Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

More memory efficient find_features_homography #1696

Merged
merged 3 commits into from
Sep 8, 2023
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion VERSION
Original file line number Diff line number Diff line change
@@ -1 +1 @@
3.2.0
3.2.1
22 changes: 22 additions & 0 deletions opendm/multispectral.py
Original file line number Diff line number Diff line change
Expand Up @@ -504,6 +504,28 @@ def find_features_homography(image_gray, align_image_gray, feature_retention=0.7

# Detect SIFT features and compute descriptors.
detector = cv2.SIFT_create(edgeThreshold=10, contrastThreshold=0.1)

h,w = image_gray.shape
max_dim = max(h, w)

max_size = 2048
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

if 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]

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))

kp_image, desc_image = detector.detectAndCompute(image_gray, None)
kp_align_image, desc_align_image = detector.detectAndCompute(align_image_gray, None)

Expand Down