更换文档检测模型
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import glob
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import os
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import os.path as osp
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import cv2
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import random
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import numpy as np
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import argparse
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import tqdm
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import json
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def mkdir_if_missing(d):
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if not osp.exists(d):
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os.makedirs(d)
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def bdd2mot_tracking(img_dir, label_dir, save_img_dir, save_label_dir):
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label_jsons = os.listdir(label_dir)
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for label_json in tqdm(label_jsons):
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with open(os.path.join(label_dir, label_json)) as f:
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labels_json = json.load(f)
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for label_json in labels_json:
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img_name = label_json['name']
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video_name = label_json['videoName']
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labels = label_json['labels']
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txt_string = ""
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for label in labels:
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category = label['category']
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x1 = label['box2d']['x1']
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x2 = label['box2d']['x2']
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y1 = label['box2d']['y1']
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y2 = label['box2d']['y2']
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width = x2 - x1
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height = y2 - y1
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x_center = (x1 + x2) / 2. / args.width
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y_center = (y1 + y2) / 2. / args.height
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width /= args.width
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height /= args.height
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identity = int(label['id'])
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# [class] [identity] [x_center] [y_center] [width] [height]
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txt_string += "{} {} {} {} {} {}\n".format(
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attr_id_dict[category], identity, x_center, y_center,
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width, height)
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fn_label = os.path.join(save_label_dir, img_name[:-4] + '.txt')
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source_img = os.path.join(img_dir, video_name, img_name)
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target_img = os.path.join(save_img_dir, img_name)
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with open(fn_label, 'w') as f:
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f.write(txt_string)
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os.system('cp {} {}'.format(source_img, target_img))
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def transBbox(bbox):
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# bbox --> cx cy w h
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bbox = list(map(lambda x: float(x), bbox))
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bbox[0] = (bbox[0] - bbox[2] / 2) * 1280
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bbox[1] = (bbox[1] - bbox[3] / 2) * 720
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bbox[2] = bbox[2] * 1280
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bbox[3] = bbox[3] * 720
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bbox = list(map(lambda x: str(x), bbox))
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return bbox
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def genSingleImageMot(inputPath, classes=[]):
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labelPaths = glob.glob(inputPath + '/*.txt')
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labelPaths = sorted(labelPaths)
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allLines = []
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result = {}
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for labelPath in labelPaths:
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frame = str(int(labelPath.split('-')[-1].replace('.txt', '')))
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with open(labelPath, 'r') as labelPathFile:
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lines = labelPathFile.readlines()
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for line in lines:
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line = line.replace('\n', '')
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lineArray = line.split(' ')
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if len(classes) > 0:
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if lineArray[0] in classes:
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lineArray.append(frame)
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allLines.append(lineArray)
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else:
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lineArray.append(frame)
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allLines.append(lineArray)
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resultMap = {}
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for line in allLines:
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if line[1] not in resultMap.keys():
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resultMap[line[1]] = []
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resultMap[line[1]].append(line)
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mot_gt = []
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id_idx = 0
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for rid in resultMap.keys():
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id_idx += 1
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for id_line in resultMap[rid]:
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mot_line = []
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mot_line.append(id_line[-1])
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mot_line.append(str(id_idx))
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id_line_temp = transBbox(id_line[2:6])
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mot_line.extend(id_line_temp)
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mot_line.append('1') # origin class: id_line[0]
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mot_line.append('1') # permanent class => 1
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mot_line.append('1')
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mot_gt.append(mot_line)
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result = list(map(lambda line: str.join(',', line), mot_gt))
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resultStr = str.join('\n', result)
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return resultStr
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def writeGt(inputPath, outPath, classes=[]):
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singleImageResult = genSingleImageMot(inputPath, classes=classes)
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outPathFile = outPath + '/gt.txt'
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mkdir_if_missing(outPath)
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with open(outPathFile, 'w') as gtFile:
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gtFile.write(singleImageResult)
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def genSeqInfo(seqInfoPath):
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name = seqInfoPath.split('/')[-2]
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img1Path = osp.join(str.join('/', seqInfoPath.split('/')[0:-1]), 'img1')
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seqLength = len(glob.glob(img1Path + '/*.jpg'))
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seqInfoStr = f'''[Sequence]\nname={name}\nimDir=img1\nframeRate=30\nseqLength={seqLength}\nimWidth=1280\nimHeight=720\nimExt=.jpg'''
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with open(seqInfoPath, 'w') as seqFile:
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seqFile.write(seqInfoStr)
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def genMotGt(dataDir, classes=[]):
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seqLists = sorted(glob.glob(dataDir))
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for seqList in seqLists:
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inputPath = osp.join(seqList, 'img1')
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outputPath = seqList.replace('labels_with_ids', 'images')
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outputPath = osp.join(outputPath, 'gt')
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mkdir_if_missing(outputPath)
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print('processing...', outputPath)
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writeGt(inputPath, outputPath, classes=classes)
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seqList = seqList.replace('labels_with_ids', 'images')
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seqInfoPath = osp.join(seqList, 'seqinfo.ini')
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genSeqInfo(seqInfoPath)
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def updateSeqInfo(dataDir, phase):
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seqPath = osp.join(dataDir, 'labels_with_ids', phase)
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seqList = glob.glob(seqPath + '/*')
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for seqName in seqList:
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print('seqName=>', seqName)
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seqName_img1_dir = osp.join(seqName, 'img1')
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txtLength = glob.glob(seqName_img1_dir + '/*.txt')
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name = seqName.split('/')[-1].replace('.jpg', '').replace('.txt', '')
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seqLength = len(txtLength)
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seqInfoStr = f'''[Sequence]\nname={name}\nimDir=img1\nframeRate=30\nseqLength={seqLength}\nimWidth=1280\nimHeight=720\nimExt=.jpg'''
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seqInfoPath = seqName_img1_dir.replace('labels_with_ids', 'images')
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seqInfoPath = seqInfoPath.replace('/img1', '')
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seqInfoPath = seqInfoPath + '/seqinfo.ini'
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with open(seqInfoPath, 'w') as seqFile:
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seqFile.write(seqInfoStr)
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def VisualDataset(datasetPath, phase='train', seqName='', frameId=1):
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trainPath = osp.join(datasetPath, 'labels_with_ids', phase)
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seq1Paths = osp.join(trainPath, seqName)
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seq_img1_path = osp.join(seq1Paths, 'img1')
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label_with_idPath = osp.join(seq_img1_path, seqName + '-' + '%07d' %
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frameId) + '.txt'
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image_path = label_with_idPath.replace('labels_with_ids', 'images').replace(
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'.txt', '.jpg')
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seqInfoPath = str.join('/', image_path.split('/')[:-2])
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seqInfoPath = seqInfoPath + '/seqinfo.ini'
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seq_info = open(seqInfoPath).read()
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width = int(seq_info[seq_info.find('imWidth=') + 8:seq_info.find(
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'\nimHeight')])
