seanbenhur

import cv2
face_cascade=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

eye_cascade=cv2.CascadeClassifier('haarcascade_eye.xml')

cap=cv2.VideoCapture(0)

while True:
    ret,img=cap.read()
    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

seanbenhur

routes = []
#write a function to find path
def find_path(node, cities, path, distance):
    #add way point
    path.append(node)
    #calcualte path length
    if len(path) > 1:
        distance += cities[path[-2]][node]
    #if path contains all cities and is not a dead end
    #add path from the first city

seanbenhur

# Load the the dataset from raw image folders
siamese_dataset = SiameseDataset(training_csv,training_dir,
                                        transform=transforms.Compose([transforms.Resize((105,105)),
                                                                      transforms.ToTensor()
                                                                      ])
                                       )

seanbenhur

# Declare Siamese Network
net = SiameseNetwork().cuda()
# Decalre Loss Function
criterion = ContrastiveLoss()
# Declare Optimizer
optimizer = th.optim.Adam(net.parameters(), lr=1e-3, weight_decay=0.0005)
#train the model
def train():
    loss=[] 
    counter=[]

seanbenhur

#create a siamese network
class SiameseNetwork(nn.Module):
    def __init__(self):
        super(SiameseNetwork, self).__init__()
        # Setting up the Sequential of CNN Layers
        self.cnn1 = nn.Sequential(
            nn.Conv2d(1, 96, kernel_size=11,stride=1),
            nn.ReLU(inplace=True),
            nn.LocalResponseNorm(5,alpha=0.0001,beta=0.75,k=2),
            nn.MaxPool2d(3, stride=2),

seanbenhur

#preprocessing and loading the dataset
class SiameseDataset():
    def __init__(self,training_csv=None,training_dir=None,transform=None):
        # used to prepare the labels and images path
        self.train_df=pd.read_csv(training_csv)
        self.train_df.columns =["image1","image2","label"]
        self.train_dir = training_dir    
        self.transform = transform

    def __getitem__(self,index):

seanbenhur

# Load the test dataset
test_dataset = SiameseDataset(training_csv=testing_csv,training_dir=testing_dir,
                                        transform=transforms.Compose([transforms.Resize((105,105)),
                                                                      transforms.ToTensor()
                                                                      ])
                                       )

test_dataloader = DataLoader(test_dataset,num_workers=6,batch_size=1,shuffle=True)
#test the network
count=0

seanbenhur

class Solution(object):
    def longestConsecutive(self, nums):
        """
        :type nums: List[int]
        :rtype: int
        """
        nums = set(nums)
        best = 0
        for x in nums:
            if x-1 not in nums:

seanbenhur

class Lenet(nn.Module):
  def __init__(self):
    super(Lenet,self).__init__()
    self.tanh = nn.Tanh()      
    self.pool = nn.AvgPool2d(kernel_size=(2,2),stride=(2,2))
    self.conv1 = nn.Conv2d(in_channels=1,out_channels=6,kernel_size=(5,5),stride=(1,1))
    self.conv2 = nn.Conv2d(in_channels=6,out_channels=16,kernel_size=(5,5),stride=(1,1))
    self.conv3 = nn.Conv2d(in_channels=16,out_channels=120,kernel_size=(5,5),stride=(1,1))
    self.linear1 = nn.Linear(120,84)
    self.linear2 = nn.Linear(84,10)

seanbenhur

class AlexNet(nn.Module):
    def __init__(self):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(),
            nn.LocalResponseNorm(size=5,alpha=0.0001,beta=0.75,k=2),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(),