python - 如何使用 python 中的枚举将值放入列表中？

Question

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])

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

这有效地打印出:

[{'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'}]

我的问题是: 1. 如何获取物体的长度。 2. 如何让它将值存储在新列表中，仅包含项目名称。

这就是我希望分配对象的方式(不打印):

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'

提前致谢!

我需要澄清一下，打印命令只是为了让我可以直观地看到我所拥有的内容。我的意思并不是只是将其打印成输出中的样子，我希望它作为对象存在，以便以后可以检索并进行计算。

Answer 1

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']

要获取长度，只需输出 len(name) 即可输出 10

编辑

要根据您的要求进行澄清，请更改此行:

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

至:

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

然后使用:

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

获取仅包含名称的列表。