def ci():
"""
Used in conjunction with travis for Continuous Integration testing
:return:
"""
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file, handle_youtube_video
from dvaapp.models import Video
from dvaapp.tasks import extract_frames, perform_indexing, perform_detection
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="video/mp4")
handle_uploaded_file(f, name, False)
for fname in glob.glob('tests/*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="application/zip")
handle_uploaded_file(f, name)
handle_youtube_video('tomorrow never dies',
'https://www.youtube.com/watch?v=gYtz5sw98Bc')
for v in Video.objects.all():
extract_frames(v.pk)
perform_indexing(v.pk)
# perform_detection(v.pk) detection is not performed in CI since it take long time on CPU
test_backup()
def ci():
"""
Used in conjunction with travis for Continuous Integration testing
:return:
"""
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file, handle_youtube_video
from dvaapp.models import Video
from dvaapp.tasks import extract_frames, perform_face_indexing, inpcetion_index_by_id, perform_yolo_ssd_detection_by_id
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="video/mp4")
handle_uploaded_file(f, name, False)
for fname in glob.glob('tests/*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="application/zip")
handle_uploaded_file(f, name)
handle_youtube_video('tomorrow never dies', 'https://www.youtube.com/watch?v=gYtz5sw98Bc')
for i,v in enumerate(Video.objects.all()):
extract_frames(v.pk)
inpcetion_index_by_id(v.pk)
if i ==0: # save travis time by just running detection on first video
perform_yolo_ssd_detection_by_id(v.pk)
perform_face_indexing(v.pk)
test_backup()
def test():
"""
Run tests by launching tasks
:return:
"""
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file, handle_youtube_video
for fname in glob.glob('tests/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="video/mp4")
handle_uploaded_file(f, name)
for fname in glob.glob('tests/*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="application/zip")
handle_uploaded_file(f, name)
handle_youtube_video('tomorrow never dies',
'https://www.youtube.com/watch?v=gYtz5sw98Bc')
def ci():
"""
Used in conjunction with travis for Continuous Integration testing
:return:
"""
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file, handle_youtube_video
from dvaapp.models import Video, Clusters, IndexEntries
from django.conf import settings
from dvaapp.tasks import extract_frames, perform_face_indexing, inception_index_by_id, perform_ssd_detection_by_id,\
perform_yolo_detection_by_id, inception_index_ssd_detection_by_id, export_video_by_id, import_video_by_id,\
perform_clustering
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="video/mp4")
handle_uploaded_file(f, name, False)
for fname in glob.glob('tests/*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="application/zip")
handle_uploaded_file(f, name)
handle_youtube_video('tomorrow never dies',
'https://www.youtube.com/watch?v=gYtz5sw98Bc')
for i, v in enumerate(Video.objects.all()):
extract_frames(v.pk)
inception_index_by_id(v.pk)
if i == 0: # save travis time by just running detection on first video
perform_ssd_detection_by_id(v.pk)
perform_yolo_detection_by_id(v.pk)
perform_face_indexing(v.pk)
inception_index_ssd_detection_by_id(v.pk)
fname = export_video_by_id(v.pk)
f = SimpleUploadedFile(fname,
file("{}/exports/{}".format(
settings.MEDIA_ROOT, fname)).read(),
content_type="application/zip")
vimported = handle_uploaded_file(f, fname)
import_video_by_id(vimported.pk)
dc = Clusters()
dc.indexer_algorithm = 'inception'
dc.included_index_entries_pk = [
k.pk for k in IndexEntries.objects.all().filter(
algorithm=dc.indexer_algorithm)
]
dc.components = 32
dc.save()
perform_clustering(dc.pk)
test_backup()
def ci():
"""
Used in conjunction with travis for Continuous Integration testing
:return:
"""
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
import base64
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file, handle_youtube_video, create_query
from dvaapp.models import Video, Clusters,IndexEntries,TEvent
from django.conf import settings
from dvaapp.tasks import extract_frames, perform_face_indexing, inception_index_by_id, perform_ssd_detection_by_id,\
perform_yolo_detection_by_id, inception_index_ssd_detection_by_id, export_video_by_id, import_video_by_id,\
inception_query_by_image, perform_clustering, assign_open_images_text_tags_by_id
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="video/mp4")
handle_uploaded_file(f, name, False)
for fname in glob.glob('tests/*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="application/zip")
handle_uploaded_file(f, name)
handle_youtube_video('tomorrow never dies', 'https://www.youtube.com/watch?v=gYtz5sw98Bc')
for i,v in enumerate(Video.objects.all()):
extract_frames(TEvent.objects.create(video=v).pk)
inception_index_by_id(TEvent.objects.create(video=v).pk)
if i ==0: # save travis time by just running detection on first video
perform_ssd_detection_by_id(TEvent.objects.create(video=v).pk)
perform_yolo_detection_by_id(TEvent.objects.create(video=v).pk)
perform_face_indexing(v.pk)
inception_index_ssd_detection_by_id(TEvent.objects.create(video=v).pk)
assign_open_images_text_tags_by_id(TEvent.objects.create(video=v).pk)
fname = export_video_by_id(TEvent.objects.create(video=v,event_type=TEvent.EXPORT).pk)
f = SimpleUploadedFile(fname, file("{}/exports/{}".format(settings.MEDIA_ROOT,fname)).read(), content_type="application/zip")
vimported = handle_uploaded_file(f, fname)
