import json
import os
import cv2
import numpy as npdef write_line(img_path, im_shape, boxes, ids, idx):h, w, c = im_shape# for header, we use minimal length 2, plus width and height# with A: 4, B: 5, C: width, D: heightA = 4B = 5C = wD = h# concat id and bboxeslabels = np.hstack((ids.reshape(-1, 1), boxes)).astype('float')# normalized bboxes (recommanded)labels[:, (1, 3)] /= float(w)labels[:, (2, 4)] /= float(h)# flattenlabels = labels.flatten().tolist()str_idx = [str(idx)]str_header = [str(x) for x in [A, B, C, D]]str_labels = [str(x) for x in labels]str_path = [img_path]line = '\t'.join(str_idx + str_header + str_labels + str_path) + '\n'return linefiles = os.listdir('train_front')
json_url = []
cnt = 0
for file in files:tmp = os.listdir('train_front/'+file)for js in tmp:if js.endswith('json'):json_url.append('train_front/'+file+'/'+js)cnt+=1
print(cnt)fwtrain = open("train.lst","w")
fwval = open("val.lst","w")first_flag = []
flag = Truecnt = 0
cnt1 = 0
cnt2 = 0
for json_url_index in json_url:file = open(json_url_index,'r')for line in file:js = json.loads(line)if 'person' in js:boxes = []ids = []for i in range(len(js['person'])):if js['person'][i]['attrs']['ignore'] == 'yes' or js['person'][i]['attrs']['occlusion']== 'heavily_occluded' or js['person'][i]['attrs']['occlusion']== 'invisible':continuebbox = js['person'][i]['data']url = '/mnt/hdfs-data-4/data/jian.yin/'+json_url_index[:-5]+'/'+js['image_key']width = js['width']height = js['height']boxes.append(bbox)ids.append(0)print(url)print(bbox)if len(boxes) > 0:if flag:flag = Falsefirst_flag = boxesids = np.array(ids)if cnt < 27853//2:line = write_line(url,(height,width,3),boxes,ids,cnt1)fwtrain.write(line)cnt1+=1if cnt >= 27853//2:line = write_line(url, (height, width, 3), boxes, ids, cnt2)fwval.write(line)cnt2+=1cnt += 1fwtrain.close()
fwval.close()
print(first_flag)

    elif dataset.lower() == 'pedestrian':lst_dataset = LstDetection('train_val.lst',root=os.path.expanduser('.'))print(len(lst_dataset))first_img = lst_dataset[0][0]print(first_img.shape)print(lst_dataset[0][1])train_dataset = LstDetection('train.lst',root=os.path.expanduser('.'))val_dataset = LstDetection('val.lst',root=os.path.expanduser('.'))classs = ('pedestrian',)val_metric = VOC07MApMetric(iou_thresh=0.5,class_names=classs)

    if args.dataset == 'voc' or args.dataset == 'pedestrian':args.epochs = int(args.epochs) if args.epochs else 20args.lr_decay_epoch = args.lr_decay_epoch if args.lr_decay_epoch else '14,20'args.lr = float(args.lr) if args.lr else 0.001args.lr_warmup = args.lr_warmup if args.lr_warmup else -1args.wd = float(args.wd) if args.wd else 5e-4

'faster_rcnn_resnet50_v1b_pedestrian': faster_rcnn_resnet50_v1b_voc,