#!/usr/bin/env python
# coding: utf-8

# # Project 1 – Introduction to Python scikit-image
# 
# ## Author
# Addison Sears-Collins
# ## Date Created
# 9/4/2019
# ## Python Version
# 3.7
# ## Description
# This program draws an E at the center of an input image.
# ## Purpose
# The purpose of this assignment is to introduce the basic functions of the Python scikit-image
# library -- a simple and popular open source library for image processing in Python. The scikitimage
# extends scipy.ndimage to provide a set of image processing routines including I/O, color
# and geometric transformations, segmentation, and other basic features.
# ## File Path

# In[1]:


# Move to the directory where the input images are located
get_ipython().run_line_magic('cd', 'D:\\Dropbox\\work')

# List the files in that directory
get_ipython().run_line_magic('ls', '')


# ## Code

# In[2]:


# Import scikit-image
import skimage

# Import module to read and write images in various formats
from skimage import io

# Import matplotlib functionality
import matplotlib.pyplot as plt

# Import numpy
import numpy as np

# Set the color of the E
# [red, green, blue]
COLOR_OF_E = [255, 0, 0]


# In[3]:


# Show the critical points of E
from IPython.display import Image
Image(filename = "e_critical_points.PNG", width = 200, height = 200)


# In[4]:


def e_generator(y_dim, x_dim):
    """
    Generates the coordinates of the E
    :param y_dim int: The y dimensions of the input image
    :param x_dim int: The x dimensions of the input image
    :return: The critical coordinates
    :rtype: list
    """
    # Set all the critical points
    A =  [int(0.407 * y_dim), int(0.423 *  x_dim)]
    B =  [int(0.407 * y_dim), int(0.589 *  x_dim)]
    C =  [int(0.488 * y_dim), int(0.423 *  x_dim)]
    D =  [int(0.488 * y_dim), int(0.589 *  x_dim)]
    E =  [int(0.572 * y_dim), int(0.423 *  x_dim)]
    F =  [int(0.572 * y_dim), int(0.581 *  x_dim)]
    G =  [int(0.657 * y_dim), int(0.423 *  x_dim)]
    H =  [int(0.657 * y_dim), int(0.581 *  x_dim)]
    I =  [int(0.735 * y_dim), int(0.423 *  x_dim)]
    J =  [int(0.735 * y_dim), int(0.589 *  x_dim)]
    K =  [int(0.819 * y_dim), int(0.423 *  x_dim)]
    L =  [int(0.819 * y_dim), int(0.589 *  x_dim)]
    M =  [int(0.407 * y_dim), int(0.47 *  x_dim)]
    N =  [int(0.819 * y_dim), int(0.47 *  x_dim)]
    
    return A,B,C,D,E,F,G,H,I,J,K,L,M,N


# In[5]:


def plot_image_with_e(image, A, B, C, D, E, F, G, H, I, J, K, L, M, N):
    """
    Plots an E on an input image
    :param image: The input image
    :param A, B, etc. list: The coordinates of the critical points
    :return: image_with_e
    :rtype: image
    """
    # Copy the image
    image_with_e = np.copy(image)

    # Top horizontal rectangle
    image_with_e[A[0]:C[0], A[1]:B[1], :] = COLOR_OF_E 

    # Middle horizontal rectangle
    image_with_e[E[0]:G[0], E[1]:F[1], :] = COLOR_OF_E

    # Bottom horizontal rectangle
    image_with_e[I[0]:K[0], I[1]:J[1], :] = COLOR_OF_E

    # Vertical connector rectangle
    image_with_e[A[0]:K[0], A[1]:M[1], :] = COLOR_OF_E

    # Display image
    plt.imshow(image_with_e);

    return image_with_e


# In[6]:


def print_image_details(image):
    """
    Prints the details of an input image
    :param image: The input image
    """
    print("Size: ", image.size)
    print("Shape: ", image.shape)
    print("Type: ", image.dtype)
    print("Max: ", image.max())
    print("Min: ", image.min())


# In[7]:


def compare(original_image, annotated_image):
    """
    Compare two images side-by-side
    :param original_image: The original input image
    :param annotated_image: The annotated-version of the original input image
    """
    # Compare the two images side-by-side
    f, (ax0, ax1) = plt.subplots(1, 2, figsize=(20,10))

    ax0.imshow(original_image)
    ax0.set_title('Original', fontsize = 18)
    ax0.axis('off')

    ax1.imshow(annotated_image)
    ax1.set_title('Annotated', fontsize = 18)
    ax1.axis('off')


# In[8]:


# Load the test image
image = io.imread("test_image.jpg")

# Store the y and x dimensions of the input image
y_dimensions = image.shape[0]
x_dimensions = image.shape[1]

# Print the image details
print_image_details(image)

# Display the image
plt.imshow(image);


# In[9]:


# Set all the critical points of the image
A,B,C,D,E,F,G,H,I,J,K,L,M,N = e_generator(y_dimensions, x_dimensions)

# Plot the image with E and store it
image_with_e = plot_image_with_e(image, A, B, C, D, E, F, G, H, I, J, K, L, M, N)

# Save the output image
plt.imsave('test_image_annotated.jpg', image_with_e)


# In[10]:


compare(image, image_with_e)


# In[11]:


# Load the first image
image = io.imread("architecture_roof_buildings_baked.jpg")

# Store the y and x dimensions of the input image
y_dimensions = image.shape[0]
x_dimensions = image.shape[1]

# Print the image details
print_image_details(image)

# Display the image
plt.imshow(image);


# In[12]:


# Set all the critical points of the image
A,B,C,D,E,F,G,H,I,J,K,L,M,N = e_generator(y_dimensions, x_dimensions)

# Plot the image with E and store it
image_with_e = plot_image_with_e(image, A, B, C, D, E, F, G, H, I, J, K, L, M, N)

# Save the output image
plt.imsave('architecture_roof_buildings_baked_annotated.jpg', image_with_e)


# In[13]:


compare(image, image_with_e)


# In[14]:


# Load the second image
image = io.imread("statue.jpg")

# Store the y and x dimensions of the input image
y_dimensions = image.shape[0]
x_dimensions = image.shape[1]

# Print the image details
print_image_details(image)

# Display the image
plt.imshow(image);


# In[15]:


# Set all the critical points of the image
A,B,C,D,E,F,G,H,I,J,K,L,M,N = e_generator(y_dimensions, x_dimensions)

# Plot the image with E and store it
image_with_e = plot_image_with_e(image, A, B, C, D, E, F, G, H, I, J, K, L, M, N)

# Save the output image
plt.imsave('statue_annotated.jpg', image_with_e)


# In[16]:


compare(image, image_with_e)


# In[ ]: