def hmb(image_path_1, image_path_2):
ui = UI()
if (image_path_2 != ''):
pic1 = Picture(image_path_1)
pic2 = Picture(image_path_2)
pic1.match_lumi(pic2)
pic1.save("lumi_result.jpg")
pic1 = Picture(image_path_1)
pic1.match_rgb(pic2)
pic1.save("rgb_result.jpg")
else:
pic1 = Picture(image_path_1)
pic1.match_GB2R()
pic1.save("GB2R.jpg")
pic1 = Picture(image_path_1)
pic1.match_RB2G()
pic1.save("RB2G.jpg")
pic1 = Picture(image_path_1)
pic1.match_RG2B()
pic1.save("RG2B.jpg")
for x in range(size_x):
r_list = []
g_list = []
b_list = []
for i in img_list:
r, g, b = i.get_RGB_value((x, y))
r_list.append(r)
g_list.append(g)
b_list.append(b)
# insert median values into output image
rm = median.get_median(r_list, user_median)
gm = median.get_median(g_list, user_median)
bm = median.get_median(b_list, user_median)
image.put_RGB_value((x, y), (rm, gm, bm))
image.save(os.path.join(IMAGE_DIRECTORY, 'output.png'))
print "Done processing!"
# compare output image to reference image, named final.png
model = Picture(filename=os.path.join(IMAGE_DIRECTORY, 'final.png'))
print "\n\nBrute Similarity: " + similarity.sim_brute(image, model)
print "Correlation Similarity: " + similarity.sim_correlation(image, model)
print "Chi Squared p-value: " + similarity.sim_chi(image, model)
print "Note: Closer to 0 is perfect for chi-squared, 1 is perfect for brute and correlation\n\n"
# displays output image to the user
image.display()
((-sqrt(10*c)-90) % 100) / 100,
.75 + .25*cos(c*pi/20),
# 1
exp(c/2-1)/(exp(c/2-1)+10)
))
pic.set(j, i, color.Color(r, g, b))
#stdio.writeln()
stdio.write('\r{:02.2f}%'.format(100))
t3 = time.time()
t = t3 - t2
ts = int(t)
tm = 1000*(t-ts)
# draws the generated image to the canvas (and saves it)
stdio.writeln()
stdio.writeln('render:')
stdio.writeln(f'{ts:d}s {tm:.0f}ms')
if f is not None:
pic.save(f)
stddraw.setCanvasSize(nx, ny)
stddraw.picture(pic)
stddraw.show()
# python mandelbrot.py 2 400 600 300 .18 -.8 .1 .15 x.png
# python mandelbrot.py 2 1440 2160 1000 .18 -.8 .1 .15 x.png
# python mandelbrot.py 2 400 600 1000 .18 -.8025 .01 .015 x.png
# python mandelbrot.py 2 800 1200 2000 .18237 -.8027 .0004 .0006 x.png
# python mandelbrot.py 2 600 600 1200 .1823 -.8027 .00005 .00005 x.png
# python mandelbrot.py 2 300 300 1600 .18231 -.80268 .00001 .00001 x.png
g_list = [] b_list = [] for i in img_list: r, g, b = i.get_RGB_value((x,y)) r_list.append(r) g_list.append(g) b_list.append(b) # insert median values into output image rm = median.get_median(r_list, user_median) gm = median.get_median(g_list, user_median) bm = median.get_median(b_list, user_median) image.put_RGB_value((x,y),(rm,gm,bm)) image.save(os.path.join(IMAGE_DIRECTORY, 'output.png')) print "Done processing!" # compare output image to reference image, named final.png model = Picture(filename = os.path.join(IMAGE_DIRECTORY, 'final.png')) print "\n\nBrute Similarity: " + similarity.sim_brute(image, model) print "Correlation Similarity: " + similarity.sim_correlation(image, model) print "Chi Squared p-value: " + similarity.sim_chi(image, model) print "Note: Closer to 0 is perfect for chi-squared, 1 is perfect for brute and correlation\n\n" # displays output image to the user image.display()
