import improc
import numpy as np
import time
img = improc.read("gilman-hall.jpg")
# removed red channel
#img_nored = improc.rm_red(img)
# convert to gray
img_gray_avg = improc.rgb_to_gray_avg(img)
#img_gray_lum = improc.rgb_to_gray_lum(img)
#bigger = improc.scale(img,1.5)
#smaller = improc.scale(bigger,2/3)
#diff = improc.difference(smaller,img)
improc.show_n([img,img_gray_avg])
#n = 500
#A = np.random.rand(n)
#A2 = np.array(A)
#t0 = time.time()
#improc.selectionsort(A,n)
#t1 = time.time()
#improc.mergesort(A2,n)
#t2 = time.time()
#print(t1-t0, t2-t1)
# I ADDED THIS LINE
import improc
import numpy as np
import time
img = improc.read("gilman-hall.jpg")
# removed red channel
#img_nored = improc.rm_red(img)
# convert to gray
img_gray_avg = improc.rgb_to_gray_avg(img)
#img_gray_lum = improc.rgb_to_gray_lum(img)
#bigger = improc.scale(img,1.5)
#smaller = improc.scale(bigger,2/3)
#diff = improc.difference(smaller,img)
improc.show_n([img, img_gray_avg])
#n = 500
#A = np.random.rand(n)
#A2 = np.array(A)
#t0 = time.time()
#improc.selectionsort(A,n)
#t1 = time.time()
#improc.mergesort(A2,n)
#t2 = time.time()
#print(t1-t0, t2-t1)
# I ADDED THIS LINE
# removed red channel
#img_nored = improc.rm_red(img)
# convert to gray
#img_gray_avg = improc.rgb_to_gray_avg(img)
#img_gray_lum = improc.rgb_to_gray_lum(img)
#bigger = improc.scale(img,1.5)
#smaller = improc.scale(bigger,2/3)
#diff = improc.difference(smaller,img)
#improc.show_n([img,smaller,diff])
#n = 500
#A = np.random.rand(n)
#A2 = np.array(A)
#t0 = time.time()
#improc.selectionsort(A,n)
#t1 = time.time()
#improc.mergesort(A2,n)
#t2 = time.time()
#print(t1-t0, t2-t1)
kern = [[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]
kern_G = [[1 / 16, 2 / 16, 1 / 16], [2 / 16, 4 / 16, 2 / 16],
[1 / 16, 2 / 16, 1 / 16]]
gauss = improc.convolution(img, kern_G)
edges = improc.convolution(img, kern)
improc.show_n([img, edges, gauss])
#bigger = improc.scale(img,1.5) #smaller = improc.scale(bigger,2/3) #diff = improc.difference(smaller,img) #improc.show_n([img,smaller,diff]) #n = 500 #A = np.random.rand(n) #A2 = np.array(A) #t0 = time.time() #improc.selectionsort(A,n) #t1 = time.time() #improc.mergesort(A2,n) #t2 = time.time() #print(t1-t0, t2-t1) #print(improc.factorial(4)) #A = np.array([1,7,6,5,0,8,9,4,2]); #print(A) #improc.mergesort(A,9) #print(A) #print(improc.binary_search(A,0,8,4)) kern = [[-1,-1,-1],[-1,8,-1],[-1,-1,-1]] kern_G = [[1/16,2/16,1/16],[2/16,4/16,2/16],[1/16,2/16,1/16]] gauss = improc.convolution(img,kern_G) edges = improc.convolution(gauss,kern) improc.show_n([img,edges,gauss])
#!/usr/bin/env python
import improc
img = improc.read("gilman-hall.jpg")
# find max values
image_max = improc.max_img(img)
# find min values
image_min = improc.min_img(img)
# convert to grayscale
img_gray_lit = improc.rgb_to_gray_lit(image_min, image_max)
# show images
improc.show_n([img, img_gray_lit])
