CrowdHuman数据集标注格式转换为YOLOv3可以使用的COCO格式
需要了解CrowdHuman的数据标注格式odgt，YOLOv3需要的COCO格式（不需要使用json文件，只需要图片位置信息和标注信息）
YOLOv3 github地址：https://github.com/eriklindernoren/PyTorch-YOLOv3
保存每一张图片的位置信息
1 import os 2 import json 3 4 5 def load_file(fpath): # fpath是具体的文件 ，作用：#str to list 6 assert os.path.exists(fpath) # assert() raise-if-not 7 with open(fpath, 'r') as fid: 8 lines = fid.readlines() 9 records = [json.loads(line.strip('\n')) for line in lines] # str to list 10 return records 11 12 13 def img2txt(odgtpath, respath): 14 records = load_file(odgtpath) # 提取odgt文件数据 15 record_list = len(records) # 获得record的长度，循环遍历所有数据。 16 print(os.getcwd()) 17 # os.mkdir(os.getcwd() + respath) 18 with open(respath, 'w') as txt: 19 for i in range(record_list): 20 file_name = records[i]['ID'] + '.jpg' 21 file_name = str("/datasets/crowdhuman/images/val/Image/" + file_name) 22 txt.write(file_name + '\n') 23 24 25 if __name__ == '__main__': 26 odgtpath = "/datasets/crowdhuman/annotation_val.odgt" 27 respath = "/datasets/crowdhuman/val_name.txt" 28 img2txt(odgtpath, respath)
保存每一张图片标注信息中的全身坐标fbox
1 import time 2 import img2txt 3 from PIL import Image 4 5 6 def tonormlabel(odgtpath, storepath): 7 records = img2txt.load_file(odgtpath) 8 record_list = len(records) 9 print(record_list) 10 categories = {} 11 # txt = open(respath, 'w') 12 for i in range(record_list): 13 txt_name = storepath + records[i]['ID'] + '.txt' 14 file_name = records[i]['ID'] + '.jpg' 15 #print(i) 16 im = Image.open("/datasets/crowdhuman/images/train_all/Image/" + file_name) 17 height = im.size[1] 18 width = im.size[0] 19 file = open(txt_name, 'w') 20 gt_box = records[i]['gtboxes'] 21 gt_box_len = len(gt_box) # 每一个字典gtboxes里，也有好几个记录，分别提取记录。 22 for j in range(gt_box_len): 23 category = gt_box[j]['tag'] 24 if category not in categories: # 该类型不在categories，就添加上去 25 new_id = len(categories) + 1 # ID递增 26 categories[category] = new_id 27 category_id = categories[category] # 重新获取它的类别ID 28 fbox = gt_box[j]['fbox'] # 获得全身框 29 norm_x = fbox[0] / width 30 norm_y = fbox[1] / height 31 norm_w = fbox[2] / width 32 norm_h = fbox[3] / height 33 ''' 34 norm_x = 0 if norm_x <= 0 else norm_x 35 norm_x = 1 if norm_x >= 1 else norm_x 36 norm_y = 0 if norm_y <= 0 else norm_y 37 norm_y = 1 if norm_y >= 1 else norm_y 38 norm_w = 0 if norm_w <= 0 else norm_w 39 norm_w = 1 if norm_w >= 1 else norm_w 40 norm_h = 0 if norm_h <= 0 else norm_h 41 norm_h = 1 if norm_h >= 1 else norm_h 42 ''' 43 blank = ' ' 44 if j == gt_box_len-1: 45 file.write(str(category_id - 1) + blank + '{:.6f}'.format(norm_x) + blank + '{:.6f}'.format(norm_y) + blank 46 + '{:.6f}'.format(norm_w) + blank + '{:.6f}'.format(norm_h)) 47 else: 48 file.write(str(category_id - 1) + blank + '{:.6f}'.format(norm_x) + blank + '{:.6f}'.format(norm_y) + blank 49 + '{:.6f}'.format(norm_w) + blank + '{:.6f}'.format(norm_h) + '\n') 50 51 52 if __name__ == '__main__': 53 odgtpath = "/datasets/crowdhuman/annotation_train.odgt" 54 storepath = "/datasets/crowdhuman/labels/train_all/Image/" 55 print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))) # 格式化输出时间 56 start = time.time() 57 tonormlabel(odgtpath, storepath) 58 end = time.time() 59 print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))) 60 print('已完成转换，共耗时{:.5f}s'.format(end - start))
