def get_pipeline(image):
filepath = utils.get_filepath(app.config['INPUT_FOLDER'], image)
if filepath is None:
return not_found_error()
utils.delete_images(app.config['OUTPUT_FOLDER_PIPELINES'])
steps = request.get_json().get('steps')
if steps is None:
response = jsonify({
'success': False,
'message': 'Field "steps" is required'
})
response.status_code = 404
return response
processing_lib.pipeline_individual(filepath, steps)
original = ['/' + filepath, image]
pipelines = utils.get_images(app.config['OUTPUT_FOLDER_PIPELINES'])
steps_count = utils.count_folders(app.config['OUTPUT_FOLDER_STEPS'])
for index in range(1, steps_count + 1):
if next((x for x in pipelines if int(x[1].split('-')[0]) == index),
None) is None:
pipelines.append(('/static/img/fail.gif',
'{}-{}'.format(index,
str(uuid.uuid4()).split('-')[0])))
pipelines.sort(key=lambda x: int(x[1].split('-')[0]))
response = jsonify({
'success': True,
'original': original,
'pipeline': pipelines
})
response.status_code = 200
return response
def processing(image):
filepath = utils.get_filepath(app.config['INPUT_FOLDER'], image)
if filepath is None:
return redirect(url_for('index'))
utils.delete_images(app.config['OUTPUT_FOLDER'])
processing_lib.individual(filepath)
original = ['/' + filepath, image]
transformations = utils.get_images(app.config['OUTPUT_FOLDER'])
transformations.sort(key=lambda x: x[1])
return render_template('processing.html', original=original, transformations=transformations)
def run_app():
warn_label = Label(frame, text='Select a date from the list', bg='#81D4FA')
if clicked.get() == 'Select date':
warn_label.place(relx=0.5, rely=0.2, relwidth=1, relheight=0.3, anchor='n')
pass
else:
generate_barcodes(get_eans(clicked.get()))
word_document(clicked.get())
os.startfile(documents_path)
delete_images()
root.destroy()
def processing(image):
filepath = utils.get_filepath(app.config['INPUT_FOLDER'], image)
if filepath is None:
return not_found_error()
utils.delete_images(app.config['OUTPUT_FOLDER'])
processing_lib.individual(filepath)
original = ['/' + filepath, image]
transformations = utils.get_images(app.config['OUTPUT_FOLDER'])
transformations.sort(key=lambda x: x[1])
response = jsonify({
'success': True,
'original': original,
'transformations': transformations
})
response.status_code = 200
return response
def pipeline(image):
filepath = utils.get_filepath(app.config['INPUT_FOLDER'], image)
if filepath is None:
return redirect(url_for('index'))
if request.method == 'POST':
utils.delete_images(app.config['OUTPUT_FOLDER_PIPELINES'])
list_transformations = request.form.get('list_transformations').split(',')
processing_lib.pipeline(filepath, list_transformations)
original = ['/' + filepath, image]
pipelines = utils.get_images(app.config['OUTPUT_FOLDER_PIPELINES'])
steps_count = utils.count_folders(app.config['OUTPUT_FOLDER_STEPS'])
for index in range(1, steps_count + 1):
if next((x for x in pipelines if int(x[1].split('-')[0]) == index), None) is None:
pipelines.append(('/static/img/fail.gif', '{}-{}'.format(index, str(uuid.uuid4()).split('-')[0])))
pipelines.sort(key=lambda x: int(x[1].split('-')[0]))
return render_template('pipeline.html', original=original, pipelines=pipelines)
def delete_image(image_id):
appid = request.headers.get('app_id')
cnt = df.delete_images(image_id, appid)
out = {
'status': 'success',
'message': 'image {} has been deleted.'.format(image_id)
}
return jsonify(out)
def delete_images():
appid = request.headers.get('app_id')
image_ids = []
try:
image_ids = df.get_request_data(request).get('images', '').split(',')
image_ids = [i.strip() for i in image_ids]
except AttributeError as ae:
image_ids = []
cnt = df.delete_images(image_ids, appid)
out = {'status': 'success', 'message': 'images are deleted.'}
return jsonify(out)
def main(image=0):
if image == 0:
init__file_results()
images = utils.get_images(config['INPUT_FOLDER'])
images.sort(key=lambda x: int(x[1]))
images = images[image:]
for image in images:
