miki998

import pyxdf
import sys
import numpy as np
import os
import matplotlib.pyplot as plt
from scipy.io.wavfile import write
# Quick script to check streams in XDF file

def plot_channel(position, channel, eeg):
    plt.subplot(position)

miki998

import pyxdf
import numpy as np
import os
import argparse
import csv
import pandas as pd
import shutil

# Script that takes a XDF file and converts all the data to CSV files.

miki998

def box(image, boxes, class_names=None):
    colors = torch.FloatTensor([[1, 0, 1], [0, 0, 1], [0, 1, 1], [0, 1, 0], [1, 1, 0], [1, 0, 0]]);
    img = image.copy()
    
    width = img.shape[0]
    height = img.shape[1]

    for i in range(len(boxes)):
        box = boxes[i]
        x1,y1 = (box[0] - box[2] / 2.0) * width, (box[1] - box[3] / 2.0) * height

miki998

class Neek(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = Conv_Bn_Activation(1024, 512, 1, 1, 'leaky')
        self.conv2 = Conv_Bn_Activation(512, 1024, 3, 1, 'leaky')
        self.conv3 = Conv_Bn_Activation(1024, 512, 1, 1, 'leaky')
        # SPP
        self.maxpool1 = MaxPoolStride1(5)
        self.maxpool2 = MaxPoolStride1(9)
        self.maxpool3 = MaxPoolStride1(13)

miki998

class Yolov4Head(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = Conv_Bn_Activation(128, 256, 3, 1, 'leaky')
        self.conv2 = Conv_Bn_Activation(256, 255, 1, 1, 'linear', bn=False)
        self.yolo1 = YoloLayer(anchor_mask=[0, 1, 2], num_classes=80,
                               anchors=[12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401],
                               num_anchors=9, stride=8)

        # R -4

miki998

class Yolov4(nn.Module):
    def __init__(self):
        super().__init__()
        # backbone
        self.down1 = DownSample1()
        self.down2 = DownSample2()
        self.down3 = DownSample3()
        self.down4 = DownSample4()
        self.down5 = DownSample5()
        # neek

miki998

#We load the image we want to create adversarial
nb_image = 0
img = X_test[nb_image]
#Get the correct label
label = np.zeros(len(AGE_CLASS)) ; label[int(y_test[nb_image])] = 1.
img = img.reshape(1,img.shape[0],img.shape[1],img.shape[2])
img = img.astype(np.float32)

#Convert it into tensor
tens = tf.convert_to_tensor(img)

miki998

num_classes = len(AGE_CLASS)
input_shape = (227,227,3)

model = Sequential()

model.add(Conv2D(32, kernel_size=(3, 3), activation='relu',input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(3,3),strides=2))
model.add(BatchNormalization())

model.add(Conv2D(64, (3, 3), activation='relu'))

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AGE_CLASS = {'(0,2)':0,'(4,6)':1 
             ,'(8,13)':2,'(15,20)':3
             ,'(25,32)':4,'(38,43)':5
             ,'(48,53)':6,'(60,100)':7}

#load images
X_train, y_train = [], []
X_test, y_test = [], []

for image in os.listdir('train'):

miki998

with h5py.File("./full_dataset_vectors.h5", "r") as hf:    

    # Split the data into training/test features/targets
    X_train = hf["X_train"][:]
    targets_train = hf["y_train"][:]
    X_test = hf["X_test"][:] 
    targets_test = hf["y_test"][:]

    # Determine sample shape
    sample_shape = (16, 16, 16, 3)