我正在尝试使用我训练的神经网络数据在jupyter笔记本中执行Tensorflow"object_detection_tutorial.py"，但它抛出了一个ValueError。上面提到的文件是Sentdexs张量流教程的一部分，用于YouTube上的对象检测。
你可以在这里找到它：(https://www.youtube.com/watch?v=srPndLNMMpk&list=PLQVvvaa0QuDcNK5GeCQnxYnSSaar2tpku&index=6(
我的图像大小：490x704。因此，这将产生 344960 阵列。
但它是：ValueError: cannot reshape array of size 344960 into shape (490,704,3)
我做错了什么？
法典：
进口
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
环境设置
# This is needed to display the images.
%matplotlib inline
# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
对象检测导入
from utils import label_map_util
from utils import visualization_utils as vis_util
变量
# What model to download.
MODEL_NAME = 'shard_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 = 90
将(冻结的(张量流模型加载到内存中。
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='')
加载标签地图
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)
帮助程序代码
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)
检波
# 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, 'frame_{}.png'.format(i)) for i in range(0, 2) ]
# 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)
脚本的最后一部分是抛出错误：
----------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-62-7493eea60222> in <module>()
14 # the array based representation of the image will be used later in order to prepare the
15 # result image with boxes and labels on it.
---> 16 image_np = load_image_into_numpy_array(image)
17 # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
18 image_np_expanded = np.expand_dims(image_np, axis=0)
<ipython-input-60-af094dcdd84a> in load_image_into_numpy_array(image)
2 (im_width, im_height) = image.size
3 return np.array(image.getdata()).reshape(
----> 4 (im_height, im_width, 3)).astype(np.uint8)
ValueError: cannot reshape array of size 344960 into shape (490,704,3)
编辑：
所以我更改了此函数的最后一行：
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)
自：
(im_height, im_width)).astype(np.uint8)
并且解决了价值错误。但是现在引发了另一个连接到数组格式的 ValueError：
----------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-107-7493eea60222> in <module>()
20 (boxes, scores, classes, num) = sess.run(
21 [detection_boxes, detection_scores, detection_classes, num_detections],
---> 22 feed_dict={image_tensor: image_np_expanded})
23 # Visualization of the results of a detection.
24 vis_util.visualize_boxes_and_labels_on_image_array(
~/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
898 try:
899 result = self._run(None, fetches, feed_dict, options_ptr,
--> 900 run_metadata_ptr)
901 if run_metadata:
902 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
~/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1109 'which has shape %r' %
1110 (np_val.shape, subfeed_t.name,
-> 1111 str(subfeed_t.get_shape())))
1112 if not self.graph.is_feedable(subfeed_t):
1113 raise ValueError('Tensor %s may not be fed.' % subfeed_t)
ValueError: Cannot feed value of shape (1, 490, 704) for Tensor 'image_tensor:0', which has shape '(?, ?, ?, 3)'
这是否意味着这个张量流模型不是为灰度图像设计的？有没有办法让它工作？
溶液
多亏了Matan Hugi，它现在工作得很好。我所要做的就是将此功能更改为：
def load_image_into_numpy_array(image):
# The function supports only grayscale images
last_axis = -1
dim_to_repeat = 2
repeats = 3
grscale_img_3dims = np.expand_dims(image, last_axis)
training_image = np.repeat(grscale_img_3dims, repeats, dim_to_repeat).astype('uint8')
assert len(training_image.shape) == 3
assert training_image.shape[-1] == 3
return training_image
以NHWC格式格式化的Tensorflow预期输入， 这意味着：(批次、高度、宽度、通道(。
步骤 1 - 添加最后一个维度：
last_axis = -1
grscale_img_3dims = np.expand_dims(image, last_axis)
步骤2 - 重复最后一个维度3次：
dim_to_repeat = 2
repeats = 3
np.repeat(grscale_img_3dims, repeats, dim_to_repeat)
所以你的函数应该是：
def load_image_into_numpy_array(image):
# The function supports only grayscale images
assert len(image.shape) == 2, "Not a grayscale input image"
last_axis = -1
dim_to_repeat = 2
repeats = 3
grscale_img_3dims = np.expand_dims(image, last_axis)
training_image = np.repeat(grscale_img_3dims, repeats, dim_to_repeat).astype('uint8')
assert len(training_image.shape) == 3
assert training_image.shape[-1] == 3
return training_image