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Decision/HLSwitch/HLSwitch_eval.py

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+# HLSwitch evaluation py
+import tensorflow as tf
+import numpy as np
+import time
+import cv2
+from keras.models import load_model
+import time
+from keras.preprocessing.image import load_img, save_img, img_to_array
+import multiprocessing
+
+
+config = tf.ConfigProto()
+config.gpu_options.allow_growth = True
+session = tf.Session(config=config)
+
+model_dir = './models/final_model_new_4.hdf5'
+
+class imageProcess():
+    def preprocess(self, img):
+        #img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        img = img.astype(np.float32)
+        img /= 255.0
+        return img
+
+    def postprocess(self, img):
+        pass
+
+
+class inference():
+    def __init__(self):
+        self.model = load_model(model_dir)
+        self.switchValue = 0.5
+        self.imageSize = (200,200)
+        self.improc = imageProcess()
+        self.showInferenceTime = True
+        self.ans_dict = {0:'GPS', 1:'VISION'}
+
+
+    def run(self, img):
+        start = time.time()
+        tempStr = ""
+        tempImage = self.improc.preprocess(img)
+        result = self.model.predict(tempImage.reshape(1, self.imageSize[0], self.imageSize[1], 1))
+        prediction_result_prob = result * 100
+        end = time.time()
+        if self.showInferenceTime:
+            print("Inference Time(s) :", end-start)
+        if result[0] > self.switchValue:
+            tempStr = "VISION"
+        else:
+            tempStr = "GPS"
+
+        return [tempStr, prediction_result_prob]
+
+
+
+class subNode():
+    def __init__(self):
+        #rospy.Subscriber('/camera/image_color', Image, self.imageCb)
+        runTh = multiprocessing.Process(target=self.run)
+        runTh.start()
+
+
+    def run(self):
+        pass
+
+    def imageCb(self, msg):
+        self.trafficIdx = msg.data
+        print(self.trafficIdx)
+        pass
+
+class pubNode():
+    def __init__(self):
+        #self.switchPub = rospy.Publisher(rootname+pubAccel, Int16, queue_size = 1)
+        pass
+    def run(self):
+        pass
+
+class main():
+    def run(self):
+        hlSwitch = inference()
+        video_dir = 'test.webm'
+        cap = cv2.VideoCapture(video_dir)
+        while(1):
+            ret, frame = cap.read()
+            if not ret:
+                continue
+            tempImage = cv2.resize(frame, (200, 200))
+            tempImage = cv2.cvtColor(tempImage, cv2.COLOR_BGR2GRAY)
+            location = (0, 40)
+            font = cv2.FONT_HERSHEY_SIMPLEX  # hand-writing style font
+            fontScale = 0.9
+            strn = str(hlSwitch.run(tempImage))
+            cv2.putText(frame, strn, location, font, fontScale, (0,100,255), 2)
+            cv2.imshow('asd', frame)
+
+
+            cv2.waitKey(1)
+
+
+if __name__ == "__main__":
+    run = main()
+    run.run()

