def compareLearningRate2(name, img1: ImageWithPoints, img2: ImageWithPoints):
pts_match_1, pts_match_2, match_good = ho.matchKeypoints(img1.kp, img1.des, img2.kp, img2.des)
_, mask = ho.findHomographyCV(pts_match_1, pts_match_2, 4)
H = np.asarray([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
H, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=200, method=0, H=H, mask=mask, learning_rate=0.3)
printResults(name+"|lr=0.3|200", H, pts_match_1, pts_match_2, mask)
H = np.asarray([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
H, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=500, method=0, H=H, mask=mask, learning_rate=0.3)
printResults(name+"|lr=0.3|500", H, pts_match_1, pts_match_2, mask)
H = np.asarray([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
H, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=200, method=0, H=H, mask=mask, learning_rate=0.5)
printResults(name+"|lr=0.5|200", H, pts_match_1, pts_match_2, mask)
H = np.asarray([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
H, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=500, method=0, H=H, mask=mask, learning_rate=0.5)
printResults(name+"|lr=0.5|500", H, pts_match_1, pts_match_2, mask)
H = np.asarray([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
H, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=200, method=0, H=H, mask=mask, learning_rate=0.7)
printResults(name+"|lr=0.7|200", H, pts_match_1, pts_match_2, mask)
H = np.asarray([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
H, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=500, method=0, H=H, mask=mask, learning_rate=0.7)
printResults(name+"|lr=0.7|500", H, pts_match_1, pts_match_2, mask)
def compareRANSACMethod(name, img1: ImageWithPoints, img2: ImageWithPoints):
'''Compares different methods for outlier detection.'''
pts_match_1, pts_match_2, match_good = ho.matchKeypoints(img1.kp, img1.des, img2.kp, img2.des)
H, mask = cv2.findHomography(pts_match_1, pts_match_2, cv2.RANSAC, 4)
mask = mask.ravel() > 0.5
# printResults(name+"|CV2-RANSAC", H, pts_match_1, pts_match_2, mask)
printResults(name+"|CV2-RANSAC", H, img1.points, img2.points, None)
H, mask = cv2.findHomography(pts_match_1, pts_match_2, cv2.LMEDS, 4)
mask = mask.ravel() > 0.5
# printResults(name+"|CV2-LMEDS", H, pts_match_1, pts_match_2, mask)
printResults(name+"|CV2-LMEDS", H, img1.points, img2.points, None)
H, mask = cv2.findHomography(pts_match_1, pts_match_2, cv2.RHO, 4)
mask = mask.ravel() > 0.5
# printResults(name+"|CV2-RHO", H, pts_match_1, pts_match_2, mask)
printResults(name+"|CV2-RHO", H, img1.points, img2.points, None)
H, mask = ho.ransac(pts_match_1, pts_match_2, 4)
H, mask = ho.findHomographyCV(pts_match_1, pts_match_2, 0, mask=mask)
# printResults(name+"|RANSAC", H, pts_match_1, pts_match_2, mask)
printResults(name+"|RANSAC", H, img1.points, img2.points, None)
H, mask = ho.msac(pts_match_1, pts_match_2, 4)
H, mask = ho.findHomographyCV(pts_match_1, pts_match_2, 0, mask=mask)
# printResults(name+"|MSAC", H, pts_match_1, pts_match_2, mask)
printResults(name+"|MSAC", H, img1.points, img2.points, None)
def compareAndShow(img1: ImageWithPoints, img2: ImageWithPoints):
pts_match_1, pts_match_2, match_good = ho.matchKeypoints(img1.kp, img1.des, img2.kp, img2.des)
H_tf, mask_tf = ho.findHomography(pts_match_1, pts_match_2, 4)
H_cv, mask_cv = ho.findHomographyCV(pts_match_1, pts_match_2, 4)
image = ho.polyline(img2.points, img2.image, color=(0, 0, 255))
image_tf = ho.polyline(ho.project(H_tf, img1.points), image, color=(255, 0, 0))
image_cv = ho.polyline(ho.project(H_cv, img1.points), image, color=(0, 255, 0))
while True:
cv2.imshow('image', image)
cv2.imshow('tf', image_tf)
cv2.imshow('cv', image_cv)
k = cv2.waitKey(10) & 0xFF
if k == 27:
break
def CompareRANSACWithout(name, img1: ImageWithPoints, img2: ImageWithPoints):
pts_match_1, pts_match_2, match_good = ho.matchKeypoints(img1.kp, img1.des, img2.kp, img2.des)
H_0, mask_0 = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=0,learning_rate=0.3)
printResults(name+"|with", H_0, pts_match_1, pts_match_2, mask_0)
error_ransac = ho.distanceError(H_0, pts_match_1, pts_match_2, mask_0).mean()
H_0 = np.asarray([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
z = 0
with ho.Graph() as graph:
while True:
z += 10
H_0, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=10, learning_rate=0.3, method=0, H=H_0, mask=mask_0, graph = graph)
error = ho.distanceError(H_0, pts_match_1, pts_match_2, mask_0).mean()
if error < error_ransac:
break
printResults(name+"|without", H_0, pts_match_1, pts_match_2, mask_0)
print("Epochs: "+str(z))
def normalizationBox(img1, img2):
pts_match_1, pts_match_2, match_good = ho.matchKeypoints(img1.kp, img1.des, img2.kp, img2.des)
