Merge pull request #1771 from pierotofy/homographyfix

Mavic 3M Support

SuperBuild/CMakeLists.txt

@@ -244,7 +244,7 @@ externalproject_add(dem2points
 externalproject_add(odm_orthophoto
     DEPENDS           opencv 
     GIT_REPOSITORY    https://github.com/OpenDroneMap/odm_orthophoto.git
-    GIT_TAG           317
+    GIT_TAG           353
     PREFIX            ${SB_BINARY_DIR}/odm_orthophoto
     SOURCE_DIR        ${SB_SOURCE_DIR}/odm_orthophoto
     CMAKE_ARGS        -DCMAKE_INSTALL_PREFIX:PATH=${SB_INSTALL_DIR}

VERSION

@@ -1 +1 @@
-3.5.2
+3.5.3

opendm/multispectral.py

@@ -427,14 +427,14 @@ def find_ecc_homography(image_gray, align_image_gray, number_of_iterations=1000,
     pyramid_levels = 0
     h,w = image_gray.shape
     max_dim = max(h, w)
+    downscale = 0
 
-    max_size = 1280
+    max_size = 2048
+    while max_dim / (2**downscale) > max_size:
+        downscale += 1
 
-    if max_dim > max_size:
-        if max_dim == w:
-            f = max_size / w
-        else:
-            f = max_size / h
+    if downscale > 0:
+        f = 1 / (2**downscale)
         image_gray = cv2.resize(image_gray, None, fx=f, fy=f, interpolation=cv2.INTER_AREA)
         h,w = image_gray.shape
 
@@ -473,7 +473,6 @@ def find_ecc_homography(image_gray, align_image_gray, number_of_iterations=1000,
 
     # Define the motion model, scale the initial warp matrix to smallest level
     warp_matrix = np.eye(3, 3, dtype=np.float32)
-    warp_matrix = warp_matrix * np.array([[1,1,2],[1,1,2],[0.5,0.5,1]], dtype=np.float32)**(1-(pyramid_levels+1))
 
     for level in range(pyramid_levels+1):
         ig = gradient(gaussian(image_gray_pyr[level]))
@@ -495,14 +494,16 @@ def find_ecc_homography(image_gray, align_image_gray, number_of_iterations=1000,
             if level != pyramid_levels:
                 log.ODM_INFO("Could not compute ECC warp_matrix at pyramid level %s, resetting matrix" % level)
                 warp_matrix = np.eye(3, 3, dtype=np.float32)
-                warp_matrix = warp_matrix * np.array([[1,1,2],[1,1,2],[0.5,0.5,1]], dtype=np.float32)**(1-(pyramid_levels+1))
             else:
                 raise e
 
         if level != pyramid_levels: 
             warp_matrix = warp_matrix * np.array([[1,1,2],[1,1,2],[0.5,0.5,1]], dtype=np.float32)
 
-    return warp_matrix
+    if downscale > 0:
+        return warp_matrix * (np.array([[1,1,2],[1,1,2],[0.5,0.5,1]], dtype=np.float32) ** downscale)
+    else:
+        return warp_matrix
 
 
 def find_features_homography(image_gray, align_image_gray, feature_retention=0.7, min_match_count=10):
@@ -512,13 +513,14 @@ def find_features_homography(image_gray, align_image_gray, feature_retention=0.7
 
     h,w = image_gray.shape
     max_dim = max(h, w)
+    downscale = 0
 
-    max_size = 2048
-    if max_dim > max_size:
-        if max_dim == w:
-            f = max_size / w
-        else:
-            f = max_size / h
+    max_size = 4096
+    while max_dim / (2**downscale) > max_size:
+        downscale += 1
+    
+    if downscale > 0:
+        f = 1 / (2**downscale)
         image_gray = cv2.resize(image_gray, None, fx=f, fy=f, interpolation=cv2.INTER_AREA)
         h,w = image_gray.shape
 
@@ -570,7 +572,13 @@ def find_features_homography(image_gray, align_image_gray, feature_retention=0.7
 
     # Find homography
     h, _ = cv2.findHomography(points_image, points_align_image, cv2.RANSAC)
-    return h
+    if h is None:
+        return None
+    
+    if downscale > 0:
+        return h * (np.array([[1,1,2],[1,1,2],[0.5,0.5,1]], dtype=np.float32) ** downscale)
+    else:
+        return h
 
 def gradient(im, ksize=5):
     im = local_normalize(im)
@@ -635,4 +643,4 @@ 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

opendm/photo.py

@@ -413,7 +413,7 @@ class ODM_Photo:
                         self.set_attr_from_xmp_tag('speed_z', xtags, [
                             '@drone-dji:FlightZSpeed',
                         ], float)
-
+                    
                     # Account for over-estimation
                     if self.gps_xy_stddev is not None:
                         self.gps_xy_stddev *= 2.0

opendm/types.py

@@ -285,7 +285,6 @@ class ODM_Reconstruction(object):
             if p.filename == filename:
                 return p
     
-
 class ODM_GeoRef(object):
     @staticmethod
     def FromCoordsFile(coords_file):

stages/dataset.py

@@ -162,7 +162,7 @@ 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
-
+                    
                 photos = []
                 with open(tree.dataset_list, 'w') as dataset_list:
                     log.ODM_INFO("Loading %s images" % len(path_files))