def predict(self,data_set):
"""
This method uses the model to assign a label to each item in the data set. The return value
is a Prediction object.
"""
test_file_name=self._test_file_name+'_'+str(LibSVMModel._file_suffix)
model_filename=self._model_file
output_filename=self._output_filename
predictor_package_path=self._predictor_package_path
item_order=self.create_validation_set_file_from_dataset(data_set,test_file_name)
args=[]
args.append(predictor_package_path)
args.append(test_file_name)
args.append(model_filename)
args.append(output_filename)
#logging.debug( 'args for predict'
#logging.debug( args
fnull=open(os.devnull,'w')
#p=subprocess.Popen(args,stdout=subprocess.PIPE)
p=subprocess.call(args, stdout=fnull)
fnull.close()
'''
for line in p.stdout:
#logging.debug( line
if(line.startswith('Accuracy')):
accuracy=float(line.split()[2].split('%')[0])/100.0
logging.debug( accuracy
break
'''
#
predicted_values=Prediction(data_set)
#logging.debug( 'Count '+str(predicted_values.predicted_count())
#logging.debug( output_filename
fin=open(output_filename,'r')
i=0
for line in fin:
#logging.debug( 'Output file :'+line
item=item_order[i]
predicted_values.set_est_label(item,line.split()[0])
logging.debug( 'setting label for '+item)
i+=1
fin.close()
#LibSVMModel._file_suffix+=1
return(predicted_values)
def myAPI(url_path):
# 2- download the image
path_of_image = downloadImage(url_path)
if path_of_image.startswith("Non"):
return render_template('error.html')
# 3- get the path of the image .... DONE IN STEP #2
# 4- form the proper main method for core_classification
result = mainWeb(path_of_image)
# 5- display the output
# 5'- slice the result into useful parts
parts = result.split('\n')
# 6- extract info from the parts to form the FinalResult object and jsonify it
predictions = []
for part in parts:
part = part.strip()
if part.startswith("Prediction") or part == "":
continue
myParts = part.split(' ')
print myParts
percentage = myParts[0] #percentage
value = myParts[2] #value prediction
prediction = Prediction(percentage, value)
predictions.append(prediction)
finalResult = FinalResult(url_path, predictions)
return jsonify(Result=finalResult.serialize)
def classifyImage():
if request.method == 'POST':
# 1- get the url
if request.form['url']:
url_path = request.form['url']
# 2- download the image
path_of_image = downloadImage(url_path)
if path_of_image.startswith("Non"):
return render_template('error.html')
# 3- get the path of the image .... DONE IN STEP #2
# 4- form the proper main method for core_classification
result = mainWeb(path_of_image)
# 5- display the output
# 5'- slice the result into useful parts
# list of percentages
predictions = []
parts = result.split('\n')
for part in parts:
part = part.strip()
if part.startswith("Prediction") or part == "":
continue
myParts = part.split(' ')
percentage = myParts[0] # percentage
value = myParts[2] # value prediction
prediction = Prediction(percentage, value)
predictions.append(prediction)
path_parts = path_of_image.split('/')
betterPath = path_parts[5] + "/" + path_parts[
6] + "/" + path_parts[7]
"""
RETRIEVAL CODE GOES HERE
"""
script_start_time = time.time()
features_array = async_result.get()
print 'async_result took %f ' % (time.time() - script_start_time, )
list_of_paths = findSimilar([path_of_image], features_array)
print list_of_paths
# parse the location from disk to a location in server
server_similar_images_parsed = parseImageSimilarPath(list_of_paths)
# pass list_of_paths which represents the similar images to the template
#return render_template('result.html', result=predictions, path_of_image=betterPath)
return render_template('result.html',
result=predictions,
path_of_image=betterPath,
similar=server_similar_images_parsed)
else:
return "REJECTED"
def predict(self,data_set): """ This method uses the model to assign a label to each item in the data set. The return value is a Prediction object. """ predicted_values=Prediction(data_set) gbm_package=self._gbm_package gbm_object=self._gbm_model_object (features,labels,items)=self.create_validation_set(data_set) predicted=gbm_package.predict_gbm(gbm_object,features,n_trees=self._n_trees,type='response',verbose=False) item_count=0 for item in items: predicted_values.set_est_label(item,self.convert_label(predicted[item_count])) item_count+=1 return(predicted_values)
def predict(self, data_set):
"""
This method uses the model to assign a label to each item in the data set. The return value
is a Prediction object.
"""
predicted_values = Prediction(data_set)
gbm_package = self._gbm_package
gbm_object = self._gbm_model_object
(features, labels, items) = self.create_validation_set(data_set)
predicted = gbm_package.predict_gbm(gbm_object,
features,
n_trees=self._n_trees,
type='response',
verbose=False)
item_count = 0
for item in items:
predicted_values.set_est_label(
item, self.convert_label(predicted[item_count]))
item_count += 1
return (predicted_values)