print('Image {}'.format(image[1]))
filepath = image[0][1:]
text = ORIGINAL_TEXTS[image[1]]
utils.delete_images(config['OUTPUT_FOLDER_PIPELINES'])
time = default_timer()
result_text, percentage = ocr.compare(text, filepath)
time_end = default_timer() - time
write_file_result(
[image[1], 'original', percentage, text, result_text, time_end])
steps, times = processing_lib.pipeline(filepath,
config['TRANSFORMATIONS'])
pipelines = utils.get_images(config['OUTPUT_FOLDER_PIPELINES'])
for pipeline in pipelines:
time = default_timer()
result_text, percentage = ocr.compare(
text,
utils.get_filepath(config['OUTPUT_FOLDER_PIPELINES'],
pipeline[1]))
time_end = default_timer() - time
write_file_result([
image[1], '\r'.join(steps[int(pipeline[1].split('-')[0])]),
percentage, text, result_text,
times[int(pipeline[1].split('-')[0])] + time_end
])
def run(conf):
'''
Visualises clusters of a given run.
:param conf:
:return:
'''
#pp = pprint.PrettyPrinter()
#pp.pprint(conf)
# Make sure the global configuration is in place
utils.run_global_visit_configuration(conf)
#pdb.set_trace()
clusterConf = utils.getValueForKeyPath(conf, 'postprocessing.clusters')
if not clusterConf:
print "No configuration for cluster postprocessing. Nothing to do."
return 0
visitConf = utils.getValueForKeyPath(clusterConf, 'visit')
if not visitConf:
print "No configuration for visuals. Nothing to do."
return 0
views = utils.getValueForKeyPath(visitConf, 'views')
# Set up background gradient, axis labels etc.
utils.setAnnotations(visitConf, 'annotationAttributes')
# Set view and annotation attributes
utils.setAnnotations(conf, 'postprocessing.clusters.visit.annotations')
if not utils.getValueForKeyPath(conf, 'resume'):
print "Removing results from previous runs"
subprocess.call(
"rm -rf images movies *.vtk *.vtr *tracking*.png *source*.png",
shell=True)
else:
print "Removing intermediary files from previous runs"
subprocess.call("rm -f *.vtk *.vtr *.vtu", shell=True)
# Figure out the ending for the cluster vtk files
conversion_config = utils.getValueForKeyPath(
conf, 'postprocessing.clusters.meanie3D-cfm2vtk')
if "--write-as-xml" in conversion_config:
cluster_vtk_extension = ".vtu"
else:
cluster_vtk_extension = ".vtk"
# Glob the netcdf directory or find the single file
uses_time = utils.getValueForKeyPath(conf, 'uses_time')
print "Current work directory: " + os.path.abspath(os.getcwd())
if uses_time:
print "Processing file " + conf['source_directory']
netcdf_file = conf['source_directory']
else:
print "Processing files in directory " + conf['source_directory']
netcdf_files = sorted(glob.glob(conf['source_directory'] + "/*.nc"))
# Keep track of number of images to allow
# forced re-set in time to circumvent the
# Visit memory leak
image_count = 0
index_range = []
if uses_time:
t1 = int(utils.getValueForKeyPath(conf, 'start_time_index'))
t2 = int(utils.getValueForKeyPath(conf, 'end_time_index'))
index_range = range(t1, t2 + 1)
else:
index_range = range(len(netcdf_files))
time_index = -1
for index in index_range:
# construct the cluster filename and find it
# in the cluster directory
if not uses_time:
netcdf_file = netcdf_files[index]
else:
time_index = index
netcdf_path, filename = os.path.split(netcdf_file)
basename = os.path.splitext(filename)[0]
if uses_time:
basename = basename + "-" + str(time_index)
cluster_file = conf[
'cluster_directory'] + os.path.sep + basename + "-clusters.nc"
# cluster file gets it's basename from the input file rather than the cluster file
cluster_vtk_file = os.path.splitext(
filename)[0] + "-clusters" + cluster_vtk_extension
# label and displacement files are based on the cluster file name
label_vtk_file = basename + "-clusters-centers.vtk"
displacement_vtk_file = basename + "-clusters-displacements.vtk"
print "netcdf_file = " + netcdf_file
print "cluster_file = " + cluster_file
# check if the files both exist
if not os.path.exists(cluster_file):
print "Cluster file does not exist. Skipping."