import_video_by_id(TEvent.objects.create(video=vimported).pk)
dc = Clusters()
dc.indexer_algorithm = 'inception'
dc.included_index_entries_pk = [k.pk for k in IndexEntries.objects.all().filter(algorithm=dc.indexer_algorithm)]
dc.components = 32
dc.save()
clustering_task = TEvent()
clustering_task.clustering = dc
clustering_task.event_type = TEvent.CLUSTERING
clustering_task.operation = 'perform_clustering'
clustering_task.save()
perform_clustering(clustering_task.pk)
query,dv = create_query(10,False,['inception',],[],'data:image/png;base64,'+base64.encodestring(file('tests/query.png').read()))
inception_query_by_image(query.pk)
query,dv = create_query(10,True,['inception',],[],'data:image/png;base64,'+base64.encodestring(file('tests/query.png').read()))
inception_query_by_image(query.pk)
test_backup()
def qt():
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="application/mp4")
v = handle_uploaded_file(f, name)
for fname in glob.glob('tests/example*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="application/zip")
v = handle_uploaded_file(f, name)
def qt():
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file
from dvaapp.models import Video, TEvent
from dvaapp.tasks import extract_frames, perform_face_detection, perform_indexing, segment_video
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="application/mp4")
v = handle_uploaded_file(f, name)
arguments_json = json.dumps({'sync': True})
segment_video(
TEvent.objects.create(video=v, arguments_json=arguments_json).pk)
perform_face_detection(TEvent.objects.create(video=v).pk)
args = json.dumps({
'index': 'facenet',
'target': 'regions',
'filter': {
'object_name__startswith': 'MTCNN_face'
}
})
perform_indexing(
TEvent.objects.create(video=v, arguments_json=args).pk)
def test():
"""
Run tests by launching tasks
:return:
"""
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file, handle_youtube_video
for fname in glob.glob('tests/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="video/mp4")
handle_uploaded_file(f, name)
for fname in glob.glob('tests/*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="application/zip")
handle_uploaded_file(f, name)
handle_youtube_video('tomorrow never dies', 'https://www.youtube.com/watch?v=gYtz5sw98Bc')
def test(ci=False):
"""
Run tests
:param ci: if True (fab test:1) tests are run on Travis this option skips creating tasks and directly calls
:return:
"""
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file, handle_youtube_video
from dvaapp.models import Video
from dvaapp.tasks import extract_frames, perform_indexing, perform_detection
if ci:
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="video/mp4")
handle_uploaded_file(f, name, False)
for fname in glob.glob('tests/*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="application/zip")
handle_uploaded_file(f, name)
handle_youtube_video('jungle book',
'https://www.youtube.com/watch?v=C4qgAaxB_pc')
for v in Video.objects.all():
extract_frames(v.pk)
perform_indexing(v.pk)
perform_detection(v.pk)
test_backup()
else:
for fname in glob.glob('tests/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="video/mp4")
handle_uploaded_file(f, name)
for fname in glob.glob('tests/*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="application/zip")
handle_uploaded_file(f, name)
handle_youtube_video('jungle book',
'https://www.youtube.com/watch?v=C4qgAaxB_pc')
def qt():
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file
from dvaapp.models import Video, TEvent
from dvaapp.tasks import extract_frames,perform_detection,perform_indexing,segment_video
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="application/mp4")
v = handle_uploaded_file(f, name)
def qt():
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file
from dvaapp.models import Video, TEvent
from dvaapp.tasks import extract_frames
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="video/mp4")
v = handle_uploaded_file(f, name, False)
extract_frames(TEvent.objects.create(video=v).pk)
def qt():
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file
from dvaapp.models import Video, TEvent
from dvaapp.tasks import extract_frames,perform_face_detection,perform_face_indexing
for fname in glob.glob('tests/example*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="application/zip")
v = handle_uploaded_file(f, name)
extract_frames(TEvent.objects.create(video=v).pk)
perform_face_detection(TEvent.objects.create(video=v).pk)
perform_face_indexing(TEvent.objects.create(video=v).pk)
def process_visual_genome():
setup_django()
import os, shutil, gzip, json
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.shared import handle_uploaded_file
from dvaapp import models
from dvaapp.models import TEvent
from dvaapp.tasks import extract_frames, export_video
from collections import defaultdict
os.system(
'aws s3api get-object --request-payer "requester" --bucket visualdatanetwork --key visual_genome_objects.txt.gz /root/DVA/visual_genome_objects.txt.gz'
)
data = defaultdict(list)
with gzip.open('/root/DVA/visual_genome_objects.txt.gz') as metadata:
for line in metadata:
entries = line.strip().split('\t')
data[entries[1]].append({
'x': int(entries[2]),
'y': int(entries[3]),
'w': int(entries[4]),
'h': int(entries[5]),
'object_id': entries[0],
'object_name': entries[6],
'text': ' '.join(entries[6:]),
})
name = "visual_genome"
fname = "visual_genome.zip"
f = SimpleUploadedFile(fname, "", content_type="application/zip")
v = handle_uploaded_file(f, name)
outpath = "/root/DVA/dva/media/{}/video/{}.zip".format(v.pk, v.pk)
os.system('rm {}'.format(outpath))
