├── config ├── __init__.py ├── config.pyc ├── __init__.pyc └── config.py ├── detection ├── __init__.py ├── nms.pyc ├── demoy.pyc ├── __init__.pyc ├── demo │ ├── detect_131.jpg │ └── detect_2889686_134437546000_2.jpg ├── calc_regionratio.py ├── demoy.py ├── objdetect.py ├── producePostxt.py ├── create_annotation.py ├── make_annotations.py ├── train_cascade.py └── nms.py ├── data ├── image │ └── pos │ │ ├── pos_1.jpg │ │ ├── pos_2.jpg │ │ ├── pos_3.jpg │ │ ├── pos_4.jpg │ │ └── pos_5.jpg └── model │ ├── params.xml │ ├── stage0.xml │ ├── stage1.xml │ ├── stage2.xml │ ├── stage3.xml │ ├── stage4.xml │ ├── stage5.xml │ ├── stage6.xml │ ├── stage7.xml │ ├── stage8.xml │ ├── stage10.xml │ ├── stage9.xml │ ├── stage11.xml │ ├── stage13.xml │ ├── stage12.xml │ ├── stage15.xml │ ├── stage14.xml │ └── cascade.xml ├── .idea ├── misc.xml ├── inspectionProfiles │ └── profiles_settings.xml ├── modules.xml ├── lantern.iml └── workspace.xml └── ReadMe.md /config/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /detection/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /config/config.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gongxijun/lantern/HEAD/config/config.pyc -------------------------------------------------------------------------------- /detection/nms.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gongxijun/lantern/HEAD/detection/nms.pyc -------------------------------------------------------------------------------- /config/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gongxijun/lantern/HEAD/config/__init__.pyc -------------------------------------------------------------------------------- /detection/demoy.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gongxijun/lantern/HEAD/detection/demoy.pyc -------------------------------------------------------------------------------- /config/config.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | # 设置项目的绝对路径 3 | pro_path = '/home/gongxijun/data/lantern' 4 | -------------------------------------------------------------------------------- /detection/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gongxijun/lantern/HEAD/detection/__init__.pyc -------------------------------------------------------------------------------- /data/image/pos/pos_1.jpg: 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-------------------------------------------------------------------------------- /data/image/pos/pos_5.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gongxijun/lantern/HEAD/data/image/pos/pos_5.jpg -------------------------------------------------------------------------------- /detection/demo/detect_131.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gongxijun/lantern/HEAD/detection/demo/detect_131.jpg -------------------------------------------------------------------------------- /detection/demo/detect_2889686_134437546000_2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gongxijun/lantern/HEAD/detection/demo/detect_2889686_134437546000_2.jpg -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 7 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/lantern.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 11 | -------------------------------------------------------------------------------- /data/model/params.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | BOOST 5 | LBP 6 | 128 7 | 90 8 | 9 | GAB 10 | 9.9500000476837158e-01 11 | 5.0000000000000000e-01 12 | 9.4999999999999996e-01 13 | 1 14 | 100 15 | 16 | 256 17 | 1 18 | 19 | -------------------------------------------------------------------------------- /detection/calc_regionratio.py: -------------------------------------------------------------------------------- 1 | #!/usr/local/bin/python 2 | # -*- coding: utf-8 -*- 3 | 4 | import argparse 5 | import os 6 | import sqlite3 7 | import sys 8 | from pprint import pprint 9 | 10 | import numpy as np 11 | 12 | 13 | def connectdb(dbname): 14 | if os.path.exists(dbname): 15 | db = sqlite3.connect(dbname) 16 | db.row_factory = sqlite3.Row 17 | else: 18 | print('cannnot open %s' % (dbname,)) 19 | return None 20 | return db 21 | 22 | 23 | def calcratio(db): 24 | try: 25 | sql = 'SELECT width, height FROM samples WHERE status=200' 26 | rv = db.execute(sql) 27 | ratio = [] 28 | for r in rv: 29 | ratio.append(float(r['width']) / float(r['height'])) 30 | arr = np.array(ratio) 31 | print(np.mean(arr), np.amax(arr), np.amin(arr), np.median(arr)) 32 | except sqlite3.OperationalError as e: 33 | print(e) 34 | sys.exit(-1) 35 | 36 | 37 | if __name__ == '__main__': 38 | dbname = 'db/samples.db' 39 | db = connectdb(dbname) 40 | calcratio(db) 41 | -------------------------------------------------------------------------------- /data/model/stage0.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 6 5 | -1.4431272745132446e+00 6 | 7 | <_> 8 | 9 | 0 -1 1259 -137363506 -33559597 1376111494 2002213727 -305401665 10 | 1408209595 1298137071 1946153983 11 | 12 | -6.8031120300292969e-01 4.7004294395446777e-01 13 | <_> 14 | 15 | 0 -1 3616514 -805634081 -1537 -787057 502661087 266919855 16 | -411905 -1049633 1610088447 17 | 18 | -5.0030064582824707e-01 4.8760640621185303e-01 19 | <_> 20 | 21 | 0 -1 3428315 -596164589 -73796737 -494004739 -11489 -27377664 22 | -11588897 -356856098 -252784641 23 | 24 | -5.0489288568496704e-01 4.8167842626571655e-01 25 | <_> 26 | 27 | 0 -1 81577 -1935416414 -1345689632 -237247665 -2886448 -35918885 28 | 1871677057 2070641663 -605554699 29 | 30 | -5.2462232112884521e-01 4.2182406783103943e-01 31 | <_> 32 | 33 | 0 -1 587 -621488658 -791228766 -2051455591 1280726020 34 | -1827710378 -770813393 1464601382 1602180199 35 | 36 | -5.5705660581588745e-01 3.5474479198455811e-01 37 | <_> 38 | 39 | 0 -1 3613807 1190461311 -1073884369 -102908653 221567747 40 | 1257699815 -88382477 -1052898 183762943 41 | 42 | -5.2202731370925903e-01 3.3614942431449890e-01 43 | 44 | -------------------------------------------------------------------------------- /detection/demoy.py: -------------------------------------------------------------------------------- 1 | #!