continue
# predict the filenames for checking on resume
number_postfix = str(image_count).rjust(4, '0')
source_open = False
skip_source = False
if conf['resume'] == True:
exists = utils.images_exist(visitConf['views'], "source",
image_count)
if exists == "all":
print "Source visualization " + number_postfix + " exists. Skipping."
skip_source = True
elif exists == "partial":
print "Deleting partial visualization " + number_postfix
utils.delete_images(conf, "source", image_count)
if skip_source == False:
if utils.getValueForKeyPath(clusterConf, 'createSourceMovie'):
# Add ancillary background data
utils.plotMapdata(visitConf, 'map')
# Add timestamp
if utils.getValueForKeyPath(clusterConf, 'showDateTime'):
utils.add_datetime(clusterConf, netcdf_file, time_index)
# Add source data and threshold it
print "Plotting source data ..."
start_time = time.time()
utils.addPseudocolorPlots(netcdf_file, visitConf,
'source.plots', time_index)
source_open = True
visit.DrawPlots()
utils.saveImagesForViews(views, "source")
visit.DeleteAllPlots()
visit.ClearWindow()
print " done. (%.2f seconds)" % (time.time() - start_time)
if utils.getValueForKeyPath(clusterConf, 'visualiseClusters'):
skip = False
if conf['resume'] == True:
exists = utils.images_exist(visitConf['views'], "tracking",
image_count)
if exists == "all":
print "Cluster visualization " + number_postfix + " exists. Skipping."
skip = True
elif exists == "partial":
print "Deleting partial cluster visualization " + number_postfix
utils.delete_images(conf, "tracking", image_count)
if skip == False:
# Run the conversion
print "-- Converting clusters to .vtr --"
start_time = time.time()
params = "-f %s %s" \
% (cluster_file,utils.getValueForKeyPath(conf,'postprocessing.clusters.meanie3D-cfm2vtk'))
if utils.getValueForKeyPath(
conf,
'postprocessing.clusters.showDisplacementVectors'):
params += " --write-displacement-vectors"
if utils.getValueForKeyPath(conf, 'data.vtkDimensions'):
vtkDimString = ",".join(
utils.getValueForKeyPath(conf, 'data.vtkDimensions'))
params += " --vtk-dimensions=%s" % vtkDimString
# pdb.set_trace();
print "meanie3D-cfm2vtk %s" % params
meanie3D.app.external.execute_command('meanie3D-cfm2vtk',
params)
print " done. (%.2f seconds)" % (time.time() - start_time)
# Move cluster output file to individual file
if uses_time:
cluster_vtk_file_dst = basename + "-clusters.vtk"
os.rename(cluster_vtk_file, cluster_vtk_file_dst)
cluster_vtk_file = cluster_vtk_file_dst
print "-- Rendering cluster scene --"
start_time = time.time()
# Add ancillary background data
utils.plotMapdata(visitConf, 'map')
# Add timestamp
if utils.getValueForKeyPath(clusterConf, 'showDateTime'):
utils.add_datetime(clusterConf, netcdf_file, time_index)
# Add background source data
if utils.getValueForKeyPath(clusterConf,
