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

# <center><img src="graphics/Logolink_OP_VVV_hor_barva_cz.jpg" width="600px"/></center>
# <center>ESF projekt Západočeské univerzity v Plzni reg. č. CZ.02.2.69/0.0/0.0/16 015/0002287</center>

# In[1]:


get_ipython().run_line_magic('pylab', 'inline --no-import-all')


# In[2]:


import scipy
import scipy.misc
from scipy import stats
import numpy as np
import urllib
import skimage
import skimage.color
import skimage.io
import skimage.exposure
import skimage.data
import matplotlib.pyplot as plt
from skimage import data
from skimage import transform as tf


# # Gray-scale image

# In[3]:


# URL = "http://uc452cam01-kky.fav.zcu.cz/snapshot.jpg"
URL = "http://www.chmi.cz/files/portal/docs/meteo/kam/pribram.jpg"
# URL = "http://plzen.cz/kamera.php?0.8989779513794929"


# In[4]:


img = skimage.io.imread(URL, as_gray=False)
plt.imshow(img)
img.shape


# In[5]:


img.shape


# In[6]:


type(img)


# In[7]:


img.dtype


# In[ ]:





# In[8]:


dir(img)


# In[9]:


np.min(img)


# In[10]:


# from skimage import color
imggray = skimage.color.rgb2gray(img)
plt.imshow(imggray, cmap='gray')
plt.colorbar()


# In[12]:


plt.imshow(imggray, vmin=0, vmax=1, cmap="Spectral")
plt.colorbar()


# # Histogram

# In[14]:


data = np.random.random([3,2])
print(data)
print(' ')
print(data.ravel())


# In[15]:


np.min(img)
img.shape


# In[28]:


a, b, c = plt.hist(img.ravel(), 40, density=False)


# In[29]:


a, b, c = plt.hist(img.ravel(), 40, cumulative=True, density=False)


# In[22]:


a, b, c = plt.hist(img[::2,::2].ravel(), bins=[0,50,100,110,120,130,200,255], 
#                    normed=False
                  )


# In[35]:


a, b, c = plt.hist(img[::2,::2].ravel(), 10)


# In[31]:


img.flatten()
img[::2,::2].ravel()
import timeit
timeit.timeit('import numpy as np; img = np.random.random([640,480]); img.ravel()', number=100)

# timeit.Timer('for i in xrange(10): oct(i)', 'gc.enable()').timeit()


# In[32]:


timeit.timeit('import numpy as np; img = np.random.random([640,480]); img.flatten()', number=100)


# In[36]:


print(a.shape)
print(b.shape)
plt.plot(b[:-1], a, '*g--')




# In[80]:


# import cv2

import numpy as np
from matplotlib import pyplot as plt
import scipy
import scipy.misc
import urllib

import matplotlib.pyplot as plt

# scipy.misc.imread(
URL = "http://uc452cam01-kky.fav.zcu.cz/snapshot.jpg"

img = skimage.io.imread(URL)

imgg = skimage.color.rgb2gray(img)
plt.imshow(imggray, cmap='gray')
plt.show()
a, b, c = plt.hist(imgg.ravel(),255)


# # Intensity manipulation
# 
# ## Lightening

# In[81]:


plt.figure()
plt.imshow(imgg, cmap='gray', clim=(0.0, 1.0))
plt.figure()
plt.imshow(imgg + 0.5, cmap='gray', clim=(0.0, 1.0))
# plt.colorbar()
plt.show()


# ## Data type overflow

# In[98]:


img.dtype

print(f"min: {np.min(img)}, max: {np.max(img)}")

plt.figure(figsize=[15,10])
plt.subplot(221)
plt.hist(img.ravel(), bins=20)

img2 = img + 50

plt.subplot(223)
plt.hist(img2.ravel(), bins=20)

print(f"min: {np.min(img2)}, max: {np.max(img2)}")

plt.subplot(222)
plt.imshow(img)
plt.subplot(224)
plt.imshow(img2)


