Merge branch 'refactor'

.gitignore

@@ -76,3 +76,4 @@ dataset
 *.pyc
 flask_monitoringdashboard.db
 uploads
+conf

api/Api.py

@@ -2,15 +2,13 @@
 """
 Custom function that will be wrapped for be HTTP compliant
 """
-import os
-import pickle
+
 import time
-import zipfile
+from datetime import datetime
 from logging import getLogger
-from os.path import join as path_join
 
 from datastructure.Response import Response
-from utils.util import print_prediction_on_image, random_string, remove_dir, unzip_data
+from utils.util import print_prediction_on_image, random_string, retrieve_dataset
 
 log = getLogger()
 
@@ -24,21 +22,22 @@ def predict_image(img_path, clf, PREDICTION_PATH):
 	:return: Response dictionary jsonizable
 	"""
 	response = Response()
-	log.debug("predict_image | Predicting {}".format(img_path))
 	if clf is None:
+		log.error("predict_image | FATAL | Classifier is None!")
 		prediction = None
 	else:
+		log.debug("predict_image | Predicting {}".format(img_path))
 		prediction = clf.predict(img_path)
-	log.debug("predict_image | Image analyzed!")
+		log.debug("predict_image | Result: {}".format(prediction))
 	# Manage success
-	if prediction is not None and isinstance(prediction, list) and len(prediction) == 1:
+	if prediction and isinstance(prediction["predictions"], list):
 		img_name = random_string() + ".png"
-		log.debug("predict_image | Generated a random name: {}".format(img_path))
+		log.debug("predict_image | Generated a random name: {}".format(img_name))
 		log.debug("predict_image | Visualizing face recognition ...")
-		print_prediction_on_image(img_path, prediction, PREDICTION_PATH, img_name)
-		response.status = "OK"
-		response.description = img_name
-		response.data = prediction[0][0]
+		print_prediction_on_image(img_path, prediction["predictions"], PREDICTION_PATH, img_name)
+		return Response(status="OK", description=img_name, data={"name": prediction["predictions"][0][0],
+		                                                         "distance": prediction[
+			                                                         "score"]}).__dict__
 
 	# Manage error
 	elif prediction is None:
@@ -61,7 +60,7 @@ def predict_image(img_path, clf, PREDICTION_PATH):
 		# TODO: Add custom algorithm that "try to understand" who has never been recognized
 		response.error = "FACE_NOT_RECOGNIZED"
 		response.description = "Seems that this face is related to nobody that i've seen before ..."
-		log.error("predict_image | Seems that this face is lated to nobody that i've seen before ...")
+		log.error("predict_image | Seems that this face is related to nobody that i've seen before ...")
 
 	elif prediction == -2:
 		response.error = "FILE_NOT_VALID"
@@ -79,23 +78,23 @@ def train_network(folder_uncompress, zip_file, clf):
 	:param clf:
 	:return:
 	"""
-	log.debug("train_network | uncompressing zip file ...")
-	folder_name = path_join(folder_uncompress, random_string())
-	zip_ref = zipfile.ZipFile(zip_file)
-	zip_ref.extractall(folder_name)
-	zip_ref.close()
-	log.debug("train_network | zip file uncompressed!")
-	clf.init_peoples_list(peoples_path=folder_name)
-	dataset = clf.init_dataset()
-	neural_model_file = clf.train(dataset["X"], dataset["Y"])
-	log.debug("train_network | Removing unzipped files")
-	remove_dir(folder_name)
-	response = Response()
-	response.status = "OK"
-	response.data = neural_model_file
-	response.description = "Model succesfully trained!"
 
-	return response.__dict__
+	log.debug("train_network | Starting training phase ...")
+	dataset = retrieve_dataset(folder_uncompress, zip_file, clf)
+
+	if dataset is None:
+		return Response(error="ERROR DURING LOADING DAT", description="Seems that the dataset is not valid").__dict__
+
+	else:
+		timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
+		neural_model_file, elapsed_time = clf.train(dataset["X"], dataset["Y"], timestamp)
+
+		response = Response(status="OK", data=neural_model_file)
+		response.description = "Model succesfully trained! | {}".format(
+			time.strftime("%H:%M:%S.%f", time.gmtime(elapsed_time)))
+		log.debug("train_network | Tuning phase finihsed! | {}".format(response.description))
+
+		return response.__dict__
 