/usr/local/bin/python 2 | # -*- coding: utf-8 -*- 3 | 4 | import argparse 5 | import os 6 | import re 7 | import sys 8 | import cv2 as cv 9 | from nms import nms 10 | import numpy as np 11 | 12 | cnt = 0; 13 | 14 | 15 | def parsearguments(): 16 | parser = argparse.ArgumentParser(description='object detection using cascade classifier') 17 | parser.add_argument('-i', '--image', help='image file name', default='/home/gongxijun/data') 18 | parser.add_argument('-c', '--cascade', dest='cascadefilename', help='cascade file name', 19 | default='/home/gongxijun/data/lantern/data/model/cascade.xml') 20 | parser.add_argument('-s', '--scalefactor', dest='scalefactor', type=float, default=1.1) 21 | parser.add_argument('-m', '--minneighbors', dest='minneighbors', type=int, default=3) 22 | parser.add_argument('-o', '--output', dest='output', 23 | default='box/detect.jpg') 24 | return parser.parse_args() 25 | 26 | 27 | def detect(img, cascadefilename, scalefactor, minneighbors): 28 | global cnt 29 | srcimg = img 30 | if srcimg is None: 31 | print('cannot load image') 32 | sys.exit(-1) 33 | cascade = cv.CascadeClassifier(cascadefilename) 34 | # print cascade 35 | objects = cascade.detectMultiScale(srcimg, scalefactor, minneighbors) 36 | _num_status = True 37 | while _num_status and len(objects) > 0: 38 | objects, _num_status = nms(list(objects), 0.6) 39 | count = len(objects) 40 | if count > 0: 41 | cnt += 1; 42 | print('detection count: %s' % (count,)) 43 | # print (objects) 44 | for (x, y, w, h) in (objects): 45 | # print(x, y, w, h) 46 | cv.rectangle(srcimg, (x, y), (x + w, y + h), (0, 0, 255), 2) 47 | return srcimg 48 | 49 | 50 | args = parsearguments() 51 | print('cascade file: %s' % (args.cascadefilename,)) 52 | 53 | def showImage(img): 54 | result=detect(img, args.cascadefilename, 55 | args.scalefactor, args.minneighbors) 56 | return result 57 | -------------------------------------------------------------------------------- /data/model/stage1.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 7 5 | -1.2098675966262817e+00 6 | 7 | <_> 8 | 9 | 0 -1 9869 -137625650 -212639453 323744174 -251138257 -766531073 10 | 325006251 1155125195 1465883311 11 | 12 | -6.9039869308471680e-01 2.6776158809661865e-01 13 | <_> 14 | 15 | 0 -1 227 713206688 -1885148288 -794307628 -202389760 -606112769 16 | -1153187597 -593497477 -69212203 17 | 18 | -6.7114043235778809e-01 3.0477219820022583e-01 19 | <_> 20 | 21 | 0 -1 3522329 -46997248 -1593577139 -1589460794 690676171 22 | -2137997280 -1039548407 -1467289087 -1490290689 23 | 24 | -5.0339454412460327e-01 3.6862725019454956e-01 25 | <_> 26 | 27 | 0 -1 3613806 1070645231 -43033 1984942019 164666813 1306582671 28 | -1847001378 -6300147 267647407 29 | 30 | -5.3666716814041138e-01 3.5385552048683167e-01 31 | <_> 32 | 33 | 0 -1 3372945 -564278194 -671109025 -2077848990 1356715391 34 | -629477376 -1308589933 -889200514 -789317905 35 | 36 | -4.5275869965553284e-01 3.8935306668281555e-01 37 | <_> 38 | 39 | 0 -1 78011 -11862066 -546581842 1870249358 -32717362 1159434716 40 | 21604953 -873477188 -539194412 41 | 42 | -4.9909380078315735e-01 3.6912557482719421e-01 43 | <_> 44 | 45 | 0 -1 3616513 -269090881 -2305569 -275306719 529595703 46 | -1897612293 -135398977 -67113010 267567039 47 | 48 | -4.4046744704246521e-01 3.8484367728233337e-01 49 | 50 | -------------------------------------------------------------------------------- /ReadMe.md: -------------------------------------------------------------------------------- 1 | 使用opencv中的haar+adaboost进行物体检测,文件目录如下,目录介绍：　　 2 | . 3 | ├── config 4 | │   ├── config.py 5 | │   ├── config.pyc 6 | │   ├── __init__.py 7 | │   └── __init__.pyc 8 | ├── data 9 | │   ├── image 10 | │   │   ├── neg-->负样本 11 | │   │   │   ├── neg_1.jpg 12 | │   │   │   ├── neg_2.jpg 13 | │   │   │   ├── neg_3.jpg 14 | │   │   │   ├── neg_4.jpg 15 | │   │   │   └── neg_5.jpg 16 | │   │   ├── neg.txt-->负样本文件目录 17 | │   │   ├── pos-->正样本 18 | │   │   │   ├── pos_1.jpg 19 | │   │   │   ├── pos_2.jpg 20 | │   │   │   ├── pos_3.jpg 21 | │   │   │   ├── pos_4.jpg 22 | │   │   │   └── pos_5.jpg 23 | │   │   └── pos.txt-->正样本文件目录 24 | │   └── model 25 | │   ├── cascade.xml 26 | │   ├── params.xml 27 | │   ├── stage0.xml 28 | │   ├── stage10.xml 29 | │   ├── stage11.xml 30 | │   ├── stage12.xml 31 | │   ├── stage13.xml 32 | │   ├── stage14.xml 33 | │   ├── stage15.xml 34 | │   ├── stage1.xml 35 | │   ├── stage2.xml 36 | │   ├── stage3.xml 37 | │   ├── stage4.xml 38 | │   ├── stage5.xml 39 | │   ├── stage6.xml 40 | │   ├── stage7.xml 41 | │   ├── stage8.xml 42 | │   └── stage9.xml 43 | ├── detection 44 | │   ├── calc_regionratio.py 45 | │   ├── create_annotation.py 46 | │   ├── demo 47 | │   │   ├── detect_01300000115459120894838850378.jpg 48 | │   │   ├── detect_01300000180919124256698376723.jpg 49 | │   │   ├── detect_01300000314631128246170511243.jpg 50 | │   │   ├── detect_131.jpg 51 | │   │   ├── detect_19300001287046131622747012866.jpg 52 | │   │   ├── detect_20071014171857166_2.jpg 53 | │   │   ├── detect_20081016103122407_2.jpg 54 | │   │   ├── detect_2889686_134437546000_2.jpg 55 | │   │   ├── detect_5451374_173506368112_2.jpg 56 | │   │   ├── detect_5736135_094819032909_2.jpg 57 | │   │   ├── detect_7953837_193544583114_2.jpg 58 | │   │   └── detect_th.jpg 59 | │   ├── demoy.py 60 | │   ├── demoy.pyc 61 | │   ├── __init__.py 62 | │   ├── __init__.pyc 63 | │   ├── make_annotations.py 64 | │   ├── nms.py 65 | │   ├── nms.pyc 66 | │   ├── objdetect.py 67 | │   ├── producePostxt.py 　-->产生样本文件. 