'showSourceBackground'):
utils.addPseudocolorPlots(netcdf_file, visitConf,
'sourceBackground.plots',
time_index)
source_open = True
# Add the clusters
add_clusters(cluster_vtk_file, conf)
# Add modes as labels
labelConf = utils.getValueForKeyPath(visitConf, 'label')
if labelConf:
utils.addLabelPlot(label_vtk_file, labelConf)
# Add displacement vectors
if utils.getValueForKeyPath(clusterConf,
'showDisplacementVectors'):
vectorConf = utils.getValueForKeyPath(
visitConf, 'displacementVectors')
if vectorConf:
utils.addVectorPlot(displacement_vtk_file, vectorConf)
visit.DrawPlots()
utils.saveImagesForViews(views, "tracking")
print " done. (%.2f seconds)" % (time.time() - start_time)
# clean up
visit.DeleteAllPlots()
visit.ClearWindow()
if source_open:
visit.CloseDatabase(netcdf_file)
visit.CloseDatabase(label_vtk_file)
utils.close_pattern(basename + "*.vtr")
utils.close_pattern(basename + "*.vtk")
utils.close_pattern(basename + "*.vtu")
if utils.getValueForKeyPath(conf, 'cleanup_vtk'):
subprocess.call("rm -f *.vt*", shell=True)
# periodically kill computing engine to
# work around the memory leak fix
image_count = image_count + 1
# TODO: check if this is still necessary and re-implement if it is.
#if image_count % 100 == 0:
# visit.CloseComputeEngine()
# close mapstuff
# TODO: might need to keep track of open database files and
# close them.
# Use imagemagick to use image sequences to make movies
movieFormats = utils.getValueForKeyPath(clusterConf, 'movieFormats')
if utils.getValueForKeyPath(clusterConf, 'createSourceMovie'):
utils.createMoviesForViews(views, "source", movieFormats)
if utils.getValueForKeyPath(clusterConf, 'createClusterMovie'):
utils.createMoviesForViews(views, "tracking", movieFormats)
# clean up
print "Cleaning up ..."
subprocess.call("mkdir images", shell=True)
subprocess.call("mv *tracking_*.png images", shell=True)
subprocess.call("mv *source_*.png images", shell=True)
subprocess.call("mkdir movies", shell=True)
subprocess.call("mv *source*.gif *tracking*.gif *.m4v movies", shell=True)
if utils.getValueForKeyPath(conf, 'cleanup_vtk'):
subprocess.call("rm -f *.vt* visitlog.py", shell=True)
return
def run(conf):
'''
Visualises clusters of a given run.
:param conf:
:return:
'''
#pp = pprint.PrettyPrinter()
#pp.pprint(conf)
# Make sure the global configuration is in place
utils.run_global_visit_configuration(conf)
#pdb.set_trace()
clusterConf = utils.getValueForKeyPath(conf,'postprocessing.clusters')
if not clusterConf:
print "No configuration for cluster postprocessing. Nothing to do."
return 0
visitConf = utils.getValueForKeyPath(clusterConf,'visit')
if not visitConf:
print "No configuration for visuals. Nothing to do."
return 0
views = utils.getValueForKeyPath(visitConf,'views')
