- Reduce distance_threshold during predict for reduce miss-classifica…

…tion

- Create a delegated method for init main data

api/Api.py

@@ -8,7 +8,7 @@
 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
+from utils.util import print_prediction_on_image, random_string, remove_dir
 
 log = getLogger()
 

conf/test.json

@@ -23,7 +23,7 @@
 	"classifier": {
 		"trainin_dir": "dataset/images/",
 		"model_path": "dataset/model/",
-		"model": "model-20190518_191827.clf",
+		"model": "model-20190519_125204.clf",
 		"n_neighbors": "",
 		"knn_algo": ""
 	},

datastructure/Classifier.py

@@ -178,7 +178,7 @@ def init_dataset(self):
 
 		return DATASET
 
-	def predict(self, X_img_path, distance_threshold=0.6):
+	def predict(self, X_img_path, distance_threshold=0.45):
 		"""
 		Recognizes faces in given image using a trained KNN classifier
 

datastructure/Person.py

@@ -64,6 +64,6 @@ def init_dataset(self):
 						log.error("initDataset | Found more than one face, too much for me Sir :&")
 			# Loading the Y [target]
 			for i in range(len(self.dataset["X"])):
-				log.debug("Adding name " + self.name)
 				self.dataset["Y"].append(self.name)
+			log.debug("Adding {} entries for {}".format(len(self.dataset["X"]), self.name))
 		return

main.py

@@ -3,7 +3,6 @@
 PyRecognizer loader
 """
 import base64
-import json
 import os
 
 import flask_monitoringdashboard as dashboard
@@ -12,22 +11,13 @@
 
 from api.Api import predict_image, train_network
 from datastructure.Classifier import Classifier
-from utils.util import load_logger
+from utils.util import init_main_data
 
 # ===== LOAD CONFIGURATION FILE =====
 # TODO: Add argument parser for manage configuration file
 CONFIG_FILE = "conf/test.json"
 
-with open(CONFIG_FILE) as f:
-	CFG = json.load(f)
-
-log = load_logger(CFG["logging"]["level"], CFG["logging"]["path"], CFG["logging"]["prefix"])
-
-# TODO: Verify the presence -> create directory
-# NOTE: create a directory every time that you need to use this folder
-TMP_UPLOAD_PREDICTION = CFG["PyRecognizer"]["temp_upload_predict"]
-TMP_UPLOAD_TRAINING = CFG["PyRecognizer"]["temp_upload_training"]
-TMP_UPLOAD = CFG["PyRecognizer"]["temp_upload"]
+CFG, log, TMP_UPLOAD_PREDICTION, TMP_UPLOAD_TRAINING, TMP_UPLOAD = init_main_data(CONFIG_FILE)
 
 # $(base64 /dev/urandom  | head -n 1 | md5sum | awk '{print $1}')
 SECRET_KEY = str(base64.b64encode(bytes(os.urandom(24)))).encode()
@@ -51,6 +41,7 @@
 clf.model_path = CFG["classifier"]["model_path"]
 clf.load_classifier_from_file(CFG["classifier"]["model"])
 
+# TODO Add check on extension
 allowed_ext = ["jpg", "jpeg", "png"]
 
 
@@ -73,7 +64,6 @@ def predict():
 		flash('No file choosed :/', category="error")
 		return redirect(request.url)  # Return to HTML page [GET]
 	file = request.files['file']
-	# TODO: Add check on extension
 	filename = secure_filename(file.filename)
 	img_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
 	file.save(img_path)

utils/util.py

@@ -2,13 +2,14 @@
 """
 Common method for reuse code
 """
-
+import json
 import logging
 import os
 import random
+import shutil
 import string
 from logging.handlers import TimedRotatingFileHandler
-import shutil
+
 from PIL import Image, ImageDraw
 
 levels = {
@@ -53,6 +54,33 @@ def print_prediction_on_image(img_path, predictions, path_to_save, file_to_save)
 	pil_image.save(os.path.join(path_to_save, file_to_save), "PNG")
 
 
+def init_main_data(config_file):
+	"""
+	Parse the configuration file and return the necessary data for initalize the tool
+	:param config_file:
+	:return:
+	"""
+	with open(config_file) as f:
+		CFG = json.load(f)
+
+	log = load_logger(CFG["logging"]["level"], CFG["logging"]["path"], CFG["logging"]["prefix"])
+
+	# TODO: Verify the presence -> create directory
+	# NOTE: create a directory every time that you need to use this folder
+	TMP_UPLOAD_PREDICTION = CFG["PyRecognizer"]["temp_upload_predict"]
+	TMP_UPLOAD_TRAINING = CFG["PyRecognizer"]["temp_upload_training"]
+	TMP_UPLOAD = CFG["PyRecognizer"]["temp_upload"]
+
+	if not os.path.exists(TMP_UPLOAD_PREDICTION):
+		os.makedirs(TMP_UPLOAD_PREDICTION)
+	if not os.path.exists(TMP_UPLOAD_TRAINING):
+		os.makedirs(TMP_UPLOAD_TRAINING)
+	if not os.path.exists(TMP_UPLOAD):
+		os.makedirs(TMP_UPLOAD)
+
+	return CFG, log, TMP_UPLOAD_PREDICTION, TMP_UPLOAD_TRAINING, TMP_UPLOAD
+
+
 def load_logger(level, path, name):
 	"""
 
@@ -85,7 +113,7 @@ def random_string(string_length=10):
 	return ''.join(random.choice(letters) for i in range(string_length))
 
 
-def zip_data(file_to_zip,path):
+def zip_data(file_to_zip, path):
 	"""
 
 	:param file_to_zip:
@@ -99,5 +127,3 @@ def zip_data(file_to_zip,path):
 def remove_dir(dir):
 	if os.path.isdir(dir):
 		shutil.rmtree(dir)
-
-