def main():
img = cv2.imread('../images/image_car.jpg', 0)
img = cv2.bitwise_not(img)
# img = cv2.resize(img, (0,0), fx= 2, fy = 2)
tmp = img
plt.imshow(img, cmap='Greys')
plt.show()
img_fs = fs(img.copy())
plt.imshow(img_fs, cmap='Greys')
plt.show()
# img_a = atkinson(img.copy())
# plt.imshow(img_a, cmap='Greys')
# plt.show()
# img_s = sierra(img.copy())
# plt.imshow(img_s, cmap='Greys')
# plt.show()
img_st = stucki(img.copy())
plt.imshow(img_st, cmap='Greys')
plt.show()
def main():
s_x = []
s_y = []
p_x = []
p_y = []
skl = 0.0625
iimg = cv2.imread('../images/image.jpg', 0)
iimg = cv2.bitwise_not(iimg)
while (skl <= 16):
img = cv2.resize(iimg, None, fx=1 / skl, fy=1 / skl)
processes = []
events = [th.Event()]
events[0].clear()
output = img.copy()
for i in range(0, output.shape[0] - 2):
if i < 1:
processes.append(
th.Thread(target=fs, args=(output, i, 'p', events)))
else:
events.append(th.Event())
events[i].clear()
processes.append(
th.Thread(target=fs, args=(
output,
i,
'p',
events,
)))
now = T.time()
for i in processes:
i.start()
for i in processes:
i.join()
p_x.append(1 / skl)
p_y.append((T.time() - now) / (len(processes)))
print('Time taken parallel : {}'.format(
(T.time() - now) / (len(processes))))
# cv2.imwrite('../images/doggo_parallel_locks.png', cv2.bitwise_not(output))
# plt.imshow(output, cmap='Greys')
# plt.show()
now = T.time()
img_fs = img.copy()
fs(img_fs)
s_y.append(T.time() - now)
s_x.append(1 / skl)
print('Time taken serial : {}'.format(T.time() - now))
# cv2.imwrite('../images/doggo_ser.png', cv2.bitwise_not(output))
# plt.imshow(img_fs, cmap='Greys')
# plt.show()
skl *= 2
# s_y_ = scaler(s_y.copy())
# p_y_ = scaler(p_y.copy())
plt.figure()
plt.plot(s_x, s_y, color='red', label='Serial')
plt.plot(p_x, p_y, color='blue', label='Parallel')
plt.xlabel('Input size scaling factor')
plt.ylabel('Time')
# plt.xscale('log', basex=2)
plt.yscale('log', basey=10)
plt.title(
"Parallel vs Serial Floyd-Steinberg algorithm input image = {}X{}".
format(iimg.shape[0], iimg.shape[1]))
plt.legend()
plt.savefig('../images/F_S_Parr_Vs_SR.png', bbox_inches='tight')
plt.show()
speed_up = []
for i in range(len(s_x)):
speed_up.append(s_y[i] / p_y[i] * (iimg.shape[0] * s_x[i]))
plt.plot(s_x, speed_up, label='Speed up')
plt.xlabel('Input size 10^5')
plt.ylabel('Time')
plt.xscale('log', basex=2)
plt.title("Speed up observed")
plt.legend()
plt.savefig('../images/F_S_Parr_Vs_SR_speedup.png', bbox_inches='tight')
plt.show()
def main():
s_x = []
s_y = []
p_x = []
p_y = []
skl = 0.0625
iimg = cv2.imread('../images/image.jpg', 0)
iimg = cv2.bitwise_not(iimg)
while (skl <= 8):
limg = cv2.resize(iimg, (0, 0), fx=1 / skl, fy=1 / skl)
img = cv2.resize(limg, (((limg.shape[0] // 8) * 8),
((limg.shape[1] // 8) * 8)))
e_processes = []
o_processes = []
events = [th.Event()]
events[0].clear()
output = img.copy()
flip = 1
for i in range(0, img.shape[0], 8):
for j in range(0, img.shape[1], 8):
if flip > 0:
flip *= -1
e_processes.append(
th.Thread(target=checkerboard_fs,
args=(output, i, j, 8, 8)))
else:
flip *= -1
o_processes.append(
th.Thread(target=checkerboard_fs,
args=(output, i, j, 8, 8)))
now = T.time()
print(len(e_processes))
for i in e_processes:
i.start()
for i in e_processes:
i.join()
for i in o_processes:
i.start()
for i in o_processes:
i.join()
p_x.append(1 / skl)
p_y.append((T.time() - now) / (len(e_processes)))
print('Time taken parallel : {}'.format(
(T.time() - now) / (len(e_processes))))
output = cv2.resize(output, (limg.shape[1], limg.shape[0]))
cv2.imwrite('../images/doggo_chker_{}.png'.format(1 / skl),
cv2.bitwise_not(output))
plt.imshow(output, cmap='Greys')
plt.show()
now = T.time()
img_fs = img.copy()
fs(img_fs)
s_y.append(T.time() - now)
s_x.append(1 / skl)
print('Time taken serial : {}'.format(T.time() - now))
cv2.imwrite('../images/doggo_chker_ser_{}.png'.format(1 / skl),
cv2.bitwise_not(output))
plt.imshow(img_fs, cmap='Greys')
plt.show()
skl *= 2
# s_y_ = scaler(s_y.copy())
# p_y_ = scaler(p_y.copy())
plt.figure()
plt.plot(s_x, s_y, color='red', label='Serial')
plt.plot(p_x, p_y, color='blue', label='Parallel')
plt.xlabel('Input size scaling factor')
plt.ylabel('Time')
# plt.xscale('log', basex=2)
plt.yscale('log', basey=10)
plt.title(
"Parallel vs Serial Floyd-Steinberg algorithm input image = {}X{}".
format(iimg.shape[0], iimg.shape[1]))
plt.legend()
plt.savefig('../images/F_S_Parr_chker_Vs_SR.png', bbox_inches='tight')
plt.show()
speed_up = []
for i in range(len(s_x)):
speed_up.append(s_y[i] / p_y[i] * (iimg.shape[0] * s_x[i]))
plt.plot(s_x, speed_up, label='Speed up')
plt.xlabel('Input size 10^5')
plt.ylabel('Time')
plt.xscale('log', basex=2)
plt.title("Speed up observed")
plt.legend()
plt.savefig('../images/F_S_Parr_ckher_Vs_SR_speedup.png',
bbox_inches='tight')
plt.show()