Decision/HLSwitch/dataset_raw/annotTool.py

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+# Dataset Gen
+
+import cv2
+import os
+
+
+videoName = str(input("Video Name : (with Extension *.mp4)  "))
+try:
+    print('./video/' + videoName)
+    cap = cv2.VideoCapture('./video/' + videoName)
+except:
+    print("Error while reading video file")# Dataset Gen
+
+idx = 0
+while(1):
+    ret, frame = cap.read()
+    print("Arrow Key LEFT : GPS\nArrow Key Right VISION\nArrow Key Up Save all and Exit")
+    if ret:
+
+        cv2.imshow('SCREEN', frame)
+        ket = cv2.waitKey(0)
+        print(ket)
+        if ket == 81 :
+            # Left
+            dir = './GPS/'
+            print("GPS")
+
+            cv2.imwrite(dir + videoName + '_' + str(idx) + '.jpg', cv2.resize(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), (640, 360)))
+            pass
+        elif ket == 83:
+            # Right
+            dir = './VISION/'
+            cv2.imwrite(dir + videoName + '_' + str(idx) + '.jpg', cv2.resize(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), (640, 360)))
+            print("VISION")
+            pass
+        elif ket == 82:
+            print("SAVE")
+            break
+
+    else:
+        break
+    idx += 1
+
+cap.release()
+cv2.destroyAllWindows()
+
+
+while(1):
+    ret, frame = cap.read()
+    print("Arrow Key LEFT : GPS\nArrow Key Right VISION\nArrow Key Up Save all and Exit")
+    if ret:
+        cv2.imshow('SCREEN', frame)
+        ket = cv2.waiyKey(0)
+
+        if ket == 2424832 :
+            # Left
+            dir = './GPS/'
+            print("GPS")
+            pass
+        elif ket == 2555904:
+            # Right
+            dir = './VISION'
+            print("VISION")
+            pass
+        elif ket == 2490368:
+            print("SAVE")
+            break
+
+    else:
+        break
+
+cap.release()
+cv2.destroyAllWindows()

Decision/HLSwitch/img2Pixel.py

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+# 이미지 정규화 하는 코드
+# 그레이스케일, 리사이징, 정규화 (/255.0) 하고 npy에 stack
+# 사진들이 분류 되어있는 폴더의 상위 디렉토리에 넣고 돌리기...
+
+# script for preparing datasets, loading fer data and generating scaled images
+import numpy as np
+import cv2
+from PIL import Image
+import sys
+import os
+
+emotions = {0:'GPS', 1:'VISION'}
+global outfile, data, data0, label, label0, desired_size
+
+def _cls():
+    os.system("cls" if os.name == "nt" else "clear")
+
+def run(data, label, desired_size):
+    data0 = data[:]
+    label0 = label[:]
+    for root, dirs, files in os.walk(os.getcwd(), topdown=False):
+
+        for name in files:
+            try:
+
+                filename = os.path.join(root, name)
+                print(filename)
+                cvim = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
+                grayCv = cv2.resize(cvim, desired_size)
+                print(grayCv.shape)
+                pdata = np.array(grayCv.reshape(-1)).astype('float32')
+                pdata = [np.divide(d, 255.0) for d in pdata]
+                try:
+                    data = np.concatenate((data, [pdata]))
+                except Exception as ex:
+                    print('first start', ex)
+                    data = np.array([pdata])
+
+                emotios_k = list(emotions.keys())
+                for key in emotios_k:
+                    if emotions[key] in filename:
+                        label = np.concatenate((label, [key]))
+                        _cls()
+                        print(f'add img2pix {name}:{emotions[key]}')
+
+            except Exception as rex:
+                print('Image OpenError!', rex)
+    print(data.shape)
+    print(f"All done! \n{len(data)} files are coverted and added ")
+    name = "Scaled_" + str(len(data)) + ".npy"
+    name_lab = "labeld_" + str(len(data)) + ".npy"
+    np.save(name, data)
+    np.save(name_lab, label)
+
+
+
+# load previous npy file if exist
+try:
+    global outfile, data, data0, label, label0, desired_size
+    var1, var2, desired_size = '', '', (320, 180)
+    var1 = sys.argv[2] #pixel
+    var2 = sys.argv[3] #label
+    data = [[]]
+    dataO = data[:]
+    label = []
+    labelO = label[:]
+    desired_size = eval(sys.argv[1])
+
+    run(data, label, desired_size)
+except Exception as ex:
+    tuto = '''
+    image2pixel by shinkansan
+    =Manual=
+    e.g) python img2pixel.py '(48, 48)' data.npy label.npy
+    argv[1] = img resize..tuple
+    argv[2] = merging existing data npy file
+    argv[3] = merging existing label npy file
+    '''
+    print(tuto)
+    print(ex)

