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from scipy.io import loadmat
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import glob,os
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import xml.dom.minidom
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from tqdm import tqdm
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from creat_xml_fordetection import make_xml_iccv
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den_path = "../data_MPKFNet/data/VOC/VOCdevkit/VOCiccvtrain/ground_truth/"
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voc_path = "../data_MPKFNet/data/VOC/VOCdevkit/VOCiccvtrain/Annotations/"
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save_path = "../data_MPKFNet/data/VOC/VOCdevkit/VOCiccvtrain/Bird_detection/"
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def get_mat_point (path):
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mat_data = loadmat(path)
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positions = mat_data['image_info'][0, 0][0, 0][0]
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count = mat_data['image_info'][0][0][0][0][1][0][0]
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return positions
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def get_xml_box (path):
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dom = xml.dom.minidom.parse(path)
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objects = dom.getElementsByTagName("object")
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all_boxes = []
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for object in objects:
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bndbox = object.getElementsByTagName('bndbox')[0]
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xmin = bndbox.getElementsByTagName('xmin')[0]
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ymin = bndbox.getElementsByTagName('ymin')[0]
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xmax = bndbox.getElementsByTagName('xmax')[0]
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ymax = bndbox.getElementsByTagName('ymax')[0]
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xmin_data = int(xmin.childNodes[0].data)
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ymin_data = int(ymin.childNodes[0].data)
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xmax_data = int(xmax.childNodes[0].data)
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ymax_data = int(ymax.childNodes[0].data)
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all_boxes.append((xmin_data,ymin_data,xmax_data,ymax_data))
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sizes = dom.getElementsByTagName("size")
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for size in sizes:
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w = size.getElementsByTagName('width')[0]
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w_data = int(w.childNodes[0].data)
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h = size.getElementsByTagName('height')[0]
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h_data = int(h.childNodes[0].data)
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return all_boxes ,w_data,h_data
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def point_in_box (site,box):
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flag = False
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if (site[0]>=box[0]) and (site[0]<=box[2]) and (site[1]>=box[1]) and (site[1]<=box[3]):
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flag = True
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return flag
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def ifsame_box(site1,site2,box):
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centerx = (box[0]+box[2])/2
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centery = (box[1]+box[3])/2
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dis1 = ((site1[0]-centerx)*(site1[0]-centerx) +(site1[1]-centery)*(site1[1]-centery))**0.5
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dis2 = ((site2[0] - centerx) * (site2[0] - centerx) + (site2[1] - centery) * (site2[1] - centery))**0.5
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if dis1>dis2:
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flag = True
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else:
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flag = False
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return flag
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def get_new(site,boxes,img_w,img_h):
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min_dis = 10e10
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min_index = 0
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for i in range(len(boxes)):
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centerx = (boxes[i][0] + boxes[i][2]) / 2
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centery = (boxes[i][1] + boxes[i][3]) / 2
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dis = ((site[0]-centerx)*(site[0]-centerx) +(site[1]-centery)*(site[1]-centery))**0.5
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if dis < min_dis:
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min_dis = dis
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min_index = i
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w = (boxes[min_index][2]-boxes[min_index][0])/2
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h = (boxes[min_index][3]-boxes[min_index][1])/2
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xmin = site[0]-w
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if xmin <0:
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xmin =0
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xmax = site[0]+w
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if xmax >img_w:
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xmax = img_w-1
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ymin = site[1]-h
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if ymin<0:
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ymin = 0
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ymax = site[1]+h
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if ymax >img_h:
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ymax = img_h-1
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return ((xmin,ymin,xmax,ymax))
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if __name__ == '__main__':
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xml_lists = os.listdir(voc_path)
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for xml_path in tqdm(xml_lists) :
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print(xml_path+"is doing")
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mat_path = xml_path.replace(".xml",".mat").replace("HatIMG","GT_HatIMG")
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ALL_points = get_mat_point(den_path+mat_path)
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ALl_boxes, image_W,image_H = get_xml_box (voc_path+xml_path)
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ALL_point_box = [] #点对应的边框的编号
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No_Label_point = []
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Yes_Label_point = []
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Yes_Box_index = []
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###############步1 完成对ALL_point的所有点的遍历，寻找每个点对应的box在ALL_boxes中的索引，最终得到一个list ALL_point_box 代表每个点对应的box的索引，没有的是100000
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for i in range(len(ALL_points)):
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ALL_point_box.append(100000)
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for j in range(len(ALl_boxes)):
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if point_in_box(ALL_points[i],ALl_boxes[j]): #判断是否在边框里面，是的话就将边框的编号放到点对应边框的数组里面，否则该点对应的边框为100000
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ALL_point_box[i]=j
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break
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#########################################步2.1 筛选出来准标注的和未标注的
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for i in range(len(ALL_point_box)):
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if ALL_point_box[i]==100000:
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No_Label_point.append(ALL_points[i])
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else:
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Yes_Label_point.append(ALL_points[i])
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Yes_Box_index.append(ALL_point_box[i])
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###################步2.2对已标记的点进行遍历，去除掉具有重复边框的点
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for i in range(5):
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for i in range(len(Yes_Label_point)):
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if i >= len(Yes_Label_point):
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break
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else:
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for j in range(i+1,len(Yes_Label_point)):
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#print(len(Yes_Label_point))
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if j >= len(Yes_Label_point):
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break
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else:
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if Yes_Box_index[i] == Yes_Box_index[j]: #当前点和遍历点是一样的
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if ifsame_box(Yes_Label_point[i],Yes_Label_point[j],ALl_boxes[Yes_Box_index[i]]):
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No_Label_point.append(Yes_Label_point[j])
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del Yes_Label_point[j]
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Yes_Box_index.remove(Yes_Box_index[j])
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else:
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No_Label_point.append(Yes_Label_point[i])
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del Yes_Label_point[i]
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Yes_Box_index.remove(Yes_Box_index[i])
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break
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##########步3推理未标记的点的边框信息 对No_Laber_point进行遍历
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final_boxes = ALl_boxes
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for i in range(len(No_Label_point)):
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final_boxes.append(get_new(No_Label_point[i],ALl_boxes,image_W,image_H))
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#########步4存储为xml
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make_xml_iccv(xml_path.replace(".xml",".jpg"),image_H,image_W,final_boxes,save_path+xml_path)
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print(final_boxes)
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