PulkitS01

for img_path in img_paths:
    print (img_path)
    mat = io.loadmat(img_path.replace('.jpg','.mat').replace('images','ground-truth').replace('IMG_','GT_IMG_'))
    img= plt.imread(img_path)
    k = np.zeros((img.shape[0],img.shape[1]))
    gt = mat["image_info"][0,0][0,0][0]
    for i in range(0,len(gt)):
        if int(gt[i][1])<img.shape[0] and int(gt[i][0])<img.shape[1]:
            k[int(gt[i][1]),int(gt[i][0])]=1
    k = gaussian_filter_density(k)

PulkitS01

# checking the accuracy of the predicted tags
from sklearn.metrics import accuracy_score
accuracy_score(predict, actual)*100

PulkitS01

# initializing two arrays
a = np.array(2)
b = np.array(1)
print(a,b)

PulkitS01

class Net(Module):   
    def __init__(self):
        super(Net, self).__init__()

        self.cnn_layers = Sequential(
            # Defining a 2D convolution layer
            Conv2d(1, 4, kernel_size=3, stride=1, padding=1),
            BatchNorm2d(4),
            ReLU(inplace=True),
            MaxPool2d(kernel_size=2, stride=2),

PulkitS01

# Add on classifier
model.classifier[6] = Sequential(
                      Linear(4096, 2))
for param in model.classifier[6].parameters():
    param.requires_grad = True

PulkitS01

torch.manual_seed(0)

class Net(Module):   
    def __init__(self):
        super(Net, self).__init__()

        self.cnn_layers = Sequential(
            # Defining a 2D convolution layer
            Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
            ReLU(inplace=True),

PulkitS01

final_train_data = []
final_target_train = []
for i in tqdm(range(train_x.shape[0])):
    final_train_data.append(train_x[i])
    final_train_data.append(rotate(train_x[i], angle=45, mode = 'wrap'))
    final_train_data.append(np.fliplr(train_x[i]))
    final_train_data.append(np.flipud(train_x[i]))
    final_train_data.append(random_noise(train_x[i],var=0.2**2))
    for j in range(5):
        final_target_train.append(train_y[i])

PulkitS01

#standard deviation for noise to be added in the image
sigma=0.155
#add random noise to the image
noisyRandom = random_noise(image,var=sigma**2)

plt.imshow(noisyRandom)
plt.title('Random Noise')

PulkitS01

# defining two strings
string_1 = 'euclidean'
string_2 = 'manhattan'

PulkitS01

%%writefile app.py
 
import pickle
import streamlit as st
 
# loading the trained model
pickle_in = open('classifier.pkl', 'rb') 
classifier = pickle.load(pickle_in)
 
@st.cache()