os.system(
'aws s3api get-object --request-payer "requester" --bucket visualdatanetwork --key visual_genome.zip {}'
.format(outpath))
extract_frames(TEvent.objects.create(video=v).pk)
video = v
models.Region.objects.all().filter(video=video).delete()
buffer = []
batch_count = 0
for frame in models.Frame.objects.all().filter(video=video):
frame_id = str(int(frame.name.split('/')[-1].split('.')[0]))
for o in data[frame_id]:
annotation = models.Region()
annotation.region_type = models.Region.ANNOTATION
annotation.video = v
annotation.frame = frame
annotation.x = o['x']
annotation.y = o['y']
annotation.h = o['h']
annotation.w = o['w']
annotation.object_name = o['object_name']
annotation.metadata = o
annotation.text = o['text']
buffer.append(annotation)
if len(buffer) == 1000:
try:
models.Region.objects.bulk_create(buffer)
batch_count += 1
print "saved {}".format(batch_count)
except:
print "encountered an error doing one by one"
for k in buffer:
try:
k.save()
except:
print "skipping"
print k.object_name
buffer = []
try:
models.Region.objects.bulk_create(buffer)
print "saved {}".format(batch_count)
except:
print "encountered an error doing one by one"
for k in buffer:
try:
k.save()
except:
print "skipping"
print k.object_name
print "exporting"
export_video(TEvent.objects.create(video=v).pk)
def create_yolo_test_data():
import json
import shutil
import numpy as np
import os
from PIL import Image
setup_django()
from dvaapp.shared import handle_uploaded_file
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.models import Region, TEvent, Frame, AppliedLabel
from dvaapp.tasks import extract_frames, export_video_by_id
try:
shutil.rmtree('tests/yolo_test')
except:
pass
try:
os.mkdir('tests/yolo_test')
except:
pass
data = np.load('shared/underwater_data.npz')
json_test = {}
json_test['anchors'] = [(0.57273, 0.677385), (1.87446, 2.06253),
(3.33843, 5.47434), (7.88282, 3.52778),
(9.77052, 9.16828)]
id_2_boxes = {}
class_names = {
0: "red_buoy",
1: "green_buoy",
2: "yellow_buoy",
3: "path_marker",
4: "start_gate",
5: "channel"
}
for i, image in enumerate(data['images'][:500]):
path = "tests/yolo_test/{}.jpg".format(i)
Image.fromarray(image).save(path)
id_2_boxes[path.split('/')[-1]] = data['boxes'][i].tolist()
local('zip tests/yolo_test.zip -r tests/yolo_test/* ')
fname = "tests/yolo_test.zip"
name = "yolo_test"
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="application/zip")
dv = handle_uploaded_file(f, name)
extract_frames(TEvent.objects.create(video=dv).pk)
for df in Frame.objects.filter(video=dv):
for box in id_2_boxes[df.name]:
r = Region()
r.video = dv
r.frame = df
c, top_x, top_y, bottom_x, bottom_y = box
r.object_name = class_names[c]
r.region_type = Region.ANNOTATION
r.x = top_x
r.y = top_y
r.w = bottom_x - top_x
r.h = bottom_y - top_y
r.save()
l = AppliedLabel()
l.frame = df
l.video = dv
l.label_name = class_names[c]
l.region = r
l.save()
export_video_by_id(TEvent.objects.create(video=dv).pk)
try:
shutil.rmtree('tests/yolo_test')
except:
pass
def ci():
"""
Used in conjunction with travis for Continuous Integration testing
:return:
"""
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
import base64
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file, handle_youtube_video, pull_vdn_list\
,import_vdn_dataset_url
from dvaapp.models import Video, Clusters,IndexEntries,TEvent,VDNServer
from django.conf import settings
from dvaapp.operations.query_processing import QueryProcessing
from dvaapp.tasks import extract_frames, inception_index_by_id, perform_ssd_detection_by_id,\
perform_yolo_detection_by_id, inception_index_regions_by_id, export_video_by_id, import_video_by_id,\
execute_index_subquery, perform_clustering, assign_open_images_text_tags_by_id, perform_face_detection,\
perform_face_indexing
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="video/mp4")
handle_uploaded_file(f, name, False)
for fname in glob.glob('tests/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="video/mp4")
handle_uploaded_file(f, name, False)
for fname in glob.glob('tests/*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="application/zip")
handle_uploaded_file(f, name)
# handle_youtube_video('world is not enough', 'https://www.youtube.com/watch?v=P-oNz3Nf50Q') # Temporarily disabled due error in travis
for i,v in enumerate(Video.objects.all()):
extract_frames(TEvent.objects.create(video=v).pk)
inception_index_by_id(TEvent.objects.create(video=v).pk)
if i ==0: # save travis time by just running detection on first video
perform_ssd_detection_by_id(TEvent.objects.create(video=v).pk)
perform_face_detection(TEvent.objects.create(video=v).pk)
inception_index_regions_by_id(TEvent.objects.create(video=v).pk)
assign_open_images_text_tags_by_id(TEvent.objects.create(video=v).pk)
fname = export_video_by_id(TEvent.objects.create(video=v,event_type=TEvent.EXPORT).pk)
f = SimpleUploadedFile(fname, file("{}/exports/{}".format(settings.MEDIA_ROOT,fname)).read(), content_type="application/zip")
vimported = handle_uploaded_file(f, fname)
import_video_by_id(TEvent.objects.create(video=vimported).pk)
dc = Clusters()
dc.indexer_algorithm = 'inception'
dc.included_index_entries_pk = [k.pk for k in IndexEntries.objects.all().filter(algorithm=dc.indexer_algorithm)]
dc.components = 32
dc.save()
clustering_task = TEvent()
clustering_task.clustering = dc
clustering_task.event_type = TEvent.CLUSTERING
clustering_task.operation = 'perform_clustering'
clustering_task.save()
perform_clustering(clustering_task.pk)
query_dict = {
'image_data_b64':base64.encodestring(file('tests/query.png').read()),
