!git clone https://github.com.cnpmjs.org/PaddlePaddle/PaddleDetection    
      #放入持久层    
      !mv PaddleDetection/ work/    
      #导入所需要的依赖    
      !pip install -r work/PaddleDetection/requirements.txt   
     

   
     
    
      """    
      按VOC格式划分数据集，train : val = 0.85 : 0.15    
      生成标签label_list.txt    
      """    
      from random import shuffle    
      
    
      dataset = 'dataset/VOCData/'    
      train_txt = os.path.join(dataset, 'train_val.txt')    
      val_txt = os.path.join(dataset, 'val.txt')    
      lbl_txt = os.path.join(dataset, 'label_list.txt')    
      
    
      classes = ["person"]    
      
    
      with open(lbl_txt, 'w') as f:    
      for l in classes:    
       f.write(l+'\n')    
      
    
      xml_base = 'Annotations'    
      img_base = 'images'    
      xmls = [v for v in os.listdir(os.path.join(dataset, xml_base)) if v.endswith('.xml')]    
      shuffle(xmls)    
      split = int(0.85 * len(xmls))    
      
    
      with open(train_txt, 'w') as f:    
      for x in tqdm(xmls[:split]):    
       m = x[:-4]+'.jpg'    
       xml_path = os.path.join(xml_base, x)    
       img_path = os.path.join(img_base, m)    
       f.write('{} {}\n'.format(img_path, xml_path))    
      
    
      with open(val_txt, 'w') as f:    
      for x in tqdm(xmls[split:]):    
       m = x[:-4]+'.jpg'    
       xml_path = os.path.join(xml_base, x)    
       img_path = os.path.join(img_base, m)    
       f.write('{} {}\n'.format(img_path, xml_path))   
     

   
     
    
      import json    
      import os    
      import shutil    
      
    
      train_path = "COCO_All/train2017/"    
      train_json_path = "COCO_All/annotations/person_keypoints_train2017.json"    
      out_train_path = "COCO_Humen/tarin2017/"    
      
    
      val_path="COCO_All/val2017/"    
      val_json_path = "COCO_All/annotations/person_keypoints_val2017.json"    
      out_val_path = "COCO_Humen/val2017/"    
      
    
      def humen_img(img_path, json_path,out_path):      
          count = 0     
      with open( json_path, 'r') as f:    
       data = json.load(f)    
      for name in data['annotations']:    
       img_file= name['image_id']    
       img_name = str(img_file).zfill(12)+".jpg"    
       img_file = img_path+ img_name #照片名字都是12位+.jpg后缀表示 如：000000319100.jpg    
      
    
      if os.path.exists(out_path + img_name):    
      # print("有重复文件！！，跳过，不移动！！！")    
      continue    
      else:    
       count += 1    
       shutil.copy(img_file, out_path)    
          print("本次生成照片数量为：",count)   
     

   
     
    
      %cd /home/aistudio/work/PaddleDetection/     
      # 导出PP-YOLOV2模型    
      !python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_voc.yml \    
       -o weights=output/ppyolov2_r50vd_dcn_voc/model_final.pdparams    
       #导出模型hrnet_w32_384x288模型    
      !python tools/export_model.py -c configs/keypoint/hrnet/hrnet_w32_384x288.yml \    
      -o weights=output/hrnet_w32_384x288/model_final.pdparams   
     

   
     
    
      # [联合部署推理]    
      !python deploy/python/keypoint_det_unite_infer.py --det_model_dir=output_inference/ppyolov2_r50vd_dcn_voc/ \    
      --keypoint_model_dir=output_inference/hrnet_w32_384x288/ --video_file=../../Test/test.mp4 --use_gpu=True   
     

   
     
    
      # 准备PaddleLite依赖    
      !pip install paddlelite     
      
    
      
    
      # 准备PaddleLite部署模型    
      # --optimize_out=output_inference/ssd_mobilenet_v1 \ 与上面同一个文件夹    
      !paddle_lite_opt \    
      --model_file=output_inference/ssd_mobilenet_v1_300_120e_voc/model.pdmodel \    
       --param_file=output_inference/ssd_mobilenet_v1_300_120e_voc/model.pdiparams \    
       --optimize_out=output_inference/ssd_mob \    
       --optimize_out_type=naive_buffer \    
       --valid_targets=arm