Python OpenCV未显示对象检测tensorflow的结果

我从网络摄像头中添加了目标检测代码，当我运行此代码时，它会显示检测2-5秒，然后在imshow窗口中显示未响应

注:

• 我与cv2.waitKey（1）一起使用，也与cv2.waitKey（0）一起使用，结果相同

• 我正在使用tensorflow gpu，它检测到我的gpu:1050ti

• 但是OpenCV使用CPU来显示图像

更新部分：

#!/usr/bin/env python
# coding: utf-8

# # Object Detection API Demo


import os
import pathlib


if "models" in pathlib.Path.cwd().parts:
  while "models" in pathlib.Path.cwd().parts:
    os.chdir('..')
elif not pathlib.Path('models').exists():
  get_ipython().system('git clone --depth 1 https://github.com/tensorflow/models')


import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import cv2

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


# Import the object detection module.

# In[5]:


from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util


# Get a reference to webcam 
video_capture = cv2.VideoCapture(0)
# Patches:

# In[6]:


# patch tf1 into `utils.ops`
utils_ops.tf = tf.compat.v1

# Patch the location of gfile
tf.gfile = tf.io.gfile


# # Model preparation 

# ## Variables
# 
# Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing the path.
# 
# 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.

# ## Loader

# In[7]:


def load_model(model_name):
  base_url = 'http://download.tensorflow.org/models/object_detection/'
  model_file = model_name + '.tar.gz'
  model_dir = tf.keras.utils.get_file(
    fname=model_name, 
    origin=base_url + model_file,
    untar=True)

  model_dir = pathlib.Path(model_dir)/"saved_model"

  model = tf.saved_model.load(str(model_dir))
  model = model.signatures['serving_default']

  return model


# ## 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[8]:


# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = 'models/research/object_detection/data/mscoco_label_map.pbtxt'
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)


# For the sake of simplicity we will test on 2 images:

# In[9]:


# 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 = pathlib.Path('models/research/object_detection/test_images')
TEST_IMAGE_PATHS = sorted(list(PATH_TO_TEST_IMAGES_DIR.glob("*.jpg")))
TEST_IMAGE_PATHS


# # Detection

# Load an object detection model:

# In[10]:


model_name = 'ssd_mobilenet_v1_coco_2017_11_17'
detection_model = load_model(model_name)


# Check the model's input signature, it expects a batch of 3-color images of type uint8: 

# In[11]:


print(detection_model.inputs)


# And retuns several outputs:

# In[12]:


detection_model.output_dtypes


# In[13]:


print(detection_model.output_shapes)


# Add a wrapper function to call the model, and cleanup the outputs:

# In[14]:


def run_inference_for_single_image(model, image):
  image = np.asarray(image)
  # The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
  input_tensor = tf.convert_to_tensor(image)
  # The model expects a batch of images, so add an axis with `tf.newaxis`.
  input_tensor = input_tensor[tf.newaxis,...]

  # Run inference
  output_dict = model(input_tensor)

  # All outputs are batches tensors.
  # Convert to numpy arrays, and take index [0] to remove the batch dimension.
  # We're only interested in the first num_detections.
  num_detections = int(output_dict.pop('num_detections'))
  output_dict = {key:value[0, :num_detections].numpy() 
                 for key,value in output_dict.items()}
  output_dict['num_detections'] = num_detections

  # detection_classes should be ints.
  output_dict['detection_classes'] = output_dict['detection_classes'].astype(np.int64)

  # Handle models with masks:
  if 'detection_masks' in output_dict:
    # Reframe the the bbox mask to the image size.
    detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
              output_dict['detection_masks'], output_dict['detection_boxes'],
               image.shape[0], image.shape[1])      
    detection_masks_reframed = tf.cast(detection_masks_reframed > 0.5,
                                       tf.uint8)
    output_dict['detection_masks_reframed'] = detection_masks_reframed.numpy()

  return output_dict


# Run it on each test image and show the results:

# In[15]:



# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.

# Grab a single frame of video
while True:
  ret, image_np = video_capture.read()
  # Actual detection.
  output_dict = run_inference_for_single_image(detection_model, image_np)
  # Visualization of the results of a detection.
  vis_util.visualize_boxes_and_labels_on_image_array(
      image_np,
      output_dict['detection_boxes'],
      output_dict['detection_classes'],
      output_dict['detection_scores'],
      category_index,
      instance_masks=output_dict.get('detection_masks_reframed', None),
      use_normalized_coordinates=True,
      line_thickness=8)

  cv2.imshow('Detected',image_np)
  if cv2.waitKey(25) & 0xFF == ord('q'):
      cv2.destroyAllWindows()
      break

[已解决] 我刚刚创建了新的conda环境，安装了tensorflow版本TFV1.15.2，并使用了link中的代码

---

现在它正在工作，但代码包含一些不推荐使用的函数。

使用以下函数调用的返回值

while True:
  ret, image_np = video_capture.read()
  if ret == False:
    break
  # Actual detection.
  output_dict = run_inference_for_single_image(detection_model, image_np)
  # Visualization of the results of a detection.
  vis_util.visualize_boxes_and_labels_on_image_array(
      image_np,
      output_dict['detection_boxes'],
      output_dict['detection_classes'],
      output_dict['detection_scores'],
      category_index,
      instance_masks=output_dict.get('detection_masks_reframed', None),
      use_normalized_coordinates=True,
      line_thickness=8)

  cv2.imshow('Detected',image_np)
  if cv2.waitKey(0) & 0xFF == ord('q'):
      break

cv2.destroyAllWindows()
video_capture.release()

另外，将

cv2.destroyAllWindows（）

移动到while条件之外

ret, image_np = video_capture.read()
if ret == False:
    break

---

请监控你的内存使用情况。它使用了56%的内存，并表示没有响应。我有8 gb的RAM视频有多长？将

cv2.destroyAllWindows（）

置于while条件之外。我也尝试了你的建议，但结果仍然相同。我在“描述”部分进行了更新，并尝试了cv2.waitKey（0），视频有多长？e、 g 5秒？这是视频捕获，我从WebCaman捕获，输出持续3-4秒。对于实时流，建议使用

cv2.waitKey（1）

。

while True:
    #your code here

    if cv2.waitKey(25) & 0xFF == ord('q'):
        break

cv2.destroyAllWindows()