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drawCube.py
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drawCube.py
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import cv2
import numpy as np
import copy
import math
def preprocessing(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gblur = cv2.GaussianBlur(gray, (19, 19), 0)
thresh = cv2.threshold(gblur, 200, 255, cv2.THRESH_BINARY)[1]
threshblur = cv2.GaussianBlur(thresh, (5, 5), 0)
kernel = np.ones((3, 3), np.uint8)
img_dilation = cv2.dilate(threshblur, kernel, iterations=3)
img_erosion = cv2.erode(img_dilation, kernel, iterations=3)
return img_erosion
def detectCorners(img):
corners = cv2.goodFeaturesToTrack(img, 15, 0.05, 50)
corners = np.int0(corners)
corners = corners.reshape(corners.shape[0], 2)
return corners
def distance(pt1, pt2):
return math.sqrt(((pt1[0] - pt2[0]) ** 2) + ((pt1[1] - pt2[1]) ** 2))
def checkIfPointNearLine(pt1, pt2, corners):
points = copy.deepcopy(corners)
for i in points:
d = np.abs(np.cross(pt2 - pt1, i - pt1) / np.linalg.norm(pt2 - pt1))
if d < 10:
corners = np.delete(corners, [np.where(corners == i)[0][0]], 0)
return corners
def tagCorners(corners):
x_min, y_min = np.argmin(corners, axis=0)
x_max, y_max = np.argmax(corners, axis=0)
pt1 = corners[x_min]
pt2 = corners[y_max]
pt3 = corners[x_max]
pt4 = corners[y_min]
corners = np.delete(corners, [x_min, y_max, x_max, y_min], 0)
corners = checkIfPointNearLine(pt1, pt2, corners)
corners = checkIfPointNearLine(pt2, pt3, corners)
corners = checkIfPointNearLine(pt3, pt4, corners)
corners = checkIfPointNearLine(pt4, pt1, corners)
x_min, y_min = np.argmin(corners, axis=0)
x_max, y_max = np.argmax(corners, axis=0)
leftmost_corner = corners[x_min]
rightmost_corner = corners[x_max]
topmost_corner = corners[y_min]
bottommost_corner = corners[y_max]
tag_corners = np.float32([leftmost_corner, bottommost_corner, rightmost_corner, topmost_corner])
return tag_corners
def homography(input_pts, output_pts):
x1 = input_pts[0][0]
x2 = input_pts[1][0]
x3 = input_pts[2][0]
x4 = input_pts[3][0]
y1 = input_pts[0][1]
y2 = input_pts[1][1]
y3 = input_pts[2][1]
y4 = input_pts[3][1]
xp1 = output_pts[0][0]
xp2 = output_pts[1][0]
xp3 = output_pts[2][0]
xp4 = output_pts[3][0]
yp1 = output_pts[0][1]
yp2 = output_pts[1][1]
yp3 = output_pts[2][1]
yp4 = output_pts[3][1]
A = np.matrix([[-x1, -y1, -1, 0, 0, 0, x1 * xp1, y1 * xp1, xp1],
[0, 0, 0, -x1, -y1, -1, x1 * yp1, y1 * yp1, yp1],
[-x2, -y2, -1, 0, 0, 0, x2 * xp2, y2 * xp2, xp2],
[0, 0, 0, -x2, -y2, -1, x2 * yp2, y2 * yp2, yp2],
[-x3, -y3, -1, 0, 0, 0, x3 * xp3, y3 * xp3, xp3],
[0, 0, 0, -x3, -y3, -1, x3 * yp3, y3 * yp3, yp3],
[-x4, -y4, -1, 0, 0, 0, x4 * xp4, y4 * xp4, xp4],
[0, 0, 0, -x4, -y4, -1, x4 * yp4, y4 * yp4, yp4]])
U, Sigma, V = np.linalg.svd(A)
H = np.reshape(V[-1, :], (3, 3))
