if k == ord('s'):
img1 = img2.copy()
cv2.imwrite('campic.png', img1)
elif k == 27:
break
# find the keypoints and descriptors with ORB
if k is not None:
cv2.destroyWindow('preview')
kp1, des1 = orb.detectAndCompute(img1, None)
kp2, des2 = orb.detectAndCompute(img2, None)
# If nothing match then continue
if des2 is None:
img3 = img3 = draw_match(img1, kp1, img2, kp2, [])
continue
des1 = des1.astype(np.uint8, copy=False) # Fix the data type
des2 = des2.astype(np.uint8, copy=False)
# Now match describers
bf = cv2.BFMatcher(cv2.NORM_HAMMING)
# matches = bf.match(des1,des2)
matches = bf.knnMatch(des1, des2, k=2)
# m = matches[0][0]
# p1, p2 = np.float32(kp1[m.queryIdx].pt), np.float32(kp2[m.trainIdx].pt)
# print m.distance, p1, p2
if len(good)>MIN_MATCH_COUNT:
src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
matchesMask = mask.ravel().tolist()
h,w = img1.shape
pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
dst = cv2.perspectiveTransform(pts,M)
cv2.polylines(img2,[np.int32(dst)],True,255,3)
else:
print "Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT)
matchesMask = None
draw_params = dict(matchColor = (0,255,0), # draw matches in green color
singlePointColor = None,
matchesMask = matchesMask, # draw only inliers
flags = 2)
# print [x for x in dir(kp1[0]) if not '__' in x]
# print [x for x in dir(good[0]) if not '__' in x]
if matchesMask:
img3 = draw_match(img1,kp1,img2,kp2,good,None,**draw_params)
cv2.imshow('matches', img3)
def find_screen_img(self, cam_img, screen_img=None, debug=False):
"""
Find screen_img in cam_img.
If executed successfully, the function return True.
Meanwhile self.recovery_matrix will be computed, which is used to
map camera image to top view
"""
try:
MATCH_THRESHOLD = 10
FLANN_INDEX_KDTREE = 0
AREA_THRESHOLD = 1000
if screen_img is None:
kp1, des1 = self._screen_features
screen_img = self._screen_img
else:
kp1, des1 = self._detector.detectAndCompute(screen_img, None)
kp2, des2 = self._detector.detectAndCompute(cam_img, None)
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)
# Perform Lowe's ratio test to select good points to proceed with.
good = [m for m, n in matches if m.distance < 0.7 * n.distance]
src_pts = np.float32([kp1[m.queryIdx].pt
for m in good]).reshape(-1, 1, 2)
dst_pts = np.float32([kp2[m.trainIdx].pt
for m in good]).reshape(-1, 1, 2)
# check the property of the corners found out there
self.screen2cam_matrix, mask = cv2.findHomography(
src_pts, dst_pts, cv2.RANSAC, 5.0)
matchesMask = mask.ravel().tolist()
h, w = self._screen_img.shape[0:2]
pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1],
[w - 1, 0]]).reshape(-1, 1, 2)
self._screen_corners = cv2.perspectiveTransform(
pts, self.screen2cam_matrix)
if debug:
cv2.imshow(
'debug',
draw_match(screen_img,
kp1,
self.draw_screen_boundary(cam_img),
kp2,
good,
matchesMask=matchesMask))
else:
cv2.destroyWindow('debug')
if False in [
cv2.isContourConvex(self._screen_corners),
cv2.contourArea(self._screen_corners) > AREA_THRESHOLD,
sum(matchesMask) > MATCH_THRESHOLD
]:
self.screen2cam_matrix = None
self._screen_corners = None
return False
self.cam2screen_matrix, _ = cv2.findHomography(
dst_pts, src_pts, cv2.RANSAC, 5.0)
return True
except cv2.error:
self.screen2cam_matrix = None
self._screen_corners = None
return False
elif k== 27:
break
# find the keypoints and descriptors with ORB
if k is not None:
cv2.destroyWindow('preview')
kp1, des1 = orb.detectAndCompute(img1,None)
kp2, des2 = orb.detectAndCompute(img2,None)
# If nothing match then continue
if des2 is None:
img3 = img3 = draw_match(img1,kp1,img2,kp2,[])
continue
des1 = des1.astype(np.uint8, copy=False) # Fix the data type
des2 = des2.astype(np.uint8, copy=False)
# Now match describers
bf = cv2.BFMatcher(cv2.NORM_HAMMING)
# matches = bf.match(des1,des2)
matches = bf.knnMatch(des1,des2, k=2)
# m = matches[0][0]
# p1, p2 = np.float32(kp1[m.queryIdx].pt), np.float32(kp2[m.trainIdx].pt)
# print m.distance, p1, p2
for m in good]).reshape(-1, 1, 2)
dst_pts = np.float32([kp2[m.trainIdx].pt
for m in good]).reshape(-1, 1, 2)
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
matchesMask = mask.ravel().tolist()
h, w = img1.shape
pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1],
[w - 1, 0]]).reshape(-1, 1, 2)
dst = cv2.perspectiveTransform(pts, M)
cv2.polylines(img2, [np.int32(dst)], True, 255, 3)
else:
print "Not enough matches are found - %d/%d" % (len(good),
MIN_MATCH_COUNT)
matchesMask = None
draw_params = dict(
matchColor=(0, 255, 0), # draw matches in green color
singlePointColor=None,
matchesMask=matchesMask, # draw only inliers
flags=2)
# print [x for x in dir(kp1[0]) if not '__' in x]
# print [x for x in dir(good[0]) if not '__' in x]
img3 = draw_match(img1, kp1, img2, kp2, good, None, **draw_params)
cv2.imshow('matches', img3)
def find_screen_img(self, cam_img, screen_img=None, debug=False):
"""
Find screen_img in cam_img.
If executed successfully, the function return True.
Meanwhile self.recovery_matrix will be computed, which is used to
map camera image to top view
"""
try:
MATCH_THRESHOLD = 10
FLANN_INDEX_KDTREE = 0
AREA_THRESHOLD = 1000
if screen_img is None:
kp1, des1 = self._screen_features
screen_img = self._screen_img
else:
kp1, des1 = self._detector.detectAndCompute(screen_img,None)
kp2, des2 = self._detector.detectAndCompute(cam_img,None)
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1,des2,k=2)
# Perform Lowe's ratio test to select good points to proceed with.
good = [m for m,n in matches if m.distance < 0.7*n.distance]
src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1,1,2)
dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1,1,2)
# check the property of the corners found out there
self.screen2cam_matrix, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
matchesMask=mask.ravel().tolist()
h,w = self._screen_img.shape[0:2]
pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
self._screen_corners = cv2.perspectiveTransform(pts, self.screen2cam_matrix)
if debug: cv2.imshow('debug', draw_match(screen_img, kp1, self.draw_screen_boundary(cam_img), kp2, good, matchesMask=matchesMask))
else: cv2.destroyWindow('debug')
if False in [cv2.isContourConvex(self._screen_corners),
cv2.contourArea(self._screen_corners) > AREA_THRESHOLD,
sum(matchesMask) > MATCH_THRESHOLD]:
self.screen2cam_matrix = None
self._screen_corners = None
print "Couldn't find screen image"
return False
self.cam2screen_matrix, _ = cv2.findHomography(dst_pts, src_pts, cv2.RANSAC,5.0)
return True
except cv2.error:
self.screen2cam_matrix = None
self._screen_corners = None
return False