Decision/HLSwitch/model/mcdropout_ferVGG.py

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+'''
+VGG in modular keras backend
+'''
+
+import keras
+from keras.layers import Dense, Conv2D, Dropout, Input, Activation, Flatten
+from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D, AveragePooling2D
+from keras.optimizers import Adam
+from keras.regularizers import l2
+from keras.losses import categorical_crossentropy
+
+
+
+def predictionMC():
+    pass
+
+
+def fer_vgg(input_shape=(48,48,1), input_classes= 7):
+    inputs = keras.Input(shape=input_shape)
+    # Conv Block 1
+    x = Conv2D(64, (3,3), activation = 'relu', padding='same')(inputs)
+    x = Dropout(0.25)(x, training = True)
+    x = Conv2D(64, (3,3), activation = 'relu', padding = 'same')(inputs)
+    x = MaxPooling2D(pool_size = (2,2) , strides = (2,2))(x)
+    x = Dropout(0.25)(x, training = True)
+
+    # Conv Block 2
+    x = Conv2D(128, (3,3), activation='relu', padding='same')(x)
+    x = Dropout(0.25)(x, training = True)
+    x = Conv2D(128, (3,3), activation='relu', padding='same', kernel_regularizer=l2(0.001))(x)
+    x = MaxPooling2D((2,2), strides=(2,2))(x)
+    x = Dropout(0.25)(x, training = True)
+
+    # Conv Block 3
+    x = Conv2D(256, (3, 3), padding='same', activation ='relu')(x)
+    x = Dropout(0.25)(x, training = True)
+    x = Conv2D(256, (3, 3), padding='same', activation = 'relu', kernel_regularizer=l2(0.001))(x)
+    x = Dropout(0.25)(x, training = True)
+    x = Conv2D(256, (3, 3), padding='same', activation = 'relu', kernel_regularizer=l2(0.001))(x)
+    x = AveragePooling2D(pool_size = (2, 2), strides=(2, 2))(x)
+    x = Dropout(0.25)(x, training=True)
+
+    # Conv Block 4
+    x = Conv2D(512, (3, 3), padding = 'same', activation = 'relu')(x)
+    x = Dropout(0.25)(x, training = True)
+    x = Conv2D(512, (3, 3), padding = 'same', activation = 'relu')(x)
+    x = Dropout(0.25)(x, training = True)
+    x = Conv2D(512, (3, 3), padding = 'same', activation = 'relu', kernel_regularizer = l2(0.001))(x)
+    x = Dropout(0.25)(x, training = True)
+    x = Conv2D(512, (3, 3), padding = 'same', activation = 'relu', kernel_regularizer = l2(0.001))(x)
+    x = AveragePooling2D(pool_size = (2,2), strides=(2,2))(x)
+    x = Dropout(0.25)(x, training=True)
+
+    # Conv Block 5
+    x = Conv2D(512, (3, 3), padding = 'same', activation = 'relu')(x)
+    x = Dropout(0.25)(x, training = True)
+    x = Conv2D(512, (3, 3), padding = 'same', activation = 'relu')(x)
+    x = Dropout(0.25)(x, training = True)
+    x = Conv2D(512, (3, 3), padding = 'same', activation = 'relu', kernel_regularizer = l2(0.001))(x)
+    x = Dropout(0.25)(x, training = True)
+    x = Conv2D(512, (3, 3), padding = 'same', activation = 'relu', kernel_regularizer = l2(0.001))(x)
+    x = AveragePooling2D(pool_size = (2,2), strides=(2,2))(x)
+    x = Dropout(0.25)(x, training=True)
+
+    # FC Layers
+    x = Flatten()(x)
+    #x = Dense(4096, activation='relu')(x)
+    #x = Dropout(0.25)(x, training = True)
+    x = Dense(512, activation='relu')(x)
+    x = Dropout(0.25)(x, training=True)
+    x = Dense(256, activation='relu')(x)
+    x = Dropout(0.25)(x)
+    output = Dense(7, activation='softmax')(x)
+
+    model = keras.Model(inputs, output)
+    return model
+
+if __name__ == "__main__":
+    model = fer_vgg()
+    model.summary()