After using TensorFlow's retrain.py
使用TensorFlow的retrain.py之后
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py
I've successfully generated the "retrained_labels.txt" and "retrained_graph.pb" files. For anybody not familiar with this process, I'm essentially following this tutorial:
我已经成功生成了“retrained_labels.txt”和“retrained_graph.pb”文件。对于任何不熟悉此过程的人,我基本上都遵循本教程:
https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0
which is effectively the same steps as this popular video:
这与这个流行的视频实际上是相同的步骤:
https://www.youtube.com/watch?v=QfNvhPx5Px8
After the retraining process, I'm attempting to write a Python script that opens all the images in a test images directory, and successively shows each image in an OpenCV window and also runs TensorFlow to classify the image.
在重新训练过程之后,我正在尝试编写一个Python脚本,打开测试图像目录中的所有图像,并在OpenCV窗口中连续显示每个图像,并运行TensorFlow对图像进行分类。
Problem is, I can't seem to work out how to open the image as a NumPy array (this is the format that the Python OpenCV wrapper uses) and then convert it into a format I can pass into TensorFlow's sess.run().
问题是,我似乎无法弄清楚如何将图像作为NumPy数组打开(这是Python OpenCV包装器使用的格式),然后将其转换为我可以传递给TensorFlow的sess.run()的格式。
Currently I'm opening the image with cv2.imread() and then opening it again with tf.gfile.FastGFile(). This is a very poor practice; I'd much rather open the image once and then convert it.
目前我正在使用cv2.imread()打开图像,然后使用tf.gfile.FastGFile()再次打开它。这是一种非常糟糕的做法;我宁愿打开图像一次然后转换它。
Here is the relevant portion of the code where I'm stuck:
以下是我遇到的代码的相关部分:
# open the image with OpenCV
openCVImage = cv2.imread(imageFileWithPath)
# show the OpenCV image
cv2.imshow(fileName, openCVImage)
# get the final tensor from the graph
finalTensor = sess.graph.get_tensor_by_name('final_result:0')
# open the image in TensorFlow
tfImage = tf.gfile.FastGFile(imageFileWithPath, 'rb').read()
# run the network to get the predictions
predictions = sess.run(finalTensor, {'DecodeJpeg/contents:0': tfImage})
After reading these posts:
看完这些帖子后:
How to convert numpy arrays to standard TensorFlow format?
如何将numpy数组转换为标准的TensorFlow格式?
Feeding image data in tensorflow for transfer learning
在张量流中馈送图像数据用于转移学习
I've tried the following:
我尝试过以下方法:
# show the OpenCV image
cv2.imshow(fileName, openCVImage)
# get the final tensor from the graph
finalTensor = sess.graph.get_tensor_by_name('final_result:0')
# convert the NumPy array / OpenCV image to a TensorFlow image
openCVImageAsArray = np.asarray(openCVImage, np.float32)
tfImage = tf.convert_to_tensor(openCVImageAsArray, np.float32)
# run the network to get the predictions
predictions = sess.run(finalTensor, {'DecodeJpeg/contents:0': tfImage})
This results in this error on the sess.run() line:
这会导致sess.run()行出现此错误:
TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, numpy ndarrays, or TensorHandles.
I've also tried this:
我也试过这个:
# show the OpenCV image
cv2.imshow(fileName, openCVImage)
# get the final tensor from the graph
finalTensor = sess.graph.get_tensor_by_name('final_result:0')
# convert the NumPy array / OpenCV image to a TensorFlow image
tfImage = np.array(openCVImage)[:, :, 0:3]
# run the network to get the predictions
predictions = sess.run(finalTensor, {'DecodeJpeg/contents:0': tfImage})
which results in this error:
这会导致此错误:
ValueError: Cannot feed value of shape (257, 320, 3) for Tensor 'DecodeJpeg/contents:0', which has shape '()'
--- EDIT ---
---编辑---
I've also tried this:
我也试过这个:
# show the OpenCV image
cv2.imshow(fileName, openCVImage)
# get the final tensor from the graph
finalTensor = sess.graph.get_tensor_by_name('final_result:0')
# convert the NumPy array / OpenCV image to a TensorFlow image
tfImage = np.expand_dims(openCVImage, axis=0)
# run the network to get the predictions
predictions = sess.run(finalTensor, feed_dict={finalTensor: tfImage})
which results in this error:
这会导致此错误:
ValueError: Cannot feed value of shape (1, 669, 1157, 3) for Tensor 'final_result:0', which has shape '(?, 2)'
and I've also tried this:
我也试过这个:
# show the OpenCV image
cv2.imshow(fileName, openCVImage)
# get the final tensor from the graph
finalTensor = sess.graph.get_tensor_by_name('final_result:0')
# convert the NumPy array / OpenCV image to a TensorFlow image
tfImage = np.expand_dims(openCVImage, axis=0)
# run the network to get the predictions
predictions = sess.run(finalTensor, feed_dict={'DecodeJpeg/contents:0': tfImage})
which results in this error:
这会导致此错误:
ValueError: Cannot feed value of shape (1, 669, 1157, 3) for Tensor 'DecodeJpeg/contents:0', which has shape '()'
I'm not sure if this is necessary, but if anyone is curious here is the entire script. Note that this works great except for having to open the image twice:
我不确定这是否有必要,但如果有人好奇,这就是整个剧本。请注意,除了必须打开图像两次之外,这很有效:
# test.py
import os
import tensorflow as tf
import numpy as np
import cv2
# module-level variables ##############################################################################################
RETRAINED_LABELS_TXT_FILE_LOC = os.getcwd() + "/" + "retrained_labels.txt"
RETRAINED_GRAPH_PB_FILE_LOC = os.getcwd() + "/" + "retrained_graph.pb"
TEST_IMAGES_DIR = os.getcwd() + "/test_images"
#######################################################################################################################
def main():
# get a list of classifications from the labels file
classifications = []