pts = np.asarray([[1, 1], [-1, 1], [-1, -1], [1, -1]])
H, mask = ho.ransac(pts_match_1, pts_match_2, 4)
N_1 = ho.findNormalizationMatrix(pts_match_1, mask)
N_1_inv = np.linalg.inv(N_1)
N_2 = ho.findNormalizationMatrix(pts_match_2, mask)
N_2 = np.matmul(N_1, np.linalg.inv(H))
N_2_inv = np.linalg.inv(N_2)
image1 = ho.polyline(ho.project(N_1_inv, pts), img1.image)
image2 = ho.polyline(ho.project(N_2_inv, pts), img2.image)
mask = (mask * 1).tolist()
both = ho.drawMatches(img1.kp, img2.kp, match_good, image1, image2, mask)
cv2.imshow("both", both)
cv2.waitKey(0)
def compareLearningRate(name, img1: ImageWithPoints, img2: ImageWithPoints):
pts_match_1, pts_match_2, match_good = ho.matchKeypoints(img1.kp, img1.des, img2.kp, img2.des)
H, mask = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=0)
H, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=50, method=0, H=H, mask=mask, learning_rate=0.5)
printResults(name+"|lr=0.3|200", H, pts_match_1, pts_match_2, mask)
H, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=300, method=0, H=H, mask=mask, learning_rate=0.5)
printResults(name+"|lr=0.3|500", H, pts_match_1, pts_match_2, mask)
H, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=50, method=0, H=H, mask=mask, learning_rate=0.8)
printResults(name+"|lr=0.5|200", H, pts_match_1, pts_match_2, mask)
H, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=300, method=0, H=H, mask=mask, learning_rate=0.8)
printResults(name+"|lr=0.5|500", H, pts_match_1, pts_match_2, mask)
H, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=50, method=0, H=H, mask=mask, learning_rate=1)
printResults(name+"|lr=0.7|200", H, pts_match_1, pts_match_2, mask)
H, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=300, method=0, H=H, mask=mask, learning_rate=1)
printResults(name+"|lr=0.7|500", H, pts_match_1, pts_match_2, mask)
def video(img1: ImageWithPoints, img2: ImageWithPoints):
pts_match_1, pts_match_2, match_good = ho.matchKeypoints(img1.kp, img1.des, img2.kp, img2.des)
H, mask = ho.findHomography(pts_match_1, pts_match_2, 2, epochs=0, learning_rate=0.3, method=2)
H = np.asarray([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
with ho.Graph() as graph:
for i in range(0, 100):
if i < 50:
H, mask = ho.findHomography(pts_match_1, pts_match_2, 2, epochs=1, learning_rate=0.15, H=H, mask=mask, method=0, graph=graph, normalization=1)
image = ho.polyline(ho.project(H, img1.points), img2.image, color=(0, 0, 255))
else:
H, mask = ho.findHomography(pts_match_1, pts_match_2, 2, epochs=5, learning_rate=0.3, H=H, mask=mask, method=0, graph=graph, normalization=1)
image = ho.polyline(ho.project(H, img1.points), img2.image, color=(0, 255, 0))
image2 = cv2.warpPerspective(img1.image, H, (image.shape[1], image.shape[0]))
image3 = image2/256*0.25 + image/256*0.75
cv2.imshow('frame4', image3)
cv2.imshow('image', image)
k = cv2.waitKey(10) & 0xFF
if k == 27:
break
def compareEpochs(name, img1: ImageWithPoints, img2: ImageWithPoints):
pts_match_1, pts_match_2, match_good = ho.matchKeypoints(img1.kp, img1.des, img2.kp, img2.des)
H_0, mask_0 = ho.findHomography(pts_match_1, pts_match_2, 2, epochs=0, learning_rate=0.3, method=2)
H_1, mask_1 = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=2, learning_rate=0.3, method=0, H=H_0, mask=mask_0)
H_2, mask_2 = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=8, learning_rate=0.3, method=0, H=H_1, mask=mask_0)
H_3, mask_3 = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=10, learning_rate=0.3, method=0, H=H_2, mask=mask_0)
H_4, mask_4 = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=20, learning_rate=0.3, method=0, H=H_3, mask=mask_0)
H_cv, mask_cv = ho.findHomographyCV(pts_match_1, pts_match_2, 4)
printResults(name+"|0 epochs", H_0, pts_match_1, pts_match_2, mask_0)
printResults(name+"|2 epochs", H_1, pts_match_1, pts_match_2, mask_0)
printResults(name+"|10 epochs", H_2, pts_match_1, pts_match_2, mask_0)
printResults(name+"|20 epochs", H_3, pts_match_1, pts_match_2, mask_0)
printResults(name+"|40 epochs", H_4, pts_match_1, pts_match_2, mask_0)
printResults(name+"|0 epochs", H_0, img1.points, img2.points, None)
printResults(name+"|2 epochs", H_1, img1.points, img2.points, None)
printResults(name+"|10 epochs", H_2, img1.points, img2.points, None)
printResults(name+"|20 epochs", H_3, img1.points, img2.points, None)
printResults(name+"|40 epochs", H_4, img1.points, img2.points, None)
printResults(name+"|cv", H_cv, img1.points, img2.points, None)
def compareWithout(name, img1: ImageWithPoints, img2: ImageWithPoints):
'''Comparation with OpenCV. Both methods use the same keypoints. Our system does not use a prior SVD to find an estimation.'''