68 | │   ├── train_cascade.py 　-->训练 69 | │   └── vec 70 | │   └── pos.txt.vec 71 | └── ReadMe.md 72 | 73 | 效果图： 74 | -------------------------------------------------------------------------------- /detection/objdetect.py: -------------------------------------------------------------------------------- 1 | #!/usr/local/bin/python 2 | # -*- coding: utf-8 -*- 3 | 4 | import argparse 5 | import os 6 | import re 7 | import sys 8 | import cv2 as cv 9 | from nms import nms 10 | import numpy as np 11 | 12 | cnt = 0; 13 | 14 | 15 | def parsearguments(): 16 | parser = argparse.ArgumentParser(description='object detection using cascade classifier') 17 | parser.add_argument('-i', '--image', help='image file name', default='/home/gongxijun/data') 18 | parser.add_argument('-c', '--cascade', dest='cascadefilename', help='cascade file name', 19 | default='/home/gongxijun/data/lantern/data/model/cascade.xml') 20 | parser.add_argument('-s', '--scalefactor', dest='scalefactor', type=float, default=1.1) 21 | parser.add_argument('-m', '--minneighbors', dest='minneighbors', type=int, default=3) 22 | parser.add_argument('-o', '--output', dest='output', 23 | default='box/detect.jpg') 24 | return parser.parse_args() 25 | 26 | 27 | def detect(imagefilename, cascadefilename, scalefactor, minneighbors): 28 | global cnt 29 | srcimg = cv.imread(imagefilename) 30 | if srcimg is None: 31 | print('cannot load image') 32 | sys.exit(-1) 33 | cascade = cv.CascadeClassifier(cascadefilename) 34 | # print cascade 35 | objects = cascade.detectMultiScale(srcimg, scalefactor, minneighbors) 36 | _num_status = True 37 | while _num_status and len(objects) > 0: 38 | objects, _num_status = nms(list(objects), 0.6) 39 | count = len(objects) 40 | if count > 0: 41 | cnt += 1; 42 | print('detection count: %s' % (count,)) 43 | # print (objects) 44 | for (x, y, w, h) in (objects): 45 | # print(x, y, w, h) 46 | cv.rectangle(srcimg, (x, y), (x + w, y + h), (0, 0, 255), 2) 47 | return srcimg 48 | 49 | 50 | if __name__ == '__main__': 51 | args = parsearguments() 52 | print('cascade file: %s' % (args.cascadefilename,)) 53 | lists = ['/home/gongxijun/data/dataset/neg', '/home/gongxijun/data/222'] 54 | imagedir = lists[-1] 55 | pattern = re.compile('.*[.](jpg|jpeg|png|bmp|gif)$') 56 | images = [image for image in os.listdir(imagedir) if re.match(pattern, image)] 57 | print('target files: %s' % (len(images),)) 58 | for i, image in enumerate(images): 59 | imagesrc = os.path.join(imagedir, images[i]) 60 | result = detect(imagesrc, args.cascadefilename, 61 | args.scalefactor, args.minneighbors) 62 | dstfilename = './demo/detect_%s' % (images[i],) 63 | cv.imwrite(dstfilename, result) 64 | print '@@@@@@@@@@@@@@@@@@@@@ {}/{}'.format(cnt, i) 65 | -------------------------------------------------------------------------------- /data/model/stage2.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 9 5 | -1.1269989013671875e+00 6 | 7 | <_> 8 | 9 | 0 -1 1226 -69230042 -532993 545300640 1910627319 -204734993 10 | -241329734 1152128751 -237634561 11 | 12 | -6.2409824132919312e-01 2.4164426326751709e-01 13 | <_> 14 | 15 | 0 -1 3616509 -805961809 -76546561 -6425745 -806241321 1240529915 16 | -1057985 -3670173 1542586363 17 | 18 | -4.5240753889083862e-01 4.2520290613174438e-01 19 | <_> 20 | 21 | 0 -1 3427157 -59883497 -144836710 1686755149 1536678807 22 | -353398754 1787435691 -88413526 -254087425 23 | 24 | -4.5018905401229858e-01 3.8418895006179810e-01 25 | <_> 26 | 27 | 0 -1 2511 -411053114 -16551522 285599693 1931949967 -1656369477 28 | -218255288 290399807 870036171 29 | 30 | -4.9687308073043823e-01 3.1973978877067566e-01 31 | <_> 32 | 33 | 0 -1 3614214 1207107583 -70517249 -1120583893 197721567 34 | 160485259 -59053062 -148918421 198328251 35 | 36 | -4.7911062836647034e-01 3.2857897877693176e-01 37 | <_> 38 | 39 | 0 -1 29903 -1566055450 -965852446 -1517588446 2137522068 40 | -219421709 -1376987715 1128902755 -704649401 41 | 42 | -4.4380256533622742e-01 3.3845117688179016e-01 43 | <_> 44 | 45 | 0 -1 3475803 -1305216606 385921792 -1107887311 -279052320 46 | -1397395 656588784 -23339552 -101851175 47 | 48 | -3.4966492652893066e-01 4.0131226181983948e-01 49 | <_> 50 | 51 | 0 -1 60015 -6330272 1511355598 -928586228 206594956 1414414404 52 | 443044726 13256783 -286101922 53 | 54 | -5.1629424095153809e-01 2.6675820350646973e-01 55 | <_> 56 | 57 | 0 -1 3616506 1341521271 -285223425 -36306984 257548101 197156859 58 | -1078263974 -536872162 267632511 59 | 60 | -4.4815850257873535e-01 3.1016173958778381e-01 61 | 62 | -------------------------------------------------------------------------------- /detection/producePostxt.py: -------------------------------------------------------------------------------- 1 | #!