def glass():
pipe.process('glass',
skip_frames=200,
n_frames=800,
resize=.5,
tracking_box=[55, 485, 82, 530],
warp_mode='t',
fps=30)
def foosball():
pipe.process('foosball',
skip_frames=566,
n_frames=1600,
resize=.5,
deanimate_mask_path='input/foosball/deanimate_mask.png',
warp_mode='h',
fps=30)
def work(self, item):
""" Feed jobs from the queue into the pipeline """
try:
data = json.loads(item)
process(source(self.item_collection, data['id']), self.pipeline)
except Exception, e:
import traceback
logger.error("Problem! " + str(e))
logger.error(traceback.format_exc())
def run(offset=0):
docs = item_collection.find(search_params, offset=offset)
for doc in docs['docs']:
#pass
process(lambda: doc, pipeline)
offset += len(docs['docs'])
if offset < docs['total']:
run(offset=offset)
else:
print 'done'
def run(offset=0):
docs = item_collection.find(search_params, offset=offset)
for doc in docs["docs"]:
# pass
process(lambda: doc, pipeline)
offset += len(docs["docs"])
# print "Running for " + str(docs['total']) + " docs"
if offset < docs["total"]:
run(offset=offset)
else:
print "done"
def main():
"""Parse stream of requests and insert into MongoDB collection.
This script will accept input from either stdin or one or more files as
arguments. Two loggers control logging--one general purpose logger for the
application and one for logging requests that fail to make it through the
pipeline. The latter is configured to route different kinds of failures to
different streams as configured. The failed requests will be logged
unmodified, as they entered the pipeline, to make later attempts at
processing easier.
Failure to send any requests through the pipeline will result in an exit
status of 1.
"""
req_buffer = []
for line in fileinput.input():
try:
request = process(line)
except apachelog.ApacheLogParserError:
# log unparseable requests
req_log.error(line.strip(), extra={'err_type': 'REQUEST_ERROR'})
continue
except requests.exceptions.RequestException:
req_log.error(line.strip(), extra={'err_type': 'DSPACE_ERROR'})
continue
except Exception, e:
log.error(e, extra={'inputfile': fileinput.filename(),
'inputline': fileinput.filelineno()})
continue
if request:
req_buffer.append(request)
if len(req_buffer) > 999:
insert(collection, req_buffer)
req_buffer = []
def execute(self, REQUEST=None, RESPONSE=None):
""" """
if not self.isActive():
return
if getattr(REQUEST, 'reset_date', False):
self.setResetDate(True)
histories = self.getDeploymentHistory()
history = histories.makeHistory()
Log.attachLogMonitor(history)
try:
try:
pipeline = self.getPipeline()
pipeline.process( self )
except:
if not DefaultConfiguration.DEPLOYMENT_DEBUG:
raise
import sys, pdb, traceback
ec, e, tb = sys.exc_info()
print ec, e
print traceback.print_tb( tb )
#pdb.post_mortem( tb )
raise
finally:
Log.detachLogMonitor(history)
#history.recordStatistics(display)
histories.attachHistory(history)
self.getDeploymentPolicy().setResetDate(False)
#Uncomment if/when you want to have users with Manager role receive email
#when a deployment completes. Helpful, for large sites with long-running
#deployments.
#self.exportNotify(REQUEST)
if RESPONSE:
return RESPONSE.redirect('manage_workspace')
return True
def execute(self, REQUEST=None, RESPONSE=None):
""" """
if not self.isActive():
return
if getattr(REQUEST, 'reset_date', False):
self.setResetDate(True)
histories = self.getDeploymentHistory()
history = histories.makeHistory()
Log.attachLogMonitor(history)
try:
try:
pipeline = self.getPipeline()
pipeline.process( self )
except:
if not DefaultConfiguration.DEPLOYMENT_DEBUG:
raise
import sys, pdb, traceback
ec, e, tb = sys.exc_info()
print ec, e
print traceback.print_tb( tb )
#pdb.post_mortem( tb )
raise
finally:
Log.detachLogMonitor(history)
#history.recordStatistics(display)
histories.attachHistory(history)
self.getDeploymentPolicy().setResetDate(False)
if RESPONSE:
return RESPONSE.redirect('manage_workspace')
return True
def calculate():
directory = directory_entry.get()
threshold = threshold_entry.get()
save = save_loc_entry.get()
include = included_check.get()
search = subdirs_check.get()
error_list = check_inputs(directory, save, threshold)
if len(error_list) > 0:
error_popup(error_list)
else:
result = process(directory, save, threshold, include, search)
if result == "Success":
messagebox.showinfo("Success", "Report generated!")
else:
messagebox.showerror("Error", result)
drawer_sr = comicolorization_sr.drawer.Drawer(
path_result_directory=args.super_resolution_model_directory,
gpu=args.gpu,
colorization_class=comicolorization_sr.colorization_task.
ComicolorizationTask,
)
drawer_sr.load_model(iteration=args.super_resolution_model_iteration)
# prepare datas
image = Image.open(args.input_image).convert('RGB')
rects = json.load(open(args.panel_rectangle))
reference_images = [
Image.open(path).convert('RGB') for path in args.reference_images
]
assert len(rects) == len(reference_images)
# prepare pipeline
pipeline = pipeline.PagePipeline(
drawer=drawer,
drawer_sr=drawer_sr,
image=image,
reference_images=reference_images,
threshold_binary=190,
threshold_line=130,
panel_rects=rects,
)
# draw
drawn_image = pipeline.process()
drawn_image.save(args.output)
def run_for_single(item_collection, doc_id):
pipeline = set_pipeline_steps(item_collection=item_collection)
process(source(item_collection, doc_id), pipeline)
filemenu.add_separator()
filemenu.add_command(label="Exit", command=exit_prog)
helpmenu = Menu(menu)
menu.add_cascade(label="Help", menu=helpmenu)
helpmenu.add_command(label="About...", command=launch_about_window)
if __name__ == "__main__":
# read path provided
if len(sys.argv) > 1:
# write path provided
if len(sys.argv) > 2:
# threshold provided
if len(sys.argv) > 3:
# include inoffensive provided
if len(sys.argv) > 4:
# include sub directories provided
if len(sys.argv) > 5:
process(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4], sys.argv[5])
else:
process(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4])
else:
process(sys.argv[1], sys.argv[2], sys.argv[3])
else:
process(sys.argv[1], sys.argv[2])
else:
process(sys.argv[1])
# path not provided
else:
mainloop()
import pipeline
import exporter
import sklearn
if __name__ == "__main__":
if len(sys.argv) == 2:
filepath = str(sys.argv[1])
if os.path.isfile(filepath) and filepath.endswith('.json'):
print("Import Google Takout data (can be long)... ", end='')
df = parser.importJson(filepath)
print("Done !")
print("Process trajectories... ", end='')
data = pipeline.process(df)
print("Done !")
print("Export to json... ", end='')
json = exporter.generateJson(data)
print("Done !")
print("Write to file... ", end='')
file = open("output.json", "w")
file.write(json)
file.close()
print("Done !")
elif not filepath.endswith('.json'):
print("File must be in JSON format.")
