I'm trying to read a directory of images files into tensorflow and I'm having a little trouble. When I run the script, the shell is just hanging (even waited 10 mins for an output) and the only output I get is the from the print(len(os.listdir()) line
My attempts stem from this guide:
https://www.tensorflow.org/api_guides/python/reading_data
import tensorflow as tf
import os
os.chdir(r'C:\Users\Moondra\Desktop\Testing')
print(len(os.listdir())) # only 2 in this case
file_names =tf.constant(os.listdir(), tf.string)
file_tensors =tf.train.string_input_producer(string_tensor = file_names)
reader =tf.WholeFileReader()
key, value = reader.read(file_tensors)
##features = tf.parse_single_example(value)
#records = reader.num_records_produced()
with tf.Session() as sess:
values =sess.run(value)
##print(records_num)
print(type(values))
The reader is supposed to read images one at a time, so I'm assuming
value will hold the image values on on the current image.
Despite this, the shell is just hanging and no output.
Thank you and --just incase #mrry is available.
tf.train.string_input_producer adds a QueueRunner to the current Graph and you need to manually start it. Otherwise it is just hanging and no output is produced.
with tf.Session() as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
values = sess.run(value)
# ....
Related
I'm currently doing an internship remotely and I got to code a Visualization Tool with D3.JS but here is not the part where I got a problem.
To fix the subject I got some file called episodes, which contain data about the path of a robot, if he succeeds or failed and the different point in cartesian coordinate.
(BTW I'm French I apologize in advance if there is some grammar issues)
So I got a small Python program that interpret these data contained in these .p files here's the code :
import pickle
import matplotlib.pyplot as plt #PyQT is require or tkinter
import numpy as np
import cv2
#This script aligns the true position to position given by orbslam.
#Load episode with id. #This loads the dictionary containing all information about an episode.
trajectory_dir = "ORBSlam/"
episode_id = 0
episode = pickle.load(open( trajectory_dir+"episodeStats"+str(episode_id)+".p", "rb" ))
#Extract useful data from the dictionary
pose_env = episode["pose_env"]
images_RGB = episode["rgb"]
images_depth = episode["depth"]
actions_orb = episode["orb_action"]
actions_best = episode["best_action"]
goal_distances = episode["goal_distance"]
success = episode["success"]
#Save observations into images.
for i in range(len(images_RGB)):
cv2.imwrite( trajectory_dir+"RGBs"+str(episode_id)+"/"+str(i)+".png", images_RGB[i] )
cv2.imwrite( trajectory_dir+"Depths"+str(episode_id)+"/"+str(i)+".png", images_depth[i]*255 )
#Display 2D trajectories.
x_env = []
y_env = []
for i in range(len(pose_env)):
#add x,y coordinates of the translation
x_env.append(pose_env[i][0,3])
y_env.append(pose_env[i][2,3])
plt.plot(x_env,y_env)
plt.axis('equal')
plt.show()
The problem here is that during the loop where it's supposed to output png image, in fact there is no output in the folder and since it's a silent function I don't know what the error is, I created both folder in ORBSlam folder (RGBs and Depths), so do you think it's something about permissions of writing or something like that ? (I'm working on macOS)
Thanks in advance for all the responses.
EDIT : I find why I've got no output images, I simply forgot to create a folder with the name of episode_id (Basically 0,1,2,...) in Depths and RGBs folder, my bad it was a dumb mistake
Solved : a folder inside where missing to get the output
I have a script that uses the MTCNN face detection library that iterates through a fair amount of directories, totaling thousands of images. An issue that I've been running into with this script is the excessive memory usage when processing all of these images, which will eventually cause my MacBook (16gb of RAM) to run out of memory. What I'm looking to do is to implement batching on a folder by folder basis, instead of a specific batch limit because none of the folders contain enough images individually that would make the system run out of memory.