/usr/local/bin/python 2 | # -*- coding: utf-8 -*- 3 | 4 | import argparse 5 | import re 6 | import os 7 | import sqlite3 8 | import sys 9 | import codecs 10 | from config import config as cf 11 | from PIL import Image 12 | from skimage import transform, color 13 | import cv2 as cv 14 | 15 | 16 | def produceNegTxt(filedirpath, imagedir, filename='neg.txt'): 17 | filedirpath = os.path.join(cf.pro_path, filedirpath); 18 | imagedir = os.path.join(cf.pro_path, imagedir); 19 | if not os.path.exists(filedirpath): 20 | os.makedirs(filedirpath) 21 | if not os.path.exists(imagedir): 22 | print 'image dir is not exists ~' 23 | sys.exit(-1) 24 | pattern = re.compile('.*[.](jpg|jpeg|png)$') 25 | images = [image for image in os.listdir(imagedir) if re.match(pattern, image)] 26 | with codecs.open(os.path.join(filedirpath, filename), 'w', encoding='utf-8') as fw: 27 | for i, image in enumerate(images): 28 | try: 29 | imagesrc = os.path.join(imagedir, images[i]) 30 | os.rename(imagesrc, os.path.join(imagedir, 'neg_' + str(i + 1) + '.jpg')) 31 | print(imagesrc) 32 | fw.write('{}\n'.format(os.path.join('/neg', 'neg_' + str(i + 1) + '.jpg'))); # x,y,w,h,status 33 | except Exception, e: 34 | print e 35 | 36 | 37 | def producePosTxt(filedirpath, imagedir, filename='pos.txt'): 38 | filedirpath = os.path.join(cf.pro_path, filedirpath); 39 | imagedir = os.path.join(cf.pro_path, imagedir); 40 | if not os.path.exists(filedirpath): 41 | os.makedirs(filedirpath) 42 | if not os.path.exists(imagedir): 43 | print 'image dir is not exists ~' 44 | sys.exit(-1) 45 | pattern = re.compile('.*[.](jpg|jpeg|png)$') 46 | images = [image for image in os.listdir(imagedir) if re.match(pattern, image)] 47 | with codecs.open(os.path.join(filedirpath, filename), 'w', encoding='utf-8') as fw: 48 | for i, image in enumerate(images): 49 | try: 50 | imagesrc = os.path.join(imagedir, images[i]) 51 | tmp_img = cv.imread(imagesrc) 52 | #tmp_img = transform.resize(tmp_img,(128,90)); 53 | #cv.imwrite(imagesrc,tmp_img*255); 54 | #os.rename(imagesrc, os.path.join(imagedir, 'pos_' + str(i + 1) + '.jpg')) 55 | print(imagesrc) 56 | fw.write('{} {} {} {} {} {}\n'.format(imagesrc, 1, 0, 0, 57 | tmp_img.shape[1], 58 | tmp_img.shape[0], 59 | 1)); # status,x,y,w,h 60 | except Exception, e: 61 | print e 62 | 63 | 64 | if __name__ == '__main__': 65 | # produceNegTxt('data/image', 'data/image/neg') 66 | producePosTxt('data/image', 'data/image/pos') 67 | -------------------------------------------------------------------------------- /data/model/stage3.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 10 5 | -1.5669338703155518e+00 6 | 7 | <_> 8 | 9 | 0 -1 6697 -812912661 -271670334 -440140816 -35914812 -444858373 10 | -337655401 -33564681 -4205577 11 | 12 | -5.3180831670761108e-01 2.8578004240989685e-01 13 | <_> 14 | 15 | 0 -1 3616508 -808255525 -264197 -170690741 223412223 197328831 16 | -524817 -1310917 267647987 17 | 18 | -4.7518107295036316e-01 3.1859636306762695e-01 19 | <_> 20 | 21 | 0 -1 2682506 1018833920 2147150045 -191300584 -54600983 22 | 1827041280 -13899617 751206030 -1602554113 23 | 24 | -6.4279401302337646e-01 2.4397149682044983e-01 25 | <_> 26 | 27 | 0 -1 926099 889696492 864924665 291088396 834116155 -1173583890 28 | 587639147 -1471174266 857715855 29 | 30 | -6.2343305349349976e-01 2.5955408811569214e-01 31 | <_> 32 | 33 | 0 -1 3613849 -957286657 -1275475977 -665104443 231856981 34 | 301329019 -145237033 -100928177 1207295967 35 | 36 | -4.3994808197021484e-01 3.2169348001480103e-01 37 | <_> 38 | 39 | 0 -1 55069 -14691793 1484440454 303752704 -902151630 1731876386 40 | 1651384595 -14484922 -473704665 41 | 42 | -3.9100563526153564e-01 3.7136620283126831e-01 43 | <_> 44 | 45 | 0 -1 1553684 1933386479 1600452575 1440690830 -1612907784 46 | 1202622617 1930176926 1661775766 -9913912 47 | 48 | -3.9102047681808472e-01 3.0988332629203796e-01 49 | <_> 50 | 51 | 0 -1 159686 -380109849 -4393505 2143125001 1741092093 52 | -1261764609 -100849171 -153171201 1777719279 53 | 54 | -3.1870624423027039e-01 3.8334307074546814e-01 55 | <_> 56 | 57 | 0 -1 3561125 -830499838 -548753105 -896037366 12838663 58 | 1801064450 143499289 -1568497074 -150995749 59 | 60 | -3.9560940861701965e-01 3.2942935824394226e-01 61 | <_> 62 | 63 | 0 -1 545024 -1974850528 -1574555118 -1895119404 402100624 64 | -70395406 -1080389141 -1348813316 -582134411 65 | 66 | -5.5045163631439209e-01 2.0038785040378571e-01 67 | 68 | -------------------------------------------------------------------------------- /data/model/stage4.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 10 5 | -1.7335377931594849e+00 6 | 7 | <_> 8 | 9 | 0 -1 81123 -136314913 -134153 -1837057 -3333 -4718593 -589439029 10 | -134217729 2145385471 11 | 12 | -5.8354979753494263e-01 3.2054635882377625e-01 13 | <_> 14 | 15 | 0 -1 393942 -168036694 -208449033 -1802401304 -207749671 16 | -259460164 -190717269 -785818883 1975386047 17 | 18 | -5.4924690723419189e-01 3.0849689245223999e-01 19 | <_> 20 | 21 | 0 -1 2813 -823731068 -931865311 -725400408 1549297728 -570736481 22 | -15939441 -606125089 1597464439 23 | 24 | -5.9341806173324585e-01 2.6302301883697510e-01 25 | <_> 26 | 27 | 0 -1 3478834 1480213504 -14780180 -803268138 1582070862 28 | -208782312 -611657086 -1945567424 -893716577 29 | 30 | -6.1474728584289551e-01 2.3925973474979401e-01 31 | <_> 32 | 33 | 0 -1 6240 -76427025 -705203949 -1823745685 1809011277 -118622324 34 | 898337498 -1079297621 1602211819 35 | 36 | -4.3725883960723877e-01 3.1791627407073975e-01 37 | <_> 38 | 39 | 0 -1 826181 1474033600 1430639435 1104014976 