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])