PulkitS01
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| %%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() |
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| # defining two strings | |
| string_1 = 'euclidean' | |
| string_2 = 'manhattan' |
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| #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') |
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| 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]) |
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| 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), |
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| # Add on classifier | |
| model.classifier[6] = Sequential( | |
| Linear(4096, 2)) | |
| for param in model.classifier[6].parameters(): | |
| param.requires_grad = True |
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| 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), |
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| # initializing two arrays | |
| a = np.array(2) | |
| b = np.array(1) | |
| print(a,b) |
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| # checking the accuracy of the predicted tags | |
| from sklearn.metrics import accuracy_score | |
| accuracy_score(predict, actual)*100 |
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| 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) |