Merge c5c3930b8a into 424d9e28a0

Fix PoissonRecon failed with n threads log message

opendm/mesh.py

@@ -187,7 +187,7 @@ def screened_poisson_reconstruction(inPointCloud, outMesh, depth = 8, samples =
             if threads < 1:
                 break
             else:
-                log.ODM_WARNING("PoissonRecon failed with %s threads, let's retry with %s..." % (threads, threads // 2))
+                log.ODM_WARNING("PoissonRecon failed with %s threads, let's retry with %s..." % (threads * 2, threads))
 
 
     # Cleanup and reduce vertex count if necessary

opendm/multispectral.py

@@ -6,7 +6,8 @@ from opendm import dls
 import numpy as np
 from opendm import log
 from opendm.concurrency import parallel_map
-from opensfm.io import imread
+from opendm.io import related_file_path
+from opensfm.io import imread, imwrite
 
 from skimage import exposure
 from skimage.morphology import disk
@@ -635,4 +636,60 @@ def resize_match(image, dimension):
                 fy=fx,
                 interpolation=(cv2.INTER_AREA if (fx < 1.0 and fy < 1.0) else cv2.INTER_LANCZOS4))
 
-    return image
+    return image
+
+def dewarp_photos(photos, images_path, scale_factor=1.0):
+    # Caution! This will make changes to the photo objects' filename
+
+    for p in photos:
+        # This should never happen
+        if os.path.splitext(p.filename)[0].endswith("_dewarped"):
+            log.ODM_WARNING("Cannot dewarp %s, already dewarped?" % (p.filename))
+            continue
+
+        if p.dewarp_data is None:
+            log.ODM_WARNING("Cannot dewarp %s, dewarp data is missing" % p.filename)
+            continue
+
+        if p.dewarp_data.count(";") != 1:
+            log.ODM_WARNING("Cannot dewarp %s, cannot parse dewarp data" % p.filename)
+            continue
+
+        datestamp, params = p.dewarp_data.split(";")
+        try:
+            params = [float(p) for p in params.split(",")]
+        except ValueError as e:
+            log.ODM_WARNING("Cannot dewarp %s, failed to parse dewarp data" % p.filename)
+            continue
+
+        if len(params) != 9:
+            log.ODM_WARNING("Cannot dewarp %s, invalid dewarp data parameters (expected 9, got: %s)" % (p.filename, len(params)))
+            continue
+
+        dewarped_filename = related_file_path(p.filename, postfix="_dewarped")
+        dewarped_path = os.path.join(images_path, dewarped_filename)
+        if os.path.isfile(dewarped_path):
+            # Already dewarped
+            p.filename = dewarped_filename
+        else:
+            image = imread(os.path.join(images_path, p.filename), unchanged=True, anydepth=True)
+            w, h = image.shape[1], image.shape[0]
+            fx, fy, cx, cy, k1, k2, p1, p2, k3 = params
+            cam_m = np.array([[fx, 0, w / 2 - cx], [0, fy, h / 2 + cy], [0, 0, 1]])
+            dist_c = np.array([k1, k2, p1, p2, k3])
+            map1, map2 = cv2.initUndistortRectifyMap(cam_m, dist_c, None, cam_m, (w, h), cv2.CV_32FC1)
+            dewarped_image = cv2.remap(image, map1, map2, cv2.INTER_LINEAR)
+
+            if scale_factor > 1.0:
+                new_w = int(w * (1.0 / scale_factor))
+                new_h = int(h * (1.0 / scale_factor))
+                center_x, center_y = w // 2, h // 2
+                crop_x1 = max(0, center_x - new_w // 2)
+                crop_y1 = max(0, center_y - new_h // 2)
+                crop_x2 = min(w, center_x + new_w // 2)
+                crop_y2 = min(h, center_y + new_h // 2)
+                cropped = dewarped_image[crop_y1:crop_y2, crop_x1:crop_x2]
+                dewarped_image = cv2.resize(cropped, (w, h), interpolation=cv2.INTER_LANCZOS4)
+            imwrite(dewarped_path, dewarped_image)
+            log.ODM_INFO("Dewarped %s --> %s" % (p.filename, dewarped_filename))
+            p.filename = dewarped_filename

opendm/photo.py

@@ -159,6 +159,9 @@ class ODM_Photo:
         self.gps_xy_stddev = None # Dilution of Precision X/Y
         self.gps_z_stddev = None # Dilution of Precision Z
 