'indexers':[
{
'algorithm':'inception',
'count':10,
'approximate':False
}
]
}
qp = QueryProcessing()
qp.create_from_json(query_dict)
execute_index_subquery(qp.indexer_queries[0].pk)
query_dict = {
'image_data_b64':base64.encodestring(file('tests/query.png').read()),
'indexers':[
{
'algorithm':'inception',
'count':10,
'approximate':True
}
]
}
qp = QueryProcessing()
qp.create_from_json(query_dict)
execute_index_subquery(qp.indexer_queries[0].pk)
server, datasets, detectors = pull_vdn_list(1)
for k in datasets:
if k['name'] == 'MSCOCO_Sample_500':
print 'FOUND MSCOCO SAMPLE'
import_vdn_dataset_url(VDNServer.objects.get(pk=1),k['url'],None)
test_backup()
def ci():
"""
Used in conjunction with travis for Continuous Integration testing
:return:
"""
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
import base64
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file, handle_youtube_video, pull_vdn_list\
,import_vdn_dataset_url
from dvaapp.models import Video, Clusters,IndexEntries,TEvent,VDNServer, DVAPQL
from django.conf import settings
from dvaapp.operations.processing import DVAPQLProcess
from dvaapp.tasks import extract_frames, perform_indexing, export_video, import_video_by_id,\
perform_clustering, perform_analysis, perform_detection,\
segment_video, crop_regions_by_id
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="video/mp4")
handle_uploaded_file(f, name, False)
if sys.platform != 'darwin':
for fname in glob.glob('tests/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="video/mp4")
handle_uploaded_file(f, name, False)
for fname in glob.glob('tests/*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="application/zip")
handle_uploaded_file(f, name)
# handle_youtube_video('world is not enough', 'https://www.youtube.com/watch?v=P-oNz3Nf50Q') # Temporarily disabled due error in travis
for i,v in enumerate(Video.objects.all()):
if v.dataset:
arguments = {'sync':True}
extract_frames(TEvent.objects.create(video=v,arguments=arguments).pk)
else:
arguments = {'sync':True}
segment_video(TEvent.objects.create(video=v,arguments=arguments).pk)
arguments = {'index': 'inception'}
perform_indexing(TEvent.objects.create(video=v,arguments=arguments).pk)
if i ==0: # save travis time by just running detection on first video
# face_mtcnn
arguments = {'detector': 'face'}
dt = TEvent.objects.create(video=v,arguments=arguments)
perform_detection(dt.pk)
arguments = {'filters':{'event_id':dt.pk},}
crop_regions_by_id(TEvent.objects.create(video=v,arguments=arguments).pk)
# coco_mobilenet
arguments = {'detector': 'coco'}
dt = TEvent.objects.create(video=v, arguments=arguments)
perform_detection(dt.pk)
arguments = {'filters':{'event_id':dt.pk},}
crop_regions_by_id(TEvent.objects.create(video=v,arguments=arguments).pk)
# inception on crops from detector
arguments = {'index':'inception','target': 'regions','filters': {'event_id': dt.pk, 'w__gte': 50, 'h__gte': 50}}
perform_indexing(TEvent.objects.create(video=v,arguments=arguments).pk)
# assign_open_images_text_tags_by_id(TEvent.objects.create(video=v).pk)
fname = export_video(TEvent.objects.create(video=v).pk)
f = SimpleUploadedFile(fname, file("{}/exports/{}".format(settings.MEDIA_ROOT,fname)).read(), content_type="application/zip")
vimported = handle_uploaded_file(f, fname)
import_video_by_id(TEvent.objects.create(video=vimported).pk)
dc = Clusters()
dc.indexer_algorithm = 'inception'
dc.included_index_entries_pk = [k.pk for k in IndexEntries.objects.all().filter(algorithm=dc.indexer_algorithm)]
dc.components = 32
dc.save()
clustering_task = TEvent()
clustering_task.arguments = {'clusters_id':dc.pk}
clustering_task.operation = 'perform_clustering'
clustering_task.save()
perform_clustering(clustering_task.pk)
query_dict = {
'process_type': DVAPQL.QUERY,
'image_data_b64':base64.encodestring(file('tests/query.png').read()),
'indexer_queries':[
{
'algorithm':'inception',
'count':10,
'approximate':False
}
]
}
qp = DVAPQLProcess()
qp.create_from_json(query_dict)
# execute_index_subquery(qp.indexer_queries[0].pk)
query_dict = {
'process_type': DVAPQL.QUERY,
'image_data_b64':base64.encodestring(file('tests/query.png').read()),
'indexer_queries':[
{
'algorithm':'inception',
'count':10,
'approximate':True
}
]
}
qp = DVAPQLProcess()
qp.create_from_json(query_dict)
# execute_index_subquery(qp.indexer_queries[0].pk)
server, datasets, detectors = pull_vdn_list(1)
for k in datasets:
if k['name'] == 'MSCOCO_Sample_500':
print 'FOUND MSCOCO SAMPLE'
import_vdn_dataset_url(VDNServer.objects.get(pk=1),k['url'],None)
test_backup()
command = 'celery -A dva worker -l info -c {} -Q {} -n {}.%h -f logs/{}.log'.format(
1, Q_DETECTOR, Q_DETECTOR, Q_DETECTOR)
elif sys.argv[2] == 'retriever':
command = 'celery -A dva worker -l info -c {} -Q {} -n {}.%h -f logs/{}.log'.format(
1, Q_RETRIEVER, Q_RETRIEVER, Q_RETRIEVER)
else:
raise NotImplementedError
print command
os.system(command)
elif sys.argv[1] == 'test':
for fname in glob.glob('tests/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="video/mp4")
handle_uploaded_file(f, name)
for fname in glob.glob('tests/*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="application/zip")
handle_uploaded_file(f, name)
handle_youtube_video('jungle book',
'https://www.youtube.com/watch?v=C4qgAaxB_pc')
elif sys.argv[1] == 'ci_test':
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="video/mp4")
handle_uploaded_file(f, name, False)
def ci():
"""
Used in conjunction with travis for Continuous Integration testing
:return:
"""
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
import base64
