使用TensorFlow的retrain.py
之后https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py
我已成功生成＆＃34; retrained_labels.txt＆＃34;和＆＃34; retrained_graph.pb＆＃34;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
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
在重新训练过程之后，我尝试编写一个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。
问题是，我似乎无法解决如何将图像作为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的格式＆＃ 39; s sess.run（）。
目前，我使用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;我更喜欢打开图像一次然后转换它。
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})
阅读这些帖子后：
How to convert numpy arrays to standard TensorFlow format?
Feeding image data in tensorflow for transfer learning
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})
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.
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})
会导致此错误：
ValueError: Cannot feed value of shape (257, 320, 3) for Tensor 'DecodeJpeg/contents:0', which has shape '()'
---编辑---
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})
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)'
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})
会导致此错误：
ValueError: Cannot feed value of shape (1, 669, 1157, 3) for Tensor 'DecodeJpeg/contents:0', which has shape '()'
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()
答案 0 :(得分：4)
你非常接近：
{'DecodeJpeg/contents:0': tfImage}解码二进制jpeg图像。
如果图像已经解码，则需要使用{'DecodeJpeg:0': tfImage}。
Read more here
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})