# open up the csv file
with open(csv_path, 'w', newline='') as file:
writer = csv.writer(file)
writer.writerow(['Index', 'Threshhold', 'Path'])
for path, subdirs, files in os.walk(path):
for name in files:
if name == '.DS_Store':
print("Skipping .DS_Store")
continue
else:
try:
image = os.path.join(path, name)
pixels = pyplot.imread(image)
print("Processing " + image)
print("Count: " + str(inc))
# calculate the area of the image
total_height = pixels.shape[0]
total_width = pixels.shape[1]
total_area = total_height * total_width
# create the detector, using default weights
detector = MTCNN()
faces = detector.detect_faces(pixels)
ax = pyplot.gca()
face_total_area = 0
if faces == []:
print("No faces detected.")
# pass in 0 for the threshold becuase there's no faces
#write_to_csv(inc, 0, image)
print()
else:
for face in faces:
# get dimensions from the face
x, y, width, height = face['box']
# calculate the area of the face
face_area = width * height
face_total_area += face_area
threshold = face_total_area / total_area
# write to csv only if the threshold is less than the limit
# change back to this eventually ^^^^^^^^^
if threshold > threshhold_limit:
print("Facial area is over the threshold - writing file path to csv.")
write_to_csv(inc, threshold, image)
else:
print("Image threshold is under the limit - good")
print(threshold)
print()
inc += 1
except:
print("Processing error - skipping image")
Is something like this possible to do? Or should it be done a different way? The idea is that batching like this will allow mtcnn to release the memory it's holding onto when it's done processing that folder.
Memory usage should not increase with this program, because it does not accumulate data from one image to the next one. So, what you are asking for will have no effect. Have you tried runnng tis same code outside of a Python notebook? As a standalone program? It may be that the notebook is keeping references to all read images.
Either that, or find a call that would really reset pyplot's internal state inside the innermost loop. (maybe pyplot.clf()).
"Batching" as you say is what takes place inside the first for loop, which will run once for each folder in your tree. The only bennefit you could possibly have would be to reset the internal state inside the first loop, but outside the second for (for name in ...), you'd have to find the exactly same call to reset the internal state.
(also, on a side note, you create a csv writer in your with block that is invalidated at the end of the block - you should refactor this code not to keep reopening the CSV file for each new line - (which happens in the not-shown write_to_csv function) )
I am trying to do image processing in all cores available in my machine(which has 4 cores and 8 processors). I chose to do Multiprocessing because it's a kind of CPU bound workload. Now, explaining the data I have a CSV file that has file paths recorded(local path), Image Category(explain what image is). The CSV has exactly 9258 categories. My Idea is to do batch processing. Assign 10 categories to each processor and loop through the images one by one, wait till all the processors complete its job, and assign the next batch.
The categories are stored in this format as_batches = [[C1, C2, ..., C10], [C11, C12, C13, ..., C20], [Cn-10, Cn-9,..., Cn]]
Here is the function that starts the process.
def get_n_process(as_batches, processes, df, q):
p = []
for i in range(processes):
work = Process(target=submit_job, args=(df, as_batches[i], q, i))
p.append(work)
work.start()
as_batches = as_batches[processes:]
return p, as_batches
Here is the main loop,
while(len(as_batches) > 0):
t = []
#dynamically check the lists
if len(as_batches) > 8:
n_process = 8
else:
n_process = len(as_batches)
print("For this it Requries {} Process".format(n_process))
process_obj_inlist, as_batches = get_n_process(as_batches, n_process, df, q)
for ind_process in process_obj_inlist:
ind_process.join()
with open("logs.txt", "a") as f:
f.write("\n")
f.write("Log Recording at: {timestamp}, Remaining N = {remaining} yet to be processed".format(
timestamp=datetime.datetime.now(),
remaining = len(as_batches)
))
f.close()
For log purposes, I am writing into a text file to see how many categories are there to process yet. And here is the main function
def do_something(fromprocess):
time.sleep(1)
print("Operation Ended for Process:{}, process Id:{}".format(
current_process().name, os.getpid()
))
return "msg"
def submit_job(df, list_items, q, fromprocess):
a = []
for items in list_items:
oneitemdf = df[df['MinorCategory']==items]['FilePath'].values.tolist()
oneitemdf = [x for x in oneitemdf if x.endswith('.png')]
result = do_something(fromprocess)
a.append(result)
q.put(a)
For now, I am just printing in the console, but in real code, I will be using KAZE algorithm to extract features from the images, store it in a list and append it to the Queue(Shared Memory) from all the processors. Now the script is running for few minutes but after some time the script is halted. It didn't run further. I tried to exit it but I couldn't. I think some deadlock might happen but I am not sure. I read online sources but couldn't figure out the solution and reason why it's happening?
