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Author Topic: HOW TO ADD FISHEYE CAMERA  (Read 21 times)

mihapajo123

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HOW TO ADD FISHEYE CAMERA
« on: May 10, 2021, 08:11:12 PM »
I'm interested in how to write in the script that I'm using a fisheye camera and that I already have the internal parameters given. These internal parameters, it would make sense to always ask me when I run the script.

Thank you very much for your help




MY SKRIPT:


import Metashape
import time, math, os, glob

TYPES = ["DNG", "JPG", "JPEG", "TIF", "TIFF"] #supported file types/extensions

def calc_reprojection(chunk):
   point_cloud = chunk.point_cloud
   points = point_cloud.points
   npoints = len(points)
   projections = chunk.point_cloud.projections
   err_sum = 0
   num = 0
   maxe = 0

   point_ids = [-1] * len(point_cloud.tracks)
   #point_errors = dict()
   for point_id in range(0, npoints):
      point_ids[points[point_id].track_id] = point_id

   for camera in chunk.cameras:
      if not camera.transform:
         continue
      for proj in projections[camera]:
         track_id = proj.track_id
         point_id = point_ids[track_id]
         if point_id < 0:
            continue
         point = points[point_id]
         if not point.valid:
            continue
         error = camera.error(point.coord, proj.coord).norm() ** 2
         err_sum += error
         num += 1
         #if point_id not in point_errors.keys():
         #   point_errors[point_id] = [error]
         #else:
         #   point_errors[point_id].append(error)
         #if error > maxe: maxe = error
            
   reproj = math.sqrt(err_sum / num)
   return (reproj) #return (sigma, point_errors, maxe)

def autoprocess():

   #project_path = "D:/Project/project.psx"
   project_path = Metashape.app.getSaveFileName("Specify project save filename:", filter = "Metashape Project (*.psx)")
   if not project_path:
      Metashape.app.messageBox("Invalid project path")
      return 0
   image_folder_path = Metashape.app.getExistingDirectory("Specify the folder with images:")
   if not image_folder_path:
      Metashape.app.messageBox("Invalid image folder path")
      return 0
   reference_path = Metashape.app.getOpenFileName("Specify reference file with targes:", filter = "CSV files (*.txt *.csv)")
   if not reference_path:
      Metashape.app.messageBox("Invalid reference file path")
      return 0
      
   doc = Metashape.Document()
   doc.save(project_path)
   doc = Metashape.app.document #open the same project in applicaiton GUI
   doc.open(project_path)
   
   chunk = doc.addChunk()
   chunk.crs = Metashape.CoordinateSystem('LOCAL_CS["Local Coordinates (m)",LOCAL_DATUM["Local Datum",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]]]') #set crs to local coordinates (m)
   
   image_list = [photo for photo in glob.iglob(image_folder_path + "\\*.*", recursive = False) if os.path.isfile(photo) and os.path.splitext(photo)[1][1:].upper() in TYPES]
   chunk.addPhotos(image_list) #add images
   doc.save()
   
   
   #align photos
   chunk.matchPhotos(downscale = 4, generic_preselection = True, keypoint_limit = 50000, tiepoint_limit = 4000, guided_matching = True)   #we recommend downscale = 1, guided_matchinf = False
   chunk.alignCameras(adaptive_fitting = False) #we recommend adaptive_fitting = False
   doc.save()
   