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height = int(seq_info[seq_info.find('imHeight=') + 9:seq_info.find(
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'\nimExt')])
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with open(label_with_idPath, 'r') as label:
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allLines = label.readlines()
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images = cv2.imread(image_path)
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print('image_path => ', image_path)
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for line in allLines:
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line = line.split(' ')
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line = list(map(lambda x: float(x), line))
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c1, c2, w, h = line[2:6]
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x1 = c1 - w / 2
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x2 = c2 - h / 2
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x3 = c1 + w / 2
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x4 = c2 + h / 2
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cv2.rectangle(
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images, (int(x1 * width), int(x2 * height)),
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(int(x3 * width), int(x4 * height)), (255, 0, 0),
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thickness=2)
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cv2.imwrite('test.jpg', images)
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def VisualGt(dataPath, phase='train'):
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seqList = sorted(glob.glob(osp.join(dataPath, 'images', phase) + '/*'))
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seqIndex = random.randint(0, len(seqList) - 1)
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seqPath = seqList[seqIndex]
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gt_path = osp.join(seqPath, 'gt', 'gt.txt')
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img_list_path = sorted(glob.glob(osp.join(seqPath, 'img1', '*.jpg')))
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imgIndex = random.randint(0, len(img_list_path))
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img_Path = img_list_path[imgIndex]
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frame_value = img_Path.split('/')[-1].replace('.jpg', '')
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frame_value = frame_value.split('-')[-1]
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frame_value = int(frame_value)
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seqNameStr = img_Path.split('/')[-1].replace('.jpg', '').replace('img', '')
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frame_value = int(seqNameStr.split('-')[-1])
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print('frame_value => ', frame_value)
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gt_value = np.loadtxt(gt_path, dtype=float, delimiter=',')
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gt_value = gt_value[gt_value[:, 0] == frame_value]
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get_list = gt_value.tolist()
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img = cv2.imread(img_Path)
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colors = [[255, 0, 0], [255, 255, 0], [255, 0, 255], [0, 255, 0],
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[0, 255, 255], [0, 0, 255]]
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for seq, _id, pl, pt, w, h, _, bbox_class, _ in get_list:
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pl, pt, w, h = int(pl), int(pt), int(w), int(h)
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print('pl,pt,w,h => ', pl, pt, w, h)
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cv2.putText(img,
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str(bbox_class), (pl, pt), cv2.FONT_HERSHEY_PLAIN, 2,
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colors[int(bbox_class - 1)])
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cv2.rectangle(
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img, (pl, pt), (pl + w, pt + h),
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colors[int(bbox_class - 1)],
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thickness=2)
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cv2.imwrite('testGt.jpg', img)
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print(seqPath, frame_value)
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return seqPath.split('/')[-1], frame_value
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def gen_image_list(dataPath, datType):
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inputPath = f'{dataPath}/labels_with_ids/{datType}'
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pathList = sorted(glob.glob(inputPath + '/*'))
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print(pathList)
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allImageList = []
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for pathSingle in pathList:
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imgList = sorted(glob.glob(osp.join(pathSingle, 'img1', '*.txt')))
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for imgPath in imgList:
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imgPath = imgPath.replace('labels_with_ids', 'images').replace(
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'.txt', '.jpg')
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allImageList.append(imgPath)
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with open(f'{dataPath}.{datType}', 'w') as image_list_file:
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allImageListStr = str.join('\n', allImageList)
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image_list_file.write(allImageListStr)
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def formatOrigin(datapath, phase):
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label_with_idPath = osp.join(datapath, 'labels_with_ids', phase)
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print(label_with_idPath)
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for txtList in sorted(glob.glob(label_with_idPath + '/*.txt')):
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print(txtList)
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seqName = txtList.split('/')[-1]
