Nachbesserung Sorted_set

projectmood/process_model.py

@@ -2,15 +2,19 @@ import cv2
 import glob
 import random
 import numpy as np
+import logging
+import sys
+import signal
 
-emotions = ["neutral", "anger", "disgust", "happy", "surprise"]  # Emotion list
-fishface = cv2.face.FisherFaceRecognizer_create()
-
-data = {}
+logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
+                    datefmt='%m-%d %H:%M',
+                    filename='logs/process_model.log')
 
+emotions = ["anger", "disgust", "happy", "neutral", "surprise"]  # Emotion list
+fishface = cv2.face.FisherFaceRecognizer_create()
 
 def get_files(emotion):  # Define function to get file list, randomly shuffle it and split 80/20
-    files = glob.glob("Basis_data\\dataset\\%s\\*" % emotion)
+    files = glob.glob('Basis_data/dataset/%s/*' % emotion)
     random.shuffle(files)
     training = files[:int(len(files) * 0.8)]  # get first 80% of file list
     prediction = files[-int(len(files) * 0.2):]  # get last 20% of file list
@@ -37,6 +41,7 @@ def make_sets():
             prediction_data.append(gray)
             prediction_labels.append(emotions.index(emotion))
 
+
     return training_data, training_labels, prediction_data, prediction_labels
 
 
@@ -64,11 +69,19 @@ def run_recognizer():
 
 # Now run it
 metascore = []
+
+logging.debug("fisherface training initialized")
 for i in range(0, 10):
     correct = run_recognizer()
     print("got", correct, "percent correct!")
     metascore.append(correct)
 
-print("\n\nend score:", np.mean(metascore), "percent correct!")
+print("\nend score:", np.mean(metascore), "percent correct!")
+
+logging.info("finished {} iterations:".format(len(metascore)))
+for idx, meta in enumerate(metascore):
+    logging.debug("{} : {}%".format(idx, meta))
+
+logging.debug("fisherface finished with {}%\n".format(np.mean(metascore)))
 
 fishface.write('Basis_data/models/detection_model.xml')
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