Logs verbessert

projectmood/process_model.py

@@ -2,24 +2,27 @@ import cv2
 import glob
 import random
 import numpy as np
-import logging
 import sys
-import signal
+import logging
 
-logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
+logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)-8s %(message)s',
                     datefmt='%m-%d %H:%M',
                     filename='logs/process_model.log')
+tags = sys.argv
+logging.debug('Fisherface training initialized')
 
-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
+def get_files_from_emotion(emotion):  # Define function to get file list, randomly shuffle it and split 80/20
     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
     return training, prediction
 
+def image_preprocessing(item):
+    image = cv2.imread(item)  # open image
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  # convert to grayscale
+    equalized = cv2.equalizeHist(gray)
+    return equalized
 
 def make_sets():
     training_data = []
@@ -27,32 +30,26 @@ def make_sets():
     prediction_data = []
     prediction_labels = []
     for emotion in emotions:
-        training, prediction = get_files(emotion)
+        training, prediction = get_files_from_emotion(emotion)
         # Append data to training and prediction list, and generate labels 0-7
         for item in training:
-            image = cv2.imread(item)  # open image
-            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  # convert to grayscale
-            training_data.append(gray)  # append image array to training data list
+            img = image_preprocessing(item)
+            training_data.append(img)  # append image array to training data list
             training_labels.append(emotions.index(emotion))
 
         for item in prediction:  # repeat above process for prediction set
-            image = cv2.imread(item)
-            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-            prediction_data.append(gray)
+            img = image_preprocessing(item)
+            prediction_data.append(img)
             prediction_labels.append(emotions.index(emotion))
 
 
     return training_data, training_labels, prediction_data, prediction_labels
 
-
 def run_recognizer():
     training_data, training_labels, prediction_data, prediction_labels = make_sets()
 
-    print("training fisher face classifier")
-    print("size of training set is:", len(training_labels), "images")
     fishface.train(training_data, np.asarray(training_labels))
 
-    print("predicting classification set")
     cnt = 0
     correct = 0
     incorrect = 0
@@ -66,22 +63,20 @@ def run_recognizer():
             cnt += 1
     return ((100 * correct) / (correct + incorrect))
 
+if len(tags) > 1:
+    message = ', '.join(tags[1:])
+    logging.debug(message.upper())
+
+emotions = ["anger", "disgust", "happy", "neutral", "surprise"]  # Emotion list
+fishface = cv2.face.FisherFaceRecognizer_create()
 
 # Now run it
 metascore = []
 
-logging.debug("fisherface training initialized")
 for i in range(0, 10):
     correct = run_recognizer()
-    print("got", correct, "percent correct!")
+    logging.debug("{} : {}%".format(i, int(correct)))
     metascore.append(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)))
-
+logging.debug("{} iterations - {}% average\n".format(len(metascore), np.mean(metascore)))
 fishface.write('basis_data/models/detection_model.xml')
\ No newline at end of file

projectmood/webcam.py

@@ -46,7 +46,6 @@ def show_webcam_and_run(model, emoticons, window_size=None, window_name='webcam'
     while read_value:
         for normalized_face, (x, y, w, h) in find_faces(webcam_image):
             prediction = model.predict(normalized_face)  # do prediction
-            cv2.imshow("Face", normalized_face)
 
             image_to_draw = emoticons[(prediction[0])]
             if x - 150 > 0 and y - 50 > 0 and w - 150 > 0 and h - 150 > 0: