc[:,"Latitude"].values.tolist() 167 | metaInfo.sideLon=metaInfo.dataSide.loc[:,"Longitude"].values.tolist() 168 | metaInfo.sideHeading=metaInfo.dataSide.loc[:,"GNSSHeading"].values.tolist() 169 | return metaInfo 170 | 171 | # Function to sort file name in alpha numeric order 172 | # This is important to process the files in the right order 173 | # input 174 | # data list with the file names 175 | # return list with file names sorted 176 | def sorted_alphanumeric(data): 177 | convert = lambda text: int(text) if text.isdigit() else text.lower() 178 | alphanum_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ] 179 | return sorted(data, key=alphanum_key) 180 | 181 | # This function takes a position and return a local coordinate system 182 | # which fits 183 | # Geocord geocoordinates 184 | # return local geo coordinate system 185 | def getLocalGeoCoordinateSystem(Geocord): 186 | utm_crs_list = query_utm_crs_info( 187 | datum_name="WGS 84", 188 | area_of_interest=AreaOfInterest( 189 | west_lon_degree=Geocord.west, 190 | south_lat_degree=Geocord.south, 191 | east_lon_degree=Geocord.east, 192 | north_lat_degree=Geocord.north, 193 | ), 194 | ) 195 | utm_crs = CRS.from_epsg(utm_crs_list[0].code) 196 | return utm_crs 197 | 198 | # convert openCV image to PIL image format 199 | def openCvToPilImage(openCvImageIn): 200 | cvRGB = cv2.cvtColor(openCvImageIn, cv2.COLOR_BGR2RGB) 201 | pilImg = Image.fromarray(cvRGB) 202 | return pilImg 203 | 204 | # convert PIL image format to openCV image format 205 | def pilToOpenCvImage(pilImageIn): 206 | numpy_image=np.array(pilImageIn) 207 | opencv_image=cv2.cvtColor(numpy_image, cv2.COLOR_RGB2BGR) 208 | return opencv_image 209 | 210 | # This function takes 2 images in and puts them in different colour canels in the final image 211 | # This is currently used to combine the Down and the Prime scan 212 | # src1 image 1 213 | # scr2 image 2 214 | def combineImages(src1,src2): 215 | 216 | #convert input pil to opencv, this is in RGB not in BGR but since we are working with grayscale here anyways, it doesn't matter 217 | src1=pilToOpenCvImage(src1) 218 | src2=pilToOpenCvImage(src2) 219 | 220 | src1=cv2.cvtColor(src1, cv2.COLOR_BGR2GRAY) 221 | src2=cv2.cvtColor(src2, cv2.COLOR_BGR2GRAY) 222 | 223 | # reshape to the shape of src1 224 | height, width = src1.shape[:2] 225 | src2=cv2.resize(src2, (width, height), interpolation = cv2.INTER_AREA) 226 | 227 | src2=cv2.convertScaleAbs(src2, alpha=1.5, beta=1.5) 228 | src1=cv2.convertScaleAbs(src1, alpha=1.5, beta=1.5) 229 | 230 | blank_image = np.zeros((height,width,1), np.uint8) 231 | blank_image.fill(100) 232 | dst1 = cv2.merge([blank_image,src1,src2 ]) 233 | 234 | # average weighting 235 | #dst1 = cv2.addWeighted(src1, 0.5, src2, 0.5, 0.0) 236 | #cv2.imwrite(pathToCombined, dst1) 237 | return dst1 238 | 239 | def check_if_scan_exsists(path:str, message): 240 | """ 241 | Checks if the given path to scan files does exsist. 242 | Throws an error if not. 243 | Returns True if path exsists 244 | 245 | """ 246 | if not os.path.exists(path): 247 | error_string = "####### Selected scan not found. Make sure preprocessing is enabled in config and your sonar file contains the scan. #######" 248 | if message == "": 249 | error_string = error_string 250 | else: 251 | error_string = message + error_string 252 | raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), path + " " + error_string) 253 | return True 254 | 255 | 256 | 257 | -------------------------------------------------------------------------------- /environment.yaml: -------------------------------------------------------------------------------- 1 | name: sonar-env 2 | 3 | dependencies: 4 | - shapely=1.8.4 5 | - rasterio=1.2.10 6 | - pillow=9.2.0 7 | - pathlib=1.0.1 8 | - pandas=1.4.4 9 | - opencv=4.6.0 10 | - numpy=1.23.1 11 | - matplotlib=3.5.2 12 | - geopandas=0.9.0 13 | - gdal=3.5.2 14 | - configparser=5.0.2 15 | - brotlipy=0.7.0 16 | - pip=22.2.2 17 | - pip: 18 | - geopy==2.2.0 19 | - contextily==1.2.0 20 | - pyproj==3.3.1 21 | - urllib3==1.26.10 22 | -------------------------------------------------------------------------------- /example/config.ini: -------------------------------------------------------------------------------- 1 | [views] 2 | downScan = True 3 | sideScan = True 4 | primary = True 5 | secondary = False 6 | georeference = False 7 | plotTrack = False 8 | combinedDownAndPrimeView = True 9 | 10 | [settings] 11 | coordinateCorrection = True 12 | colourMap =jet 13 | maxDepth=50 14 | 15 | [settingsGeoreference] 16 | north = 53.68230 17 | south = 53.67932 18 | east = 10.75654 19 | west = 10.75250 20 | pixelSizeMeters=0.5 21 | 22 | [files] 23 | basepath = C:/Path 24 | files = testfile.sl2 25 | 26 | [preprocessing] 27 | callIt = True 28 | 29 | # experimental functions 30 | [ai] 31 | segmentation = False -------------------------------------------------------------------------------- /example/testfile.sl2: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SquidProjects/SonarMapper/92dcf126afab28ba9d7cdc0c1952d6d070445b93/example/testfile.sl2 -------------------------------------------------------------------------------- /image1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SquidProjects/SonarMapper/92dcf126afab28ba9d7cdc0c1952d6d070445b93/image1.png -------------------------------------------------------------------------------- /image2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SquidProjects/SonarMapper/92dcf126afab28ba9d7cdc0c1952d6d070445b93/image2.jpg -------------------------------------------------------------------------------- /image3.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SquidProjects/SonarMapper/92dcf126afab28ba9d7cdc0c1952d6d070445b93/image3.jpg --------------------------------------------------------------------------------