import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

import torch
import torch.nn as nn
import torch.nn.functional as F

imgUrl = "../../assets/5.CNN-Layers-Feature-Visualization/"

tesla_img = cv2.imread(imgUrl + "tesla.jpg")
gray_img = cv2.cvtColor(tesla_img, cv2.COLOR_BGR2GRAY)

# Normalize, rescale entries to lie in [0, 1]
gray_img = gray_img.astype("float32")/255

plt.imshow(gray_img, cmap="gray")
plt.show()

filter_vals = np.array([[-1, -1, 1, 1], [-1, -1, 1, 1], [-1, -1, 1, 1], [-1, -1, 1, 1]])

# Define four filters
filter_1 = filter_vals
filter_2 = -filter_1
filter_3 = filter_1.T
filter_4 = -filter_3

filters = np.array([filter_1, filter_2, filter_3, filter_4])

fig = plt.figure(figsize=(10, 5))
for i in range(4):
    ax = fig.add_subplot(1, 4, i+1, xticks=[], yticks=[])
    ax.imshow(filters[i], cmap='gray')
    ax.set_title('Filter %s' % str(i+1))
    width, height = filters[i].shape
    for x in range(width):
        for y in range(height):
            ax.annotate(str(filters[i][x][y]), xy=(y,x),
                        horizontalalignment='center',
                        verticalalignment='center',
                        color='white' if filters[i][x][y]<0 else 'black')

class Net(nn.Module):
    
    def __init__(self, weight):
        super(Net, self).__init__()
        
        # Initializes the weights of the convolutional layer to be the weights of the 4 defined filters
        k_height, k_width = weight.shape[2:]
        
        # Assumes there are 4 grayscale filters
        self.conv = nn.Conv2d(1, 4, kernel_size=(k_height, k_width), bias=False)
        
        self.conv.weight = torch.nn.Parameter(weight)
    
    def forward(self, x):
        # Calculates the output of a convolutional layer
        conv_x = self.conv(x)
        activated_x = F.relu(conv_x)
        
        return conv_x, activated_x

# Instantiate the model ad set the weights
weight = torch.from_numpy(filters).unsqueeze(1).type(torch.FloatTensor)
model = Net(weight)

print(model)

def visualize_layer(layer, n_filters=4):
    fig = plt.figure(figsize=(20, 20))
    
    for i in range(n_filters):
        ax = fig.add_subplot(1, n_filters, i+1, xticks=[], yticks=[])
        # Grab layer outputs
        ax.imshow(np.squeeze(layer[0,i].data.numpy()), cmap="gray")
        ax.set_title('Output %s' % str(i+1))

# Plot original image
plt.imshow(gray_img, cmap='gray')

# Visualize all filters
fig = plt.figure(figsize=(12, 6))
fig.subplots_adjust(left=0, right=1.5, bottom=0.8, top=1, hspace=0.05, wspace=0.05)
for i in range(4):
    ax = fig.add_subplot(1, 4, i+1, xticks=[], yticks=[])
    ax.imshow(filters[i], cmap='gray')
    ax.set_title('Filter %s' % str(i+1))

# Convert the image into an input Tensor
gray_img_tensor = torch.from_numpy(gray_img).unsqueeze(0).unsqueeze(1)

# Get the convolutional layer (pre and post activation)
conv_layer, activated_layer = model(gray_img_tensor)

visualize_layer(conv_layer)

# Visualize the output of an activated conv layer
visualize_layer(activated_layer)