   #gradual selection Reconstruction uncertainty
   #criterion = True
   #n = 0
   #while criterion:
   #   n += 1
   #   TARGET_PERCENT = 50
    #
   #   f = Metashape.PointCloud.Filter()
   #   f.init(chunk, criterion = Metashape.PointCloud.Filter.ReconstructionUncertainty)
   #   list_values = f.values
   #   list_values_valid = list()
   #   for i in range(len(list_values)):
   #      if points.valid:
   #         list_values_valid.append(list_values)
   #   list_values_valid.sort()
   #   target = int(len(list_values_valid) * TARGET_PERCENT / 100)
   #   threshold = list_values_valid[target]
   #   if threshold < 50:
   #      f.selectPoints(threshold)
   #      f.removePoints(threshold)
   #   else:
   #      f.selectPoints(50)
   #      f.removePoints(50)
   #   chunk.optimizeCameras(fit_f = True, fit_cx = True, fit_cy = True, fit_k1 = True, fit_k2 = True, fit_k3 = True, fit_p1 = True, fit_p2 = True, fit_b1 = True, fit_b2 = True, fit_corrections = False, tiepoint_covariance = True)
   #   
   #   reproj =  calc_reprojection(chunk)
   #   if (float(chunk.meta['OptimizeCameras/sigma0']) >= 1) or (reproj >= 3.0):
   #      criterion = True
   #   else:
   #      criterion = False
   #   if (threshold <= 10) and (n > 2):
   #      criterion = False
   #doc.save()
   #
   ##gradual selection Projection accuracy
   #criterion = True
   #n = 0
   #while criterion:
   #   n += 1
   #   TARGET_PERCENT = 50
    #
   #   f = Metashape.PointCloud.Filter()
   #   f.init(chunk, criterion = Metashape.PointCloud.Filter.ProjectionAccuracy)
   #   list_values = f.values
   #   list_values_valid = list()
   #   for i in range(len(list_values)):
   #      if points.valid:
   #         list_values_valid.append(list_values)
   #   list_values_valid.sort()
   #   target = int(len(list_values_valid) * TARGET_PERCENT / 100)
   #   threshold = list_values_valid[target]
   #   if threshold < 2:
   #      f.selectPoints(2)
   #      f.removePoints(2)
   #   elif threshold < 3:
   #      f.selectPoints(threshold)
   #      f.removePoints(threshold)
   #   else:
   #      f.selectPoints(3)
   #      f.removePoints(3)
   #   chunk.optimizeCameras(fit_f = True, fit_cx = True, fit_cy = True, fit_k1 = True, fit_k2 = True, fit_k3 = True, fit_p1 = True, fit_p2 = True, fit_b1 = True, fit_b2 = True, fit_corrections = False, tiepoint_covariance = True)
   #   
   #   if (threshold <= 2) and (n > 2):
   #      criterion = False
   #doc.save()
   #
   ##modify tie point accuracy
   #
   #chunk.tiepoint_accuracy = 0.1
   #criterion = True
   #n = 0
   #while criterion:
   #   n += 1
   #   chunk.optimizeCameras(fit_f = True, fit_cx = True, fit_cy = True, fit_k1 = True, fit_k2 = True, fit_k3 = True, fit_p1 = True, fit_p2 = True, fit_b1 = True, fit_b2 = True, fit_corrections = False, tiepoint_covariance = True)
   #   reproj =  calc_reprojection(chunk)
   #   if (float(chunk.meta['OptimizeCameras/sigma0']) >= 1) or (reproj >= 3.0):
   #      criterion = True
   #   else:
   #      criterion = False
   #   if (n > 5):
   #      criterion = False
   #doc.save()

   ####

   #detect markers
   chunk.detectMarkers(target_type = Metashape.CircularTarget12bit, tolerance = 50)
   chunk.importReference(reference_path, format = Metashape.ReferenceFormatCSV, columns = "nxyz", delimiter = ",") #import coordinates from file, delimiter = "comma"
   chunk.marker_location_accuracy = Metashape.Vector([0.002, 0.002, 0.002])
   chunk.optimizeCameras(fit_f = True, fit_cx = True, fit_cy = True, fit_k1 = True, fit_k2 = True, fit_k3 = True, fit_k4 = True, fit_p1 = True, fit_p2 = True, fit_b1 = True, fit_b2 = True, fit_corrections = False, tiepoint_covariance = True) #we recommend fit_corrections = False
   chunk.updateTransform()
   doc.save()
   
   ###add scalebars???
   
   ##gradual selection Reprojction Error
   #criterion = True
   #n = 0
   #while criterion:
   #   n += 1
   #   TARGET_PERCENT = 90
    #
   #   f = Metashape.PointCloud.Filter()
   #   f.init(chunk, criterion = Metashape.PointCloud.Filter.ReprojectionError)
   #   list_values = f.values
   #   list_values_valid = list()
   #   for i in range(len(list_values)):
   #      if points.valid:
   #         list_values_valid.append(list_values)
   #   list_values_valid.sort()
   #   target = int(len(list_values_valid) * TARGET_PERCENT / 100)
   #   threshold = list_values_valid[target]
   #   if threshold >= 3:
   #      f.selectPoints(threshold)
   #      f.removePoints(threshold)
   #   else:
   #      f.selectPoints(3)
   #      f.removePoints(3)
   #   chunk.optimizeCameras(fit_f = True, fit_cx = True, fit_cy = True, fit_k1 = True, fit_k2 = True, fit_k3 = True, fit_p1 = True, fit_p2 = True, fit_b1 = True, fit_b2 = True, fit_corrections = False, tiepoint_covariance = True)
   #   
   #   reproj =  calc_reprojection(chunk)
   #   if (float(chunk.meta['OptimizeCameras/sigma0']) >= 1) or (reproj >= 3.0):
   #      criterion = True
   #   else:
   #      criterion = False
   #   if (threshold < 3) and (n > 3):
   #      criterion = False
   #doc.save()
   ####
   
   chunk.resetRegion()
   #build dense cloud
   chunk.buildDepthMaps(downscale = 4, filter_mode = Metashape.AggressiveFiltering)
   doc.save()
   chunk.buildDenseCloud(point_colors = True, point_confidence = True)
   doc.save()
   
   ##build mesh
   #chunk.depth_maps = None
   #chunk.buildDepthMaps(downscale = 4, filter_mode = Metashape.MildFiltering)
   #doc.save()
   #chunk.buildModel(surface_type = Metashape.Arbitrary, source_data = Metashape.DataSource.DenseCloudData, face_count = Metashape.HighFaceCount, vertex_colors = True)
   #doc.save()
   #
   ##build texture
   #chunk.buildUV(mapping_mode = Metashape.GenericMapping, texture_size = 8192)
   #chunk.buildTexture(blending_mode = Metashape.MosaicBlending, texture_size = 8192, fill_holes = True, ghosting_filter = True, texture_type = Metashape.Model.TextureType.DiffuseMap)
   #doc.save()
   
   #export
   export_base_path = os.path.dirname(doc.path)
   try:
      os.mkdir(export_base_path + "/REZULTATI")
   except:
      print("Can't create directory")
      return 0
   
   if chunk.dense_cloud:
      chunk.exportPoints(path = export_base_path + "/REZULTATI/1_OBLAK_TOCK.las", format = Metashape.PointsFormatLAS, source_data = Metashape.DataSource.DenseCloudData, save_colors = True)
   
   #if chunk.model:
   #   chunk.exportModel(path = export_base_path + "/REZULTATI/2_3D_MODEL.obj", format = Metashape.ModelFormatOBJ, texture_format = Metashape.ImageFormatJPEG, save_texture = True, save_uv = True, save_normals = True, save_colors = True)
   
   chunk.exportReport(path = export_base_path + "/REZULTATI/3_Fotogrametrično_poročilo.pdf")
   
   #undistort
   #for camera in chunk.cameras:
   #   image = camera.image()
   #   calib = camera.sensor.calibration
   #   undist = image.undistort(calib, center_principal_point = True, square_pixels = True)
   #   label = camera.label
   #   undist.save(export_base_path + "/REZULTATI/" + camera.label + os.path.basename(camera.photo.path)[-4:])
   
   print("ODLIČNO, BREZ NAPAK JE OBDELALO FOTOGRAMETRIČNO OBDELAVO!!")
   Metashape.app.messageBox("ODLIČNO, BREZ NAPAK JE OBDELALO FOTOGRAMETRIČNO OBDELAVO!!")
   return 1
   
   
autoprocess()