def cluster_by_rho(theta_groups):
result_group = [] # 合并后的分组线
rho_differ = 7
for gp in theta_groups:
# 按 rho 的大小排序的 line 列表
rho_lines = sorted([li for li in gp], key=lambda li: li.rho)
rho_ls = [li.rho for li in rho_lines]
rho_diff = np.diff(rho_ls) # calculate diff in theta_ls which have been sorted
rho_diff_idx = np.where(rho_diff > rho_differ)[0] + 1 # slice by std value
rho_diff_idx = np.append(rho_diff_idx, [len(rho_lines)]) # line_ls 长度作为最后一个
rho_groups = [] # 按 rho 分组
gp_rho_ls = []
for i, idx in enumerate(rho_diff_idx): # 邻近的diff theta 分组
if i == 0:
rho_groups.append(rho_lines[0: idx])
gp_rho_ls.append(rho_ls[0: idx])
else:
rho_groups.append(rho_lines[rho_diff_idx[i - 1]:idx])
gp_rho_ls.append(rho_ls[rho_diff_idx[i - 1]:idx])
# 在分组内(rho 和 theta 都接近的情况下)
new_lines = []
for lis in rho_groups:
if len(lis) > 1: # 合并在同一直线上的直线段
pts = []
for li in lis:
pts.extend(li.getLinePts(includeEndPt=True))
new_li = GLine().fit(pts)
calculate_theta_rho(new_li)
new_lines.append(new_li)
elif len(lis) == 1:
new_lines.append(lis[0])
result_group.append(new_lines)
return result_group
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 fuse(baseline, line):
tlen = baseline.getLen() + line.getLen()
lineTh = max(tlen * maxRatio, minDis) # 直线间隔阈值
segTh = tlen * segRatio # 线段间隔阈值
pts = [line.pt1, line.pt2]
lineDis = [baseline.distance(p, mode=0) for p in pts] # 直线距离取最大
segDis = [baseline.distance(p, mode=1) for p in pts] # 线段距离取最小
# 直线距离超过阈值, 或者断开距离超过阈值, 不能融合
if max(lineDis) > lineTh or min(segDis) > segTh: return False
pt1, pt2, dis = getMaxLinesLen([baseline, line])
if abs(dis - baseline.getLen()) < 0.01: return True
pts = baseline.getLinePts(True)
pts.extend(line.getLinePts(True))
resline = GLine().fit(pts)
pt1 = resline.getVertPt(pt1)
pt2 = resline.getVertPt(pt2)
baseline.initPts(pt1, pt2)
return True
def isParallel(self, line, PDDR=0.05, overlapRatio=0.3, lineMindis=-1, lineMaxdis=-1):
# 考虑因素,直线距离, 重合率, 线段长度差异, 平行距离差异
longLine, shortLine = (self, line) if self.getLen() > line.getLen() else (line, self)
llen, slen = longLine.getLen(), shortLine.getLen()
if float(slen) / llen < overlapRatio: return False
pts = [shortLine.pt1, shortLine.pt2]
lineDis = [longLine.distance(p, mode=0) for p in pts] # 直线距离
lineDif = lineDis[0] + lineDis[1] if longLine.isCross(shortLine) else abs(lineDis[0] - lineDis[1])
# 线是否平行
if PDDR < 1 and lineDif > slen * PDDR: return False
if PDDR > 1 and lineDif > PDDR: return False
if overlapRatio <= 0: return True
# 平行线的距离大于两线距离, 为干扰线
if sum(lineDis) / 2 > slen + llen: return False
if lineMindis > 0 and lineDis < lineMindis: return False
if lineMaxdis > 0 and lineDis > lineMaxdis: return False
# 计算重合率
pt1 = longLine.getVertPt(shortLine.pt1)
pt2 = longLine.getVertPt(shortLine.pt2)
pt1.flag = longLine.isPtAtLineSeg(pt1)
pt2.flag = longLine.isPtAtLineSeg(pt2)
if pt1.flag and pt2.flag: return True # 完全重合的情况
if not pt1.flag and not pt2.flag: return False # 完全不重合的请教
# 计算重合率
tmpLine = GLine(pt1, pt2)
pt3 = longLine.pt1 if tmpLine.isPtAtLineSeg(longLine.pt1) else longLine.pt2
dis1 = pt3.distance(pt1) if pt1.flag else pt3.distance(pt2)
dis2 = pt1.distance(pt2)
return dis1 / dis2 > overlapRatio
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)
def getExtendLine(self, ratio=1.5):
midPt = self.midPoint()
x1, y1 = self.pt1.x + (self.pt1.x - midPt.x) * ratio, self.pt1.y + (self.pt1.y - midPt.y) * ratio
x2, y2 = self.pt2.x + (self.pt2.x - midPt.x) * ratio, self.pt2.y + (self.pt2.y - midPt.y) * ratio
return GLine().initXY(x1, y1, x2, y2)
def getParaLine(self, pt):
line = GLine()
line.a, line.b, line.c = self.a, self.b, -self.a * pt.fx - self.b * pt.fy
line.d = math.sqrt(line.a ** 2 + line.b ** 2)
return line
def getYAngle(self):
line = GLine()
line.a, line.b, line.c = 1, 0, 0
return self.angle(line)
def getXAngle(self):
line = GLine()
line.a, line.b, line.c = 0, 1, 0
return self.angle(line)
def midLine(self, line):
if checkAnyNone(self.pt1, self.pt2, line.pt1, line.pt2): return None
if self.pt1.distance(line.pt1) > self.pt1.distance(line.pt2):
return GLine(self.pt1.midpoint(line.pt2), self.pt2.midpoint(line.pt1))
else:
return GLine(self.pt1.midpoint(line.pt1), self.pt2.midpoint(line.pt2))