Image Stitching Engine
Guide to panorama creation, homography, and blending techniques.
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Topics Covered
- Stitcher Class - High-level panorama API
- Feature Matching - SIFT vs ORB, BFMatcher vs FLANN
- Homography - RANSAC estimation, image warping
- Blending - Alpha, feather, multi-band techniques
- Projections - Planar, cylindrical, spherical
Tutorial Files
| File |
Description |
01_panorama.py |
High-level Stitcher API, basic manual stitch |
02_manual_stitching.py |
Step-by-step pipeline: features, matching, RANSAC, warping |
03_blending_techniques.py |
Blending comparison: none, alpha, feather, multi-band |
04_cylindrical_pano.py |
Cylindrical/spherical projections, wide panoramas |
Key Concepts
Stitching Pipeline
Feature Detection → Feature Matching → Homography → Warping → Blending
Blending Methods
| Method |
Quality |
Speed |
Best For |
| No blending |
Poor |
Fast |
Preview only |
| Alpha |
Medium |
Fast |
Similar exposure |
| Feather |
Good |
Medium |
General use |
| Multi-band |
Best |
Slow |
Professional quality |
Projection Types
| Projection |
Use Case |
| Planar |
Small rotations (<90 deg) |
| Cylindrical |
360 degree horizontal panoramas |
| Spherical |
Full 360 x 180 degree VR content |
Key Functions
# High-level API
stitcher = cv2.Stitcher_create(cv2.Stitcher_PANORAMA)
status, panorama = stitcher.stitch(images)
# Manual pipeline
H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
warped = cv2.warpPerspective(img, H, (width, height))
# Blending
blender = cv2.detail.MultiBandBlender()
blender = cv2.detail.FeatherBlender()
# Projections
warper = cv2.PyRotationWarper('cylindrical', focal_length)