親們，首先請看下圖：
為什么霍夫線檢測和繪制的線與足球場的白線并不真正平行？
我需要那個交點準確地放在影像上
那么它需要在影像中用白線繪制平行的直線誰可以幫助我解決這個問題
提前致謝
如果你想使用代碼，這是原始影像 d.jpg：
這是我的python代碼：
import numpy as np
import cv2
from collections import defaultdict
import sys
import math
img2 = cv2.imread("e:/d.jpg")
edges2 = cv2.Canny(img2, 40, 55)
def segment_by_angle_kmeans(lines, k=2, **kwargs):
# Define criteria = (type, max_iter, epsilon)
default_criteria_type = cv2.TERM_CRITERIA_EPS cv2.TERM_CRITERIA_MAX_ITER
criteria = kwargs.get('criteria', (default_criteria_type, 10, 1.0))
flags = kwargs.get('flags', cv2.KMEANS_RANDOM_CENTERS)
attempts = kwargs.get('attempts', 10)
# Get angles in [0, pi] radians
angles = np.array([line[0][1] for line in lines])
# Multiply the angles by two and find coordinates of that angle on the Unit Circle
pts = np.array([[np.cos(2*angle), np.sin(2*angle)] for angle in angles], dtype=np.float32)
# Run k-means
if sys.version_info[0] == 2:
# python 2.x
ret, labels, centers = cv2.kmeans(pts, k, criteria, attempts, flags)
else:
# python 3.x, syntax has changed.
labels, centers = cv2.kmeans(pts, k, None, criteria, attempts, flags)[1:]
labels = labels.reshape(-1) # Transpose to row vector
# Segment lines based on their label of 0 or 1
segmented = defaultdict(list)
for i, line in zip(range(len(lines)), lines):
segmented[labels[i]].append(line)
segmented = list(segmented.values())
print("Segmented lines into two groups: %d, %d" % (len(segmented[0]), len(segmented[1])))
return segmented
def intersection(line1, line2):
"""
Find the intersection of two lines
specified in Hesse normal form.
Returns closest integer pixel locations.
"""
rho1, theta1 = line1[0]
rho2, theta2 = line2[0]
A = np.array([[np.cos(theta1), np.sin(theta1)],
[np.cos(theta2), np.sin(theta2)]])
b = np.array([[rho1], [rho2]])
x0, y0 = np.linalg.solve(A, b)
x0, y0 = int(np.round(x0)), int(np.round(y0))
return [[x0, y0]]
def segmented_intersections(lines):
"""
Find the intersection between groups of lines.
"""
intersections = []
for i, group in enumerate(lines[:-1]):
for next_group in lines[i 1:]:
for line1 in group:
for line2 in next_group:
intersections.append(intersection(line1, line2))
return intersections
def drawLines(img, lines, color=(0,0,255)):
"""
Draw lines on an image
"""
for line in lines:
for rho,theta in line:
a = np.cos(theta)
b = np.sin(theta)
x0 = a*rho
y0 = b*rho
x1 = int(x0 1000*(-b))
y1 = int(y0 1000*(a))
x2 = int(x0 - 1000*(-b))
y2 = int(y0 - 1000*(a))
cv2.line(img, (x1,y1), (x2,y2), color, 2)
# Detect lines
rho = 2.7
theta = np.pi/50
thresh = 410
lines = cv2.HoughLines(edges2, rho, theta, thresh)
print("Found lines: %d" % (len(lines)))
# Draw all Hough lines
img_with_all_lines = np.copy(2)
drawLines(img_with_all_lines, lines)
# Cluster line angles into 2 groups (vertical and horizontal)
segmented = segment_by_angle_kmeans(lines, 2)
# Find the intersections of each vertical line with each horizontal line
intersections = segmented_intersections(segmented)
img_with_segmented_lines = np.copy(img2)
# Draw vertical lines
vertical_lines = segmented[1]
img_with_vertical_lines = np.copy(img2)
drawLines(img_with_segmented_lines, vertical_lines, (255,255,0))
# Draw horizontal lines
horizontal_lines = segmented[0]
img_with_horizontal_lines = np.copy(img2)
drawLines(img_with_segmented_lines, horizontal_lines, (0,255,255))
# Draw intersection points
intersections.pop(5)
intersections.pop(4)
mn = intersections[3]
mk = intersections[2]
intersections = intersections[:2]
intersections.append(mn)
intersections.append(mk)
print(intersections)
for point in intersections:
pt = (point[0][0], point[0][1])
length = 5
cv2.circle(img_with_segmented_lines, pt, 5 , (255, 0, 255), -1)
cv2.imshow("Segmented lines", img_with_segmented_lines)
cv2.waitKey()
uj5u.com熱心網友回復：
Hough 使用具有有限解析度的累加器，該解析度決定了精度。您可以減少 theta（以運行時間為代價，并且可能會損失可靠性）。
如果必須這樣做，我可能會添加一些后處理以更精確地檢測沿霍夫線的線，使用方向梯度濾波器和穩健的線擬合。
轉載請註明出處，本文鏈接：https://www.uj5u.com/caozuo/345427.html