from cam import MyCam
from fmatch import draw_match
MIN_MATCH_COUNT = 10
# Initiate SIFT detector
orb = cv2.ORB()
cam = MyCam()
cam.size = (640, 480)
img1 = img1 = cv2.imread('box.png', 0)
cv2.imshow('source', img1)
while True:
img2 = cv2.flip(cv2.cvtColor(cam.read(), cv2.COLOR_BGR2GRAY), 1)
k = cv2.waitKey(5)
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
from random import randint
win_size = (600, 800, 3)
cam = MyCam()
sf = ScreenFinder()
black = np.full(win_size, (0, 0, 0), np.uint8)
while True:
k = waitKey(5)
if k == 27:
break
if k == ord('r'):
sf.reset()
cam_img = cam.read()
on = True
while sf.screens is None:
on = not on
x = on * 255
screen_img = np.full(win_size, (x, x, x), np.uint8)
imshow('main', screen_img)
imshow('screen finder', cam_img)
# wait so that the camera can capture images completely loaded
k = waitKey(20)
cam_img = cam.read()
sf.put_calibrate_images(screen_img, cam_img)
# delay again to make sure that the image will not change during the camera taking picture
k = waitKey(20)
from fmatch import draw_match
MIN_MATCH_COUNT = 10
# Initiate SIFT detector
sift = cv2.SIFT()
cam = MyCam()
cam.size = (640, 480)
img1 = img1 = cv2.imread('box.png', 0)
cv2.imshow('source', img1)
while True:
img2 = cv2.flip(cv2.cvtColor(cam.read(), cv2.COLOR_BGR2GRAY), 1)
k = cv2.waitKey(5)
if k == ord('s'):
img1 = img2.copy()
cv2.imwrite('campic.png', img1)
elif k== 27:
break
# find the keypoints and descriptors with SIFT
if k is not None:
cv2.destroyWindow('preview')
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)
cross_product = np.cross(tmp[1] - tmp[0], tmp[2] - tmp[1])[0]
if cross_product * orientation > 0:
tmp = np.flipud(tmp)
# rearrange the points so that the returned point array always start with
# the point that is closest to origin
tmp = tmp.reshape(-1, 2)
distance_from_origin = map(lambda pt: pt[0]**2 + pt[1]**2, tmp)
val, idx = min(
(val, idx) for (idx, val) in enumerate(distance_from_origin))
if idx > 0:
up, down = np.vsplit(tmp, [idx])
tmp = np.vstack((down, up))
polygons.append(tmp)
return polygons
if __name__ == "__main__":
cam = MyCam()
while True:
img = cam.read()
polygons = find_polygons(img, 4)
for ctr in polygons:
draw_oriented_polylines(img, ctr, 0, (0, 0, 255), 2, (255, 0, 0))
cv2.imshow('find quadrangles', img)
k = cv2.waitKey(5)
if k == 27:
break
self.diff = cv2.bitwise_and(d1, d2)
def feed_image(self, image):
self._index = (self._index + 1) % (2 * self._N)
self._frame[self._index] = image
self.diff_img()
if __name__ == '__main__':
winName = "cam test"
cv2.namedWindow(winName, cv2.CV_WINDOW_AUTOSIZE)
# Read three images first:
cam = MyCam()
md = MotionDetector(N=2, shape=cam.read().shape)
while True:
md.feed_image(cam.read())
cv2.imshow(winName, md.diff)
key = cv2.waitKey(10)
if key == 27 or key == 32:
cam.release()
cv2.destroyWindow(winName)
break
print "Goodbye"
f0, f1, f2 = self.three_frames()
d1 = cv2.absdiff(f1, f0)
d2 = cv2.absdiff(f2, f1)
self.diff = cv2.bitwise_and(d1, d2)
def feed_image(self, image):
self._index = (self._index + 1) % (2 * self._N)
self._frame[self._index] = image
self.diff_img()
winName = "cam test"
cv2.namedWindow(winName, cv2.CV_WINDOW_AUTOSIZE)
# Read three images first:
cam = MyCam()
md = MotionDetector(N=2, shape=cam.read().shape)
while True:
md.feed_image(cam.read())
cv2.imshow(winName, md.diff)
key = cv2.waitKey(10)
if key == 27 or key == 32:
md.cam.release()
cv2.destroyWindow(winName)
break
print "Goodbye"
# now sort the points in counter clock wise so we can eliminate similar solutions
cross_product = np.cross(tmp[1] - tmp[0], tmp[2] - tmp[1])[0]
if cross_product * orientation > 0:
tmp = np.flipud(tmp)
# rearrange the points so that the returned point array always start with
# the point that is closest to origin
tmp = tmp.reshape(-1,2)
distance_from_origin = map(lambda pt: pt[0] ** 2 + pt[1] ** 2, tmp)
val, idx = min((val, idx) for (idx, val) in enumerate(distance_from_origin))
if idx > 0:
up, down = np.vsplit(tmp, [idx])
tmp = np.vstack((down, up))
polygons.append(tmp)
return polygons
if __name__ == "__main__":
cam = MyCam()
while True:
img = cam.read()
polygons = find_polygons(img, 4)
for ctr in polygons:
draw_oriented_polylines(img, ctr, 0, (0,0,255), 2, (255,0,0))
cv2.imshow('find quadrangles',img)
k = cv2.waitKey(5)
if k == 27:
break
def test_feature_matching_realtime(detetor=cv2.ORB()):
from cam import MyCam
"""
Press 's' to take a picture or 'l' to load one and start real-time
"""
MIN_MATCH_COUNT = 10
cam = MyCam()
cam.size = (640, 480)
img1 = img1 = cv2.imread('box.png', 0)
cv2.imshow('source', img1)
while True:
img2 = cv2.flip(cv2.cvtColor(cam.read(), cv2.COLOR_BGR2GRAY), 1)
k = cv2.waitKey(5)
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 = detetor.detectAndCompute(img1,None)
kp2, des2 = detetor.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
# Apply ratio test
good = []
try:
for m,n in matches:
if m.distance < 0.7*n.distance:
good.append(m)
except ValueError:
good = []
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, (0,0,255) ,3)
else:
# print "Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT)
matchesMask = None
good = []
img3 = draw_match(img1,kp1,img2,kp2,good, matchesMask=matchesMask)
cv2.imshow('matches', img3)
print 'press any key to continue'