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file, handle_youtube_video, pull_vdn_list\
,import_vdn_dataset_url
from dvaapp.models import Video, Clusters,IndexEntries,TEvent,VDNServer
from django.conf import settings
from dvaapp.operations.query_processing import QueryProcessing
from dvaapp.tasks import extract_frames, perform_indexing, export_video_by_id, import_video_by_id,\
perform_clustering, perform_analysis, perform_detection,\
segment_video, crop_regions_by_id
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="video/mp4")
handle_uploaded_file(f, name, False)
if sys.platform != 'darwin':
for fname in glob.glob('tests/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="video/mp4")
handle_uploaded_file(f, name, False)
for fname in glob.glob('tests/*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname, file(fname).read(), content_type="application/zip")
handle_uploaded_file(f, name)
# handle_youtube_video('world is not enough', 'https://www.youtube.com/watch?v=P-oNz3Nf50Q') # Temporarily disabled due error in travis
for i,v in enumerate(Video.objects.all()):
if v.dataset:
arguments_json = json.dumps({'sync':True})
extract_frames(TEvent.objects.create(video=v,arguments_json=arguments_json).pk)
else:
arguments_json = json.dumps({'sync':True})
segment_video(TEvent.objects.create(video=v,arguments_json=arguments_json).pk)
arguments_json = json.dumps({'index': 'inception'})
perform_indexing(TEvent.objects.create(video=v,arguments_json=arguments_json).pk)
if i ==0: # save travis time by just running detection on first video
# face_mtcnn
arguments_json = json.dumps({'detector': 'face'})
dt = TEvent.objects.create(video=v,arguments_json=arguments_json)
perform_detection(dt.pk)
arguments_json = json.dumps({'filters':{'event_id':dt.pk},})
crop_regions_by_id(TEvent.objects.create(video=v,arguments_json=arguments_json).pk)
# coco_mobilenet
arguments_json = json.dumps({'detector': 'coco'})
dt = TEvent.objects.create(video=v, arguments_json=arguments_json)
perform_detection(dt.pk)
arguments_json = json.dumps({'filters':{'event_id':dt.pk},})
crop_regions_by_id(TEvent.objects.create(video=v,arguments_json=arguments_json).pk)
# inception on crops from detector
arguments_json = json.dumps({'index':'inception','target': 'regions','filters': {'event_id': dt.pk, 'w__gte': 50, 'h__gte': 50}})
perform_indexing(TEvent.objects.create(video=v,arguments_json=arguments_json).pk)
# assign_open_images_text_tags_by_id(TEvent.objects.create(video=v).pk)
fname = export_video_by_id(TEvent.objects.create(video=v,event_type=TEvent.EXPORT).pk)
f = SimpleUploadedFile(fname, file("{}/exports/{}".format(settings.MEDIA_ROOT,fname)).read(), content_type="application/zip")
vimported = handle_uploaded_file(f, fname)
import_video_by_id(TEvent.objects.create(video=vimported).pk)
dc = Clusters()
dc.indexer_algorithm = 'inception'
dc.included_index_entries_pk = [k.pk for k in IndexEntries.objects.all().filter(algorithm=dc.indexer_algorithm)]
dc.components = 32
dc.save()
clustering_task = TEvent()
clustering_task.clustering = dc
clustering_task.event_type = TEvent.CLUSTERING
clustering_task.operation = 'perform_clustering'
clustering_task.save()
perform_clustering(clustering_task.pk)
query_dict = {
'image_data_b64':base64.encodestring(file('tests/query.png').read()),
'indexers':[
{
'algorithm':'inception',
'count':10,
'approximate':False
}
]
}
qp = QueryProcessing()
qp.create_from_json(query_dict)
# execute_index_subquery(qp.indexer_queries[0].pk)
query_dict = {
'image_data_b64':base64.encodestring(file('tests/query.png').read()),
'indexers':[
{
'algorithm':'inception',
'count':10,
'approximate':True
}
]
}
qp = QueryProcessing()
qp.create_from_json(query_dict)
# execute_index_subquery(qp.indexer_queries[0].pk)
server, datasets, detectors = pull_vdn_list(1)
for k in datasets:
if k['name'] == 'MSCOCO_Sample_500':
print 'FOUND MSCOCO SAMPLE'
import_vdn_dataset_url(VDNServer.objects.get(pk=1),k['url'],None)
test_backup()
def ci():
"""
Perform Continuous Integration testing using Travis
"""
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
import base64
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file, pull_vdn_list \
, import_vdn_dataset_url
from dvaapp.models import Video, IndexEntries, TEvent, VDNServer, DVAPQL, Retriever
from django.conf import settings
from dvaapp.operations.processing import DVAPQLProcess
from dvaapp.tasks import perform_dataset_extraction, perform_indexing, perform_export, perform_import, \
perform_retriever_creation, perform_detection, \
perform_video_segmentation, perform_transformation
for fname in glob.glob('tests/ci/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="video/mp4")
handle_uploaded_file(f, name, False)
if sys.platform != 'darwin':
for fname in glob.glob('tests/*.mp4'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="video/mp4")
handle_uploaded_file(f, name, False)
for fname in glob.glob('tests/*.zip'):
name = fname.split('/')[-1].split('.')[0]
f = SimpleUploadedFile(fname,
file(fname).read(),
content_type="application/zip")
handle_uploaded_file(f, name)
for i, v in enumerate(Video.objects.all()):
if v.dataset:
arguments = {'sync': True}
perform_dataset_extraction(
TEvent.objects.create(video=v, arguments=arguments).pk)
else:
arguments = {'sync': True}
perform_video_segmentation(
TEvent.objects.create(video=v, arguments=arguments).pk)
arguments = {'index': 'inception', 'target': 'frames'}
perform_indexing(
TEvent.objects.create(video=v, arguments=arguments).pk)
if i == 0: # save travis time by just running detection on first video