1267717388 40 | -512767138 1997126355 2059395016 2079326092 41 | 42 | -5.2264249324798584e-01 2.6250457763671875e-01 43 | <_> 44 | 45 | 0 -1 37626 -5257309 -450401529 -1124663485 2146021360 -22676501 46 | -1235222561 1150809091 -673187913 47 | 48 | -3.3301791548728943e-01 4.3883842229843140e-01 49 | <_> 50 | 51 | 0 -1 76459 -735068096 -606452970 -568599992 352124367 -90226664 52 | -68955958 992954888 -1612778561 53 | 54 | -3.9259910583496094e-01 3.4787562489509583e-01 55 | <_> 56 | 57 | 0 -1 3616510 1408499677 -608699942 2142744147 259391455 58 | -1483698249 -19203350 -171984303 184023882 59 | 60 | -4.7743609547615051e-01 2.7286788821220398e-01 61 | <_> 62 | 63 | 0 -1 3358214 -1290879064 -1291653080 -845952222 -1222916173 64 | 102675964 562664195 155319268 -1658204255 65 | 66 | -5.1532548666000366e-01 2.7348074316978455e-01 67 | 68 | -------------------------------------------------------------------------------- /detection/create_annotation.py: -------------------------------------------------------------------------------- 1 | #!/usr/local/bin/python 2 | # -*- coding: utf-8 -*- 3 | 4 | import argparse 5 | import re 6 | import os 7 | import sqlite3 8 | from pprint import pprint 9 | 10 | 11 | def parsearguments(): 12 | parser = argparse.ArgumentParser(description='creates annotation data') 13 | parser.add_argument('-d','--dbname', help='database name', default='catcafe') 14 | parser.add_argument('-p', '--positive', dest='positivefilename', 15 | default='positive.dat') 16 | parser.add_argument('-n', '--negative', dest='negativefilename', 17 | default='negative.dat') 18 | return parser.parse_args() 19 | 20 | 21 | def connectdb(dbname): 22 | if os.path.exists(dbname): 23 | db = sqlite3.connect(dbname) 24 | db.row_factory = sqlite3.Row 25 | else: 26 | print('cannnot open %s' % (dbname,)) 27 | return None 28 | return db 29 | 30 | 31 | def createannotations(db): 32 | annotationdata = '' 33 | sql = 'SELECT filepath, x, y, width, height FROM samples WHERE status=200' 34 | try: 35 | rv = db.execute(sql) 36 | for r in rv: 37 | annotationdata += "%s 1 %d %d %d %d\n" % (r['filepath'], r['x'], r['y'], r['width'], r['height']) 38 | print(annotationdata) 39 | except sqlite3.OperationalError as e: 40 | print(e) 41 | return None 42 | return annotationdata 43 | 44 | 45 | def createnegativefilelist(db): 46 | negativefilelist = '' 47 | sql = 'SELECT filepath FROM samples WHERE status=100' 48 | try: 49 | rv = db.execute(sql) 50 | for r in rv: 51 | negativefilelist += "%s\n" % (r['filepath'],) 52 | print(negativefilelist) 53 | except sqlite3.OperationalError as e: 54 | print(e) 55 | return None 56 | return negativefilelist 57 | 58 | 59 | def appendnegativefilelist(dirpath, dirname): 60 | pattern = re.compile('.*[.](jpg|jpeg|png|bmp|gif)$') 61 | imagedir = os.path.join(dirpath, dirname) 62 | images = [image for image in os.listdir(imagedir) if re.match(pattern, image)] 63 | negativefilelist = '' 64 | for i, image in enumerate(images): 65 | imagesrc = os.path.join(imagedir, images[i]) 66 | negativefilelist += "%s\n" % (imagesrc,) 67 | return negativefilelist 68 | 69 | 70 | if __name__ == '__main__': 71 | args = parsearguments() 72 | dbname = args.dbname 73 | db = connectdb(dbname) 74 | positivefile = open(args.positivefilename, 'wb') 75 | negativefile = open(args.negativefilename, 'wb') 76 | annotationdata = createannotations(db) 77 | 78 | if annotationdata: 79 | positivefile.write(annotationdata) 80 | positivefile.close() 81 | 82 | # negativefilelist = createnegativefilelist(db) 83 | negativefilelist = appendnegativefilelist('static/negative/images', 'other') 84 | 85 | if negativefilelist: 86 | negativefile.write(negativefilelist) 87 | negativefile.close() 88 | 89 | db.close() 90 | -------------------------------------------------------------------------------- /data/model/stage5.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 11 5 | -1.4208899736404419e+00 6 | 7 | <_> 8 | 9 | 0 -1 1303 -9175041 -170002177 -173015331 -1215841 -77856785 10 | -68193077 -269505025 1879045119 11 | 12 | -5.8139532804489136e-01 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-544222685 -385126845 50549515 -220774397 47 | 48 | -5.2852547168731689e-01 2.1428774297237396e-01 49 | <_> 50 | 51 | 0 -1 3562745 -545265129 116049647 210708590 1077407467 -71406046 52 | -1787333002 -252130054 -999293705 53 | 54 | -3.1097722053527832e-01 3.7170669436454773e-01 55 | <_> 56 | 57 | 0 -1 3616513 -875241601 -548281601 -405377611 196136902 58 | -1947669638 -847511553 -541131152 200342807 59 | 60 | -5.3021776676177979e-01 2.2613856196403503e-01 61 | <_> 62 | 63 | 0 -1 1733743 -940858456 606917441 -83690536 -1148521601 64 | -540678731 -1621390751 533646336 -1230048519 65 | 66 | -4.6793484687805176e-01 2.3651477694511414e-01 67 | <_> 68 | 69 | 0 -1 973 -285216778 -227358541 -1395080324 2010438992 -120069633 70 | -18503660 -1398990087 2075655541 71 | 72 | -3.6179101467132568e-01 2.9109182953834534e-01 73 | 74 | -------------------------------------------------------------------------------- /data/model/stage6.