Use Reach M2 with Phantom 4 Pro

dataset :

photos with timestamps

UBX from M2 and from base station

Processing:

1- Post-Process base station and get corrected coordinates

2- with Emlid Studio (or RTKLib) calculate events (triggers) positions from UBX

as output one gets an …._events.pos file that gives corrected coordinates for each event together with a timestamp

3- extract timestamps from exif of pictures

use exiftool with this command

exiftool -n -csv -filemodifydate -api TimeZone=UTC *.JPG > metadata.csv

the output file “metadata.csv” gives the name of the picture and the timestamp in UTC format

4- Merge …_events.pos and pictures events

For now I’ve got this ugly Python script:

#! /usr/bin/env python

"""
Update Emlid Reach Survey points with PPK position output from RTKLIB
David Shean
dshean@gmail.com
Edited to fix Pandas datetime/Timestamp tz issues, and a few key changes likely based on Emlid updates
"""

import os
import argparse
import numpy as np
import pandas as pd

def getparser():
    parser = argparse.ArgumentParser(description='Update Emlid Reach Survey points with \
            PPK positions from RTKLIB')
    parser.add_argument('survey_pts_csv_fn', type=str, help='Survey point csv filename')
    parser.add_argument('ppk_pos_fn', type=str, help='PPK pos filename')
    return parser


def main():
    parser = getparser()
    args = parser.parse_args()

    survey_pts_csv_fn = args.survey_pts_csv_fn
    ppk_pos_fn = args.ppk_pos_fn

    print('Loading: %s' % survey_pts_csv_fn)
    survey_pts = pd.read_csv(survey_pts_csv_fn, parse_dates=[1], header=0)
    survey_pts['date']=pd.to_datetime(survey_pts['FileModifyDate'],format="%Y:%m:%d %H:%M:%S+00:00")
    survey_pts.sort_values('date', inplace=True)
    survey_pts.index=survey_pts['date']
    print(survey_pts.dtypes)
    print(survey_pts)
    header = 'Date UTC latitude(deg) longitude(deg)  height(m)   Q  ns   sdn(m)   sde(m)   sdu(m)  sdne(m)  sdeu(m)  sdun(m) age(s)  ratio'
    print('Loading: %s' % ppk_pos_fn)
    ppk_pos = pd.read_csv(ppk_pos_fn, comment='%', delim_whitespace=True, names=header.split(), parse_dates=[[0,1]])
    ppk_pos['date']=pd.to_datetime(ppk_pos['Date_UTC'])
    ppk_pos.index=ppk_pos['Date_UTC']
    print(ppk_pos.dtypes)
    print(ppk_pos)

    # Applying merge_asof on data and store it
    # in a variable
    merged_dataframe = pd.merge_asof(ppk_pos, survey_pts, right_index=True,left_index=True,direction='nearest',tolerance=pd.Timedelta("1s"))

    print(merged_dataframe)

    #Write out new file
    out_fn = os.path.splitext(survey_pts_csv_fn)[0]+'_merged.csv'
    print("Writing out: %s" % out_fn)
    merged_dataframe.to_csv(out_fn)    

if __name__ == "__main__":
    main()

5- In Metashape, import the coordinates of the cameras from the new file

Metashape script

API Python voir https://www.agisoft.com/pdf/metashape_python_api_1_5_0.pdf

FAQ scripting : https://agisoft.freshdesk.com/support/solutions/folders/31000114192

Liste des scripts: https://github.com/agisoft-llc/metashape-scripts

exemple d’utilisation de la console Python dans Metashape, décocher les orientations de caméras :

doc=Metashape.app.document
chunk=doc.chunk
for camera in chunk.cameras:
   camera.reference.rotation_enabled=0

Crop RPC

Using MicMac tools :

example:

mm3d SateLib CropRPC Ori-RPC-d0/GB-Orientation-IMG_PHR1A_P_201902190719128_SEN_3788788101-001.TIF.xml Ori-RPC-d0/GB.* Cropped Org=[3000,4000] Sz=[10500,8500]

parameters:

  1. image to use for the definition of crop zone (Org and Sz correspond to this image)
  2. pattern of orientation files for images to be cropped
  3. name of folder to store cropped RPC
  4. Org : Origin of the box to crop
  5. Sz : Size of the box to crop

Traitement MicMac d’images satellites

Workflow complet pour traiter des images satellites (multi-) stéréoscopiques

avec MicMac pour le traitement de photogrammétrie, et GDAL et OTB pour les post-traitements

Génération d’images Fusionnées (Pan-Sharpen) ortho-rectifiées avec MicMac

Le process est le suivant, utilisable avec le script https://github.com/JulesFleury/JulesFleury/blob/master/Script_Decomposition_Band.sh

#0- réaliser la fusion du bundle d’origine avec otb_bundletoperfectsensor

#1-
split Pan-sharpen bands
avec otbcli_splitimage.....

#2-
copie et renommage des orientations initiales et ajustées de micmac dans le dossier Ori-XXX-adj (cf. script)

#3-
#relancer MicMac Malt pour générer les ortho sur les différents canaux, plusieurs options sont possibles

  • a- si Malt n’a pas encore tourné

#mm3d Malt Ortho "Pattern de toutes les images" "dossier_orientations" DirMEC="dossier de la MEC des panchromatiques" ImMNT="Pattern des panchromatiques" DoMEC=1 DoOrtho=1 ImOrtho="Pattern des différentes bandes multispectrales" DirOF="dossier des orthophotos" EZA=1 ZoomF=4 

#exemple : mm3d Malt Ortho IMG_PHR1A_P.*.TIF Ori-RPC-d0-adj/ DirMEC=MEC-Malt_forPMS_Zf8 ImMNT=IMG_PHR1A_P_202107250554.*.TIF DoMEC=1 DoOrtho=1 ImOrtho=IMG_PHR1A_PMS_202107250554.*.TIF DirOF=Ortho-MEC-Malt-PMS EZA=1 ZoomF=8

  • b- si Malt a déjà tourné sur les panchro

#mm3d Malt Ortho "Pattern de toutes les images" "dossier_orientations" DoMEC=0 DoOrtho=1 ImOrtho="Pattern des différentes bandes multispectrales" DirOF="dossier des orthophotos pour le multispectral"
#exemple : mm3d Malt Ortho "IMG.*.TIF" Ori-RPC-d0-adj DirMEC="MEC-Malt-Zf4" DoMEC=0 ImMNT="IMG.*(00[123]).TIF" ImOrtho="IMG.*_b.*.TIF" DirOF="Ortho_Fusion" DoOrtho=1 ZoomF=4 EZA=1

#4-
#Rassembler les différents canaux des images pan-sharpen
#otbcli_ConcatenateImages ....