def f2():
img = cv2.imread(get_filename('0d648f99c.jpg', 'Train'),
cv2.IMREAD_GRAYSCALE)
img = gaussian(img, 0.5)
# img = cv2.resize(img, dsize=tile_size)
em_object = EM(img)
em_object.run_it()
def process_image(image_id, thread_number, locale):
try:
print("%d start" % thread_number)
img = cv2.imread(get_filename(image_id, "final"), cv2.IMREAD_GRAYSCALE)
img = gaussian(img, 0.5) # TODO maybe change to 1
final_img = img.copy()
img = cv2.resize(img, dsize=tile_size)
print("%d go EM" % thread_number)
em = EM(img, thread_number)
em.run_it()
print("%d done EM" % thread_number)
st = timer()
prediction_matrix = create_prediction_image(final_img, em.miu,
em.sigma, em.pgk,
em.clusters)
fs = timer()
print('Prediciton matrix %d' % (fs - st))
out_file = open("../EM_Result/%s" % image_id.split(".")[0], "w")
for line in prediction_matrix:
for el in line:
out_file.write("%d " % el)
out_file.write("\n")
out_file.close()
thread_file = open("../EM_Result/_Thread_%d" % thread_number, "a")
thread_file.write(image_id)
thread_file.write("\n")
thread_file.close()
except Exception as e:
print(e)
th_err = open("../EM_Result/_Thread_Error_%d" % thread_number, "a")
th_err.write(image_id)
th_err.write("\n")
th_err.close()
