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@@ -197,6 +197,175 @@ def table_net(input_shape=(1152, 896, 3), num_classes=1):
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model = table_net((None, None, 3), 2)
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model = table_net((None, None, 3), 2)
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+def drawpixel(pred):
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+ import matplotlib.pyplot as plt
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+ _array = []
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+ for _h in range(len(pred)):
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+ _line = []
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+ for _w in range(len(pred[_h])):
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+ _prob = pred[_h][_w]
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+ if _prob[0]>0.5:
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+ _line.append((0,255,255))
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+ elif _prob[1]>0.5:
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+ _line.append((255,255,0))
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+ else:
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+ _line.append((255,255,255))
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+ _array.append(_line)
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+ plt.imshow(np.array(_array))
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+ plt.show()
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+
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+def points2lines(pred,sourceP_LB=True,prob=0.2,line_width=7,padding=3,min_len=10,cell_width=13):
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+
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+ def inBbox(bbox,point,line_width):
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+ x,y = point
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+ if x>=bbox[0]-line_width and x<=bbox[2]+line_width and y>=bbox[1]-line_width and y<=bbox[3]+line_width:
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+ return True,[min(x,bbox[0]),min(y,bbox[1]),max(x,bbox[2]),max(y,bbox[3])]
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+ return False,None
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+ height = len(pred)
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+ width = len(pred[0])
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+
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+ clust_horizontal = []
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+ clust_vertical = []
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+ h_index = -1
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+ _step = line_width
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+ _sum = list(np.sum(np.array((pred[...,1]>prob)).astype(int),axis=1))
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+ _last = False
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+ _current = False
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+ while 1:
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+ h_index += 1
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+ if h_index>=height:
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+ break
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+ w_index = -1
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+ if sourceP_LB:
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+ h_i = height-1-h_index
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+ else:
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+ h_i = h_index
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+ if _sum[h_index]<min_len:
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+ continue
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+ while 1:
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+ w_index += 2
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+ if w_index>=width:
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+ break
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+ _v,_h = pred[h_index][w_index]
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+
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+ if _h>prob:
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+
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+ _find = False
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+ _point = (w_index,h_i)
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+ for l_h_i in range(len(clust_vertical)):
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+ l_h = clust_vertical[len(clust_vertical)-l_h_i-1]
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+ bbox = l_h.get("bbox")
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+ b_in,_bbox = inBbox(bbox,_point,line_width)
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+ if b_in:
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+ _find = True
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+ l_h.get("points").append(_point)
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+ l_h["bbox"] = _bbox
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+ break
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+ if not _find:
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+ clust_vertical.append({"points":[_point],"bbox":[w_index,h_i,w_index,h_i]})
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+ w_index = -1
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+ _sum = list(np.sum(np.array((pred[...,0]>prob)).astype(int),axis=0))
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+ while 1:
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+ w_index += 1
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+ if w_index>=width:
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+ break
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+ h_index = -1
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+ if _sum[w_index]<min_len:
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+ continue
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+ while 1:
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+ h_index += 2
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+ if h_index>=height:
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+ break
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+
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+ if sourceP_LB:
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+ h_i = height-1-h_index
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+ else:
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+ h_i = h_index
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+
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+ _v,_h = pred[h_index][w_index]
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+
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+ if _v>prob:
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+ _find = False
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+ _point = (w_index,h_i)
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+ for l_h_i in range(len(clust_horizontal)):
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+ l_h = clust_horizontal[len(clust_horizontal)-l_h_i-1]
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+ bbox = l_h.get("bbox")
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+ b_in,_bbox = inBbox(bbox,_point,line_width)
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+ if b_in:
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+ _find = True
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+ l_h.get("points").append(_point)