+        # DJI
+        self.dewarp_data = None
+
         # Misc SFM
         self.camera_projection = 'brown'
         self.focal_ratio = 0.85
@@ -413,6 +416,12 @@ class ODM_Photo:
                         self.set_attr_from_xmp_tag('speed_z', xtags, [
                             '@drone-dji:FlightZSpeed',
                         ], float)
+                    
+                    # DJI dewarp data
+                    if '@drone-dji:DewarpData' in xtags:
+                        self.set_attr_from_xmp_tag('dewarp_data', xtags, [
+                            '@drone-dji:DewarpData'
+                        ])
 
                     # Account for over-estimation
                     if self.gps_xy_stddev is not None:

opendm/types.py

@@ -22,12 +22,13 @@ from opendm.photo import ODM_Photo
 warnings.filterwarnings("ignore")
 
 class ODM_Reconstruction(object):
-    def __init__(self, photos):
+    def __init__(self, photos, images_path):
         self.photos = photos
         self.georef = None
         self.gcp = None
         self.multi_camera = self.detect_multi_camera()
         self.filter_photos()
+        self.dewarp_photos(images_path)
         
     def detect_multi_camera(self):
         """
@@ -148,13 +149,7 @@ class ODM_Reconstruction(object):
 
             if 'rgb' in bands or 'redgreenblue' in bands:
                 if 'red' in bands and 'green' in bands and 'blue' in bands:
-                    bands_to_remove.append(bands['rgb'] if 'rgb' in bands else bands['redgreenblue'])
-                
-                # Mavic 3M's RGB camera lens are too different than the multispectral ones
-                # so we drop the RGB channel instead
-                elif self.photos[0].is_make_model("DJI", "M3M") and 'red' in bands and 'green' in bands:
-                    bands_to_remove.append(bands['rgb'] if 'rgb' in bands else bands['redgreenblue'])
-                
+                    bands_to_remove.append(bands['rgb'] if 'rgb' in bands else bands['redgreenblue'])                
                 else:
                     for b in ['red', 'green', 'blue']:
                         if b in bands:
@@ -285,6 +280,23 @@ class ODM_Reconstruction(object):
             if p.filename == filename:
                 return p
     
+    def dewarp_photos(self, images_path):
+        if not self.multi_camera:
+            return # Nothing to do
+        else:
+            bands = {}
+            for b in self.multi_camera:
+                bands[b['name'].lower()] = b['photos']
+
+            # Mavic 3M's RGB camera lens are too different than the multispectral ones
+            # so we unwarp them before reconstruction when needed
+            if self.photos[0].is_make_model("DJI", "M3M") and 'nir' in bands and ('rgb' in bands or 'redgreenblue' in bands):
+                log.ODM_INFO("Dewarping RGB photos before processing")
+                rgb = 'rgb' if 'rgb' in bands else 'redgreenblue'
+                upscale = max(1.0, bands['nir'][0].focal_ratio / bands[rgb][0].focal_ratio)
+                if upscale != 1.0:
+                    log.ODM_INFO("Adjusting focal distance of RGB images by %s" % upscale)
+                multispectral.dewarp_photos(bands['rgb'] if 'rgb' in bands else bands['redgreenblue'], images_path, upscale)
 
 class ODM_GeoRef(object):
     @staticmethod

stages/dataset.py

@@ -61,7 +61,7 @@ class ODMLoadDatasetStage(types.ODM_Stage):
 
         def valid_filename(filename, supported_extensions):
             (pathfn, ext) = os.path.splitext(filename)
-            return ext.lower() in supported_extensions and pathfn[-5:] != "_mask"
+            return ext.lower() in supported_extensions and pathfn[-5:] != "_mask" and pathfn[-9:] != "_dewarped"
 
         # Get supported images from dir
         def get_images(in_dir):
@@ -162,6 +162,10 @@ class ODMLoadDatasetStage(types.ODM_Stage):
                     (p, ext) = os.path.splitext(r)
                     if p[-5:] == "_mask" and ext.lower() in context.supported_extensions:
                         masks[p] = r
+                    
+                    # Remove dewarped images on re-run
+                    if p[-9:] == "_dewarped" and self.rerun():
+                        os.unlink(os.path.join(images_dir, p + ext))
 
                 photos = []
                 with open(tree.dataset_list, 'w') as dataset_list:
@@ -298,7 +302,7 @@ class ODMLoadDatasetStage(types.ODM_Stage):
         log.logger.log_json_images(len(photos))
 
         # Create reconstruction object
-        reconstruction = types.ODM_Reconstruction(photos)
+        reconstruction = types.ODM_Reconstruction(photos, tree.dataset_raw)
         
         if tree.odm_georeferencing_gcp and not args.use_exif:
             reconstruction.georeference_with_gcp(tree.odm_georeferencing_gcp,