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 12 5 | -1.3258671760559082e+00 6 | 7 | <_> 8 | 9 | 0 -1 1 -102761473 -2622213 -586226987 -175117795 -19931953 10 | -102900573 -6341665 2147483519 11 | 12 | -6.0085785388946533e-01 9.9705509841442108e-02 13 | <_> 14 | 15 | 0 -1 14890 -827326749 -295441174 -203563390 -546310718 16 | -274727961 -19925785 2008938471 -203958345 17 | 18 | -4.3862017989158630e-01 2.7953514456748962e-01 19 | <_> 20 | 21 | 0 -1 3427178 -989678048 2047593185 -536153967 979364799 22 | -2011121632 -68959570 -1364934991 1027599871 23 | 24 | -5.4623425006866455e-01 2.1587164700031281e-01 25 | <_> 26 | 27 | 0 -1 1232 -1258604640 -1147423797 551694369 -170446729 -1608783 28 | -1450923544 -1332022785 1432219614 29 | 30 | -4.5928683876991272e-01 2.4170990288257599e-01 31 | <_> 32 | 33 | 0 -1 3616512 -617545745 -13369362 1536940911 99868155 -961536583 34 | -1397572673 -2245745 264400571 35 | 36 | -4.4457331299781799e-01 2.7112343907356262e-01 37 | <_> 38 | 39 | 0 -1 65396 -102799778 -1195515244 43178052 -603269026 40 | -1073086400 1575575890 1385040490 -236001513 41 | 42 | -4.1125467419624329e-01 2.7592518925666809e-01 43 | <_> 44 | 45 | 0 -1 3166525 -292880768 -1358921048 547656369 -2084181056 46 | 1677514665 -1894780150 162166832 -908079135 47 | 48 | -4.2582458257675171e-01 2.4507157504558563e-01 49 | <_> 50 | 51 | 0 -1 3613848 431007723 -824197633 -547651837 1509059889 52 | 463010746 -1074533202 -70586438 263302043 53 | 54 | -4.7433590888977051e-01 2.1511457860469818e-01 55 | <_> 56 | 57 | 0 -1 56958 1441743847 -685310507 1570363741 -1045248 1155353807 58 | -861385509 -644273940 1336298151 59 | 60 | -4.1037479043006897e-01 2.6458358764648438e-01 61 | <_> 62 | 63 | 0 -1 1456729 1733491118 1425484947 -2096491526 -83265654 64 | -1275085187 1934409921 -553935162 -2020768881 65 | 66 | -3.2530993223190308e-01 3.2269874215126038e-01 67 | <_> 68 | 69 | 0 -1 3558203 -668332962 -690044342 -402065394 -742328710 70 | 1926121802 281976843 -263288662 2082393415 71 | 72 | -4.6887257695198059e-01 2.1346296370029449e-01 73 | <_> 74 | 75 | 0 -1 3286728 -132087616 553660464 1112740418 1352664192 76 | -1002387192 1146096448 42242307 -2129780374 77 | 78 | -4.2727327346801758e-01 2.3190428316593170e-01 79 | 80 | -------------------------------------------------------------------------------- /data/model/stage7.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 13 5 | -1.4888938665390015e+00 6 | 7 | <_> 8 | 9 | 0 -1 80617 -1 -2097697 1427111935 -572523521 -69861649 10 | -610018069 -34865681 2147478431 11 | 12 | -5.4434251785278320e-01 1.9716311991214752e-01 13 | <_> 14 | 15 | 0 -1 3430522 -93332965 -9569185 -92107402 -1325446833 -89993716 16 | 2112850527 -423696226 -226496774 17 | 18 | -3.7605828046798706e-01 3.1678086519241333e-01 19 | <_> 20 | 21 | 0 -1 310 -1431321072 -1956087328 -909973223 159041248 1939856299 22 | -359841660 -204546161 -143166091 23 | 24 | -5.3246909379959106e-01 1.9799698889255524e-01 25 | <_> 26 | 27 | 0 -1 3616475 -806223873 -537660449 1536597255 420536135 28 | -2022070373 1040170685 -6344949 533974923 29 | 30 | -3.7055054306983948e-01 3.0032548308372498e-01 31 | <_> 32 | 33 | 0 -1 74249 -145817914 -687605372 -13899192 -74517138 -288687921 34 | 1464658502 -623127604 2075065285 35 | 36 | -4.8952755331993103e-01 2.2786329686641693e-01 37 | <_> 38 | 39 | 0 -1 7223 -136512430 -787167999 1074938284 493687560 276146851 40 | 589013515 -865656437 1239239771 41 | 42 | -5.3250396251678467e-01 1.9003206491470337e-01 43 | <_> 44 | 45 | 0 -1 38588 -145759958 -821049914 -1078337216 -33608032 46 | 1423538085 -950140963 -162011277 -136845913 47 | 48 | -3.0383500456809998e-01 3.1146323680877686e-01 49 | <_> 50 | 51 | 0 -1 3616514 1139866877 2147483579 -721073 301259775 45154299 52 | -4327429 -269533409 229752671 53 | 54 | -4.5057049393653870e-01 2.0139770209789276e-01 55 | <_> 56 | 57 | 0 -1 77876 -20984736 -4949545 1448263706 920189577 1244448832 58 | 1875771437 -1057342948 -538430483 59 | 60 | -4.0678599476814270e-01 2.2909586131572723e-01 61 | <_> 62 | 63 | 0 -1 3588731 -1702174440 62965896 -1065320304 -1457761656 64 | -326805376 -633110391 -2106828700 -619713015 65 | 66 | -4.4796335697174072e-01 2.1863941848278046e-01 67 | <_> 68 | 69 | 0 -1 1853505 -234889820 -666225189 -1399322265 1964819540 70 | -1249840208 751140889 -1867275363 1896469407 71 | 72 | -3.6860093474388123e-01 2.7174666523933411e-01 73 | <_> 74 | 75 | 0 -1 3061509 -1020590966 -642048879 1325310994 1395630707 76 | -547047934 69222928 1212995731 -80798150 77 | 78 | -2.7938663959503174e-01 3.5098463296890259e-01 79 | <_> 80 | 81 | 0 -1 78987 -1026242332 -1153832753 1292814068 -27099373 82 | 948969215 1050933529 1586010798 2130502223 83 | 84 | -4.3503525853157043e-01 2.4240022897720337e-01 85 | 86 | -------------------------------------------------------------------------------- /detection/make_annotations.py: -------------------------------------------------------------------------------- 1 | #!/usr/local/bin/python 2 | # -*- coding: utf-8 -*- 3 | 4 | import os 5 | import re 6 | import shutil 7 | import sqlite3 8 | from xml.etree import ElementTree 9 | 10 | 11 | ## ll | egrep 'Abyssinian|Bengal|Birman|Bombay|British|Egyptian|Main|Persian|Ragdoll|Russian|Siamese|Sphynx' | wc -l'' 12 | 13 | def removedogimages(): 14 | imagedir = 'static/oxford/images' 15 | pattern = re.compile('.*[.](jpg|jpeg|png|bmp|gif)$') 16 | images = [image for image in os.listdir(imagedir) if re.match(pattern, image)] 17 | for i, imagefile in enumerate(images): 18 | if imagefile[0].islower(): 19 | imagesrc = os.path.join(imagedir, imagefile) 20 | os.remove(imagesrc) 21 | 22 | 23 | def removedogxmls(): 24 | xmldir = 'static/oxford/annotations/xmls' 25 | pattern = re.compile('.