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

# In[1]:


import cv2 as cv
import numpy as np


# In[2]:


img = cv.imread('reflections.jpg')


# In[3]:


squarechunk = img[100:200,100:200]


# In[4]:


cv.imshow('image', img)
cv.waitKey(0)
cv.destroyAllWindows()


# In[5]:


squarechunk[:,:,2] = 200


# In[6]:


cv.imshow('image', img)
cv.waitKey(0)
cv.destroyAllWindows()


# In[7]:


cv.imshow('image', squarechunk)
cv.waitKey(0)
cv.destroyAllWindows()


# In[9]:


blurmask = np.array([[0.1, 0.1, 0.1],[0.1, 0.2, 0.1],[0.1, 0.1, 0.1]])


# In[10]:


help(cv.filter2D)


# In[11]:


blurimg = cv.filter2D(img, -1, blurmask)


# In[12]:


cv.imshow('image', blurimg)
cv.waitKey(0)
cv.destroyAllWindows()


# In[13]:


cv.imshow('image', img)
cv.waitKey(0)
cv.destroyAllWindows()


# In[15]:


blurimg2 = cv.GaussianBlur(img, [11, 11], 4)


# In[16]:


cv.imshow('image', blurimg2)
cv.waitKey(0)
cv.destroyAllWindows()


# In[17]:


smoothmask2 = cv.getGaussianKernel(11,4)


# In[18]:


smoothmask2


# In[19]:


smoothmask2.transpose()


# In[21]:


smoothmask2 = smoothmask2.transpose() * smoothmask2


# In[22]:


smoothmask2


# In[23]:


blurimg5 = cv.filter2D(img, -1, smoothmask2)


# In[24]:


cv.imshow('image', blurimg5)
cv.waitKey(0)
cv.destroyAllWindows()


# In[25]:


np.sctypeDict.keys()


# In[26]:


np.sctypes


# In[27]:


help(np.ones)


# In[28]:


help(np.zeros)


# In[29]:


help(np.empty)


# In[30]:


np.ones([2,2], np.float32)


# In[31]:


np.zeros([2,2], np.float32)


# In[32]:


np.empty([2,2], np.float32)


# In[33]:


img.shape


# In[34]:


img.dtype


# In[40]:


def BGR2YCbCr(bgrimg):
    ycbcrimg = np.empty(bgrimg.shape, np.float32)
    for r in range(bgrimg.shape[0]):
        for c in range(bgrimg.shape[1]):
            #ycbcrimg[r,c,0] = bgrimg[r,c,0]*0.114 + bgrimg[r,c,1]*0.587 + bgimg[r,c,2]*0.299
            ycbcrimg[r,c,0] = sum(bgrimg[r,c]*np.array([0.114,0.587,0.299]))
            ycbcrimg[r,c,1] = 128 + sum(bgimg[r,c] * np.array([0.5, -0.331264,-0.168736]))
            ycbcrimg[r,c,2] = 128 + sum(bgimg[r,c] * np.array([-0.08132, -0.418688,0.5]))
    return ycbcrimg


# In[36]:


img.shape


# In[38]:


img[0,0] * np.array([0.114, 0.587, 0.299])


# In[39]:


sum(img[0,0] * np.array([0.114, 0.587, 0.299]))


# In[ ]: