Better support for AV Quantix drones

opendm/multispectral.py

@@ -421,6 +421,18 @@ def compute_homography(image_filename, align_image_filename):
 def find_ecc_homography(image_gray, align_image_gray, number_of_iterations=1000, termination_eps=1e-8, start_eps=1e-4):
     pyramid_levels = 0
     h,w = image_gray.shape
+    max_dim = max(h, w)
+
+    max_size = 1280
+
+    if max_dim > max_size:
+        if max_dim == w:
+            f = max_size / w
+        else:
+            f = max_size / h
+        image_gray = cv2.resize(image_gray, None, fx=f, fy=f, interpolation=cv2.INTER_AREA)
+        h,w = image_gray.shape
+
     min_dim = min(h, w)
 
     while min_dim > 300:
@@ -434,10 +446,10 @@ def find_ecc_homography(image_gray, align_image_gray, number_of_iterations=1000,
         align_image_gray = to_8bit(align_image_gray)
         image_gray = to_8bit(image_gray)
 
-        fx = align_image_gray.shape[1]/image_gray.shape[1]
-        fy = align_image_gray.shape[0]/image_gray.shape[0]
+        fx = image_gray.shape[1]/align_image_gray.shape[1]
+        fy = image_gray.shape[0]/align_image_gray.shape[0]
 
-        image_gray = cv2.resize(image_gray, None, 
+        align_image_gray = cv2.resize(align_image_gray, None, 
                         fx=fx, 
                         fy=fy,
                         interpolation=(cv2.INTER_AREA if (fx < 1.0 and fy < 1.0) else cv2.INTER_LANCZOS4))

opendm/photo.py

@@ -459,6 +459,21 @@ class ODM_Photo:
                 # self.set_attr_from_xmp_tag('bandwidth', xtags, [
                 #     'Camera:WavelengthFWHM'
                 # ], float)
+        
+        # Special case band handling for AeroVironment Quantix images
+        # for some reason, they don't store band information in EXIFs
+        if self.camera_make.lower() == 'aerovironment' and \
+            self.camera_model.lower() == 'quantix':
+            matches = re.match("IMG_(\d+)_(\w+)\.\w+", self.filename, re.IGNORECASE)
+            if matches:
+                band_aliases = {
+                    'GRN': 'Green',
+                    'NIR': 'Nir',
+                    'RED': 'Red',
+                    'RGB': 'RedGreenBlue',
+                }
+                self.capture_uuid = matches.group(1)
+                self.band_name = band_aliases.get(matches.group(2), matches.group(2))
 
         # Sanitize band name since we use it in folder paths
         self.band_name = re.sub('[^A-Za-z0-9]+', '', self.band_name)

opendm/types.py

@@ -76,17 +76,27 @@ class ODM_Reconstruction(object):
             bands = {}
             for b in self.multi_camera:
                 bands[b['name'].lower()] = b['name']
+            
+            bands_to_remove = []
 
-            if ('rgb' in bands or 'redgreenblue' in bands) and \
-                ('red' in bands and 'green' in bands and 'blue' in bands):
-                band_to_remove = bands['rgb'] if 'rgb' in bands else bands['redgreenblue']
+            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'])
+                else:
+                    for b in ['red', 'green', 'blue']:
+                        if b in bands:
+                            bands_to_remove.append(bands[b])
 
-                self.multi_camera = [b for b in self.multi_camera if b['name'] != band_to_remove]
-                photos_before = len(self.photos)
-                self.photos = [p for p in self.photos if p.band_name != band_to_remove]
-                photos_after = len(self.photos)
+            if len(bands_to_remove) > 0:
+                log.ODM_WARNING("Redundant bands detected, probably because RGB images are mixed with single band images. We will trim some bands as needed")
 
-                log.ODM_WARNING("RGB images detected alongside individual Red/Green/Blue images, we will use individual bands (skipping %s images)" % (photos_before - photos_after))
+                for band_to_remove in bands_to_remove:
+                    self.multi_camera = [b for b in self.multi_camera if b['name'] != band_to_remove]
+                    photos_before = len(self.photos)
+                    self.photos = [p for p in self.photos if p.band_name != band_to_remove]
+                    photos_after = len(self.photos)
+
+                    log.ODM_WARNING("Skipping %s band (%s images)" % (band_to_remove, photos_before - photos_after))
 
     def is_georeferenced(self):
         return self.georef is not None

stages/run_opensfm.py

@@ -104,9 +104,9 @@ class ODMOpenSfMStage(types.ODM_Stage):
                 image = func(shot_id, image)
             return image
 
-        def resize_thermal_images(shot_id, image):
+        def resize_secondary_images(shot_id, image):
             photo = reconstruction.get_photo(shot_id)
-            if photo.is_thermal():
+            if photo.band_name != primary_band_name:
                 return thermal.resize_to_match(image, largest_photo)
             else:
                 return image
@@ -138,8 +138,8 @@ class ODMOpenSfMStage(types.ODM_Stage):
                 return image
 
         if reconstruction.multi_camera:
-            largest_photo = find_largest_photo(photos)
-            undistort_pipeline.append(resize_thermal_images)
+            largest_photo = find_largest_photo([p for p in photos if p.band_name == primary_band_name])
+            undistort_pipeline.append(resize_secondary_images)
 
         if args.radiometric_calibration != "none":
             undistort_pipeline.append(radiometric_calibrate)