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seqName = str.join('-', seqName.split('-')[0:-1]).replace('.txt', '')
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seqPath = osp.join(label_with_idPath, seqName, 'img1')
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mkdir_if_missing(seqPath)
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os.system(f'mv {txtList} {seqPath}')
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def copyImg(fromRootPath, toRootPath, phase):
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fromPath = osp.join(fromRootPath, 'images', phase)
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toPathSeqPath = osp.join(toRootPath, 'labels_with_ids', phase)
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seqList = sorted(glob.glob(toPathSeqPath + '/*'))
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for seqPath in seqList:
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seqName = seqPath.split('/')[-1]
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imgTxtList = sorted(glob.glob(osp.join(seqPath, 'img1') + '/*.txt'))
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img_toPathSeqPath = osp.join(seqPath, 'img1')
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img_toPathSeqPath = img_toPathSeqPath.replace('labels_with_ids',
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'images')
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mkdir_if_missing(img_toPathSeqPath)
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for imgTxt in imgTxtList:
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imgName = imgTxt.split('/')[-1].replace('.txt', '.jpg')
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imgfromPath = osp.join(fromPath, seqName, imgName)
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print(f'cp {imgfromPath} {img_toPathSeqPath}')
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os.system(f'cp {imgfromPath} {img_toPathSeqPath}')
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='BDD100K to MOT format')
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parser.add_argument("--data_path", default='bdd100k')
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parser.add_argument("--phase", default='train')
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parser.add_argument("--classes", default='2,3,4,9,10')
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parser.add_argument("--img_dir", default="bdd100k/images/track/")
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parser.add_argument("--label_dir", default="bdd100k/labels/box_track_20/")
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parser.add_argument("--save_path", default="bdd100kmot_vehicle")
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parser.add_argument("--height", default=720)
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parser.add_argument("--width", default=1280)
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args = parser.parse_args()
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attr_dict = dict()
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attr_dict["categories"] = [{
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"supercategory": "none",
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"id": 0,
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"name": "pedestrian"
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}, {
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"supercategory": "none",
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"id": 1,
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"name": "rider"
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}, {
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"supercategory": "none",
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"id": 2,
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"name": "car"
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}, {
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"supercategory": "none",
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"id": 3,
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"name": "truck"
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}, {
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"supercategory": "none",
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"id": 4,
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"name": "bus"
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}, {
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"supercategory": "none",
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"id": 5,
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"name": "train"
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}, {
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"supercategory": "none",
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"id": 6,
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"name": "motorcycle"
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}, {
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"supercategory": "none",
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"id": 7,
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"name": "bicycle"
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}, {
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"supercategory": "none",
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"id": 8,
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"name": "other person"
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}, {
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"supercategory": "none",
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"id": 9,
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"name": "trailer"
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}, {
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"supercategory": "none",
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"id": 10,
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"name": "other vehicle"
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}]
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attr_id_dict = {i['name']: i['id'] for i in attr_dict['categories']}
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# create bdd100kmot_vehicle training set in MOT format
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print('Loading and converting training set...')
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train_img_dir = os.path.join(args.img_dir, 'train')
