text: import torchvision  # 0.3.0  version
  from torchvision import transforms as T
  import cv2  # 4.1.1  version
  import matplotlib.pyplot as plt  # 3.0.0  version
  from PIL import Image  # 5.3.0  version
  import random
  import os
  import torch
  # os.environ['TORCH_HOME'] = "model"  # 将模型下载到指定位置
  
  # import PIL, matplotlib
  # print("torchvision", torchvision.__version__,
  #   "cv2", cv2.__version__, "matplotlib",matplotlib.__version__,"PIL", PIL.__version__)
  
  model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)  #  加载模型
  model.eval()
  
  COCO_INSTANCE_CATEGORY_NAMES = [
      '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
      'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign',
      'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
      'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A',
      'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
      'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
      'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
      'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
      'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table',
      'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
      'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book',
      'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
  ]
  
  
  def get_prediction(img_path, threshold):
      img = Image.open(img_path)  # Load the image  加载图片
      transform = T.Compose([T.ToTensor()]) # Defing PyTorch Transform
      img = transform(img)  # Apply the transform to the image  转换成 torch 形式
      pred = model([img])  # Pass the image to the model  开始推理
      pred_class = [COCO_INSTANCE_CATEGORY_NAMES[i] for i in list(pred[0]['labels'].numpy())]  # Get the Prediction Score  获取预测的类别
      pred_boxes = [[(i[0], i[1]), (i[2], i[3])] for i in list(pred[0]['boxes'].detach().numpy())]  # Bounding boxes  获取各个类别的边框
      pred_score = list(pred[0]['scores'].detach().numpy())  #  获取各个类别的分数
      # Get list of index with score greater than threshold.
      pred_t = [pred_score.index(x) for x in pred_score if x > threshold][-1]  #  判断
      pred_boxes = pred_boxes[:pred_t+1]
      pred_class = pred_class[:pred_t+1]
      return pred_boxes, pred_class
  
  
  def object_detection_api(img_path, threshold=0.5, rect_th=3, text_size=3, text_th=3):
      boxes, pred_cls = get_prediction(img_path, threshold)  # Get predictions
      img = cv2.imread(img_path)  # Read image with cv2
      img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert to RGB
      result_dict = {}  #  用来保存每个类别的名称及数量
      for i in range(len(boxes)):
          color = tuple(random.randint(0, 255) for i in range(3))
          cv2.rectangle(img,
                        boxes[i][0],
                        boxes[i][1],
                        color=color,
                        thickness=rect_th)  # Draw Rectangle with the coordinates
  
          cv2.putText(img,
                      pred_cls[i],
                      boxes[i][0],
                      cv2.FONT_HERSHEY_SIMPLEX, 
                      text_size,  
                      color,
                      thickness=text_th)  # Write the prediction class
          #  将各个预测的结果保存到一个字典里
          if pred_cls[i] not in result_dict:
              result_dict[pred_cls[i]] = 1
          else:
              result_dict[pred_cls[i]] += 1
      print(result_dict)
  #     plt.figure(figsize=(20, 30))  # display the output image
  #     plt.imshow(img)
  #     plt.xticks([])
  #     plt.yticks([])
  #     plt.show()
  
  
  # if __name__ == "__main__":
  %time object_detection_api('./people.jpg', threshold=0.8)
  
  
  