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butterflyColors.py
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butterflyColors.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Mar 02 15:08:22 2016
@author: Kaiya
"""
## part 1
import matplotlib.pyplot as plt
import numpy as np
img = plt.imread('C:/Users/Kaiya/Dropbox/Docs for Brian/THESIS/Field Work/FIGURES/box_with_PCA_means_forAOU.png') #Read in image from bf1.png
plt.imshow(img) #Load image into matplotlib
plt.show()
height = img.shape[0]
width = img.shape[1]
redImage = np.zeros((height,width,3))
greImage = np.zeros((height,width,3))
bluImage = np.zeros((height,width,3))
blaImage = np.zeros((height,width,3))
for i in range(height):
print "processing row", i
for j in range(width):
# only red channel
redImage[i,j,0] = img[i,j,0] ## red channel
redImage[i,j,1] = 0 ## green channel
redImage[i,j,2] = 0 ## blue channel
# only green channel
greImage[i,j,0] = 0 ## red channel
greImage[i,j,1] = img[i,j,1] ## green channel
greImage[i,j,2] = 0 ## blue channel
# only blue channel
bluImage[i,j,0] = 0 ## red channel
bluImage[i,j,1] = 0 ## green channel
bluImage[i,j,2] = img[i,j,2] ## blue channel
# black and white
avg = np.mean(img[i,j,0:3])
blaImage[i,j,0] = avg ## red channel
blaImage[i,j,1] = avg ## green channel
blaImage[i,j,2] = avg ## blue channel
plt.imshow(redImage) #Open window to show image (close to continue)
plt.show()
plt.imsave('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/redScale1.png',redImage)
plt.imshow(greImage) #Open window to show image (close to continue)
plt.show()
plt.imsave('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/greenScale1.png',greImage)
plt.imshow(bluImage) #Open window to show image (close to continue)
plt.show()
plt.imsave('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/blueScale1.png',bluImage)
plt.imshow(blaImage) #Open window to show image (close to continue)
plt.show()
plt.imsave('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/greyScale1.png',blaImage)
## part 2 : butterfly picture threshhold
import matplotlib.pyplot as plt
import numpy as np
img = plt.imread('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/bf1.png') #Read in image from bf1.png
plt.imshow(img) #Load image into matplotlib
plt.show()
height = img.shape[0]
width = img.shape[1]
butImage = np.zeros((height,width,3))
for i in range(height):
for j in range(width):
if np.sum(img[i,j,0:3]) < 2.7:
butImage[i,j,0] = 0 ## red channel
butImage[i,j,1] = 0 ## green channel
butImage[i,j,2] = 0 ## blue channel
else:
butImage[i,j,0] = 1
butImage[i,j,1] = 1
butImage[i,j,2] = 1
plt.imshow(butImage) #Open window to show image (close to continue)
plt.show()
plt.imsave('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/whiteOnly1.png',butImage)
## part 3 : butterfly image difs
import matplotlib.pyplot as plt
import numpy as np
img1 = plt.imread('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/bf1.png') #Read in image from bf1.png
#plt.imshow(img1) #Load image into matplotlib
#plt.show()
img2 = plt.imread('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/bf2.png') #Read in image from bf1.png
#plt.imshow(img2) #Load image into matplotlib
#plt.show()
height1 = img1.shape[0]
width1 = img1.shape[1]
#height2 = img1.shape[0]
#width2 = img1.shape[1]
#if height1 == height2 and width1 == width2:
# print "MATCH"
#else:
# print "NO MATCH - CHECK IMAGES"
limit = 0.15
difImage = np.zeros((height1,width1,3))
for i in range(height1):
for j in range(width1):
redDif = np.absolute(np.subtract(img1[i,j,0],img2[i,j,0]))
greDif = np.absolute(np.subtract(img1[i,j,1],img2[i,j,1]))
bluDif = np.absolute(np.subtract(img1[i,j,2],img2[i,j,2]))
#print (redDif+greDif+bluDif)/3
if ((redDif+greDif+bluDif)/3) > (limit):
difImage[i,j,0:3] = np.asarray([0,0,0])
else:
difImage[i,j] = img1[i,j,0:3]
#difImage[i,j] = np.asarray([redDif,greDif,bluDif])
#if np.sum(img[i,j,0:3]) < 2.7:
# difImage[i,j,0] = 0 ## red channel
# difImage[i,j,1] = 0 ## green channel
# difImage[i,j,2] = 0 ## blue channel
#else:
# difImage[i,j,0] = 1
# difImage[i,j,1] = 1
# difImage[i,j,2] = 1
plt.imshow(difImage) #Open window to show image (close to continue)
plt.show()
plt.imsave('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/difImage2.png',difImage)
## part 4 : CA drought
import matplotlib.pyplot as plt
import numpy as np
img2011 = plt.imread('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/cali2011.png') #Read in image from bf1.png
img2013 = plt.imread('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/cali2013.png') #Read in image from bf1.png
img2014 = plt.imread('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/cali2014.png') #Read in image from bf1.png
height1 = img2011.shape[0]
width1 = img2011.shape[1]
limit = 0.05
sno11Image = np.zeros((height1,width1,3))