 
 def tune_network(folder_uncompress, zip_file, clf):
@@ -106,50 +105,19 @@ def tune_network(folder_uncompress, zip_file, clf):
 	:param clf:
 	:return:
 	"""
-	log.debug("tune_network | uncompressing zip file ...")
-	check = verify_extension(zip_file.filename)
-	if check == "zip":  # Image provided
-		folder_name = unzip_data(folder_uncompress, zip_file)
-		log.debug("tune_network | zip file uncompressed!")
-		clf.init_peoples_list(peoples_path=folder_name)
-		dataset = clf.init_dataset()
-	elif check == "dat":
-		dataset = pickle.load(zip_file)
+	log.debug("tune_network | Starting tuning phase ...")
+	dataset = retrieve_dataset(folder_uncompress, zip_file, clf)
+
+	if dataset is None:
+		return Response(error="ERROR DURING LOADING DAT", description="Seems that the dataset is not valid").__dict__
+
 	else:
-		dataset = None
-
-	if dataset is not None:
-		start_time = time.time()
-		neural_model_file = clf.tuning(dataset["X"], dataset["Y"])
-		elapsed_time = time.time() - start_time
-
-		log.debug("tune_network | Removing unzipped files")
-		if check == "zip":
-			# TODO: Refactor this method :/
-			remove_dir(folder_name)
-		response = Response()
-		response.status = "OK"
-		response.data = neural_model_file
+		timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
+		neural_model_file, elapsed_time = clf.tuning(dataset["X"], dataset["Y"], timestamp)
+
+		response = Response(status="OK", data=neural_model_file)
 		response.description = "Model succesfully trained! | {}".format(
 			time.strftime("%H:%M:%S.%f", time.gmtime(elapsed_time)))
-	else:
-		response = Response()
-		response.error = "ERROR DURING LOADING DAT"
-	return response.__dict__
+		log.debug("train_network | Tuning phase finihsed! | {}".format(response.description))
 
-
-def verify_extension(file):
-	"""
-	Wrapper for validate file
-	:param file:
-	:return:
-	"""
-	extension = os.path.splitext(file)[1]
-	log.debug("verify_extension | File: {} | Ext: {}".format(file, extension))
-	if extension == ".zip":
-		# In this case we have to analyze the photos
-		return "zip"
-	elif extension == ".dat":
-		# Photos have been alredy analyzed, dataset is ready!
-		return "dat"
-	return None
+		return response.__dict__

api/templates/train.html

@@ -10,6 +10,7 @@ <h1>Upload a zip file with all person that you want to save!</h1>
 <form enctype=multipart/form-data method=post>
     <input name=file type=file>
     <input type=submit value=Upload>
+    <input name=_csrf_token type=hidden value="{{ csrf_token() }}">
 </form>
 {% with messages = get_flashed_messages() %}
 {% if messages %}

api/templates/upload.html

@@ -10,6 +10,7 @@ <h1>Upload new File</h1>
 <form enctype=multipart/form-data method=post>
     <input name=file type=file>
     <input type=submit value=Upload>
+    <input name=_csrf_token type=hidden value="{{ csrf_token() }}">
 </form>
 {% with messages = get_flashed_messages() %}
 {% if messages %}

conf/dashboard.ini

@@ -15,4 +15,4 @@ GUEST_PASSWORD = ['guest', 'password']
 
 [database]
 TABLE_PREFIX = fmd
-DATABASE = sqlite:///log/flask_monitoringdashboard.db
+DATABASE = sqlite:///conf/flask_monitoringdashboard.db

conf/ssl/localhost.crt

@@ -0,0 +1,18 @@
+-----BEGIN CERTIFICATE-----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+-----END CERTIFICATE-----

conf/ssl/localhost.key

@@ -0,0 +1,28 @@
+-----BEGIN PRIVATE KEY-----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+-----END PRIVATE KEY-----

conf/test.json

@@ -1,6 +1,6 @@
 {
 	"PyRecognizer": {
-		"Version": "0.0.1",
+		"Version": "0.1.2",
 		"temp_upload_training": "uploads/training/",
 		"temp_upload_predict": "uploads/predict/",
 		"temp_upload": "uploads/upload"
@@ -11,21 +11,26 @@
 		"level": "debug"
 	},
 	"network": {
-		"host": "locahost",
+		"host": "0.0.0.0",
 		"port": 11001,
 		"templates": "api/templates/",
 		"SSL": {
-			"enabled": false,
-			"cert.pub": "/dev/null",
-			"cert.priv": "/dev/null"
+			"enabled": true,
+			"cert.pub": "conf/ssl/localhost.crt",
+			"cert.priv": "conf/ssl/localhost.key"
 		}
 	},
 	"classifier": {
 		"trainin_dir": "dataset/images/",
 		"model_path": "dataset/model/",
-		"model": "model-20190518_191827.clf",
-		"n_neighbors": "",
-		"knn_algo": ""
+		"timestamp": "20190522_170246",
+		"params": {
+			"algorithm": "ball_tree",
+			"metric": "minkowski",
+			"n_neighbors": 80,
+			"p": 2,
+			"weights": "distance"
+		}
 	},
 	"data": {
 		"test_data": "/tmp/test_data/"

dataset/model/model-20190519_210950.json → dataset/model/20190522_170246/model.json

@@ -1,9 +1,9 @@
 {
-	"classifier_file": "dataset/model/model-20190519_210950",
+	"classifier_file": "20190522_170246/model.clf",
 	"params": {
 		"algorithm": "ball_tree",
 		"metric": "minkowski",
-		"n_neighbors": 78,
+		"n_neighbors": 80,
 		"p": 2,
 		"weights": "distance"
 	}