pts_match_1, pts_match_2, match_good = ho.matchKeypoints(img1.kp, img1.des, img2.kp, img2.des)
H_0 = np.asarray([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
# H_0, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=0,learning_rate=0.3,ransac=2)
H_cv, mask_0 = ho.findHomographyCV(pts_match_1, pts_match_2, 4)
printResults(name+"|cv", H_cv, pts_match_1, pts_match_2, mask_0)
# printResults(name+"|cv", H_cv, img1.points, img2.points, None)
H_0, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=100, learning_rate=0.3, method=0, H=H_0, mask=mask_0)
printResults(name+"|100 epochs", H_0, pts_match_1, pts_match_2, mask_0)
# printResults(name+"|100 epochs", H_0, img1.points, img2.points, None)
# H_0 = H_0 / H_0[2, 2]
# print(np.linalg.norm(H_0- H_cv))
H_0, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=100, learning_rate=0.3, method=0, H=H_0, mask=mask_0)
# printResults(name+"|200 epochs", H_0, pts_match_1, pts_match_2, mask_0)
# printResults(name+"|200 epochs", H_0, img1.points, img2.points, None)
# H_0 = H_0 / H_0[2, 2]
# print(np.linalg.norm(H_0- H_cv))
H_0, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=100, learning_rate=0.3, method=0, H=H_0, mask=mask_0)
printResults(name+"|300 epochs", H_0, pts_match_1, pts_match_2, mask_0)
# printResults(name+"|300 epochs", H_0, img1.points, img2.points, None)
# H_0 = H_0 / H_0[2, 2]
# print(np.linalg.norm(H_0- H_cv))
H_0, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=100, learning_rate=0.3, method=0, H=H_0, mask=mask_0)
# printResults(name+"|400 epochs", H_0, pts_match_1, pts_match_2, mask_0)
# printResults(name+"|400 epochs", H_0, img1.points, img2.points, None)
# H_0 = H_0 / H_0[2, 2]
# print(np.linalg.norm(H_0- H_cv))
H_0, _ = ho.findHomography(pts_match_1, pts_match_2, 4, epochs=100, learning_rate=0.3, method=0, H=H_0, mask=mask_0)
printResults(name+"|500 epochs", H_0, pts_match_1, pts_match_2, mask_0)
diff1 = cv2.cvtColor(diff1, cv2.COLOR_BGR2GRAY)
diff1 = cv2.GaussianBlur(diff1, (9, 9), 0)
_, diff1 = cv2.threshold(diff1, 16, 255, cv2.THRESH_BINARY)
cv2.imshow('diff1', diff1)
diff2 = cv2.absdiff(ref2, frame2)
diff2 = cv2.cvtColor(diff2, cv2.COLOR_BGR2GRAY)
diff2 = cv2.GaussianBlur(diff2, (9, 9), 0)
_, diff2 = cv2.threshold(diff2, 16, 255, cv2.THRESH_BINARY)
cv2.imshow('diff2', diff2)
kp_1, des_1 = ho.findKeypoints(frame1, diff1)
kp_2, des_2 = ho.findKeypoints(frame2, diff2)
if len(kp_1) > 20 and len(kp_2) > 20:
pts_1, pts_2, good = ho.matchKeypoints(kp_1, des_1, kp_2, des_2)
if pts_1.shape[0] >= 10:
H, mask = ho.findHomography(pts_1, pts_2, 3, graph=graph)
if mask.sum() >= 10:
frame1Warp = cv2.warpPerspective(
frame1, H, (frame1.shape[1], frame1.shape[0]))
frameBlend = frame1Warp / 256 * 0.5 + frame2 / 256 * 0.5
cv2.imshow('frame4', frameBlend)
cv2.imshow('frame1', frame1)
cv2.imshow('frame2', frame2)
k = cv2.waitKey(1) & 0xFF
if k == 27:
break
