Topics

1

Python and Java API / How do you access total tie point number in Python

« on: August 31, 2018, 03:42:35 PM »

Hi,

 In the tie point information PS gives you y of x tie points. You can get the active number of tie points (y) by using len([i for i in chunk.point_cloud.points]). How do I access x, i.e the original number of tie points in Python, I can't find it in the reference doc?

Cheers
M.

2

Python and Java API / Automated PS script to process single or multiple chunks - opinions please

« on: August 21, 2018, 05:17:53 PM »

***Please see later posts for updated script

Hi,

 I started a script a while ago to automate some steps, which turned into a full blown automated script, including gradual selection. I use it for UAV and close range work.

 I'd like some opinions from you good people as to the underlying processing logic. I often have to run multiple models with different settings, so I create one PS doc and have multiple chunks, although it does work with just one chunk. I set up the images, coord sys for each chunk, add GCP where applicable and then, so I don't need to supervise, I just run the script, leave it for the weekend and have all data ready come Monday. Seems to work...

The script creates menu options to cater to my specific needs, but can be adapted.

Some points I could use some clarification on:
I use adaptive, although it is now set to False by default (http://www.agisoft.com/forum/index.php?topic=9401.0). I'm not sure why that was changed and when it should be used.
 
I use optimizeCameras without parameters, trusting PS to select the right ones. Should I set parameters?

My automated gradual selection process is:
Reconstruction uncertainty to 10 - optimize
Projection Accuracy to 10 (models) or 3 (UAV) - optimize
Reprojection error - self-adjusting to find the right value to leave me with 10% of the original points - optimize

Suggestions very welcome
Cheers
M

3

Python and Java API / Why is Adaptive to false now?

« on: July 26, 2018, 04:09:43 PM »

Hi,

Given the complexity of PS I'm trying to work through settings and why we set them, and when to chose on over the other. In laymans terms, why was Adaptive set to False by default in the latest update, and when should or shouldn't we use it. I know the latter has been answered before, but perhaps you can provide some specific reasons to do it or not to do it. What are the decision criteria I should go through for a project?

Cheers
Martin

4

Python and Java API / Dense cloud not in project after processing

« on: February 14, 2018, 01:28:28 AM »

Hi,

 I'm processing images from UAV to generate DEM and Orthos. I wrote a Python script to process multiple chunks at the same time (and to learn using Python for PS).

 I'm now using the script and in the build dense cloud step after processing for several hours the dense cloud is not there, even though the log file says it ran successfully. Depth maps don't seem to be there either.  I'm on 1.4.

This has worked in a previous project with multiple chunks, although one one occasion I had the same problem, but on a smaller image set and I just ran it again, this time successfully. I run each step (align, dense, dem, ortho, export) separately, as I check the results and clean up before continuing.

Why is the Dense Cloud missing, what did I miss?

Cheers
Martin


code snippet that runs dense cloud:
def dense_c(self):
        for _ in self.cks:
            _.buildDepthMaps(quality=PhotoScan.HighQuality,
                                filter=PhotoScan.MildFiltering)           
            _.buildDenseCloud(point_colors=True)

Log (last few lines):
2018-02-13 16:45:27
2018-02-13 16:45:27 Depth reconstruction devices performance:
2018-02-13 16:45:27  - 5%    done by CPU
2018-02-13 16:45:27  - 95%    done by GeForce GTX 980
2018-02-13 16:45:27 Total time: 6356.12 seconds
2018-02-13 16:45:27
2018-02-13 16:45:28 Finished processing in 6375.36 sec (exit code 1)
2018-02-13 16:45:28 BuildDenseCloud
2018-02-13 16:45:28 Generating dense cloud...
2018-02-13 16:45:28 Generating dense point cloud...
2018-02-13 16:45:32 selected 284 cameras in 4.853 sec
2018-02-13 16:45:32 working volume: 8580x3829x2775
2018-02-13 16:45:32 tiles: 2x1x1
2018-02-13 16:45:32 selected 135 cameras in 0.001 sec
2018-02-13 16:45:32 preloading data... done in 3.479 sec
2018-02-13 16:45:36 filtering depth maps... done in 881.324 sec
2018-02-13 17:00:18 preloading data... done in 7.266 sec
2018-02-13 17:00:25 accumulating data... done in 12.639 sec
2018-02-13 17:00:39 building point cloud... done in 2.298 sec
2018-02-13 17:00:42 selected 232 cameras in 0.001 sec
2018-02-13 17:00:42 preloading data... done in 5.479 sec
2018-02-13 17:00:47 filtering depth maps... done in 10398 sec
2018-02-13 19:54:06 preloading data... done in 13.742 sec
2018-02-13 19:54:20 accumulating data... done in 29.615 sec
2018-02-13 19:54:51 building point cloud... done in 2.034 sec
2018-02-13 19:54:54 52859925 points extracted
2018-02-13 19:54:55 Finished processing in 11367.2 sec (exit code 1)

