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@@ -33,13 +33,13 @@ def get_average_xy(list_input):
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xmax = float(blub[2])
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if float(xmax)-float(xmin) > 1.5*(float(ymax)-float(ymin)):
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ausrichtung = 0 #horizontal
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- print("horizontal")
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+ #print("horizontal")
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if 1.5*(float(xmax)-float(xmin)) < float(ymax)-float(ymin):
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ausrichtung = 1 #vertikal
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- print("vertikal")
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+ #print("vertikal")
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else:
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ausrichtung = 3 #sonstiges
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- print("sonstiges")
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+ #print("sonstiges")
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xavg_elem = xavg_elem/len(element)
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#print(xavg_elem)
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yavg_elem = yavg_elem/len(element)
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@@ -57,8 +57,26 @@ def get_average_xy(list_input):
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#print(new_list)
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return csv_name
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-def intersects(self, other):
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- return not (self.top_right.x < other.bottom_left.x or self.bottom_left.x > other.top_right.x or self.top_right.y < other.bottom_left.y or self.bottom_left.y > other.top_right.y)
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+def intersects(rectangle1, rectangle2): #using the separating axis theorem
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+ #print(rectangle2[0])
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+ #for rect in rectangle1:
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+
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+ rect_1_min = eval(rectangle1[0])
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+ rect_1_max = eval(rectangle1[3])
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+ rect1_bottom_left_x= rect_1_min[0]
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+ rect1_top_right_x=rect_1_max[0]
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+ rect1_bottom_left_y= rect_1_max[1]
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+ rect1_top_right_y= rect_1_min[1]
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+
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+ rect_2_min = eval(rectangle2[0])
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+ rect_2_max = eval(rectangle2[3])
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+ rect2_bottom_left_x= rect_2_min[0]
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+ rect2_top_right_x=rect_2_max[0]
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+ rect2_bottom_left_y= rect_2_max[1]
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+ rect2_top_right_y=rect_2_min[1]
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+
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+ return not (rect1_top_right_x < rect2_bottom_left_x or rect1_bottom_left_x > rect2_top_right_x or rect1_top_right_y > rect2_bottom_left_y or rect1_bottom_left_y < rect2_top_right_y)
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+
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def dist(rectangle1, rectangle2):
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#get minimal distance between two rectangles
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@@ -77,11 +95,14 @@ def dist(rectangle1, rectangle2):
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distance = dist
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if rectangle1[4] != rectangle2[4]:
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distance = dist + 100
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- #print(rectangle2[4], "- ", rectangle1[4])
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+ print(intersects(rectangle1,rectangle2))
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+ if intersects(rectangle1, rectangle2):
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+ distance = 0
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+ #print(rectangle1)
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return distance
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def clustering(distance_matrix):
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- db = DBSCAN(eps=0.0001, min_samples=1, metric="precomputed").fit(dm) ##3.93 until now, bei 5 shon mehr erkannt, 7 noch mehr erkannt aber auch schon zu viel; GV12 ist 4.5 gut für LH zu wenig
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+ db = DBSCAN(eps=0.001, min_samples=1, metric="precomputed").fit(dm) ##3.93 until now, bei 5 shon mehr erkannt, 7 noch mehr erkannt aber auch schon zu viel; GV12 ist 4.5 gut für LH zu wenig
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#db = OPTICS(min_samples=1,xi=0.1, metric="precomputed").fit(dm)
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labels = db.labels_
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# Number of clusters in labels
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@@ -97,10 +118,11 @@ def clustering(distance_matrix):
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#file = "/home/bscheibel/PycharmProjects/dxf_reader/drawings/5152166_Rev04.html"
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file = "/home/bscheibel/PycharmProjects/dxf_reader/drawings/5129275_Rev01-GV12.html"
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result = order_bounding_boxes_in_each_block.get_bound_box(file)
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-print(result)
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-"""get_average_xy(result)
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+#print(result)
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+get_average_xy(result)
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#rectangle1 = [[450,286],[464,286],[450,376],[464,376]]
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#rectangle2 = [[450,316],[456,316],[450,329],[456,329]]
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+#rectangle3 = [[23,45],[35,45],[23,60],[35,60]]
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#print(dist(rectangle1,rectangle2))
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data = pandas.read_csv("/home/bscheibel/PycharmProjects/dxf_reader/temporary/list_to_csv_with_corner_points.csv", sep=";")
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data = data[["point_xmi_ymi","point_xma_ymi","point_xmi_yma","point_xma_yma","ausrichtung"]].replace("'","")
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@@ -112,5 +134,7 @@ with open('blub.csv') as csvfile:
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result = list(readCSV)
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dm = np.asarray([[dist(p1, p2) for p2 in result] for p1 in result])
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-#print(dm)
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-clustering(dm)"""
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+
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+with np.printoptions(threshold=np.inf):
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+ print(dm)
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+clustering(dm)
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bscheibel