# face_mtcnn
arguments = {'detector': 'face'}
dt = TEvent.objects.create(video=v, arguments=arguments)
perform_detection(dt.pk)
arguments = {
'filters': {
'event_id': dt.pk
},
}
perform_transformation(
TEvent.objects.create(video=v, arguments=arguments).pk)
# coco_mobilenet
arguments = {'detector': 'coco'}
dt = TEvent.objects.create(video=v, arguments=arguments)
perform_detection(dt.pk)
arguments = {
'filters': {
'event_id': dt.pk
},
}
perform_transformation(
TEvent.objects.create(video=v, arguments=arguments).pk)
# inception on crops from detector
arguments = {
'index': 'inception',
'target': 'regions',
'filters': {
'event_id': dt.pk,
'w__gte': 50,
'h__gte': 50
}
}
perform_indexing(
TEvent.objects.create(video=v, arguments=arguments).pk)
# assign_open_images_text_tags_by_id(TEvent.objects.create(video=v).pk)
temp = TEvent.objects.create(video=v,
arguments={'destination': "FILE"})
perform_export(temp.pk)
temp.refresh_from_db()
fname = temp.arguments['file_name']
f = SimpleUploadedFile(fname,
file("{}/exports/{}".format(
settings.MEDIA_ROOT, fname)).read(),
content_type="application/zip")
vimported = handle_uploaded_file(f, fname)
perform_import(
TEvent.objects.create(video=vimported,
arguments={
"source": "LOCAL"
}).pk)
# dc = Retriever()
# args = {}
#
# dc.included_index_entries_pk = [k.pk for k in IndexEntries.objects.all().filter(algorithm=dc.indexer_algorithm)]
# dc.components = 32
# dc.save()
# clustering_task = TEvent()
# clustering_task.arguments = {'clusters_id': dc.pk}
# clustering_task.operation = 'perform_clustering'
# clustering_task.save()
# perform_clustering(clustering_task.pk)
# query_dict = {
# 'process_type': DVAPQL.QUERY,
# 'image_data_b64': base64.encodestring(file('tests/query.png').read()),
# 'indexer_queries': [
# {
# 'algorithm': 'inception',
# 'count': 10,
# 'approximate': False
# }
# ]
# }
# qp = DVAPQLProcess()
# qp.create_from_json(query_dict)
# # execute_index_subquery(qp.indexer_queries[0].pk)
# query_dict = {
# 'process_type': DVAPQL.QUERY,
# 'image_data_b64': base64.encodestring(file('tests/query.png').read()),
# 'indexer_queries': [
# {
# 'algorithm': 'inception',
# 'count': 10,
# 'approximate': True
# }
# ]
# }
# qp = DVAPQLProcess()
# qp.create_from_json(query_dict)
# # execute_index_subquery(qp.indexer_queries[0].pk)
server, datasets, detectors = pull_vdn_list(1)
for k in datasets:
if k['name'] == 'MSCOCO_Sample_500':
print 'FOUND MSCOCO SAMPLE'
import_vdn_dataset_url(VDNServer.objects.get(pk=1), k['url'], None,
k)
test_backup()
def create_yolo_test_data():
import json
import shutil
import numpy as np
import os
from PIL import Image
setup_django()
from dvaapp.shared import handle_uploaded_file
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.models import Region,TEvent,Frame, AppliedLabel
from dvaapp.tasks import extract_frames,export_video_by_id
try:
shutil.rmtree('tests/yolo_test')
except:
pass
try:
os.mkdir('tests/yolo_test')
except:
pass
data = np.load('shared/underwater_data.npz')
json_test = {}
json_test['anchors'] = [(0.57273, 0.677385), (1.87446, 2.06253), (3.33843, 5.47434), (7.88282, 3.52778), (9.77052, 9.16828)]
id_2_boxes = {}
class_names = {
0:"red_buoy",
1:"green_buoy",
2:"yellow_buoy",
3:"path_marker",
4:"start_gate",
5:"channel"
}
for i,image in enumerate(data['images'][:500]):
path = "tests/yolo_test/{}.jpg".format(i)
Image.fromarray(image).save(path)
id_2_boxes[path.split('/')[-1]] = data['boxes'][i].tolist()
local('zip tests/yolo_test.zip -r tests/yolo_test/* ')
fname = "tests/yolo_test.zip"
name = "yolo_test"
f = SimpleUploadedFile(fname, file(fname).read(), content_type="application/zip")
dv = handle_uploaded_file(f, name)
extract_frames(TEvent.objects.create(video=dv).pk)
for df in Frame.objects.filter(video=dv):
for box in id_2_boxes[df.name]:
r = Region()
r.video = dv
r.frame = df
c , top_x, top_y, bottom_x, bottom_y = box
r.object_name = class_names[c]
r.region_type = Region.ANNOTATION
r.x = top_x
r.y = top_y
r.w = bottom_x - top_x
r.h = bottom_y - top_y
r.save()
l = AppliedLabel()
l.frame = df
l.video = dv
l.label_name = class_names[c]
l.region = r
l.save()
export_video_by_id(TEvent.objects.create(video=dv).pk)
try:
shutil.rmtree('tests/yolo_test')
except:
pass
def process_visual_genome():
setup_django()
import os, shutil, gzip, json
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.shared import handle_uploaded_file
from dvaapp import models
from dvaapp.models import TEvent
from dvaapp.tasks import extract_frames, export_video_by_id
from collections import defaultdict
os.system('aws s3api get-object --request-payer "requester" --bucket visualdatanetwork --key visual_genome_objects.txt.gz /root/DVA/visual_genome_objects.txt.gz')
data = defaultdict(list)
with gzip.open('/root/DVA/visual_genome_objects.txt.gz') as metadata:
for line in metadata:
entries = line.strip().split('\t')
data[entries[1]].append({
'x': int(entries[2]),
'y': int(entries[3]),
'w': int(entries[4]),
'h': int(entries[5]),
'object_id': entries[0],
'object_name': entries[6],
'metadata_text': ' '.join(entries[6:]), })
name = "visual_genome"
fname = "visual_genome.zip"
f = SimpleUploadedFile(fname, "", content_type="application/zip")
v = handle_uploaded_file(f, name)
outpath = "/root/DVA/dva/media/{}/video/{}.zip".format(v.pk, v.pk)
os.system('rm {}'.format(outpath))
os.system(
'aws s3api get-object --request-payer "requester" --bucket visualdatanetwork --key visual_genome.zip {}'.format(
outpath))
extract_frames(TEvent.objects.create(video=v).pk)
video = v
models.Region.objects.all().filter(video=video).delete()
buffer = []
