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

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')
from matplotlib import pyplot as plt


# In[2]:


import imutils
import cv2
import numpy as np


# In[3]:


cv2.__version__


# In[4]:


def detectAndDescribe(image):
    # convert the image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    descriptor = cv2.xfeatures2d.SIFT_create()
    (kps, features) = descriptor.detectAndCompute(image, None)
    # convert the keypoints from KeyPoint objects to NumPy
    # arrays
    kps = np.float32([kp.pt for kp in kps])
    # return a tuple of keypoints and features
    return (kps, features)


# In[5]:


def matchKeypoints(kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh):
    # compute the raw matches and initialize the list of actual
    # matches
    matcher = cv2.DescriptorMatcher_create("BruteForce")
    rawMatches = matcher.knnMatch(featuresA, featuresB, 2)
    matches = []
        
    # loop over the raw matches
    for m in rawMatches:
        # ensure the distance is within a certain ratio of each
        # other (i.e. Lowe's ratio test)
        if len(m) == 2 and m[0].distance < m[1].distance * ratio:
            matches.append((m[0].trainIdx, m[0].queryIdx))
                
    # computing a homography requires at least 4 matches
    if len(matches) > 4:
        # construct the two sets of points
        ptsA = np.float32([kpsA[i] for (_, i) in matches])
        ptsB = np.float32([kpsB[i] for (i, _) in matches])

        # compute the homography between the two sets of points
        (H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC,
            reprojThresh)
                
                
        # return the matches along with the homograpy matrix
        # and status of each matched point
        return (matches, H, status)
            
    # otherwise, no homograpy could be computed
    return None


# In[6]:


# load the two images
imageA = cv2.imread('1orig.png')
imageB = cv2.imread('2orig.png')


# TODO:
# 
# ```
# Habcam images are "along-track" to adapt to the existing code i'm rotating the images here this is bad because is an extra step in the workflow
# ```

# In[7]:


#imageB = np.rot90(imageB_v, k=3)
#imageA = np.rot90(imageA_v, k=3)


# In[8]:


(kpsA, featuresA) = detectAndDescribe(imageA)
(kpsB, featuresB) = detectAndDescribe(imageB)


# In[9]:


# match features between the two images
ratio=0.75
reprojThresh=4.0

M = matchKeypoints(kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh)


# TODO:
# 
# ```
# I need to study the parameters ratio and reprojThresh
# ```

# In[10]:


(matches, H, status) = M
result = cv2.warpPerspective(imageA, H, (imageA.shape[1], imageA.shape[0] + imageB.shape[0]))
result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB


# In[11]:


plt.figure(figsize=(10,10))
plt.imshow(result)
plt.title('My fisrt mosaic');


# In[12]:


plt.figure(figsize=(10,10))
plt.imshow(imageA)
plt.title('First image');


# In[13]:


plt.figure(figsize=(10,10))
plt.imshow(imageB,'gray')
plt.title('Second Image');


# In[14]:


from IPython.core.display import Image


# In[15]:


Image('desired.png')


# Desired output (only 2 images for now):
#     
# * black-band on the border:
# 
#     ```The algorithm used by the Habcam devs for the geometric correction of each image, produces a black curved band on the border. The shape and area of those black-band vary very little between images and depends on the camera's attitude parameters and lens distortion.```
# 
# 
# 
# * image alignment 'cut':
# 
#     ```The images are not perfectly aligned (see desired output compared with the resulting image, the lower border is cutting put part of ImageA)```
# 
# 
# Note the output shown here (I made it cheating with a gimp) has:
# 
# * black-band set to transpart adding an alpha channel and manually detected the black area from the image
# 
# Potential solution with opencv:
# 
#    ```run a moving average window along the 4 borders (with an user defined width) performing an edge detection to mark the curved lines between the image and the black-band. Use the line a "cutting-tool" to set to null/transparent the pixels falling between the line and the border```