Full code:
import PhotoScan
import os

# Checking compatibility
compatible_major_version = "1.4"
found_major_version = ".".join(PhotoScan.app.version.split('.')[:2])
if found_major_version != compatible_major_version:
    raise Exception("Incompatible PhotoScan version: {} != {}".format(found_major_version, compatible_major_version))

class ChunksProc(object):
   
    def __init__(self, cks):
        self.cks = cks

    def remove_align(self):
        for _ in self.cks:
            for c in _.cameras:
                c.transform = None
               
    def align(self):
        for _ in self.cks:
            _.matchPhotos(accuracy=PhotoScan.HighAccuracy,
                            generic_preselection=False,
                            reference_preselection=True,
                            filter_mask=True,
                            keypoint_limit=40000,
                            tiepoint_limit=0)
            _.alignCameras()

    def dense_c(self):
        for _ in self.cks:
            _.buildDepthMaps(quality=PhotoScan.HighQuality,
                                filter=PhotoScan.MildFiltering)           
            _.buildDenseCloud(point_colors=True)

    def dem(self):
        for _ in self.cks:
            _.buildDem(source=PhotoScan.DenseCloudData,
                        interpolation=PhotoScan.EnabledInterpolation)

    def ortho(self):
        for _ in self.cks:
            _.buildOrthomosaic(surface=PhotoScan.ElevationData,
                                blending=PhotoScan.MosaicBlending,
                                fill_holes=False)

    def export(self, pth, prefix):
        for _ in self.cks:
            num = str(_)[-3]
            file = '{}_DSM_{}.tif'.format(prefix, num)
            _.exportDem(path = os.path.join(pth, file),
                        write_world = True)
            file = '{}_Ortho_{}.tif'.format(prefix, num)
            _.exportOrthomosaic(path = os.path.join(pth, file),
                                write_world = True)

    def exp_jpg(self, pth, prefix, w = None, h = None):
        doc.save()
        for _ in self.cks:
            num = str(_)[-3]
            file = '{}_Ortho_{}.jpg'.format(prefix, num)
            _.exportOrthomosaic(path = os.path.join(pth, file),
                                write_world = True)   

doc = PhotoScan.app.document
if not len(doc.chunks):
        raise Exception("No chunks!")
cks = ChunksProc(doc.chunks)
pth = r'E:\Dominica\Data Output Folder'
jpgpth = r'E:\Dominica\Data Output Folder\JPG Orthos'
prefix = input('Enter file prefix: ')
cks.align()
doc.save()
cks.dense_c()
doc.save()
cks.dem()
doc.save()
cks.ortho()
doc.save()
cks.export(pth, prefix)
cks.exp_jpg(jpgpth, prefix)
doc.save()

5

General / Documentation on smoothing mesh

« on: June 27, 2017, 06:25:11 PM »

Hi,

 I'm smoothing a mesh in Tools and am wondering if there's any documentation on the tool. What do the numbers mean?

Cheers
Martin

6

General / Compact Dense Point cloud does not remove deleted points

« on: June 19, 2017, 12:45:34 PM »

Hi,

 I've got a rather large point cloud and I'd like to mesh just a section of it, as PS will not mesh the whole thing.

 I've cropped to a selection and ran compact dense cloud, which, as I understand it, should remove deleted points. I also ran update dense cloud, but the point number remains stubbornly high.

How do I delete deleted points completely?

Thanks
Martin

7

General / Aerial photography overlap overspecified? What works?

« on: October 02, 2015, 06:17:33 PM »

Hi,

 The PhotoScan manual specifies 60% of side overlap + 80% of forward overlap for aerial photography. I'm struggling to plan a mission for this in Mission Planner, as the interval between pictures drops below 2 second if you do this. My A6000 camera cannot keep up with this pace and I get fewer images than CAM triggers; I need to keep above 2s for continuous shooting without dropping images. If I reduce the flying speed my mission time increases beyond the capacity of my battery. A balance needs to be struck.

 I have tested PS derived DEM vs differential GPS and with a 60% overlap and 70% side overlap I have achieved mean 4.3cm elevation error on a DEM. That's not bad at all.

 What are the experiences of other users? What overlaps do you specify and what works for you? Do you increase altitude or drop speed for instance? Change focal length (I shoot 35mm)?

Cheers

 

8

General / Sony Arw files not recognised

« on: September 29, 2015, 07:27:04 PM »

Hi,

 We're only just starting out on PhotoScan and are trying different things. PS reads my Canon RAW beautifully, but the Sony RAW format ARW from our A6000 isn't recognised. Can PS somehow read ARW or do I need to convert to tif if I don't want to use jpg?

Cheers
M.