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 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 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
H = np.identity(3)
_, mask = ho.findHomography(pts_match_1, pts_match_2)
# wait
cv2.imshow('frame', image_test)
k = cv2.waitKey(1000) & 0xFF
with ho.Graph() as graph:
iter = 0
for i in range(0, 140):
if i < 50:
iter += 1
# 50 times 1 epoch
H, mask = ho.findHomography(pts_match_1, pts_match_2, 2, epochs=1, learning_rate=0.3, H=H, mask=mask, method=0, graph=graph, normalization=1)
error = ho.distanceError(H, pts_match_1, pts_match_2, mask).mean()
image = ho.polyline(ho.project(H, corners), image_test, color=(0, 0, 255)) #red
image = cv2.putText(image,"iteration "+str(iter)+", error: "+str(error),(10,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,0),2,cv2.LINE_AA)
else:
iter += 5
# 90 times 5 epochs
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)
error = ho.distanceError(H, pts_match_1, pts_match_2, mask).mean()
image = ho.polyline(ho.project(H, corners), image_test, color=(0, 255, 0)) #green
image = cv2.putText(image,"iteration "+str(iter)+", error: "+str(error),(10,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,0),2,cv2.LINE_AA)
image2 = cv2.warpPerspective(image_reference, H, (image_test.shape[1], image_test.shape[0]))
image3 = image2/256*0.5 + image/256*0.5
cv2.imshow('frame', image3)
k = cv2.waitKey(10) & 0xFF
if k == 27:
"""Finds keypoints between two images and calculates a homography.
Must be run from the parent directory, e.g.: .../Mehrkamera/src> python -m examples.example1
"""
import numpy as np
import cv2
import modules.homography as ho
image_test = cv2.imread("res/graffiti/1.png")
image_reference = cv2.imread("res/graffiti/ref.png")
corners = np.float32([[54, 26], [2672, 54], [2679, 1905], [21, 1900]])
# resize the reference image
image_reference = cv2.resize(image_reference, (0, 0), fx=0.25, fy=0.25)
corners /= 4
points_reference, points_test, _ = ho.match(image_reference, image_test)
matrix, _ = ho.findHomography(points_reference, points_test)
corners_projected = ho.project(matrix, corners)
test_with_corners = ho.polyline(corners_projected, image_test)
cv2.imshow('test', test_with_corners)
cv2.waitKey(0)
cv2.destroyAllWindows()