For the full code, here is the gist link Full Source Code Link. What I am doing wrong here? I am new to Multiprocessing and MultiThreading. I would like to understand the concept in-depth. Links/Resources related to this topic are much appreciated.
UPDATE - The same code working perfectly on Mac OS.
With the tf.contrib module gone from Tensorflow, and with tf.train.Saver() also gone, I cannot find a way to store a set of embeddings and their corresponding thumbnails, so that the Tensorboard Projector can read them.
The Tensorboard documentation for Tensorflow 2.0 explains how to create plots and summaries, and how to use the summary tool in general, but nothing about the projector tool. Has anyone found how to store datasets for visualization?
If possible, I would appreciate a (minimal) code example.
It seems there are some issues left in tensorboard. However, there are some
workarounds (for now) for preparing embeddings for projector with tensorflow2:
(bug report at: https://github.com/tensorflow/tensorboard/issues/2471)
tensorflow1 code would look something like that:
embeddings = tf.compat.v1.Variable(latent_data, name='embeddings')
CHECKPOINT_FILE = TENSORBOARD_DIR + '/model.ckpt'
# Write summaries for tensorboard
with tf.compat.v1.Session() as sess:
saver = tf.compat.v1.train.Saver([embeddings])
sess.run(embeddings.initializer)
saver.save(sess, CHECKPOINT_FILE)
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = embeddings.name
embedding.metadata_path = TENSORBOARD_METADATA_FILE
projector.visualize_embeddings(tf.summary.FileWriter(TENSORBOARD_DIR), config)
when using eager mode in tensorflow2 this should (?) look somehow like this:
embeddings = tf.Variable(latent_data, name='embeddings')
CHECKPOINT_FILE = TENSORBOARD_DIR + '/model.ckpt'
ckpt = tf.train.Checkpoint(embeddings=embeddings)
ckpt.save(CHECKPOINT_FILE)
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = embeddings.name
embedding.metadata_path = TENSORBOARD_METADATA_FILE
writer = tf.summary.create_file_writer(TENSORBOARD_DIR)
projector.visualize_embeddings(writer, config)
however, there are 2 issues:
the writer created with tf.summary.create_file_writer does not have the function get_logdir() required by projector.visualize_embeddings, a simple workaround is to patch the visualize_embeddings function to take the logdir as parameter.
the checkpoint format has changed, when reading the checkpoint with load_checkpoint (which seems to be the tensorboard way of loading the file), the variable names change. e.g. embeddings changes to something like embeddings/.ATTRIBUTES/VARIABLE_VALUE (also there are additional variables in the map extracted by get_variable_to_shape_map()but they are empty anyways).
the second issue was solved with the following quick-and-dirty workaround (and logdir is now a parameter of visualize_embeddings())
embeddings = tf.Variable(latent_data, name='embeddings')
CHECKPOINT_FILE = TENSORBOARD_DIR + '/model.ckpt'
ckpt = tf.train.Checkpoint(embeddings=embeddings)
ckpt.save(CHECKPOINT_FILE)
reader = tf.train.load_checkpoint(TENSORBOARD_DIR)
map = reader.get_variable_to_shape_map()
key_to_use = ""
for key in map:
if "embeddings" in key:
key_to_use = key
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = key_to_use
embedding.metadata_path = TENSORBOARD_METADATA_FILE
writer = tf.summary.create_file_writer(TENSORBOARD_DIR)
projector.visualize_embeddings(writer, config,TENSORBOARD_DIR)
I did not find any examples on how to use tensorflow2 to directly write the embeddings for tensorboard, so I am not sure if this is the right way, but if it is, then those two issues would need to be addressed, and at least for now there is a workaround.
