def lineDetect(img, mode=0, threshold=-1, minLineLength=0, maxLineGap=0):
img = cv2.cvtColor(img, code=cv2.COLOR_BGR2GRAY) if img.ndim == 3 else img
lines = []
if mode == 0: # 霍夫直线检测
# 参数 rho:累加器距离分辨率, theta:累加器角度分辨率 threshold:投票阈值, minLineLength:直线的最小长度 maxLineGap:直线合并最大断开距离
datas = cv2.HoughLinesP(img, rho=1, theta=np.pi / 360., threshold=int(threshold), minLineLength=minLineLength,
maxLineGap=maxLineGap)
if datas is None:
return lines
for d in datas.reshape(-1, 4):
line = GLine().initXY(*d)
if line.getLen() <= 0: continue
lines.append(line)
elif mode == 1: # 直线检测器检测
# 直线检测器检测 cv2.createLineSegmentDetector
# 参数1 refine(LSD_REFINE_STD,): 调优策略, cv2.LSD_REFINE_NONE:无调优策略, cv2.LSD_REFINE_STD:标准调优策略,将弧分解成多个小线段, cv2.LSD_REFINE_ADV:使用NFA指标评价检测直线,调整参数近一步调优检测直线
# 参数2 scale(0.8): 缩放图像比例 参数3 sigma_scale(0.6): 高斯模糊核sigma=sigma_scale/scale 参数4 quant(2.0):梯度量化误差
# 参数5 ang_th(22.5):直线段角度容差 参数6 log_eps(0): NFA检测阈值 参数7 density_th(0.7):直线段密度阈值 参数8 n_bins(1024):梯度归一化数量
lsd = cv2.createLineSegmentDetector(cv2.LSD_REFINE_STD)
datas, width, prec, nfa = lsd.detect(img)
if datas is None and width is None and prec is None and nfa is None: print("检测失败，未能检测到直线");return lines
datas = datas.reshape(-1, 4)
width = width.reshape(-1).tolist()
prec = prec.reshape(-1).tolist()
for i in range(len(datas)):
line = GLine().initXY(*datas[i])
if line.getLen() <= 0: continue
line.width, line.prec = width[i], prec[i]
lines.append(line)
else:
pass
return lines
def disLine0(a, b):
if (a == b).all(): return 0
linea = GLine().initXY(a[0], a[1], a[2], a[3])
lineb = GLine().initXY(b[0], b[1], b[2], b[3])
longline, shortline = (linea, lineb) if linea.getLen() > lineb.getLen() else (lineb, linea)
pts = [shortline.pt1, shortline.pt2]
dis = min([longline.distance(pt, mode=1) for pt in pts])
angle = longline.angle(shortline)
if angle > minAngle and dis < minDis: dis = minDis # 当超过角度, 距离至少位mindis(使其不符号条件)
return dis * (math.sin(math.radians(angle)) + 1)