Python编程视频检测教程＃2

Question

我在Ubuntu 16.04中使用python 3.5 https://www.learnopencv.com/install-opencv3-on-ubuntu/ 用这个链接下载opencv3

File "<ipython-input-12-e1defa92c813>", line 1, in <module>
  runfile('/home/abhishek/models/research/object_detection/Video_detection.py', wdir='/home/abhishek/models/research/object_detection')

File "/home/abhishek/anaconda3/lib/python3.5/site-packages/spyder/utils/site/sitecustomize.py", line 710, in runfile
  execfile(filename, namespace)

File "/home/abhishek/anaconda3/lib/python3.5/site-packages/spyder/utils/site/sitecustomize.py", line 101, in execfile
  exec(compile(f.read(), filename, 'exec'), namespace)

File "/home/abhishek/models/research/object_detection/Video_detection.py", line 139, in <module>
  feed_dict={image_tensor: image_np_expanded})

File "/home/abhishek/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 895, in run
  run_metadata_ptr)

File "/home/abhishek/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1093, in _run
  np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)

File "/home/abhishek/anaconda3/lib/python3.5/site-packages/numpy/core/numeric.py", line 531, in asarray
  return array(a, dtype, copy=False, order=order)

TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'

Python编程视频检测教程＃2

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

import cv2
cap = cv2.VideoCapture(0)

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


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

# In[3]:

from utils import label_map_util

from utils import visualization_utils 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/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[4]:

# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
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
print(12)

# ## Download Model

# In[5]:

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())

print(13)

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

# In[6]:

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='')

print(14)
# ## 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[7]:

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)
print(15)

# ## Helper code

# In[8]:

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[9]:

# 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)


# In[10]:

with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    while True:
      ret, image_np = cap.read()

      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
      image_np_expanded = np.expand_dims(image_np, axis=0)
      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.
      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.
      scores = detection_graph.get_tensor_by_name('detection_scores:0')
      classes = detection_graph.get_tensor_by_name('detection_classes:0')
      num_detections = detection_graph.get_tensor_by_name('num_detections:0')
      # Actual detection.
      (boxes, scores, classes, num_detections) = sess.run(
          [boxes, scores, 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)

      cv2.imshow('object detection', cv2.resize(image_np, (800,600)))
      if cv2.waitKey(25) & 0xFF == ord('q'):
        cv2.destroyAllWindows()
        break
>

请帮我继续。我已经添加了大部分从https://pythonprogramming.net/video-tensorflow-object-detection-api-tutorial/

复制的代码

Answer 1

我遇到了同样的问题。如果您的opencv已正确安装，请首先进行交叉检查。在您从网络摄像头获取物体检测源之前，请尝试使用库存照片检查它是否有效。

稍后将opencv升级为opencv3。

conda install opencv3

如果问题仍然存在，请检查网络摄像头是否有输入问题。仅当您的网络摄像头没有捕获任何帧时，才会返回非类型。

Answer 2

有同样的问题，文件是图片时不存在，可能来自视频或cam时无法在图片中转换。检查您的输入

对象检测来自openCV中的视频（int（）参数必须是字符串，类字节对象或数字，而不是＆＃39; NoneType＆＃39;）

Python编程视频检测教程＃2

2 个答案: