third_party/opencv
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Add Java and Python code for Image Segmentation with Distance Transform and Watershed Algorithm tutorial. Use more Pythonic code.

Browse Source
This commit is contained in:
catree
2018-06-27 18:48:32 +02:00
parent db48f7b5d1
commit 7469981d1a
7 changed files with 555 additions and 89 deletions
Download Patch File Download Diff File Expand all files Collapse all files
samples/python/tutorial_code/features2D/feature_flann_matcher/SURF_FLANN_matching_Demo.py
+3 -4
View File
@@ -28,10 +28,9 @@ knn_matches = matcher.knnMatch(descriptors1, descriptors2, 2)
#-- Filter matches using the Lowe's ratio test
ratio_thresh = 0.7
good_matches = []
for matches in knn_matches:
if len(matches) > 1:
if matches[0].distance / matches[1].distance <= ratio_thresh:
good_matches.append(matches[0])
for m,n in knn_matches:
if m.distance / n.distance <= ratio_thresh:
good_matches.append(m)
#-- Draw matches
img_matches = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1], 3), dtype=np.uint8)
samples/python/tutorial_code/features2D/feature_homography/SURF_FLANN_matching_homography_Demo.py
+3 -4
View File
@@ -28,10 +28,9 @@ knn_matches = matcher.knnMatch(descriptors_obj, descriptors_scene, 2)
#-- Filter matches using the Lowe's ratio test
ratio_thresh = 0.75
good_matches = []
for matches in knn_matches:
if len(matches) > 1:
if matches[0].distance / matches[1].distance <= ratio_thresh:
good_matches.append(matches[0])
for m,n in knn_matches:
if m.distance / n.distance <= ratio_thresh:
good_matches.append(m)
#-- Draw matches
img_matches = np.empty((max(img_object.shape[0], img_scene.shape[0]), img_object.shape[1]+img_scene.shape[1], 3), dtype=np.uint8)