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

python: 'cv2.' -> 'cv.' via 'import cv2 as cv'

Browse Source
This commit is contained in:
Alexander Alekhin
2017-12-11 12:55:03 +03:00
parent 9665dde678
commit 5560db73bf
162 changed files with 2083 additions and 2084 deletions
Download Patch File Download Diff File Expand all files Collapse all files
samples/python/asift.py
+13 -13
View File
@@ -23,7 +23,7 @@ USAGE
from __future__ import print_function
import numpy as np
import cv2
import cv2 as cv
# built-in modules
import itertools as it
@@ -51,18 +51,18 @@ def affine_skew(tilt, phi, img, mask=None):
A = np.float32([[c,-s], [ s, c]])
corners = [[0, 0], [w, 0], [w, h], [0, h]]
tcorners = np.int32( np.dot(corners, A.T) )
x, y, w, h = cv2.boundingRect(tcorners.reshape(1,-1,2))
x, y, w, h = cv.boundingRect(tcorners.reshape(1,-1,2))
A = np.hstack([A, [[-x], [-y]]])
img = cv2.warpAffine(img, A, (w, h), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
img = cv.warpAffine(img, A, (w, h), flags=cv.INTER_LINEAR, borderMode=cv.BORDER_REPLICATE)
if tilt != 1.0:
s = 0.8*np.sqrt(tilt*tilt-1)
img = cv2.GaussianBlur(img, (0, 0), sigmaX=s, sigmaY=0.01)
img = cv2.resize(img, (0, 0), fx=1.0/tilt, fy=1.0, interpolation=cv2.INTER_NEAREST)
img = cv.GaussianBlur(img, (0, 0), sigmaX=s, sigmaY=0.01)
img = cv.resize(img, (0, 0), fx=1.0/tilt, fy=1.0, interpolation=cv.INTER_NEAREST)
A[0] /= tilt
if phi != 0.0 or tilt != 1.0:
h, w = img.shape[:2]
mask = cv2.warpAffine(mask, A, (w, h), flags=cv2.INTER_NEAREST)
Ai = cv2.invertAffineTransform(A)
mask = cv.warpAffine(mask, A, (w, h), flags=cv.INTER_NEAREST)
Ai = cv.invertAffineTransform(A)
return img, mask, Ai
@@ -119,8 +119,8 @@ if __name__ == '__main__':
fn1 = '../data/aero1.jpg'
fn2 = '../data/aero3.jpg'
img1 = cv2.imread(fn1, 0)
img2 = cv2.imread(fn2, 0)
img1 = cv.imread(fn1, 0)
img2 = cv.imread(fn2, 0)
detector, matcher = init_feature(feature_name)
if img1 is None:
@@ -137,7 +137,7 @@ if __name__ == '__main__':
print('using', feature_name)
pool=ThreadPool(processes = cv2.getNumberOfCPUs())
pool=ThreadPool(processes = cv.getNumberOfCPUs())
kp1, desc1 = affine_detect(detector, img1, pool=pool)
kp2, desc2 = affine_detect(detector, img2, pool=pool)
print('img1 - %d features, img2 - %d features' % (len(kp1), len(kp2)))
@@ -147,7 +147,7 @@ if __name__ == '__main__':
raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2
p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
if len(p1) >= 4:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
H, status = cv.findHomography(p1, p2, cv.RANSAC, 5.0)
print('%d / %d inliers/matched' % (np.sum(status), len(status)))
# do not draw outliers (there will be a lot of them)
kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag]
@@ -159,5 +159,5 @@ if __name__ == '__main__':
match_and_draw('affine find_obj')
cv2.waitKey()
cv2.destroyAllWindows()
cv.waitKey()
cv.destroyAllWindows()