I'm trying to run object tracking on a folder containing multiple videos. There are 5 videos in my bucket and following the documentation from here, it suggests using the wildcard (*) operator. However, when I run the entire script, only 1 video gets annotated and not the entire folder containing 5 videos. Also, response2.json does not get created as the output_uri in my GCS bucket.
To identify multiple videos, a video URI may include wildcards in the object-id. Supported wildcards: ' * ' to match 0 or more characters; ‘?’ to match 1 character.
https://googleapis.dev/python/videointelligence/latest/gapic/v1/types.html
Which is what I've done in my input_uri bit of the code:
gcs_uri = 'gs://video_intel/*'
If you check the screenshot, it should the bucket id name and shows multiple videos in the same folder.
Can anyone pls help with this question. Thanks.
Full script:
import os
os.environ['GOOGLE_APPLICATION_CREDENTIALS']='poc-video-intelligence-da5d4d52cb97.json'
"""Object tracking in a video stored on GCS."""
from google.cloud import videointelligence
video_client = videointelligence.VideoIntelligenceServiceClient()
features = [videointelligence.enums.Feature.OBJECT_TRACKING]
gcs_uri = 'gs://video_intel/*'
output_uri = 'gs://video_intel/response2.json'
operation = video_client.annotate_video(input_uri=gcs_uri, features=features, output_uri=output_uri)
print("\nProcessing video for object annotations.")
result = operation.result(timeout=300)
print("\nFinished processing.\n")
# The first result is retrieved because a single video was processed.
object_annotations = result.annotation_results[0].object_annotations
for object_annotation in object_annotations:
print("Entity description: {}".format(object_annotation.entity.description))
if object_annotation.entity.entity_id:
print("Entity id: {}".format(object_annotation.entity.entity_id))
print(
"Segment: {}s to {}s".format(
object_annotation.segment.start_time_offset.seconds
+ object_annotation.segment.start_time_offset.nanos / 1e9,
object_annotation.segment.end_time_offset.seconds
+ object_annotation.segment.end_time_offset.nanos / 1e9,
)
)
print("Confidence: {}".format(object_annotation.confidence))
# Here we print only the bounding box of the first frame in the segment
frame = object_annotation.frames[0]
box = frame.normalized_bounding_box
print(
"Time offset of the first frame: {}s".format(
frame.time_offset.seconds + frame.time_offset.nanos / 1e9
)
)
print("Bounding box position:")
print("\tleft : {}".format(box.left))
print("\ttop : {}".format(box.top))
print("\tright : {}".format(box.right))
print("\tbottom: {}".format(box.bottom))
print("\n")
please modify 'gcs_uri = 'gs://video_intel/'' to gcs_uri = 'gs://video_intel/.*'
Related
I am trying to download the image from Dataverse using Dynamics Web API.
I am able to succeed in that using {{webapiurl}}sample_imageattributedemos(d66ecb6c-4fd1-ec11-a7b5-6045bda5603f)/entityimage/$value
But when I try to download the full/actual size image - I am getting the file with the reduced size - {{webapiurl}}sample_imageattributedemos(d66ecb6c-4fd1-ec11-a7b5-6045bda5603f)/entityimage/$value?fullsize=true.
I tried to download the image using the sample code where additionally I have added CanStoreFullImage = true attribute.
Please find below code snippet for the reference:
CreateAttributeRequest createEntityImageRequest = new CreateAttributeRequest
{
EntityName = _customEntityName.ToLower(),
Attribute = new ImageAttributeMetadata
{
SchemaName = "EntityImage", //The name is always EntityImage
//Required level must be AttributeRequiredLevel.None
RequiredLevel = new AttributeRequiredLevelManagedProperty(AttributeRequiredLevel.None),
DisplayName = new Microsoft.Xrm.Sdk.Label("Image", 1033),
Description = new Microsoft.Xrm.Sdk.Label("An image to show with this demonstration.", 1033),
CanStoreFullImage = true,
IsPrimaryImage = false,
}
};
How can I achieve this - to download the full size image using Web API?
the correct syntax is size=full, not fullsize=true
to build such requests you can use my tool Dataverse REST Builder, you can find the operations to deal with Image fields under the Manage Image Data request type
I am using HERE-Android-SDK to build a simple navigation solution with offline maps.
While using the offline mode for the search addresses and calculation of a route, I can see that there are results returned from the address-search, which are not included in the installed offline map datasets. Is there anything additional which I need to do, in order to get only search results which are located inside the offline-map data installed on my device?
I am using the following code snippets.
download offline maps for a specific country:
mapsLoader.selectDataGroup(MapPackage.SelectableDataGroup.TruckAttributes)
mapsLoader.installMapPackages(listOf(mapPackageId))
search request for addresses:
val term = "New York"
val center = GeoCoordinate(lastWayPoint.latitude, lastWayPoint.longitude)
val request = SearchRequest(term)
request.connectivity = Request.Connectivity.OFFLINE
request.locale = Locale.GERMAN
request.setSearchCenter(center)
request.collectionSize = 5
request.execute { data, error ->
if (error != ErrorCode.NONE) return
// handle search results here
}
Thanks for all of your help in advance!