batch_count = 0
for frame in models.Frame.objects.all().filter(video=video):
frame_id = str(int(frame.name.split('/')[-1].split('.')[0]))
for o in data[frame_id]:
annotation = models.Region()
annotation.region_type = models.Region.ANNOTATION
annotation.video = v
annotation.frame = frame
annotation.x = o['x']
annotation.y = o['y']
annotation.h = o['h']
annotation.w = o['w']
annotation.object_name = o['object_name']
annotation.metadata_json = json.dumps(o)
annotation.metadata_text = o['metadata_text']
buffer.append(annotation)
if len(buffer) == 1000:
try:
models.Region.objects.bulk_create(buffer)
batch_count += 1
print "saved {}".format(batch_count)
except:
print "encountered an error doing one by one"
for k in buffer:
try:
k.save()
except:
print "skipping"
print k.object_name
buffer = []
try:
models.Region.objects.bulk_create(buffer)
print "saved {}".format(batch_count)
except:
print "encountered an error doing one by one"
for k in buffer:
try:
k.save()
except:
print "skipping"
print k.object_name
print "exporting"
export_video_by_id(TEvent.objects.create(video=v).pk)
def generate_vdn(fast=False):
kill()
setup_django()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file, handle_youtube_video
from dvaapp import models
from dvaapp.models import TEvent
from dvaapp.tasks import extract_frames, perform_face_detection_indexing_by_id, inception_index_by_id, \
perform_ssd_detection_by_id, perform_yolo_detection_by_id, inception_index_regions_by_id, \
export_video_by_id
dirname = get_coco_dirname()
local('wget https://www.dropbox.com/s/2dq085iu34y0hdv/coco_input.zip?dl=1 -O coco.zip')
local('unzip coco.zip')
with lcd(dirname):
local("zip coco_input.zip -r *.jpg")
fname = '{}/coco_input.zip'.format(dirname)
with open('{}/coco_sample_metadata.json'.format(dirname)) as datafile:
data = json.load(datafile)
f = SimpleUploadedFile("coco_input.zip", file(fname).read(), content_type="application/zip")
v = handle_uploaded_file(f, 'mscoco_sample_500')
extract_frames(TEvent.objects.create(video=v).pk)
video = v
models.Region.objects.all().filter(video=video).delete()
for frame in models.Frame.objects.all().filter(video=video):
frame_id = str(int(frame.name.split('_')[-1].split('.')[0]))
annotation = models.Region()
annotation.region_type = models.Region.ANNOTATION
annotation.video = v
annotation.frame = frame
annotation.full_frame = True
annotation.metadata_json = json.dumps(data[frame_id]['image'])
annotation.object_name = 'metadata'
annotation.save()
for frame in models.Frame.objects.all().filter(video=video):
frame_id = str(int(frame.name.split('_')[-1].split('.')[0]))
for a in data[frame_id][u'annotations']:
annotation = models.Region()
annotation.region_type = models.Region.ANNOTATION
annotation.video = v
annotation.frame = frame
annotation.metadata_json = json.dumps(a)
annotation.full_frame = False
annotation.x = a['bbox'][0]
annotation.y = a['bbox'][1]
annotation.w = a['bbox'][2]
annotation.h = a['bbox'][3]
annotation.object_name = 'coco_instance/{}/{}'.format(a[u'category'][u'supercategory'], a[u'category'][u'name'])
annotation.save()
for a in data[frame_id][u'keypoints']:
annotation = models.Region()
annotation.region_type = models.Region.ANNOTATION
annotation.video = v
annotation.frame = frame
annotation.metadata_json = json.dumps(a)
annotation.x = a['bbox'][0]
annotation.y = a['bbox'][1]
annotation.w = a['bbox'][2]
annotation.h = a['bbox'][3]
annotation.object_name = 'coco_keypoints/{}/{}'.format(a[u'category'][u'supercategory'], a[u'category'][u'name'])
annotation.save()
for caption in data[frame_id][u'captions']:
annotation = models.Region()
annotation.region_type = models.Region.ANNOTATION
annotation.video = v
annotation.frame = frame
annotation.metadata_text = caption['caption']
annotation.full_frame = True
annotation.object_name = 'caption'
annotation.save()
if not fast:
inception_index_by_id(TEvent.objects.create(video=v).pk)
perform_ssd_detection_by_id(TEvent.objects.create(video=v).pk)
perform_face_detection_indexing_by_id(TEvent.objects.create(video=v).pk)
inception_index_regions_by_id(TEvent.objects.create(video=v).pk)
export_video_by_id(TEvent.objects.create(video=v).pk)
def generate_visual_genome(fast=False):
kill()
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file, handle_youtube_video
from dvaapp import models
from dvaapp.models import TEvent
import gzip
from dvaapp.tasks import extract_frames, perform_face_detection_indexing_by_id, inception_index_by_id, \
perform_ssd_detection_by_id, inception_index_regions_by_id, \
export_video_by_id
dirname = get_visual_genome_dirname()
with lcd(dirname):
if not os.path.isfile("{}/vg.zip".format(dirname)):
local(
'wget https://www.dropbox.com/s/7g2c1j5n318eovr/visual_genome_sample.zip?dl=1 -O vg.zip'
)
local(
'wget https://www.dropbox.com/s/589tyg6vn3uxqcc/visual_genome_objects.txt.gz?dl=1 -O visual_genome_objects.txt.gz'
)
data = defaultdict(list)
with gzip.open(
'{}/visual_genome_objects.txt.gz'.format(dirname)) as metadata:
for line in metadata:
entries = line.strip().split('\t')
data[entries[1]].append({
'x': int(entries[2]),
'y': int(entries[3]),
'w': int(entries[4]),
'h': int(entries[5]),
'object_id': entries[0],
'object_name': entries[6],
'metadata_text': ' '.join(entries[6:]),
})
f = SimpleUploadedFile("vg.zip",
file('{}/vg.zip'.format(dirname)).read(),
content_type="application/zip")
v = handle_uploaded_file(f, 'visual genome sample')
extract_frames(TEvent.objects.create(video=v).pk)
video = v
models.Region.objects.all().filter(video=video).delete()
buffer = []
for frame in models.Frame.objects.all().filter(video=video):
