Created
August 10, 2017 12:41
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Implementation of merging line segments by TAVARES and PADILHA
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# TAVARES and PADILHA approach | |
def merge_line_segments(line_i, line_j, use_log=False): | |
# line distance | |
line_i_length = math.hypot(line_i[1][0] - line_i[0][0], line_i[1][1] - line_i[0][1]) | |
line_j_length = math.hypot(line_j[1][0] - line_j[0][0], line_j[1][1] - line_j[0][1]) | |
# centroids | |
Xg = line_i_length*(line_i[0][0]+line_i[1][0]) + line_j_length*(line_j[0][0]+line_j[1][0]) | |
Xg /= 2 * (line_i_length + line_j_length) | |
Yg = line_i_length*(line_i[0][1]+line_i[1][1]) + line_j_length*(line_j[0][1]+line_j[1][1]) | |
Yg /= 2 * (line_i_length + line_j_length) | |
# orientation | |
orientation_i = math.atan2((line_i[0][1]-line_i[1][1]),(line_i[0][0]-line_i[1][0])) | |
orientation_j = math.atan2((line_j[0][1]-line_j[1][1]),(line_j[0][0]-line_j[1][0])) | |
orientation_r = math.pi | |
if(abs(orientation_i - orientation_j) <= math.pi/2): | |
orientation_r = line_i_length*orientation_i + line_j_length*orientation_j | |
orientation_r /= line_i_length + line_j_length | |
else: | |
orientation_r = line_i_length*orientation_i + line_j_length*(orientation_j - math.pi*orientation_j/abs(orientation_j)) | |
orientation_r /= line_i_length + line_j_length | |
# coordinate transformation | |
# δXG = (δy - yG)sinθr + (δx - xG)cosθr | |
# δYG = (δy - yG)cosθr - (δx - xG)sinθr | |
a_x_g = (line_i[0][1] - Yg)*math.sin(orientation_r) + (line_i[0][0] - Xg) * math.cos(orientation_r) | |
a_y_g = (line_i[0][1] - Yg)*math.cos(orientation_r) - (line_i[0][0] - Xg) * math.sin(orientation_r) | |
b_x_g = (line_i[1][1] - Yg)*math.sin(orientation_r) + (line_i[1][0] - Xg) * math.cos(orientation_r) | |
b_y_g = (line_i[1][1] - Yg)*math.cos(orientation_r) - (line_i[1][0] - Xg) * math.sin(orientation_r) | |
c_x_g = (line_j[0][1] - Yg)*math.sin(orientation_r) + (line_j[0][0] - Xg) * math.cos(orientation_r) | |
c_y_g = (line_j[0][1] - Yg)*math.cos(orientation_r) - (line_j[0][0] - Xg) * math.sin(orientation_r) | |
d_x_g = (line_j[1][1] - Yg)*math.sin(orientation_r) + (line_j[1][0] - Xg) * math.cos(orientation_r) | |
d_y_g = (line_j[1][1] - Yg)*math.cos(orientation_r) - (line_j[1][0] - Xg) * math.sin(orientation_r) | |
# line distance relative | |
line_i_rel_length = math.hypot(b_x_g - a_x_g, b_y_g - a_y_g) | |
line_j_rel_length = math.hypot(d_x_g - c_x_g, d_y_g - c_y_g) | |
# orthogonal projections over the axis X | |
start_f = min(a_x_g,b_x_g,c_x_g,d_x_g) | |
end_f = max(a_x_g,b_x_g,c_x_g,d_x_g) | |
length_f = math.hypot(end_f - start_f, 0 - 0) | |
#start_f = line_i_rel_length * math.cos(orientation_r) | |
#end_f = line_j_rel_length * math.cos(orientation_r) | |
start_x = int(Xg - start_f * math.cos(orientation_r)) | |
start_y = int(Yg - start_f * math.sin(orientation_r)) | |
end_x = int(Xg - end_f * math.cos(orientation_r)) | |
end_y = int(Yg - end_f * math.sin(orientation_r)) | |
# log process | |
if(use_log): | |
print("distance between lines:", get_distance(line_i, line_j)) | |
print("real lines angle:", math.degrees(orientation_i), math.degrees(orientation_j)) | |
print("orientation angle:", math.degrees(orientation_r)) | |
print("centroids:", Xg, Yg) | |
print("relative lines length:", line_i_rel_length, line_j_rel_length) | |
print("real lines length:", line_i_length, line_j_length) | |
print("final line length", length_f) | |
print("final line endpoints", (start_x, start_y), (end_x, end_y)) | |
# Create a black image | |
img = np.zeros((1400,3100,3), np.uint8) | |
img = cv2.line(img, line_i[0],line_i[1],(255,0,0),5) | |
img = cv2.line(img,line_j[0],line_j[1],(255,0,0),5) | |
plt.imshow(img) | |
plt.show() | |
img = cv2.circle(img,(int(Xg),int(Yg)),10,(255,0,100),6) | |
img = cv2.line(img, (start_x, start_y), (end_x, end_y),(255,0,255),5) | |
plt.imshow(img) | |
plt.show() | |
return [(start_x, start_y), (end_x, end_y)] |
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Nice implementation :)