# ## Contrast - Intensity rescale
# 
# [doc skimage.exposure_rescale_intensity](https://scikit-image.org/docs/dev/api/skimage.exposure.html#skimage.exposure.rescale_intensity)

# In[99]:


# img = skimage.color.rgb2gray(skimage.data.astronaut())
im = skimage.data.astronaut()
print(stats.describe(im, axis=None))
plt.imshow(im)
im_rescal = skimage.exposure.rescale_intensity(im, in_range=(0,255), out_range=(30,100))
plt.figure()
plt.imshow(im_rescal)


# # Rotation and scale

# In[103]:


print(imgg[:5,:5])
imgg05 = imgg*0.5
print(imgg05[:5,:5])
plt.imshow(imgg05, cmap='gray', vmax=1, vmin=0)
plt.show()


# In[106]:


imr = skimage.transform.rotate(imgg, 30)
plt.imshow(imr, cmap='gray')
plt.show()


# In[105]:


imgg_res = scipy.misc.imresize(imgg, 0.1)
plt.imshow(imgg_res, cmap='gray')
plt.show()


# ## Transform matrix
# 
# ![Affine](https://upload.wikimedia.org/wikipedia/commons/thumb/2/2c/2D_affine_transformation_matrix.svg/449px-2D_affine_transformation_matrix.svg.png)
# 
# [Transformation matrix on wiky](https://en.wikipedia.org/wiki/Affine_transformation)

# In[4]:


lena = skimage.io.imread("https://i.stack.imgur.com/3T6Gc.jpg")


# In[108]:


theta = np.pi/16
mat = np.array(
    [[1.5, 0, 0], 
     [0, 1.5, 0], 
     [0,   0, 1]]
)

tform0 = tf.ProjectiveTransform(matrix=mat)
warped = tf.warp(lena, tform0) 

fig, ax = plt.subplots(nrows=2, figsize=(10, 8))

ax[0].imshow(lena, cmap=plt.cm.gray)
ax[1].imshow(warped, cmap=plt.cm.gray)

for a in ax:
    a.axis('off')

plt.tight_layout()

plt.show()


# In[61]:


theta = np.pi/16
mat = np.array(
    [[np.cos(theta), -np.sin(theta), 0], 
     [np.sin(theta),  np.cos(theta), 0], 
     [0,              0,             1]]
)


tform0 = tf.ProjectiveTransform(matrix=mat)
warped = tf.warp(lena, tform0) #, output_shape=(600, 600))

fig, ax = plt.subplots(nrows=2, figsize=(8, 8))

ax[0].imshow(lena, cmap=plt.cm.gray)
ax[1].imshow(warped, cmap=plt.cm.gray)

for a in ax:
    a.axis('off')

plt.tight_layout()

plt.show()


# ## Transformation matrix parameters detection

# In[9]:


text = data.text()

src = np.array([[0, 0], [0, 50], [300, 50], [300, 0]])
dst = np.array([[155, 15], [65, 40], [260, 130], [360, 95]])

tform3 = tf.ProjectiveTransform()
tform3.estimate(src, dst)
warped = tf.warp(text, tform3, output_shape=(50, 300))

fig, ax = plt.subplots(nrows=2, figsize=(8, 3))

ax[0].imshow(text, cmap=plt.cm.gray)
ax[0].plot(dst[:, 0], dst[:, 1], '.r')
ax[1].imshow(warped, cmap=plt.cm.gray)

for a in ax:
    a.axis('off')

plt.tight_layout()

plt.show()


# In[11]:


tform3.params


# ## Usefull tool

# In[11]:


import matplotlib.pylab as plt
import skimage.io
get_ipython().run_line_magic('matplotlib', 'qt5')
plt.imshow(lena)
uu = plt.ginput(n=4)
# plt.show()
uu


# # Thresholding

# In[11]:


imthr = imggray > 0.5
plt.imshow(imthr, cmap='gray')


# # Image counting

# In[ ]:


import skimage.measure
imlabel = skimage.measure.label(imthr, background=0)
np.max(imlabel) + 1