如何使用python中的enumerate将值放入列表中？

我有以下代码：

# coding: utf-8

# # Object Detection Demo
# Welcome to the object detection inference walkthrough!  This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the [installation instructions](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md) before you start.

# # Imports

# In[ ]:


import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile

from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image

if tf.__version__ < '1.4.0':
  raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')


# ## Env setup

# In[ ]:


# This is needed to display the images.
get_ipython().magic('matplotlib inline')

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")


# ## Object detection imports
# Here are the imports from the object detection module.

# In[ ]:


from utils import label_map_util

from utils import visualization_utils3 as vis_util


# # Model preparation 

# ## Variables
# 
# Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file.  
# 
# By default we use an "SSD with Mobilenet" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.

# In[ ]:


# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90


# ## Download Model

# In[ ]:

# =============================================================================
# 
# opener = urllib.request.URLopener()
# opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
# tar_file = tarfile.open(MODEL_FILE)
# for file in tar_file.getmembers():
#   file_name = os.path.basename(file.name)
#   if 'frozen_inference_graph.pb' in file_name:
#     tar_file.extract(file, os.getcwd())
# =============================================================================


# ## Load a (frozen) Tensorflow model into memory.

# In[ ]:


detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')


# ## Loading label map
# Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`.  Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine

# In[ ]:


label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

def load_image_into_numpy_array(image):
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)



# # Detection    
# In[ ]:


# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]

# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)

with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    # Definite input and output Tensors for detection_graph
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
    # Each box represents a part of the image where a particular object was detected.
    detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
    # Each score represent how level of confidence for each of the objects.
    # Score is shown on the result image, together with the class label.
    detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
    detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
    num_detections = detection_graph.get_tensor_by_name('num_detections:0')
    for image_path in TEST_IMAGE_PATHS:
      image = Image.open(image_path)
      # the array based representation of the image will be used later in order to prepare the
      # result image with boxes and labels on it.
      image_np = load_image_into_numpy_array(image)
      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
      image_np_expanded = np.expand_dims(image_np, axis=0)
      # Actual detection.
      (boxes, scores, classes, num) = sess.run(
          [detection_boxes, detection_scores, detection_classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})


      # Visualization of the results of a detection.
      vis_util.visualize_boxes_and_labels_on_image_array(
          image_np,
          np.squeeze(boxes),
          np.squeeze(classes).astype(np.int32),
          np.squeeze(scores),
          category_index,
          use_normalized_coordinates=True,
          line_thickness=8)
      plt.figure(figsize=IMAGE_SIZE)
      plt.imshow(image_np)


      print ([category_index.get(value) for index,value in enumerate(classes[0]) if scores[0,index] > 0.5])

我的问题是: 1.如何获取对象的长度。 2.我如何让它将值存储在一个新的列表中，只使用项的名称

这就是我希望对象被指定（而不是打印）的方式：

提前谢谢

---

我需要澄清一点，print命令只在那里，所以我可以可视化我所拥有的。我并不是要把它打印成输出中的样子，我希望它以对象的形式存在，我可以稍后检索并使用它进行计算。

您可以使用第二个列表理解来解决这个问题，或者如果您发布初始数据，可能有一种方法可以避免创建第二个列表：

your_list = [{'id': 1, 'name': 'person'}, {'id': 1, 'name': 'person'}, {'id': 3, 'name': 'car'}, {'id': 1, 'name': 'person'}, {'id': 1, 'name': 'person'}, {'id': 1, 'name': 'person'}, {'id': 1, 'name': 'person'}, {'id': 3, 'name': 'car'}, {'id': 1, 'name': 'person'}, {'id': 3, 'name': 'car'}]


name = [item['name'] for item in your_list]

输出：

['person', 'person', 'car', 'person', 'person', 'person', 'person', 'car', 'person', 'car']

length = 10

name[0] = 'person'
name[1] = 'person'
name[2] = 'car'
name[3] = 'person'
name[4] = 'person'
name[5] = 'person'
name[6] = 'person'
name[7] = 'car'
name[8] = 'person'
name[9] = 'car'

要获得长度，只需输出

len（name）

，输出

10

编辑

要根据您提出的问题进行澄清，请更改此行：

print ([category_index.get(value) for index,value in enumerate(classes[0]) if scores[0,index] > 0.5])

致：

然后使用：

name = [item['name'] for item in your_list]

仅使用名称获取列表。Python 3中引入了

，如果您想要与问题中显示的输出格式完全相同，则可以稍微简化打印逻辑：

items = [{'id': 1, 'name': 'person'}, {'id': 1, 'name': 'person'}, {'id': 3, 'name': 'car'}, {'id': 1, 'name': 'person'}, {'id': 1, 'name': 'person'}, {'id': 1, 'name': 'person'}, {'id': 1, 'name': 'person'}, {'id': 3, 'name': 'car'}, {'id': 1, 'name': 'person'}, {'id': 3, 'name': 'car'}]

print(f"length = {len(items)}\n")
for i, item in enumerate(items):
    print(f"name[{i}] = {item['name']!r}")

输出：

['person', 'person', 'car', 'person', 'person', 'person', 'person', 'car', 'person', 'car']

length = 10

name[0] = 'person'
name[1] = 'person'
name[2] = 'car'
name[3] = 'person'
name[4] = 'person'
name[5] = 'person'
name[6] = 'person'
name[7] = 'car'
name[8] = 'person'
name[9] = 'car'

---

我们能看到示例输入数据吗？

len（object）

给出了长度列表理解只会让试图帮助您的人感到困惑，而只是按照@JacobIRR的注释显示示例输入我添加了整个上下文代码，谢谢。您刚才发布的代码都没有任何帮助。事实上，它几乎不可能看到你在做什么。您发布的代码很好，我们希望看到输入数据（基本上是

类的输出）谢谢！但问题是“您的列表”没有编码，这是输出，这是我想从枚举中收集的。您说您当前拥有的代码输出了我标记的
您的列表
，您可以使用第二个列表理解，仅从您的初始输出中获取名称。我不太确定如何做到这一点，你在“你的清单”上的内容是打印出来的，但我希望它存在于一个清单中，我不想手动将其放入，因为这个值总是会改变的。我添加了更多的上下文，因此您可以看到它的来源。您打印列表的代码行，而不是打印，将其分配给变量，然后使用我发布的代码。谢谢！我需要澄清一点，print命令只在那里，所以我可以可视化我所拥有的。我并不是要把它打印成输出中的样子，我希望它以对象的形式存在，我可以在以后检索它并用它进行计算。@basviccc那么，这将是一个很好的信息，可以包含在您的问题中！在这种情况下，按照克里斯在回答中使用的方法来做。
length = 10

name[0] = 'person'
name[1] = 'person'
name[2] = 'car'
name[3] = 'person'
name[4] = 'person'
name[5] = 'person'
name[6] = 'person'
name[7] = 'car'
name[8] = 'person'
name[9] = 'car'