# Set up background gradient, axis labels etc.
utils.setAnnotations(visitConf,'annotationAttributes')
# Set view and annotation attributes
utils.setAnnotations(conf,'postprocessing.clusters.visit.annotations')
if not utils.getValueForKeyPath(conf,'resume'):
print "Removing results from previous runs"
subprocess.call("rm -rf images movies *.vtk *.vtr *tracking*.png *source*.png", shell=True)
else:
print "Removing intermediary files from previous runs"
subprocess.call("rm -f *.vtk *.vtr *.vtu", shell=True)
# Figure out the ending for the cluster vtk files
conversion_config = utils.getValueForKeyPath(conf,'postprocessing.clusters.meanie3D-cfm2vtk')
if "--write-as-xml" in conversion_config:
cluster_vtk_extension = ".vtu"
else:
cluster_vtk_extension = ".vtk"
# Glob the netcdf directory or find the single file
uses_time = utils.getValueForKeyPath(conf,'uses_time')
print "Current work directory: " + os.path.abspath(os.getcwd())
if uses_time:
print "Processing file " + conf['source_directory']
netcdf_file = conf['source_directory']
else:
print "Processing files in directory " + conf['source_directory']
netcdf_files = sorted(glob.glob(conf['source_directory']+"/*.nc"))
# Keep track of number of images to allow
# forced re-set in time to circumvent the
# Visit memory leak
image_count = 0
index_range = []
if uses_time:
t1 = int(utils.getValueForKeyPath(conf,'start_time_index'))
t2 = int(utils.getValueForKeyPath(conf,'end_time_index'))
index_range = range(t1,t2+1)
else:
index_range = range(len(netcdf_files))
time_index = -1
for index in index_range:
# construct the cluster filename and find it
# in the cluster directory
if not uses_time:
netcdf_file = netcdf_files[index]
else:
time_index = index
netcdf_path,filename = os.path.split(netcdf_file)
basename = os.path.splitext(filename)[0]
if uses_time:
basename = basename + "-" + str(time_index)
cluster_file = conf['cluster_directory'] + os.path.sep + basename + "-clusters.nc"
# cluster file gets it's basename from the input file rather than the cluster file
cluster_vtk_file = os.path.splitext(filename)[0] + "-clusters" + cluster_vtk_extension
# label and displacement files are based on the cluster file name
label_vtk_file = basename + "-clusters-centers.vtk"
displacement_vtk_file = basename + "-clusters-displacements.vtk"
print "netcdf_file = " + netcdf_file
print "cluster_file = " + cluster_file
# check if the files both exist
if not os.path.exists(cluster_file):
print "Cluster file does not exist. Skipping."
continue
# predict the filenames for checking on resume
number_postfix = str(image_count).rjust(4,'0')
source_open = False
skip_source = False
if conf['resume'] == True:
exists = utils.images_exist(visitConf['views'],"source",image_count)
if exists == "all":
print "Source visualization " + number_postfix + " exists. Skipping."
skip_source = True
elif exists == "partial":
print "Deleting partial visualization " + number_postfix
utils.delete_images(conf,"source",image_count)
if skip_source == False:
if utils.getValueForKeyPath(clusterConf,'createSourceMovie'):
# Add ancillary background data
utils.plotMapdata(visitConf,'map')
# Add timestamp
if utils.getValueForKeyPath(clusterConf,'showDateTime'):
utils.add_datetime(clusterConf,netcdf_file,time_index)
# Add source data and threshold it
print "Plotting source data ..."
start_time = time.time()
utils.addPseudocolorPlots(netcdf_file,visitConf,'source.plots',time_index)
source_open = True
visit.DrawPlots()
utils.saveImagesForViews(views,"source")
visit.DeleteAllPlots()
visit.ClearWindow()
print " done. (%.2f seconds)" % (time.time()-start_time)
if utils.getValueForKeyPath(clusterConf,'visualiseClusters'):
skip = False
if conf['resume'] == True:
exists = utils.images_exist(visitConf['views'],"tracking",image_count)
if exists == "all":
print "Cluster visualization "+number_postfix+" exists. Skipping."