# for each line in the label file . . .
for currentLine in tf.gfile.GFile(RETRAINED_LABELS_TXT_FILE_LOC):
# remove the carriage return
classification = currentLine.rstrip()
# and append to the list
classifications.append(classification)
# end for
# show the classifications to prove out that we were able to read the label file successfully
print("classifications = " + str(classifications))
# load the graph from file
with tf.gfile.FastGFile(RETRAINED_GRAPH_PB_FILE_LOC, 'rb') as retrainedGraphFile:
# instantiate a GraphDef object
graphDef = tf.GraphDef()
# read in retrained graph into the GraphDef object
graphDef.ParseFromString(retrainedGraphFile.read())
# import the graph into the current default Graph, note that we don't need to be concerned with the return value
_ = tf.import_graph_def(graphDef, name='')
# end with
# if the test image directory listed above is not valid, show an error message and bail
if not os.path.isdir(TEST_IMAGES_DIR):
print("the test image directory does not seem to be a valid directory, check file / directory paths")
return
# end if
with tf.Session() as sess:
# for each file in the test images directory . . .
for fileName in os.listdir(TEST_IMAGES_DIR):
# if the file does not end in .jpg or .jpeg (case-insensitive), continue with the next iteration of the for loop
if not (fileName.lower().endswith(".jpg") or fileName.lower().endswith(".jpeg")):
continue
# end if
# show the file name on std out
print(fileName)
# get the file name and full path of the current image file
imageFileWithPath = os.path.join(TEST_IMAGES_DIR, fileName)
# attempt to open the image with OpenCV
openCVImage = cv2.imread(imageFileWithPath)
# if we were not able to successfully open the image, continue with the next iteration of the for loop
if openCVImage is None:
print("unable to open " + fileName + " as an OpenCV image")
continue
# end if
# show the OpenCV image
cv2.imshow(fileName, openCVImage)
# get the final tensor from the graph
finalTensor = sess.graph.get_tensor_by_name('final_result:0')
# ToDo: find a way to convert from a NumPy array / OpenCV image to a TensorFlow image
# instead of opening the file twice, these attempts don't work
# attempt 1:
# openCVImageAsArray = np.asarray(openCVImage, np.float32)
# tfImage = tf.convert_to_tensor(openCVImageAsArray, np.float32)
# attempt 2:
# tfImage = np.array(openCVImage)[:, :, 0:3]
# open the image in TensorFlow
tfImage = tf.gfile.FastGFile(imageFileWithPath, 'rb').read()
# run the network to get the predictions
predictions = sess.run(finalTensor, {'DecodeJpeg/contents:0': tfImage})
# sort predictions from most confidence to least confidence
sortedPredictions = predictions[0].argsort()[-len(predictions[0]):][::-1]
print("---------------------------------------")
# keep track of if we're going through the next for loop for the first time so we can show more info about
# the first prediction, which is the most likely prediction (they were sorted descending above)
onMostLikelyPrediction = True
# for each prediction . . .
for prediction in sortedPredictions:
strClassification = classifications[prediction]
# if the classification (obtained from the directory name) ends with the letter "s", remove the "s" to change from plural to singular
if strClassification.endswith("s"):
strClassification = strClassification[:-1]
# end if
# get confidence, then get confidence rounded to 2 places after the decimal
confidence = predictions[0][prediction]
# if we're on the first (most likely) prediction, state what the object appears to be and show a % confidence to two decimal places
if onMostLikelyPrediction:
scoreAsAPercent = confidence * 100.0
print("the object appears to be a " + strClassification + ", " + "{0:.2f}".format(scoreAsAPercent) + "% confidence")
onMostLikelyPrediction = False
# end if
# for any prediction, show the confidence as a ratio to five decimal places
print(strClassification + " (" + "{0:.5f}".format(confidence) + ")")
# end for
# pause until a key is pressed so the user can see the current image (shown above) and the prediction info
cv2.waitKey()
# after a key is pressed, close the current window to prep for the next time around
cv2.destroyAllWindows()
# end for
# end with
# write the graph to file so we can view with TensorBoard
tfFileWriter = tf.summary.FileWriter(os.getcwd())
tfFileWriter.add_graph(sess.graph)
tfFileWriter.close()
# end main
#######################################################################################################################
if __name__ == "__main__":
main()
1 个解决方案
#1
3
You were pretty close:
你非常接近:
{'DecodeJpeg/contents:0': tfImage} decodes a binary jpeg image.
{'DecodeJpeg / contents:0':tfImage}解码二进制jpeg图像。
You need to use {'DecodeJpeg:0': tfImage} instead if the image is already decoded. Read more here
如果图像已经解码,则需要使用{'DecodeJpeg:0':tfImage}。在这里阅读更多
So your code should look like this:
所以你的代码应该是这样的:
tfImage = np.array(openCVImage)[:, :, 0:3]
# run the network to get the predictions
predictions = sess.run(finalTensor, {'DecodeJpeg:0': tfImage})
#1
3
You were pretty close:
你非常接近:
{'DecodeJpeg/contents:0': tfImage} decodes a binary jpeg image.
{'DecodeJpeg / contents:0':tfImage}解码二进制jpeg图像。
You need to use {'DecodeJpeg:0': tfImage} instead if the image is already decoded. Read more here
如果图像已经解码,则需要使用{'DecodeJpeg:0':tfImage}。在这里阅读更多
So your code should look like this:
所以你的代码应该是这样的:
tfImage = np.array(openCVImage)[:, :, 0:3]
# run the network to get the predictions
predictions = sess.run(finalTensor, {'DecodeJpeg:0': tfImage})