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+ l_h["bbox"] = _bbox
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+ break
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+ if not _find:
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+ clust_horizontal.append({"points":[_point],"bbox":[w_index,h_i,w_index,h_i]})
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+
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+
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+
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+ tmp_vertical = []
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+ for _dict in clust_vertical:
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+ _bbox = _dict.get("bbox")
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+ if _bbox[2]-_bbox[0]>=min_len or _bbox[3]-_bbox[1]>=min_len:
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+ tmp_vertical.append([(_bbox[0]+_bbox[2])/2,_bbox[1]-padding,(_bbox[0]+_bbox[2])/2,_bbox[3]+padding])
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+ tmp_horizontal = []
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+ for _dict in clust_horizontal:
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+ _bbox = _dict.get("bbox")
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+ if _bbox[2]-_bbox[0]>=min_len or _bbox[3]-_bbox[1]>=min_len:
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+ tmp_horizontal.append([_bbox[0]-padding,(_bbox[1]+_bbox[3])/2,_bbox[2]+padding,(_bbox[1]+_bbox[3])/2])
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+
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+ #merge lines
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+ tmp_vertical.sort(key=lambda x:x[3],reverse=True)
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+ tmp_horizontal.sort(key=lambda x:x[0])
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+
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+ pop_index = []
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+ final_vertical = []
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+ for _line in tmp_vertical:
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+ _find = False
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+ x0,y0,x1,y1 = _line
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+ for _line2 in final_vertical:
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+ x2,y2,x3,y3 = _line2
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+ if abs(x0-x2)<line_width and abs(y0-y3)<cell_width or abs(y1-y2)<cell_width:
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+ _find = True
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+ final_vertical.append([x0,min(y0,y2),x1,max(y1,y3)])
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+ break
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+ if not _find:
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+ final_vertical.append(_line)
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+
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+ final_horizontal = []
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+ for _line in tmp_horizontal:
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+ _find = False
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+ x0,y0,x1,y1 = _line
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+ for _line2 in final_horizontal:
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+ x2,y2,x3,y3 = _line2
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+ if abs(y0-y2)<line_width and abs(x0-x3)<cell_width or abs(x1-x2)<cell_width:
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+ _find = True
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+ final_horizontal.append([min(x0,x2),y0,max(x1,x3),y1])
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+ break
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+ if not _find:
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+ final_horizontal.append(_line)
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+
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+ list_line = []
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+ for _line in final_vertical:
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+ list_line.append(_line)
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+ for _line in final_horizontal:
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+ list_line.append(_line)
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+
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+
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+ import matplotlib.pyplot as plt
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+ plt.figure()
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+ for _line in list_line:
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+ x0,y0,x1,y1 = _line
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+ plt.plot([x0,x1],[y0,y1])
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+ # for _line in list_line:
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+ # x0,y0,x1,y1 = _line.bbox
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+ # plt.plot([x0,x1],[y0,y1])
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+ # for point in list_crosspoints:
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+ # plt.scatter(point.get("point")[0],point.get("point")[1])
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+ plt.show()
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+
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+
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+
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+
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+
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def table_line(img, model, size=(512, 1024), hprob=0.5, vprob=0.5, row=50, col=30, alph=15):
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def table_line(img, model, size=(512, 1024), hprob=0.5, vprob=0.5, row=50, col=30, alph=15):
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sizew, sizeh = size
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sizew, sizeh = size
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# [..., ::-1] 最后一维内部反向输出
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# [..., ::-1] 最后一维内部反向输出
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@@ -210,6 +379,11 @@ def table_line(img, model, size=(512, 1024), hprob=0.5, vprob=0.5, row=50, col=3
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# logging.info("into table_line 2")
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# logging.info("into table_line 2")
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pred = pred[0]
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pred = pred[0]
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+ drawpixel(pred)
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+ _time = time.time()
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+ points2lines(pred)
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+ logging.info("points2lines takes %ds"%(time.time()-_time))
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+
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vpred = pred[..., 1] > vprob # 横线
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vpred = pred[..., 1] > vprob # 横线
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hpred = pred[..., 0] > hprob # 竖线
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hpred = pred[..., 0] > hprob # 竖线
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vpred = vpred.astype(int)
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vpred = vpred.astype(int)
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