skip = True
elif exists == "partial":
print "Deleting partial cluster visualization " + number_postfix
utils.delete_images(conf,"tracking",image_count)
if skip == False:
# Run the conversion
print "-- Converting clusters to .vtr --"
start_time = time.time()
params = "-f %s %s" \
% (cluster_file,utils.getValueForKeyPath(conf,'postprocessing.clusters.meanie3D-cfm2vtk'))
if utils.getValueForKeyPath(conf,'postprocessing.clusters.showDisplacementVectors'):
params += " --write-displacement-vectors"
if utils.getValueForKeyPath(conf,'data.vtkDimensions'):
vtkDimString = ",".join(utils.getValueForKeyPath(conf,'data.vtkDimensions'))
params += " --vtk-dimensions=%s" % vtkDimString
# pdb.set_trace();
print "meanie3D-cfm2vtk %s" % params
meanie3D.app.external.execute_command('meanie3D-cfm2vtk', params)
print " done. (%.2f seconds)" % (time.time()-start_time)
# Move cluster output file to individual file
if uses_time:
cluster_vtk_file_dst = basename + "-clusters.vtk"
os.rename(cluster_vtk_file,cluster_vtk_file_dst)
cluster_vtk_file = cluster_vtk_file_dst
print "-- Rendering cluster scene --"
start_time = time.time()
# Add ancillary background data
utils.plotMapdata(visitConf,'map')
# Add timestamp
if utils.getValueForKeyPath(clusterConf,'showDateTime'):
utils.add_datetime(clusterConf,netcdf_file,time_index)
# Add background source data
if utils.getValueForKeyPath(clusterConf,'showSourceBackground'):
utils.addPseudocolorPlots(netcdf_file,visitConf,'sourceBackground.plots',time_index)
source_open = True
# Add the clusters
add_clusters(cluster_vtk_file, conf)
# Add modes as labels
labelConf = utils.getValueForKeyPath(visitConf,'label')
if labelConf:
utils.addLabelPlot(label_vtk_file,labelConf)
# Add displacement vectors
if utils.getValueForKeyPath(clusterConf,'showDisplacementVectors'):
vectorConf = utils.getValueForKeyPath(visitConf,'displacementVectors')
if vectorConf:
utils.addVectorPlot(displacement_vtk_file,vectorConf)
visit.DrawPlots()
utils.saveImagesForViews(views,"tracking")
print " done. (%.2f seconds)" % (time.time()-start_time)
# clean up
visit.DeleteAllPlots();
visit.ClearWindow()
if source_open:
visit.CloseDatabase(netcdf_file)
visit.CloseDatabase(label_vtk_file)
utils.close_pattern(basename+"*.vtr")
utils.close_pattern(basename+"*.vtk")
utils.close_pattern(basename+"*.vtu")
if utils.getValueForKeyPath(conf,'cleanup_vtk'):
subprocess.call("rm -f *.vt*", shell=True)
# periodically kill computing engine to
# work around the memory leak fix
image_count=image_count+1;
# TODO: check if this is still necessary and re-implement if it is.
#if image_count % 100 == 0:
# visit.CloseComputeEngine()
# close mapstuff
# TODO: might need to keep track of open database files and
# close them.
# Use imagemagick to use image sequences to make movies
movieFormats = utils.getValueForKeyPath(clusterConf,'movieFormats')
if utils.getValueForKeyPath(clusterConf,'createSourceMovie'):
utils.createMoviesForViews(views,"source", movieFormats)
if utils.getValueForKeyPath(clusterConf,'createClusterMovie'):
utils.createMoviesForViews(views,"tracking", movieFormats)
# clean up
print "Cleaning up ..."
subprocess.call("mkdir images", shell=True)
subprocess.call("mv *tracking_*.png images", shell=True)
subprocess.call("mv *source_*.png images", shell=True)
subprocess.call("mkdir movies", shell=True)
subprocess.call("mv *source*.gif *tracking*.gif *.m4v movies", shell=True)
if utils.getValueForKeyPath(conf,'cleanup_vtk'):
subprocess.call("rm -f *.vt* visitlog.py", shell=True)
return
def api_root():
msg = 'Image-Classification-Toolkit permite realizar una clasificación dual a partir de dos conjuntos de imágenes (positivas y negativas) con los algoritmos SVM-KNN-BPNN-CNN y Transfer Learning .'
utils.delete_images(app.config['FOLDER_POSITIVE'])
utils.delete_images(app.config['FOLDER_NEGATIVE'])
return render_template('index.html', msg=msg)