我就廢話不多說了，還是直接上代碼吧！

import osimport xml.dom.minidomimport cv2 as cv ImgPath = ’C:/Users/49691/Desktop/gangjin/gangjin_test/JPEGImages/’AnnoPath = ’C:/Users/49691/Desktop/gangjin/gangjin_test/Annotations/’ #xml文件地址save_path = ’’def draw_anchor(ImgPath,AnnoPath,save_path): imagelist = os.listdir(ImgPath) for image in imagelist: image_pre, ext = os.path.splitext(image) imgfile = ImgPath + image xmlfile = AnnoPath + image_pre + ’.xml’ # print(image) # 打開xml文檔 DOMTree = xml.dom.minidom.parse(xmlfile) # 得到文檔元素對象 collection = DOMTree.documentElement # 讀取圖片 img = cv.imread(imgfile) filenamelist = collection.getElementsByTagName('filename') filename = filenamelist[0].childNodes[0].data print(filename) # 得到標簽名為object的信息 objectlist = collection.getElementsByTagName('object') for objects in objectlist: # 每個object中得到子標簽名為name的信息 namelist = objects.getElementsByTagName(’name’) # 通過此語句得到具體的某個name的值 objectname = namelist[0].childNodes[0].data bndbox = objects.getElementsByTagName(’bndbox’) # print(bndbox) for box in bndbox:x1_list = box.getElementsByTagName(’xmin’)x1 = int(x1_list[0].childNodes[0].data)y1_list = box.getElementsByTagName(’ymin’)y1 = int(y1_list[0].childNodes[0].data)x2_list = box.getElementsByTagName(’xmax’) #注意坐標，看是否需要轉換x2 = int(x2_list[0].childNodes[0].data)y2_list = box.getElementsByTagName(’ymax’)y2 = int(y2_list[0].childNodes[0].data)cv.rectangle(img, (x1, y1), (x2, y2), (255, 255, 255), thickness=2)cv.putText(img, objectname, (x1, y1), cv.FONT_HERSHEY_COMPLEX, 0.7, (0, 255, 0), thickness=2)# cv.imshow(’head’, img)cv.imwrite(save_path+’/’+filename, img) #save picture

補充知識：深度學習python之用Faster-rcnn 檢測結果（txt文件） 在原圖畫出box

使用Faster-rcnn 的test_net.py 檢測網絡的mAP等精度會生成一個檢測結果（txt文件），格式如下：

000004 0.972 302.8 94.5 512.0 150.0000004 0.950 348.1 166.1 512.0 242.9000004 0.875 1.0 25.7 292.6 126.3000004 0.730 1.0 138.5 488.3 230.0000004 0.699 1.0 120.9 145.5 139.9000004 0.592 54.4 227.4 431.9 343.4000004 0.588 1.0 159.8 18.8 231.6000004 0.126 1.0 247.1 342.3 270.0000004 0.120 1.0 225.4 185.7 309.3

每行分別為 名稱 檢測概率 xmin ymin xmax ymax

問題在于每一行只顯示一個box數據，每幅圖像可能包括多個box，需要判斷提取的多行數據是不是屬于同一圖片

下面使用python提取這些數據，在原圖上畫出box并且保存起來

import osimport os.pathimport numpy as npimport xml.etree.ElementTree as xmlETfrom PIL import Image, ImageDrawimport cPickle as pickle txt_name = ’comp4_8a226fd7-753d-40fc-8013-f68d2a465579_det_test_ship.txt’file_path_img = ’/home/JPEGImages’save_file_path = ’/home/detect_results’source_file = open(txt_name)img_names = []for line in source_file: staff = line.split() img_name = staff[0] img_names.append(img_name)name_dict = {}for i in img_names: if img_names.count(i)>0: name_dict[i] = img_names.count(i) source_file.close()source_file = open(txt_name)for idx in name_dict: img = Image.open(os.path.join(file_path_img, idx + ’.jpg’)) draw = ImageDraw.Draw(img) for i in xrange(name_dict[idx]): line = source_file.readline() staff = line.split() score = staff[1] box = staff[2:6] draw.rectangle([int(np.round(float(box[0]))), int(np.round(float(box[1]))), int(np.round(float(box[2]))), int(np.round(float(box[3])))], outline=(255, 0, 0)) img.save(os.path.join(save_file_path, idx + ’.jpg’)) source_file.close()

運行完即可在保存文件夾中得到效果圖。

以上這篇python目標檢測給圖畫框,bbox畫到圖上并保存案例就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持好吧啦網。