frame_id = str(int(frame.name.split('_')[-1].split('.')[0]))
for o in data[frame_id]:
annotation = models.Region()
annotation.region_type = models.Region.ANNOTATION
annotation.video = v
annotation.frame = frame
annotation.full_frame = False
annotation.x = o['x']
annotation.y = o['y']
annotation.h = o['h']
annotation.w = o['w']
annotation.object_name = o['object_name']
annotation.metadata_json = json.dumps(o)
annotation.metadata_text = o['metadata_text']
buffer.append(annotation)
if len(buffer) == 1000:
models.Region.objects.bulk_create(buffer)
print "saving"
buffer = []
models.Region.objects.bulk_create(buffer)
print "saving final"
if not fast:
inception_index_by_id(TEvent.objects.create(video=v).pk)
default_args = {'region_type': 'A', 'w__gte': 50, 'h__gte': 50}
inception_index_regions_by_id(
TEvent.objects.create(video=v,
arguments_json=json.dumps(default_args)).pk)
export_video_by_id(TEvent.objects.create(video=v).pk)
def generate_vdn(fast=False):
kill()
import django
sys.path.append(os.path.dirname(__file__))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "dva.settings")
django.setup()
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file, handle_youtube_video
from dvaapp import models
from dvaapp.models import TEvent
from dvaapp.tasks import extract_frames, perform_face_detection_indexing_by_id, inception_index_by_id, \
perform_ssd_detection_by_id, perform_yolo_detection_by_id, inception_index_regions_by_id, \
export_video_by_id
dirname = get_coco_dirname()
local(
'wget https://www.dropbox.com/s/2dq085iu34y0hdv/coco_input.zip?dl=1 -O coco.zip'
)
local('unzip coco.zip')
with lcd(dirname):
local("zip coco_input.zip -r *.jpg")
fname = '{}/coco_input.zip'.format(dirname)
with open('{}/coco_sample_metadata.json'.format(dirname)) as datafile:
data = json.load(datafile)
f = SimpleUploadedFile("coco_input.zip",
file(fname).read(),
content_type="application/zip")
v = handle_uploaded_file(f, 'mscoco_sample_500')
extract_frames(TEvent.objects.create(video=v).pk)
video = v
models.Region.objects.all().filter(video=video).delete()
for frame in models.Frame.objects.all().filter(video=video):
frame_id = str(int(frame.name.split('_')[-1].split('.')[0]))
annotation = models.Region()
annotation.region_type = models.Region.ANNOTATION
annotation.video = v
annotation.frame = frame
annotation.full_frame = True
annotation.metadata_json = json.dumps(data[frame_id]['image'])
annotation.object_name = 'metadata'
annotation.save()
for frame in models.Frame.objects.all().filter(video=video):
frame_id = str(int(frame.name.split('_')[-1].split('.')[0]))
for a in data[frame_id][u'annotations']:
annotation = models.Region()
annotation.region_type = models.Region.ANNOTATION
annotation.video = v
annotation.frame = frame
annotation.metadata_json = json.dumps(a)
annotation.full_frame = False
annotation.x = a['bbox'][0]
annotation.y = a['bbox'][1]
annotation.w = a['bbox'][2]
annotation.h = a['bbox'][3]
annotation.object_name = 'coco_instance/{}/{}'.format(
a[u'category'][u'supercategory'], a[u'category'][u'name'])
annotation.save()
for a in data[frame_id][u'keypoints']:
annotation = models.Region()
annotation.region_type = models.Region.ANNOTATION
annotation.video = v
annotation.frame = frame
annotation.metadata_json = json.dumps(a)
annotation.x = a['bbox'][0]
annotation.y = a['bbox'][1]
annotation.w = a['bbox'][2]
annotation.h = a['bbox'][3]
annotation.object_name = 'coco_keypoints/{}/{}'.format(
a[u'category'][u'supercategory'], a[u'category'][u'name'])
annotation.save()
for caption in data[frame_id][u'captions']:
annotation = models.Region()
annotation.region_type = models.Region.ANNOTATION
annotation.video = v
annotation.frame = frame
annotation.metadata_text = caption['caption']
annotation.full_frame = True
annotation.object_name = 'caption'
annotation.save()
if not fast:
inception_index_by_id(TEvent.objects.create(video=v).pk)
perform_ssd_detection_by_id(TEvent.objects.create(video=v).pk)
perform_face_detection_indexing_by_id(
TEvent.objects.create(video=v).pk)
inception_index_regions_by_id(TEvent.objects.create(video=v).pk)
export_video_by_id(TEvent.objects.create(video=v).pk)
annotation['category'] = kp_id_to_category[annotation['category_id']]
data[annotation['image_id']]['keypoints'].append(annotation)
with open('coco_sample_metadata.json', 'w') as output:
json.dump(data, output)
os.system("zip coco_input.zip -r {}".format(dirname))
from django.core.files.uploadedfile import SimpleUploadedFile
from dvaapp.views import handle_uploaded_file, handle_youtube_video
from dvaapp.models import Video
from dvaapp import models
from dvaapp.tasks import extract_frames, perform_face_indexing, inception_index_by_id, perform_ssd_detection_by_id, perform_yolo_detection_by_id, inception_index_ssd_detection_by_id
fname = 'coco_input.zip'
f = SimpleUploadedFile(fname,
file('coco_input.zip').read(),
content_type="application/zip")
v = handle_uploaded_file(f, 'mscoco_sample_500')
extract_frames(v.pk)
data = json.load(file('coco_sample_metadata.json'))
video = v
models.Annotation.objects.all().filter(video=video).delete()
for frame in models.Frame.objects.all().filter(video=video):
frame_id = str(int(frame.name.split('_')[-1].split('.')[0]))
annotation = models.Annotation()
annotation.video = v
annotation.frame = frame
annotation.full_frame = True
annotation.metadata_text = json.dumps(data[frame_id]['image'])
annotation.label = 'metadata'
annotation.save()
for frame in models.Frame.objects.all().filter(video=video):
frame_id = str(int(frame.name.split('_')[-1].split('.')[0]))
