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()