Lambda = H[-1, -1]
H = H / Lambda
return H
def projectionMatrix(K, H):
K_inv = np.linalg.inv(K)
B_tilde = np.matmul(K_inv, H)
if np.linalg.det(B_tilde) < 0:
B_tilde = B_tilde * (-1)
Lambda = 2 / (np.linalg.norm(np.matmul(K_inv, H[:, 0])) + np.linalg.norm(np.matmul(K_inv, H[:, 1])))
r1 = Lambda * B_tilde[:, 0]
r2 = Lambda * B_tilde[:, 1]
r3 = np.cross(r1, r2, axis=0)
t = Lambda * B_tilde[:, 2]
B = np.column_stack((r1, r2, r3, t))
P = np.matmul(K, B)
return P
def findCubeCorners(P):
pt1 = np.array([[0], [0], [-1], [1]])
pt2 = np.array([[0], [1], [-1], [1]])
pt3 = np.array([[1], [1], [-1], [1]])
pt4 = np.array([[1], [0], [-1], [1]])
cubept1 = np.matmul(P, pt1)
cubept1 = cubept1 / cubept1[-1]
cubept2 = np.matmul(P, pt2)
cubept2 = cubept2 / cubept2[-1]
cubept3 = np.matmul(P, pt3)
cubept3 = cubept3 / cubept3[-1]
cubept4 = np.matmul(P, pt4)
cubept4 = cubept4 / cubept4[-1]
cubept1 = cubept1[:-1]
cubept2 = cubept2[:-1]
cubept3 = cubept3[:-1]
cubept4 = cubept4[:-1]
cubept1 = np.asarray(cubept1)
cubept2 = np.asarray(cubept2)
cubept3 = np.asarray(cubept3)
cubept4 = np.asarray(cubept4)
cube_corners = np.float32([cubept1, cubept2, cubept3, cubept4])
return cube_corners
def drawCube(img, tag_corners, cube_corners):
processed_img = copy.deepcopy(img)
cube_corners = cube_corners.astype(int)
for i in range(4):
p1 = (cube_corners[i][0][0], cube_corners[i][1][0])
p2 = (tag_corners[i][0], tag_corners[i][1])
processed_img = cv2.line(processed_img, p1, p2, (0, 255, 0), thickness=3, lineType=8)
for i in range(3):
p1 = (cube_corners[i][0][0], cube_corners[i][1][0])
p2 = (cube_corners[i + 1][0][0], cube_corners[i + 1][1][0])
processed_img = cv2.line(processed_img, p1, p2, (0, 255, 0), thickness=3, lineType=8)
p1 = (cube_corners[0][0][0], cube_corners[0][1][0])
p2 = (cube_corners[3][0][0], cube_corners[3][1][0])
processed_img = cv2.line(processed_img, p1, p2, (0, 255, 0), thickness=3, lineType=8)
return processed_img
def virtualCube(video, K):
print("Generating video ouput named 'virtual_cube'...\n")
out = cv2.VideoWriter('virtual_cube.avi', cv2.VideoWriter_fourcc(*'XVID'), 27, (1920, 1080))
april_tag_wc = np.float32([[0, 0], [0, 1], [1, 1], [1, 0]])
while True:
try:
isTrue, img = video.read()
if isTrue == False:
break
processed_img = preprocessing(img)
corners = detectCorners(processed_img)
tag_corners = tagCorners(corners)
tag_corners = np.int0(tag_corners)
H = homography(april_tag_wc, tag_corners)
P = projectionMatrix(K, H)
cube_corners = findCubeCorners(P)
cube_img = drawCube(img, tag_corners, cube_corners)
out.write(cube_img)
except:
isTrue, img = video.read()
continue
out.release()
print("Video output generated.\n")
K = np.array([[1346.100595, 0, 932.1633975], [0, 1355.933136, 654.8986796], [0, 0, 1]])
capture = cv2.VideoCapture('data/1tagvideo.mp4')
virtualCube(capture, K)