sno13Image = np.zeros((height1,width1,3))
sno14Image = np.zeros((height1,width1,3))
count11 = 0
count13 = 0
count14 = 0
for i in range(height1):
print "processing row", i
for j in range(width1):
if np.mean(img2011[i,j,0:3]) > (1-limit):
count11 += 1
sno11Image[i,j] = img2011[i,j,0:3]
else:
sno11Image[i,j,0:3] = np.asarray([0,0,0])
if np.mean(img2013[i,j,0:3]) > (1-limit):
count13 += 1
sno13Image[i,j] = img2013[i,j,0:3]
else:
sno13Image[i,j,0:3] = np.asarray([0,0,0])
if np.mean(img2014[i,j,0:3]) > (1-limit):
count14 += 1
sno14Image[i,j] = img2014[i,j,0:3]
else:
sno14Image[i,j,0:3] = np.asarray([0,0,0])
#redDif13 = np.absolute(np.subtract(img2011[i,j,0],img2013[i,j,0]))
#greDif13 = np.absolute(np.subtract(img2011[i,j,1],img2013[i,j,1]))
#bluDif13 = np.absolute(np.subtract(img2011[i,j,2],img2013[i,j,2]))
#if ((redDif13+greDif13+bluDif13)/3) <= (limit):
# if np.mean(img2011[i,j,0:3]) > (1-limit):
# count13 += 1
# sno13Image[i,j] = img2011[i,j,0:3]
#else:
# sno13Image[i,j,0:3] = np.asarray([0,0,0])
#
#redDif14 = np.absolute(np.subtract(img2011[i,j,0],img2014[i,j,0]))
#greDif14 = np.absolute(np.subtract(img2011[i,j,1],img2014[i,j,1]))
#bluDif14 = np.absolute(np.subtract(img2011[i,j,2],img2014[i,j,2]))
#if ((redDif14+greDif14+bluDif14)/3) <= (limit):
# if np.mean(img2011[i,j,0:3]) > (1-limit):
# count14 += 1
# sno14Image[i,j] = img2011[i,j,0:3]
#else:
# sno14Image[i,j,0:3] = np.asarray([0,0,0])
plt.imshow(sno11Image) #Open window to show image (close to continue)
plt.show()
plt.imsave('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/snow11Image1.png',sno11Image)
plt.imshow(sno13Image) #Open window to show image (close to continue)
plt.show()
plt.imsave('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/snow13Image1.png',sno13Image)
plt.imshow(sno14Image) #Open window to show image (close to continue)
plt.show()
plt.imsave('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/snow14Image1.png',sno14Image)
print "SNOWY PIXELS (5% THRESHHOLD):\n2011\t2013\t2014\n"+str(count11)+"\t"+str(count13)+"\t"+str(count14)
#SNOWY PIXELS (5% THRESHHOLD):
#2011 2013 2014
#100243 30239 13524
print "SNOW AS DIFFERENCES:\n2011\t2013\t2014\n"+str(float(count11*100)/count11)+"\t"+str((count13*100)/float(count11))+"\t"+str((count14*100)/float(count11))
#SNOW AS DIFFERENCES:
#2011 2013 2014
#100.0 30.1656973554 13.4912163443
## part 5: korea
import matplotlib.pyplot as plt
import numpy as np
kor1981 = plt.imread('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/korea1989.png')
kor2013 = plt.imread('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/korea2013.png')
plt.imshow(kor1981)
height = kor1981.shape[0]
width = kor1981.shape[1]
limit = 0.2
korImage = np.zeros((height,width,3))
for i in range(height):
for j in range(width):
## only red channel
redDif = np.absolute(np.subtract(kor1981[i,j,0],kor2013[i,j,0]))
greDif = np.absolute(np.subtract(kor1981[i,j,1],kor2013[i,j,1]))
if ((redDif+greDif)/2) > (limit) and kor1981[i,j,0] < limit:
korImage[i,j,0:3] = np.asarray([1,0,0])
else:
korImage[i,j] = kor1981[i,j,0:3]
plt.imshow(korImage) #Open window to show image (close to continue)
plt.show()
plt.imsave('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/korImage3.png',korImage)
## part 6: texas
import matplotlib.pyplot as plt
import numpy as np
tex2013 = plt.imread('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/TX2013.png')
tex2014 = plt.imread('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/TX2014.png')
height = tex2013.shape[0]
width = tex2013.shape[1]
plt.imshow(tex2014)
texImage = np.zeros((height,width,3))
texImage2 = np.zeros((height,width,3))
green13 = 0 ## 99912
green14 = 0 ## 428066
## need to calculate the amount of green relative to the amount of all colors - green/greyscale?
for i in range(height):
print "row",i
for j in range(width):
greyscale14 = np.mean(tex2014[i,j,0:3])
greyscale13 = np.mean(tex2013[i,j,0:3])
#texImage[i,j,0:3] = np.array([greyscale,greyscale,greyscale])
#greyGreen = tex2014[i,j,1]-greyscale
#if greyGreen >= 0:
# texImage2[i,j,0:3] = np.array([0,1,0])
#else:
# texImage[i,j,0:3] = np.array([greyscale,greyscale,greyscale])
if tex2014[i,j,1] > tex2014[i,j,0] and tex2014[i,j,1] > tex2014[i,j,2]:
texImage2[i,j] = np.array([0,tex2014[i,j,1],0])
green14 += 1
else:
texImage2[i,j] = np.array([greyscale14,greyscale14,greyscale14])
if tex2013[i,j,1] > tex2013[i,j,0] and tex2013[i,j,1] > tex2013[i,j,2]:
texImage[i,j] = np.array([0,tex2013[i,j,1],0])
green13 += 1
else:
texImage[i,j] = np.array([greyscale13,greyscale13,greyscale13])
plt.imshow(texImage2)
plt.imshow(texImage)
plt.imsave('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/texImage1.png',texImage)
plt.imsave('C:/Users/Kaiya/Documents/Columbia/Classes/Algo Bio/texImage2.png',texImage2)