*[.](xml)$') 26 | xmls = [xml for xml in os.listdir(xmldir) if re.match(pattern, xml)] 27 | for i, xml in enumerate(xmls): 28 | if xmls[i][0].islower(): 29 | xmlsrc = os.path.join(xmldir, xmls[i]) 30 | # print(xmlsrc) 31 | os.remove(xmlsrc) 32 | 33 | 34 | def insertannotation(dbname): 35 | db = sqlite3.connect(dbname) 36 | db.row_factory = sqlite3.Row 37 | # sql = "SELECT * FROM samples WHERE filepath LIKE '%Abyssinian%'" 38 | # rv = db.execute(sql) 39 | # for r in rv: 40 | # print(r) 41 | 42 | pattern = re.compile('.*[.](xml)$') 43 | xmldir = 'static/oxford/annotations/xmls' 44 | imgdir = 'static/oxford/images/' 45 | xmls = [xml for xml in os.listdir(xmldir) if re.match(pattern, xml)] 46 | annotationdata = '' 47 | sql = "UPDATE samples SET x=?, y=?, width=?, height=?, status=200 WHERE filepath=?" 48 | for i, xml in enumerate(xmls): 49 | xmlsrc = os.path.join(xmldir, xmls[i]) 50 | tree = ElementTree.parse(xmlsrc) 51 | elem = tree.getroot() 52 | filename = elem.find('.//filename').text 53 | if filename[0].isupper(): 54 | # print(filename) 55 | xmin = elem.find('.//xmin').text 56 | ymin = elem.find('.//ymin').text 57 | xmax = elem.find('.//xmax').text 58 | ymax = elem.find('.//ymax').text 59 | width = int(xmax) - int(xmin) 60 | height = int(ymax) - int(ymin) 61 | # print(xmin,ymin,xmax,ymax) 62 | imgsrc = imgdir + filename 63 | db.execute(sql, (int(xmin), int(ymin), width, height, imgsrc)) 64 | db.commit() 65 | db.close() 66 | 67 | 68 | def createannotationfile(): 69 | f = open('annotation.dat', 'wb') 70 | pattern = re.compile('.*[.](xml)$') 71 | xmldir = 'static/oxford/annotations/xmls' 72 | imgdir = 'static/oxford/images/' 73 | xmls = [xml for xml in os.listdir(xmldir) if re.match(pattern, xml)] 74 | annotationdata = '' 75 | for i, xml in enumerate(xmls): 76 | xmlsrc = os.path.join(xmldir, xmls[i]) 77 | tree = ElementTree.parse(xmlsrc) 78 | elem = tree.getroot() 79 | filename = elem.find('.//filename').text 80 | if filename[0].isupper(): 81 | # print(filename) 82 | xmin = elem.find('.//xmin').text 83 | ymin = elem.find('.//ymin').text 84 | xmax = elem.find('.//xmax').text 85 | ymax = elem.find('.//ymax').text 86 | width = int(xmax) - int(xmin) 87 | height = int(ymax) - int(ymin) 88 | # print(xmin,ymin,xmax,ymax) 89 | imgsrc = imgdir + filename 90 | annotationdata += "%s 1 %d %d %d %d\n" % (imgsrc, int(xmin), int(ymin), width, height) 91 | f.write(annotationdata) 92 | f.close() 93 | 94 | 95 | if __name__ == '__main__': 96 | dbname = 'samples.db' 97 | createannotationfile() 98 | insertannotation(dbname) 99 | removedogimages() 100 | removedogxmls() 101 | -------------------------------------------------------------------------------- /detection/train_cascade.py: -------------------------------------------------------------------------------- 1 | #!/usr/local/bin/python 2 | # -*- coding: utf-8 -*- 3 | 4 | import argparse 5 | import os 6 | import subprocess 7 | import sys 8 | import time 9 | from pprint import pprint 10 | 11 | 12 | def parsearguments(): 13 | parser = argparse.ArgumentParser(description='run cascade training') 14 | parser.add_argument('-pos', '--positivefilename', type=str, help='positive sample file', 15 | default='/home/gongxijun/data/lantern/data/image/pos.txt') 16 | parser.add_argument('-neg', '--negativefilename', type=str, help='negative sample file', 17 | default='/home/gongxijun/data/lantern/data/image/neg.txt') 18 | parser.add_argument('-m', '--maxfarate', help='max false alarm rate', 19 | type=float, default=0.5) 20 | parser.add_argument('-d', '--dstdir', help='destination directory', 21 | type=str, default='/home/gongxijun/data/lantern/data/model') 22 | parser.add_argument('-f', '--feature', help='feature type', 23 | type=str, default='LBP') 24 | parser.add_argument('-w', '--width', help='width', type=int, default=90) 25 | parser.add_argument('-height', '--height', help='height', type=int, default=128) 26 | return parser.parse_args() 27 | 28 | 29 | def createsamples(positivefile, vecdir='./vec', width=24, height=24): 30 | os.environ['PATH'] = '/bin:/usr/bin:/usr/local/bin' 31 | if not os.path.isdir(vecdir): 32 | os.mkdir(vecdir) 33 | numpos = len(open(positivefile).readlines()) 34 | print('samples: %d' % (numpos,)) 35 | vecfile = vecdir + '/' + positivefile.split('/')[-1] + '.vec' 36 | cmdline = ['opencv_createsamples', '-info', positivefile, 37 | '-vec', vecfile, '-num', str(numpos), '-w', str(width), '-h', str(height)] 38 | print(' '.join(cmdline)) 39 | try: 40 | p = subprocess.Popen(cmdline, cwd='./', shell=False, 41 | stdin=subprocess.PIPE, 42 | stdout=subprocess.PIPE, 43 | stderr=subprocess.STDOUT, close_fds=True) 44 | except OSError as e: 45 | print(e) 46 | sys.exit(-1) 47 | 48 | while True: 49 | line = p.stdout.readline() 50 | if not line: 51 | break 52 | print(line.rstrip()) 53 | ret = p.wait() 54 | print('') 55 | 56 | return (vecfile, numpos) 57 | 58 | 59 | def traincascade(dstdir, vecfile, numpos, negativefilename, featuretype='LBP', maxfarate=0.5, 60 | width=24, height=24): 61 | if not os.path.isdir(dstdir): 62 | os.mkdir(dstdir) 63 | numpos = int(round(numpos * 0.85)) 64 | numneg = len(open(negativefilename).readlines()) 65 | cmdline = [ 66 | 'opencv_traincascade', '-data', dstdir,'-numStages',str(20), '-vec', vecfile, 67 | '-bg', negativefilename, '-numPos', str(numpos), '-numNeg', str(numneg), 68 | '-featureType', featuretype, '-maxFalseAlarmRate', str(maxfarate), 69 | '-w', str(width), '-h', str(height) 70 | ] 71 | print(' '.join(cmdline)) 72 | 73 | try: 74 | print('Start cascade training') 75 | p = subprocess.Popen(cmdline, cwd='./', shell=False, 76 | stdin=subprocess.PIPE, 