I have a google-cloud-ml model that I can run prediction by passing a 3 dimensional array of float32...
{ 'instances' [ { 'input' : '[ [ [ 0.0 ], [ 0.5 ], [ 0.8 ] ] ... ] ]' } ] }
However this is not an efficient format to transmit images, so I'd like to pass base64 encoded png or jpeg. This document talks about doing that, but what is not clear is what the entire json object looks like. Does the { 'b64' : 'x0welkja...' } go in place of the '[ [ [ 0.0 ], [ 0.5 ], [ 0.8 ] ] ... ] ]', leaving the enclosing 'instances' and 'input' the same? Or some other structure? Or does the tensorflow model have to be trained on base64?
The TensorFlow model does not have to be trained on base64 data. Leave your training graph as is. However, when exporting the model, you'll need to export a model that can accept PNG or jpeg (or possibly raw, if it's small) data. Then, when you export the model, you'll need to be sure to use a name for the output that ends in _bytes. This signals to CloudML Engine that you will be sending base64 encoded data. Putting it all together would like something like this:
from tensorflow.contrib.saved_model.python.saved_model import utils
# Shape of [None] means we can have a batch of images.
image = tf.placeholder(shape = [None], dtype = tf.string)
# Decode the image.
decoded = tf.image.decode_jpeg(image, channels=3)
# Do the rest of the processing.
scores = build_model(decoded)
# The input name needs to have "_bytes" suffix.
inputs = { 'image_bytes': image }
outputs = { 'scores': scores }
utils.simple_save(session, export_dir, inputs, outputs)
The request you send will look something like this:
{
"instances": [{
"b64": "x0welkja..."
}]
}
If you just want an efficient way to send images to a model (and not necessarily base-64 encode it), I would suggest uploading your images(s) to Google Cloud Storage and then having your model read off GCS. This way, you are not limited by image size and you can take advantage of multi-part, multithreaded, resumable uploads etc. that the GCS API provides.
TensorFlow's tf.read_file will directly off GCS. Here's an example of a serving input_fn that will do this. Your request to CMLE would send it an image URL (gs://bucket/some/path/to/image.jpg)
def read_and_preprocess(filename, augment=False):
# decode the image file starting from the filename
# end up with pixel values that are in the -1, 1 range
image_contents = tf.read_file(filename)
image = tf.image.decode_jpeg(image_contents, channels=NUM_CHANNELS)
image = tf.image.convert_image_dtype(image, dtype=tf.float32) # 0-1
image = tf.expand_dims(image, 0) # resize_bilinear needs batches
image = tf.image.resize_bilinear(image, [HEIGHT, WIDTH], align_corners=False)
#image = tf.image.per_image_whitening(image) # useful if mean not important
image = tf.subtract(image, 0.5)
image = tf.multiply(image, 2.0) # -1 to 1
return image
def serving_input_fn():
inputs = {'imageurl': tf.placeholder(tf.string, shape=())}
filename = tf.squeeze(inputs['imageurl']) # make it a scalar
image = read_and_preprocess(filename)
# make the outer dimension unknown (and not 1)
image = tf.placeholder_with_default(image, shape=[None, HEIGHT, WIDTH, NUM_CHANNELS])
features = {'image' : image}
return tf.estimator.export.ServingInputReceiver(features, inputs)
Your training code will train off actual images, just as in rhaertel80's suggestion above. See https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive/08_image/flowersmodel/trainer/task.py#L27 for what the training/evaluation input functions would look like.
I was trying to use #Lak's answer (thanks Lak) to get online predictions for multiple instances in one json file, but kept getting the following error (I had two instances in my test json, hence the shape [2]):
input filename tensor must be scalar but had shape [2]
The problem is that ML engine apparently batches all the instances together and passes them to the serving inpur receiver function, but #Lak's sample code assumes the input is a single instance (it indeed works fine if you have a single instance in your json). I altered the code so that it can process a batch of inputs. I hope it will help someone:
def read_and_preprocess(filename):
image_contents = tf.read_file(filename)
image = tf.image.decode_image(image_contents, channels=NUM_CHANNELS)
image = tf.image.convert_image_dtype(image, dtype=tf.float32) # 0-1
return image
def serving_input_fn():
inputs = {'imageurl': tf.placeholder(tf.string, shape=(None))}
filename = inputs['imageurl']
image = tf.map_fn(read_and_preprocess, filename, dtype=tf.float32)
# make the outer dimension unknown (and not 1)
image = tf.placeholder_with_default(image, shape=[None, HEIGHT, WIDTH, NUM_CHANNELS])
features = {'image': image}
return tf.estimator.export.ServingInputReceiver(features, inputs)
The key changes are that 1) you don't squeeze the input tensor (that would cause trouble in the special case when your json contains only one instance) and, 2) use tf.map_fn to apply the read_and_preprocess function to a batch of input image urls.
