KeyError：“名称'image_tensor：0'表示一个不存在的张量。图中的操作'image_tensor'不存在。”
是运行以下脚本后出现的错误：
@author: azach
#run only in object detection folder
import numpy as np
import os
from PIL import ImageGrab
import sys
import tensorflow as tf
import cv2
# 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.
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
# # Model preparation
# What model to use.
#folder with frozen_interference_graph.pb
MODEL_NAME = 'bauernhof_graph'
# 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('training', 'object-detection.pbtxt')
NUM_CLASSES = 1
# ## Load a (frozen) Tensorflow model into memory.
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_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)
# ## Helper code
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 & screen capturing
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
#open webcam
while(True):
#load videos from screen
printscreen_pil = ImageGrab.grab(bbox=(0,40,800,640))
image_np = np.array(printscreen_pil.getdata(),dtype='uint8')\
.reshape((printscreen_pil.size[1],printscreen_pil.size[0],3))
# 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)
#Count detected objects
taco = [category_index.get(value) for index,
value in enumerate(classes[0]) if scores[0,index] > 0.5]
#create string for display
count = str(len(taco))
pretext = str("Number of Bauernhof:")
space = str(" ")
text = pretext + space + count
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(image_np, text, (0, 50), font, 0.8, (255, 255, 0), 2,
cv2.LINE_AA)
cv2.imshow('frame',image_np)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
我用CMD创建了Frozen_interference_graph：
python export_inference_graph.py \
--input_type image_tensor \
--pipeline_config_path training/ssd_mobilenet_v1_pets.config \
--trained_checkpoint_prefix training/model.ckpt-16491 \
--output_directory bauernhof_graph
工作正常，没有任何错误。因此，我最终得到了文件夹bauernhof_graph，其中包含：
您可以在这里查看这些文件：https://www.dropbox.com/s/c3s64527ip4mr6p/checkpoint?dl=0