77 | stdout=subprocess.PIPE, 78 | stderr=subprocess.STDOUT, close_fds=True) 79 | except OSError as e: 80 | print(e) 81 | sys.exit(-1) 82 | 83 | while True: 84 | line = p.stdout.readline() 85 | if not line: 86 | break 87 | print(line.rstrip()) 88 | ret = p.wait() 89 | 90 | 91 | if __name__ == '__main__': 92 | args = parsearguments() 93 | positivefilename = args.positivefilename 94 | negativefilename = args.negativefilename 95 | maxfarate = args.maxfarate 96 | dstdir = args.dstdir 97 | feature = args.feature 98 | width = args.width 99 | (vecfile, numpos) = createsamples(positivefilename, width=width, height=args.height) 100 | ts = time.time() 101 | traincascade(dstdir, vecfile, numpos, negativefilename, feature, maxfarate, width, args.height) 102 | processtime = int(time.time() - ts) 103 | print('process time: %s' % (str(processtime),)) 104 | -------------------------------------------------------------------------------- /detection/nms.py: -------------------------------------------------------------------------------- 1 | def overlapping_area(detection_1, detection_2): 2 | ''' 3 | Function to calculate overlapping area'si 4 | `detection_1` and `detection_2` are 2 detections whose area 5 | of overlap needs to be found out. 6 | Each detection is list in the format -> 7 | [x-top-left, y-top-left, confidence-of-detections, width-of-detection, height-of-detection] 8 | The function returns a value between 0 and 1, 9 | which represents the area of overlap. 10 | 0 is no overlap and 1 is complete overlap. 11 | Area calculated from -> 12 | http://math.stackexchange.com/questions/99565/simplest-way-to-calculate-the-intersect-area-of-two-rectangles 13 | ''' 14 | # Calculate the x-y co-ordinates of the 15 | # rectangles 16 | x1_tl = detection_1[0] 17 | x2_tl = detection_2[0] 18 | x1_br = detection_1[0] + detection_1[2] 19 | x2_br = detection_2[0] + detection_2[2] 20 | y1_tl = detection_1[1] 21 | y2_tl = detection_2[1] 22 | y1_br = detection_1[1] + detection_1[3] 23 | y2_br = detection_2[1] + detection_2[3] 24 | # Calculate the overlapping Area 25 | x_overlap = max(0, min(x1_br, x2_br) - max(x1_tl, x2_tl)) 26 | y_overlap = max(0, min(y1_br, y2_br) - max(y1_tl, y2_tl)) 27 | overlap_area = x_overlap * y_overlap 28 | area_1 = detection_1[2] * detection_1[3] 29 | area_2 = detection_2[2] * detection_2[3] 30 | # print overlap_area, area_1, area_2 31 | return overlap_area / (float(area_2) if (area_1 > area_2) else float(area_1)); 32 | # total_area = area_1 + area_2 - overlap_area 33 | # return overlap_area / float(total_area) 34 | 35 | 36 | def appendRect(detection_1, detection_2): 37 | """ 38 | append the rect. 39 | :return: 40 | """ 41 | _detection = [0] * 4; 42 | x1_tl = detection_1[0] 43 | x2_tl = detection_2[0] 44 | x1_br = detection_1[0] + detection_1[2] 45 | x2_br = detection_2[0] + detection_2[2] 46 | y1_tl = detection_1[1] 47 | y2_tl = detection_2[1] 48 | y1_br = detection_1[1] + detection_1[3] 49 | y2_br = detection_2[1] + detection_2[3] 50 | _detection[0] = min(x1_tl, x2_tl) 51 | _detection[1] = min(y1_tl, y2_tl) 52 | _detection[2] = max(x1_br, x2_br) - _detection[0] 53 | _detection[3] = max(y1_br, y2_br) - _detection[1] 54 | return _detection; 55 | 56 | 57 | def nms(detections, threshold=.5): 58 | ''' 59 | This function performs Non-Maxima Suppression. 60 | `detections` consists of a list of detections. 61 | Each detection is in the format -> 62 | [x-top-left, y-top-left, confidence-of-detections, width-of-detection, height-of-detection] 63 | If the area of overlap is greater than the `threshold`, 64 | the area with the lower confidence score is removed. 65 | The output is a list of detections. 66 | ''' 67 | if len(detections) == 0: 68 | return [] 69 | # Unique detections will be appended to this list 70 | new_detections = [] 71 | # Append the first detection 72 | new_detections.append(detections[0]) 73 | # Remove the detection from the original list 74 | del detections[0] 75 | # For each detection, calculate the overlapping area 76 | # and if area of overlap is less than the threshold set 77 | # for the detections in `new_detections`, append the 78 | # detection to `new_detections`. 79 | # In either case, remove the detection from `detections` list. 80 | change_status=False; 81 | for index, detection in enumerate(detections): 82 | flag = True; 83 | for new_index, new_detection in enumerate(new_detections): 84 | if overlapping_area(detection, new_detection) > threshold: 85 | #print appendRect(detection, new_detection), detection, new_detection 86 | new_detections[new_index] = appendRect(detection, new_detection); 87 | #del detections[index] 88 | flag = False; 89 | change_status=True; 90 | #break 91 | if (flag): 92 | new_detections.append(detection) 93 | #del detections[index] 94 | return new_detections,change_status 95 | 96 | 97 | if __name__ == "__main__": 98 | # Example of how to use the NMS Module 99 | detections = [[31, 31, 10, 10], [31, 31, 10, 10], [100, 34, 10, 10]] 100 | print "Detections before NMS = {}".format(detections) 101 | print "Detections after NMS = {}".format(nms(detections)) 102 | -------------------------------------------------------------------------------- /data/model/stage8.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 5 | -1.3324991464614868e+00 6 | 7 | <_> 8 | 9 | 0 -1 392777 -393217 -35925097 -538715729 -33569889 -549716757 10 | -75636018 -124911618 2146434991 11 | 12 | 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