本文介绍了如何在 HoughLinesP 之后合并行?的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着跟版网的小编来一起学习吧！
问题描述
我的任务是找到线(startX、startY、endX、endY)和矩形(4 线)的坐标.这是输入文件:
我使用下一个代码:
img = cv2.imread(image_src)灰色 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)ret, thresh1 = cv2.threshold(灰色,127,255,cv2.THRESH_BINARY)边缘 = cv2.Canny(thresh1,50,150,apertureSize = 3)minLineLength = 100最大线间隙 = 10线 = cv2.HoughLinesP(edges,1,np.pi/180,10,minLineLength,maxLineGap)打印(长度(行))对于行中的行:cv2.line(img,(line[0][0],line[0][1]),(line[0][2],line[0][3]),(0,0,255),6)我得到下一个结果:
从最后一张图片中，您可以看到大量的小红线.
问题:
- 合并小线条的最佳方法是什么?
- 为什么有很多HoughLinesP 检测不到的小部分?
解决方案
我终于完成了流水线:
- 修正了不正确的参数(Dan 建议)
- 开发了我自己的合并线段"算法.
还有 572 行.在我的合并线段"之后，我们只有 89 行
My task is to find coordinates of lines (startX, startY, endX, endY) and rectangles (4 lines). Here is input file:
I use the next code:
img = cv2.imread(image_src) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ret, thresh1 = cv2.threshold(gray,127,255,cv2.THRESH_BINARY) edges = cv2.Canny(thresh1,50,150,apertureSize = 3) minLineLength = 100 maxLineGap = 10 lines = cv2.HoughLinesP(edges,1,np.pi/180,10,minLineLength,maxLineGap) print(len(lines)) for line in lines: cv2.line(img,(line[0][0],line[0][1]),(line[0][2],line[0][3]),(0,0,255),6)I get the next results:
From the last image you can see big amount of small red lines.
Questions:
- What is the best way to merge small lines?
- Why there are a lot of small portions that are not detected by HoughLinesP?
解决方案I have finally completed the pipeline:
- fixed incorrect parameters (as were suggested by Dan)
- developed my own 'merging line segments' algorithm. I had bad results when I implemented TAVARES and PADILHA algorithm (as were suggested by Andrew).
- I have skipped Canny and got better results (as were suggested by Alexander)
Please find the code and results:
def get_lines(lines_in): if cv2.__version__ < '3.0': return lines_in[0] return [l[0] for l in lines_in] def process_lines(image_src): img = mpimg.imread(image_src) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ret, thresh1 = cv2.threshold(gray,127,255,cv2.THRESH_BINARY) thresh1 = cv2.bitwise_not(thresh1) edges = cv2.Canny(thresh1, threshold1=50, threshold2=200, apertureSize = 3) lines = cv2.HoughLinesP(thresh1, rho=1, theta=np.pi/180, threshold=50, minLineLength=50, maxLineGap=30) # l[0] - line; l[1] - angle for line in get_lines(lines): leftx, boty, rightx, topy = line cv2.line(img, (leftx, boty), (rightx,topy), (0,0,255), 6) # merge lines #------------------ # prepare _lines = [] for _line in get_lines(lines): _lines.append([(_line[0], _line[1]),(_line[2], _line[3])]) # sort _lines_x = [] _lines_y = [] for line_i in _lines: orientation_i = math.atan2((line_i[0][1]-line_i[1][1]),(line_i[0][0]-line_i[1][0])) if (abs(math.degrees(orientation_i)) > 45) and abs(math.degrees(orientation_i)) < (90+45): _lines_y.append(line_i) else: _lines_x.append(line_i) _lines_x = sorted(_lines_x, key=lambda _line: _line[0][0]) _lines_y = sorted(_lines_y, key=lambda _line: _line[0][1]) merged_lines_x = merge_lines_pipeline_2(_lines_x) merged_lines_y = merge_lines_pipeline_2(_lines_y) merged_lines_all = [] merged_lines_all.extend(merged_lines_x) merged_lines_all.extend(merged_lines_y) print("process groups lines", len(_lines), len(merged_lines_all)) img_merged_lines = mpimg.imread(image_src) for line in merged_lines_all: cv2.line(img_merged_lines, (line[0][0], line[0][1]), (line[1][0],line[1][1]), (0,0,255), 6) cv2.imwrite('prediction/lines_gray.jpg',gray) cv2.imwrite('prediction/lines_thresh.jpg',thresh1) cv2.imwrite('prediction/lines_edges.jpg',edges) cv2.imwrite('prediction/lines_lines.jpg',img) cv2.imwrite('prediction/merged_lines.jpg',img_merged_lines) return merged_lines_all def merge_lines_pipeline_2(lines): super_lines_final = [] super_lines = [] min_distance_to_merge = 30 min_angle_to_merge = 30 for line in lines: create_new_group = True group_updated = False for group in super_lines: for line2 in group: if get_distance(line2, line) < min_distance_to_merge: # check the angle between lines orientation_i = math.atan2((line[0][1]-line[1][1]),(line[0][0]-line[1][0])) orientation_j = math.atan2((line2[0][1]-line2[1][1]),(line2[0][0]-line2[1][0])) if int(abs(abs(math.degrees(orientation_i)) - abs(math.degrees(orientation_j)))) < min_angle_to_merge: #print("angles", orientation_i, orientation_j) #print(int(abs(orientation_i - orientation_j))) group.append(line) create_new_group = False group_updated = True break if group_updated: break if (create_new_group): new_group = [] new_group.append(line) for idx, line2 in enumerate(lines): # check the distance between lines if get_distance(line2, line) < min_distance_to_merge: # check the angle between lines orientation_i = math.atan2((line[0][1]-line[1][1]),(line[0][0]-line[1][0])) orientation_j = math.atan2((line2[0][1]-line2[1][1]),(line2[0][0]-line2[1][0])) if int(abs(abs(math.degrees(orientation_i)) - abs(math.degrees(orientation_j)))) < min_angle_to_merge: #print("angles", orientation_i, orientation_j) #print(int(abs(orientation_i - orientation_j))) new_group.append(line2) # remove line from lines list #lines[idx] = False # append new group super_lines.append(new_group) for group in super_lines: super_lines_final.append(merge_lines_segments1(group)) return super_lines_final def merge_lines_segments1(lines, use_log=False): if(len(lines) == 1): return lines[0] line_i = lines[0] # orientation orientation_i = math.atan2((line_i[0][1]-line_i[1][1]),(line_i[0][0]-line_i[1][0])) points = [] for line in lines: points.append(line[0]) points.append(line[1]) if (abs(math.degrees(orientation_i)) > 45) and abs(math.degrees(orientation_i)) < (90+45): #sort by y points = sorted(points, key=lambda point: point[1]) if use_log: print("use y") else: #sort by x points = sorted(points, key=lambda point: point[0]) if use_log: print("use x") return [points[0], points[len(points)-1]] # https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html # https://stackoverflow.com/questions/32702075/what-would-be-the-fastest-way-to-find-the-maximum-of-all-possible-distances-betw def lines_close(line1, line2): dist1 = math.hypot(line1[0][0] - line2[0][0], line1[0][0] - line2[0][1]) dist2 = math.hypot(line1[0][2] - line2[0][0], line1[0][3] - line2[0][1]) dist3 = math.hypot(line1[0][0] - line2[0][2], line1[0][0] - line2[0][3]) dist4 = math.hypot(line1[0][2] - line2[0][2], line1[0][3] - line2[0][3]) if (min(dist1,dist2,dist3,dist4) < 100): return True else: return False def lineMagnitude (x1, y1, x2, y2): lineMagnitude = math.sqrt(math.pow((x2 - x1), 2)+ math.pow((y2 - y1), 2)) return lineMagnitude #Calc minimum distance from a point and a line segment (i.e. consecutive vertices in a polyline). # https://nodedangles.wordpress.com/2010/05/16/measuring-distance-from-a-point-to-a-line-segment/ # http://paulbourke.net/geometry/pointlineplane/ def DistancePointLine(px, py, x1, y1, x2, y2): #http://local.wasp.uwa.edu.au/~pbourke/geometry/pointline/source.vba LineMag = lineMagnitude(x1, y1, x2, y2) if LineMag < 0.00000001: DistancePointLine = 9999 return DistancePointLine u1 = (((px - x1) * (x2 - x1)) + ((py - y1) * (y2 - y1))) u = u1 / (LineMag * LineMag) if (u < 0.00001) or (u > 1): #// closest point does not fall within the line segment, take the shorter distance #// to an endpoint ix = lineMagnitude(px, py, x1, y1) iy = lineMagnitude(px, py, x2, y2) if ix > iy: DistancePointLine = iy else: DistancePointLine = ix else: # Intersecting point is on the line, use the formula ix = x1 + u * (x2 - x1) iy = y1 + u * (y2 - y1) DistancePointLine = lineMagnitude(px, py, ix, iy) return DistancePointLine def get_distance(line1, line2): dist1 = DistancePointLine(line1[0][0], line1[0][1], line2[0][0], line2[0][1], line2[1][0], line2[1][1]) dist2 = DistancePointLine(line1[1][0], line1[1][1], line2[0][0], line2[0][1], line2[1][0], line2[1][1]) dist3 = DistancePointLine(line2[0][0], line2[0][1], line1[0][0], line1[0][1], line1[1][0], line1[1][1]) dist4 = DistancePointLine(line2[1][0], line2[1][1], line1[0][0], line1[0][1], line1[1][0], line1[1][1]) return min(dist1,dist2,dist3,dist4)There are still 572 lines. After my "merging line segments" we have only 89 lines
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