SortedSet mal ein bisschen krass gepimpt um Ergebnisse zu validieren. Output...

SortedSet mal ein bisschen krass gepimpt um Ergebnisse zu validieren. Output wird jetzt in Logfile gespeichert

projectmood/sorted_set_facedetector.py

 import cv2
 import glob
+import time
+import logging
 
 faceDet = cv2.CascadeClassifier('Haarcascade/haarcascade_frontalface_default.xml')
 faceDet_two = cv2.CascadeClassifier('Haarcascade/haarcascade_frontalface_alt2.xml')
 faceDet_three = cv2.CascadeClassifier('Haarcascade/haarcascade_frontalface_alt.xml')
 faceDet_four = cv2.CascadeClassifier('Haarcascade/haarcascade_frontalface_alt_tree.xml')
 
+logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
+                    datefmt='%m-%d %H:%M',
+                    filename='logs/sorted_set_facedetector.log',
+                    filemode='w')
+
 emotions = ["neutral", "anger", "disgust", "happy", "surprise"]  # Define emotions
 
+totalFiles:int = 0
+totalFaces:int = 0
+undetected:list = []
 
 def detect_faces(emotion):
-    files = glob.glob('Basis_data/sorted_set/%s/*w' % emotion)  # Get list of all images with emotion
+    files = glob.glob('Basis_data/sorted_set/%s/*' % emotion)  # Get list of all images with emotion
+
+    global undetected
+    global totalFaces
+    global totalFiles
 
-    filenumber = 0
+    logging.debug("%d files in folder %s" % (len(files), emotion))
+
+    fileNumber = 0
     for f in files:
         frame = cv2.imread(f)  # Open image
         gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # Convert image to grayscale
 
-        scaleFactor = 1.05
-        minNeighbors = 10
-        minSize = (20, 20)
-
         # Detect face using 4 different classifiers
-        face = faceDet.detectMultiScale(gray, scaleFactor, minNeighbors, minSize,
+        face = faceDet.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
                                         flags=cv2.CASCADE_SCALE_IMAGE)
-        face_two = faceDet_two.detectMultiScale(gray, scaleFactor, minNeighbors, minSize,
+        face_two = faceDet_two.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
                                                 flags=cv2.CASCADE_SCALE_IMAGE)
-        face_three = faceDet_three.detectMultiScale(gray, scaleFactor, minNeighbors, minSize,
+        face_three = faceDet_three.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
                                                     flags=cv2.CASCADE_SCALE_IMAGE)
-        face_four = faceDet_four.detectMultiScale(gray, scaleFactor, minNeighbors, minSize,
+        face_four = faceDet_four.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
                                                   flags=cv2.CASCADE_SCALE_IMAGE)
 
         # Go over detected faces, stop at first detected face, return empty if no face.
@@ -41,20 +53,30 @@ def detect_faces(emotion):
         elif len(face_four) == 1:
             facefeatures = face_four
         else:
-            facefeatures = ""
+            undetected.append(f)
 
         # Cut and save face
         for (x, y, w, h) in facefeatures:  # get coordinates and size of rectangle containing face
-            print("face found in file: %s" % f)
+            logging.debug("face found in file: %s" % f)
+            totalFaces += 1
             gray = gray[y:y + h, x:x + w]  # Cut the frame to size
 
             try:
                 out = cv2.resize(gray, (250, 250))  # Resize face so all images have same size
-                cv2.imwrite('Basis_data/dataset/%s/%s.jpg' % (emotion, filenumber), out)  # Write image
+                cv2.imwrite('Basis_data/dataset/%s/%s.jpg' % (emotion, fileNumber), out)  # Write image
             except:
                 pass  # If error, pass file
-        filenumber += 1  # Increment image number
+
+        totalFiles += 1 # Increment image number
+        fileNumber += 1
 
 
 for emotion in emotions:
-    detect_faces(emotion)  # Call functiona
+    detect_faces(emotion)  # Call functional
+    logging.debug("\n")
+
+logging.debug(str(totalFaces) + " Gesichter in " + str(totalFiles) + " Dateien gefunden.\n")
+
+logging.debug("Kein Gesicht wurde gefunden in:")
+for f in undetected:
+    logging.debug(f)
\ No newline at end of file