It seems plenty of people as myself are having problems using Tensorboard Projector in TF2.x due to the lack of documentation. I have managed to make it work and in this comment on GitHub I provide some minimal code examples. I know the questions was also about using thumbnails (sprites), but I did not need it and wanted to keep the examples simple, so making sprites work is left as an exercise to the reader.
# Some initial code which is the same for all the variants
import os
import numpy as np
import tensorflow as tf
from tensorboard.plugins import projector
def register_embedding(embedding_tensor_name, meta_data_fname, log_dir):
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = embedding_tensor_name
embedding.metadata_path = meta_data_fname
projector.visualize_embeddings(log_dir, config)
def get_random_data(shape=(100,100)):
x = np.random.rand(*shape)
y = np.random.randint(low=0, high=2, size=shape[0])
return x, y
def save_labels_tsv(labels, filepath, log_dir):
with open(os.path.join(log_dir, filepath), 'w') as f:
for label in labels:
f.write('{}\n'.format(label))
LOG_DIR = 'tmp' # Tensorboard log dir
META_DATA_FNAME = 'meta.tsv' # Labels will be stored here
EMBEDDINGS_TENSOR_NAME = 'embeddings'
EMBEDDINGS_FPATH = os.path.join(LOG_DIR, EMBEDDINGS_TENSOR_NAME + '.ckpt')
STEP = 0
x, y = get_random_data((100,100))
register_embedding(EMBEDDINGS_TENSOR_NAME, META_DATA_FNAME, LOG_DIR)
save_labels_tsv(y, META_DATA_FNAME, LOG_DIR)
VARIANT A (Works in TF2.0 and TF2.1, but not in eager mode)
# Size of files created on disk: 163kB
tf.compat.v1.disable_eager_execution()
tensor_embeddings = tf.Variable(x, name=EMBEDDINGS_TENSOR_NAME)
sess = tf.compat.v1.InteractiveSession()
sess.run(tf.compat.v1.global_variables_initializer())
saver = tf.compat.v1.train.Saver()
saver.save(sess, EMBEDDINGS_FPATH, STEP)
sess.close()
VARIANT B (Works in both TF2.0 and TF2.1 in Eager mode)
# Size of files created on disk: 80.5kB
tensor_embeddings = tf.Variable(x, name=EMBEDDINGS_TENSOR_NAME)
saver = tf.compat.v1.train.Saver([tensor_embeddings]) # Must pass list or dict
saver.save(sess=None, global_step=STEP, save_path=EMBEDDINGS_FPATH)
I want to thank other developers for bits of code from their Stack answers, GitHub comments or personal blog posts, which helped me to put these examples together. You are the real MVP.
I am trying to write a small program which is able to classify certain pictures into categories. I create a list with pictures in the main code and pass them to the function in a loop. Code is working perfectly fine except for the fact that it does not free my memory and with each iteration the program uses more until it completely crashes.
Already tried to use "gc.collect()" in the function to force it to clear the memory, but that does not help. Shouldn't the memory be cleared automatically after checking one file or did I miss anything here?
def classify_pictures(self, files):
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# Read the image_data
image_data = tf.gfile.FastGFile(files, 'rb').read()
# Loads label file, strips off carriage return
label_lines = [line.rstrip() for line
in tf.gfile.GFile("tf_files/retrained_labels.txt")]
# Unpersists graph from file
with tf.gfile.FastGFile("tf_files/retrained_graph.pb", 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
with tf.Session() as sess:
# Feed the image_data as input to the graph and get first prediction
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
predictions = sess.run(softmax_tensor, \
{'DecodeJpeg/contents:0': image_data})
# Sort to show labels of first prediction in order of confidence
top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
for each_picture in range(0, 10):
human_string = label_lines[top_k[0]]
if human_string == "selfie":
return ("selfie")
if "passport" in human_string:
return("passport")
if "statement" in human_string:
return("bill")
If you call this function in a loop the computational graph is being rebuild on every iteration and the previous graph is of course still there too. That is what uses all the memory.
To solve this only do the session.run() call inside the loop.
In general when writing Tensorflow code you should always try to keep code that generates the graph separate from code which executes the graph. In your case you are doing both inside a single function, which is than called multiple times so a new graph is rebuild every time.