Folder Classifier hinzugefuegt zum Testen von Gesichtserkennung mit Maschine Learning

projectmood/Classifier/master/classifier.py

+# The Emotion Face detection Scripts
+# You can modify this script as you wish
+import cv2
+import glob as gb
+import random
+import numpy as np
+
+# Emotion list
+emojis = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"]
+# Initialize fisher face classifier
+fisher_face = cv2.createFisherFaceRecognizer()
+data = {}
+
+
+# Function defination to get file list, randomly shuffle it and split 67/33
+def getFiles(emotion):
+    files = gb.glob("final_dataset\\%s\\*" % emotion)
+    random.shuffle(files)
+    training = files[:int(len(files) * 0.67)]  # get first 67% of file list
+    prediction = files[-int(len(files) * 0.33):]  # get last 33% of file list
+    return training, prediction
+
+
+def makeTrainingAndValidationSet():
+    training_data = []
+    training_labels = []
+    prediction_data = []
+    prediction_labels = []
+    for emotion in emojis:
+        training, prediction = getFiles(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
+            training_labels.append(emojis.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)
+            prediction_labels.append(emojis.index(emotion))
+    return training_data, training_labels, prediction_data, prediction_labels
+
+
+def runClassifier():
+    training_data, training_labels, prediction_data, prediction_labels = makeTrainingAndValidationSet()
+
+    print("training fisher face classifier suing the training data")
+    print("size of training set is:", len(training_labels), "images")
+    fisher_face.train(training_data, np.asarray(training_labels))
+    print("classification prediction")
+    counter = 0
+    right = 0
+    wrong = 0
+    for image in prediction_data:
+        pred, conf = fisher_face.predict(image)
+        if pred == prediction_labels[counter]:
+            right += 1
+            counter += 1
+        else:
+            wrong += 1
+            counter += 1
+    return (100 * right) / (right + wrong)
+
+
+# Now run the classifier
+metascore = []
+for i in range(0, 10):
+    right = runClassifier()
+    print("got", right, "percent right!")
+    metascore.append(right)
+print("\n\nend score:", np.mean(metascore), "percent right!")
\ No newline at end of file

projectmood/Classifier/master/face_detection.py

+import cv2
+import glob as gb
+
+face_detector1 = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
+face_detector2 = cv2.CascadeClassifier("haarcascade_frontalface_alt2.xml")
+face_detector3 = cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")
+face_detector4 = cv2.CascadeClassifier("haarcascade_frontalface_alt_tree.xml")
+emotion_list = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"]
+
+
+def faceDetection(emotion):
+    files = gb.glob("selected_set\\%s\\*" % emotion)  # Get list of all images with emotion
+    filenumber = 0
+    for f in files:
+        frame = cv2.imread(f)  # Open image
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # Convert image to grayscale
+
+        # Detect face using 4 different classifiers
+        face1 = face_detector1.detectMultiScale(gray, scaleFactor=1.1,
+                                                minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
+        face2 = face_detector2.detectMultiScale(gray, scaleFactor=1.1,
+                                                minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
+        face3 = face_detector3.detectMultiScale(gray, scaleFactor=1.1,
+                                                minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
+        face4 = face_detector4.detectMultiScale(gray, scaleFactor=1.1,
+                                                minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
+        # Go over detected faces, stop at first detected face, return empty if no face.
+        if len(face1) == 1:
+            facefeatures = face1
+        elif len(face2) == 1:
+            facefeatures == face2
+        elif len(face3) == 1:
+            facefeatures = face3
+        elif len(face4) == 1:
+            facefeatures = face4
+        else:
+            facefeatures = ""
+
+        # 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)
+            gray = gray[y:y + h, x:x + w]  # Cut the frame to size
+
+            try:
+                out = cv2.resize(gray, (350, 350))  # Resize face so all images have same size
+                cv2.imwrite("final_dataset\\%s\\%s.jpg" % (emotion, filenumber), out)  # Write image
+            except:
+                pass  # pass the file on error
+        filenumber += 1  # Increment image number
+
+
+if __name__ == '__main__':
+    for emotion in emotion_list:
+        faceDetection(emotion)  # Call our face detection module

projectmood/Classifier/master/img_seq.py

+import glob as gb
+from shutil import copyfile
+
+emotions_list = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"]
+emotions_folders = gb.glob("emotions\\*")  # Returns a list of all folders with participant numbers
+
+
+def imageWithEmotionEtraction():
+    for x in emotions_folders:
+        participant = "%s" % x[-4:]  # store current participant number
+        for sessions in gb.glob("%s\\*" % x):
+            for files in gb.glob("%s\\*" % sessions):
+                current_session = files[20:-30]
+                file = open(files, 'r')
+
+                emotion = int(float(file.readline()))
+                # get path for last image in sequence, which contains the emotion
+                sourcefile_emotion = gb.glob("images\\%s\\%s\\*" % (participant, current_session))[-1]
+                # do same for neutral image
+                sourcefile_neutral = gb.glob("images\\%s\\%s\\*" % (participant, current_session))[0]
+                # Generate path to put neutral image
+                dest_neut = "selected_set\\neutral\\%s" % sourcefile_neutral[25:]
+                # Do same for emotion containing image
+                dest_emot = "selected_set\\%s\\%s" % (emotions_list[emotion], sourcefile_emotion[25:])
+
+                copyfile(sourcefile_neutral, dest_neut)  # Copy file
+                copyfile(sourcefile_emotion, dest_emot)  # Copy file
+
+
+if __name__ == '__main__':
+    imageWithEmotionEtraction()
\ No newline at end of file

projectmood/Haarcascade

@@ -12,11 +12,11 @@ cap = cv2.VideoCapture(0)
 
 while True:
 
-    # Read the Video
-    ret, img = cap.read()
+    # Capture frame-by-frame
+    ret, frame = cap.read(100)
 
     # convert the video to gray scale
-    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 
     # Contrast Limited Adaptive Histogram Equalization
     clahe = cv2.createCLAHE(clipLimit=4.0, tileGridSize=(8, 8))