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train_label_dir = os.path.join(args.label_dir, 'train')
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save_img_dir = os.path.join(args.save_path, 'images', 'train')
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save_label_dir = os.path.join(args.save_path, 'labels_with_ids', 'train')
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if not os.path.exists(save_img_dir): os.makedirs(save_img_dir)
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if not os.path.exists(save_label_dir): os.makedirs(save_label_dir)
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bdd2mot_tracking(train_img_dir, train_label_dir, save_img_dir,
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save_label_dir)
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# create bdd100kmot_vehicle validation set in MOT format
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print('Loading and converting validation set...')
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val_img_dir = os.path.join(args.img_dir, 'val')
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val_label_dir = os.path.join(args.label_dir, 'val')
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save_img_dir = os.path.join(args.save_path, 'images', 'val')
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save_label_dir = os.path.join(args.save_path, 'labels_with_ids', 'val')
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if not os.path.exists(save_img_dir): os.makedirs(save_img_dir)
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if not os.path.exists(save_label_dir): os.makedirs(save_label_dir)
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bdd2mot_tracking(val_img_dir, val_label_dir, save_img_dir, save_label_dir)
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# gen gt file
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dataPath = args.data_path
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phase = args.phase
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classes = args.classes.split(',')
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formatOrigin(osp.join(dataPath, 'bdd100kmot_vehicle'), phase)
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dataDir = osp.join(
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osp.join(dataPath, 'bdd100kmot_vehicle'), 'labels_with_ids',
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phase) + '/*'
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genMotGt(dataDir, classes=classes)
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copyImg(dataPath, osp.join(dataPath, 'bdd100kmot_vehicle'), phase)
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updateSeqInfo(osp.join(dataPath, 'bdd100kmot_vehicle'), phase)
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gen_image_list(osp.join(dataPath, 'bdd100kmot_vehicle'), phase)
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os.system(f'rm -r {dataPath}/bdd100kmot_vehicle/images/' + phase + '/*.jpg')
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@@ -0,0 +1,16 @@
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data_path=bdd100k
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img_dir=${data_path}/images/track
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label_dir=${data_path}/labels/box_track_20
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save_path=${data_path}/bdd100kmot_vehicle
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phasetrain=train
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phaseval=val
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classes=2,3,4,9,10
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# gen mot dataset
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python bdd100k2mot.py --data_path=${data_path} --phase=${phasetrain} --classes=${classes} --img_dir=${img_dir} --label_dir=${label_dir} --save_path=${save_path}
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python bdd100k2mot.py --data_path=${data_path} --phase=${phaseval} --classes=${classes} --img_dir=${img_dir} --label_dir=${label_dir} --save_path=${save_path}
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# gen new labels_with_ids
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python gen_labels_MOT.py --mot_data=${data_path} --phase=${phasetrain}
|
||||
python gen_labels_MOT.py --mot_data=${data_path} --phase=${phaseval}
|
||||
@@ -0,0 +1,72 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import os
|
||||
import os.path as osp
|
||||
import numpy as np
|
||||
import argparse
|
||||
|
||||
|
||||
def mkdirs(d):
|
||||
if not osp.exists(d):
|
||||
os.makedirs(d)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description='BDD100K to MOT format')
|
||||
parser.add_argument(
|
||||
"--mot_data", default='./bdd100k')
|
||||
parser.add_argument("--phase", default='train')
|
||||
args = parser.parse_args()
|
||||
|
||||
MOT_data = args.mot_data
|
||||
phase = args.phase
|
||||
seq_root = osp.join(MOT_data, 'bdd100kmot_vehicle', 'images', phase)
|
||||
label_root = osp.join(MOT_data, 'bdd100kmot_vehicle', 'labels_with_ids',
|
||||
phase)
|
||||
mkdirs(label_root)
|
||||
seqs = [s for s in os.listdir(seq_root)]
|
||||
tid_curr = 0
|
||||
tid_last = -1
|
||||
|
||||
os.system(f'rm -r {MOT_data}/bdd100kmot_vehicle/labels_with_ids')
|
||||
for seq in seqs:
|
||||
print('seq => ', seq)
|
||||
seq_info = open(osp.join(seq_root, seq, 'seqinfo.ini')).read()
|
||||
seq_width = int(seq_info[seq_info.find('imWidth=') + 8:seq_info.find(
|
||||
'\nimHeight')])
|
||||
seq_height = int(seq_info[seq_info.find('imHeight=') + 9:seq_info.find(
|
||||
'\nimExt')])
|
||||
|
||||
gt_txt = osp.join(seq_root, seq, 'gt', 'gt.txt')
|
||||
gt = np.loadtxt(gt_txt, dtype=np.float64, delimiter=',')
|
||||
|
||||
seq_label_root = osp.join(label_root, seq, 'img1')
|
||||
mkdirs(seq_label_root)
|
||||
|
||||
for fid, tid, x, y, w, h, mark, label, _ in gt:
|
||||
fid = int(fid)
|
||||
tid = int(tid)
|
||||
if not tid == tid_last:
|
||||
tid_curr += 1
|
||||
tid_last = tid
|
||||
x += w / 2
|
||||
y += h / 2
|
||||
label_fpath = osp.join(seq_label_root,
|
||||
seq + '-' + '{:07d}.txt'.format(fid))
|
||||
label_str = '0 {:d} {:.6f} {:.6f} {:.6f} {:.6f}\n'.format(
|
||||
tid_curr, x / seq_width, y / seq_height, w / seq_width,
|
||||
h / seq_height)
|
||||
with open(label_fpath, 'a') as f:
|
||||
f.write(label_str)
|
||||
Reference in New Issue
Block a user