I am rying to use tensorboard embeddings page in order to visualize the results of word2vec. After debugging, digging of lots of codes i came to a point that tensorboard successfully runs, reads the confguration file, reads the tsv files but now the embeddings page does not show data.
( the page is opened , i can see the menus , items etc) this is my config file:
embeddings {
tensor_name: 'word_embedding'
metadata_path: 'c:\data\metadata.tsv'
tensor_path: 'c:\data\tensors2.tsv'
}
What can be the problem?
The tensor file originally is 1gb. in size, if i try that file , the app crashes becasue of the memory. So i copy and paste 1 or 2 pages of the original file into tensor2.tsv and use this file. May be this is the problem. May be i need to create more data by copy/ paste.
thx
tolga
Try following code snippet to get visualized word embedding in tensorboard. Open tensorboard with logdir, check localhost:6006 for viewing your embedding.
tensorboard --logdir="visual/1"
# code
fname = "word2vec_model_1000"
model = gensim.models.keyedvectors.KeyedVectors.load(fname)
# project part of vocab, max of 100 dimension
max = 1000
w2v = np.zeros((max,100))
with open("prefix_metadata.tsv", 'w+') as file_metadata:
for i,word in enumerate(model.wv.index2word[:max]):
w2v[i] = model.wv[word]
file_metadata.write(word + '\n')
# define the model without training
sess = tf.InteractiveSession()
with tf.device("/cpu:0"):
embedding = tf.Variable(w2v, trainable=False, name='prefix_embedding')
tf.global_variables_initializer().run()
path = 'visual/1'
saver = tf.train.Saver()
writer = tf.summary.FileWriter(path, sess.graph)
# adding into projector
config = projector.ProjectorConfig()
embed= config.embeddings.add()
embed.tensor_name = 'prefix_embedding'
embed.metadata_path = 'prefix_metadata.tsv'
# Specify the width and height of a single thumbnail.
projector.visualize_embeddings(writer, config)
saver.save(sess, path+'/prefix_model.ckpt', global_step=max)
So I have an instance of Liquidsoap, which I am using to stream to an Icecast server.
I'd like to record any live broadcasts that take place automatically, which I am now doing and is working well.
What I'd like to do is use the metadata (specifically the songname) of the live show when creating the mp3 archive.
#!/usr/local/bin/liquidsoap
set("log.file",true)
set("log.file.path","/var/log/liquidsoap/radiostation.log")
set("log.stdout",true)
set("log.level",3)
#-------------------------------------
set("harbor.bind_addr","0.0.0.0")
#-------------------------------------
backup_playlist = playlist("/home/radio/playlists/playlist.pls",conservative=true,reload_mode="watch")
output.dummy(fallible=true,backup_playlist)
#-------------------------------------
live_dj = input.harbor(id="live",port=9000,password="XXX", "live")
date = '%m-%d-%Y'
time = '%H:%M:%S'
output.file(%mp3, "/var/www/recorded-shows/#{Title} - Recorded On #{date} At #{time}.mp3", live_dj, fallible=true)
#time_stamp = '%m-%d-%Y, %H:%M:%S'
#output.file(%mp3, "/var/www/recorded-shows/live_dj_#{time_stamp}.mp3", live_dj, fallible=true)
#-------------------------------------
on_fail = single("/home/radio/fallback/Enei -The Moment Feat DRS.mp3")
#-------------------------------------
source = fallback(track_sensitive=false,
[live_dj, backup_playlist, on_fail])
# We output the stream to icecast
output.icecast(%mp3,id="icecast",
mount="myradio.mp3",
host="localhost", password="XXX",
icy_metadata="true",description="cool radio",
url="http://myradio.fm",
source)
I have added #{title} where I would like my song title to appear, sadly though I am unable to get this populate.
My Dj's use BUTT and the show title is connected as part of their connection, so the data should be available pre recording.
Any advice is much appreciated!
This is far from being as easy as it seems.
The title metadata is dynamic, thus not available as a variable on script initialization
The filename argument of output.file is compiled when the script is initialized
A solution would consist in:
Defining a variable reference title to populate with live metadata
Output to a temporary file
Rename the file on close using on_close argument with output.file (in this case, we can just prepend the title)
This would give the following code (on a Linux box, change mv with ren on Windows):
date = '%m-%d-%Y'
time = '%H:%M:%S'
# Title is a reference
title = ref ""
# Populate with metadata
def get_title(m)
title := m['title']
end
live_dj = on_metadata(get_title,live_dj)
# Rename file on close
def on_close(filename)
# Generate new file name
new_filename = "#{path.dirname(filename)}/#{!title} - #{basename(filename)}"
# Rename file
system("mv '#{filename}' '#{new_filename}'")
end
output.file(on_close=on_close, %mp3, "/var/www/recorded-shows/Recorded On #{date} At #{time}.mp3", live_dj, fallible=true)
I tested a similar scenario and it works just well. Just beware that this will create a new file every time a DJ disconnects or updates the title. Also keep in mind that time stamps will be resolved by output.file.
This is based on the following example from a Liquidsoap dev: https://github.com/savonet/liquidsoap/issues/661#issuecomment-439935854)