def distort_like_page(dst_dir,
start_index,
x_min,
x_max,
y_min,
y_max,
w_res,
h_res,
write_npy=True):
move_maps = []
start_time = time.time()
Check_dir_exist_and_build(data_access_path + dst_dir + "/" +
"distort_mesh_visuals")
Check_dir_exist_and_build(data_access_path + dst_dir + "/" + "move_maps")
Check_dir_exist_and_build(data_access_path + dst_dir + "/" +
"distort_infos")
### 可以用step2_d去試 我想要的參數喔！
distort_time = 1
vert_y = 0
move_x = 0
dis_type = "curl"
alpha = 1.5
index = start_index
for go_move_y in range(-13, 13): ### 可以用step2_d去試 我想要的參數喔！
for go_vert_x in range(120, 180): ### 可以用step2_d去試 我想要的參數喔！
dis_start_time = time.time()
result_move_f = np.zeros(shape=(h_res * w_res, 2),
dtype=np.float64) ### 初始化 move容器
move_f, _ = get_dis_move_map(x_min,
x_max,
y_min,
y_max,
w_res,
h_res,
go_vert_x,
vert_y,
move_x,
go_move_y,
dis_type,
alpha,
debug=False) ### 用參數去得到 扭曲move_f
result_move_f = result_move_f + move_f ### 把我要的move_f加進去容器內～
distrore_info_log(data_access_path + dst_dir + "/" +
"distort_infos", index, h_res, w_res,
distort_time, go_vert_x, vert_y, move_x,
go_move_y, dis_type, alpha) ### 紀錄扭曲參數
result_move_map = result_move_f.reshape(
h_res, w_res, 2) ### (..., 2)→(h_res, w_res, 2)
result_move_map = result_move_map.astype(np.float32)
# save_distort_mesh_visual(h_res, w_res, result_move_f, index) ### 紀錄扭曲視覺化的結果
np.save(data_access_path + dst_dir + "/" +
"move_maps/%06i" % index,
result_move_map) ### 把move_map存起來，記得要轉成float32！
print("%06i process 1 mesh cost time:" % index,
"%.3f" % (time.time() - dis_start_time), "total_time:",
time_util(time.time() - start_time))
index += 1
move_maps.append(result_move_map)
return np.array(move_maps.astype(np.float32))
def I_Generate_M_see(model_G, phase, index, in_img, in_img_pre, gt_mask_coord, _4, rec_hope=None, exp_obj=None, training=True, see_reset_init=True, postprocess=False, add_loss=False, bgr2rgb=False):
current_ep = exp_obj.current_ep
current_time = exp_obj.current_time
if (phase == "see"): used_sees = exp_obj.result_obj.sees
elif(phase == "test"): used_sees = exp_obj.result_obj.tests
'''
bgr2rgb： tf2 讀出來是 rgb， 但 cv2 存圖是bgr， 所以此狀況記得要轉一下ch 把 bgr2rgb設True！
'''
in_img, pred_mask, pred_mask_visual, gt_mask = I_Generate_M_basic_data(model_G, in_img, in_img_pre, gt_mask_coord, exp_obj, training, bgr2rgb=True)
private_write_dir = used_sees[index].see_write_dir ### 每個 see 都有自己的資料夾 存 in/gt 之類的 輔助檔案 ，先定出位置
private_mask_write_dir = used_sees[index].mask_write_dir ### 每個 see 都有自己的資料夾 存 model生成的結果，先定出位置
public_write_dir = "/".join(used_sees[index].see_write_dir.replace("\\", "/").split("/")[:-1]) ### private 的上一層資料夾
# print('public_write_dir:', public_write_dir)
if(current_ep == 0 or see_reset_init): ### 第一次執行的時候，建立資料夾 和 寫一些 進去資料夾比較好看的東西
Check_dir_exist_and_build(private_write_dir) ### 建立 放輔助檔案 的資料夾
Check_dir_exist_and_build(private_mask_write_dir) ### 建立 model生成的結果 的資料夾
cv2.imwrite(private_write_dir + "/" + "0a_u1a0-dis_img(in_img).jpg", in_img) ### 寫一張 in圖進去，進去資料夾時比較好看，0a是為了保證自動排序會放在第一張
cv2.imwrite(private_write_dir + "/" + "0b_u1b1-gt_mask.bmp", gt_mask) ### 寫一張 gt圖進去，進去資料夾時比較好看，0b是為了保證自動排序會放在第二張
cv2.imwrite( private_mask_write_dir + "/" + "epoch_%04i_u1b1_mask.bmp" % current_ep, pred_mask_visual) ### 我覺得不可以直接存npy，因為太大了！但最後為了省麻煩還是存了，相對就減少see的數量來讓總大小變小囉～
if(postprocess):
current_see_name = used_sees[index].see_name.replace("/", "-") ### 因為 test 會有多一層 "test_db_name"/test_001， 所以把 / 改成 - ，下面 Save_fig 才不會多一層資料夾
from matplot_fig_ax_util import Matplot_single_row_imgs
imgs = [ in_img , pred_mask_visual , gt_mask]
img_titles = ["in_img", "pred_mask", "gt_mask"]
single_row_imgs = Matplot_single_row_imgs(
imgs =imgs, ### 把要顯示的每張圖包成list
img_titles=img_titles, ### 把每張圖要顯示的字包成list
fig_title ="%s, epoch=%04i" % (current_see_name, int(current_ep)), ### 圖上的大標題
add_loss =add_loss,
bgr2rgb =bgr2rgb)
single_row_imgs.Draw_img()
single_row_imgs.Save_fig(dst_dir=public_write_dir, name=current_see_name) ### 如果沒有要接續畫loss，就可以存了喔！
'''
Fake_F 的部分
'''
gather_mask_dir = public_write_dir + "/pred_mask"
Check_dir_exist_and_build(gather_mask_dir)
cv2.imwrite(f"{gather_mask_dir}/{current_see_name}.bmp", pred_mask_visual)
h, w = pred_mask.shape[:2]
fake_name = current_see_name.split(".")[0]
print("")
###############################################################################
gather_fake_F_dir = public_write_dir + "/pred_mask/fake_F"
Check_dir_exist_and_build(gather_fake_F_dir)
fake_C = np.zeros(shape=(h, w, 2), dtype=np.float32)
fake_F = np.concatenate((pred_mask, fake_C), axis=-1)
fake_F = fake_F.astype(np.float32)
gather_fake_F_npy_dir = gather_fake_F_dir + "/1 npy"
gather_fake_F_knpy_dir = gather_fake_F_dir + "/2 knpy"
Check_dir_exist_and_build(gather_fake_F_npy_dir)
Check_dir_exist_and_build(gather_fake_F_knpy_dir)
fake_F_npy_path = f"{gather_fake_F_npy_dir}/{fake_name}.npy"
fake_F_knpy_path = f"{gather_fake_F_knpy_dir}/{fake_name}.knpy"
np.save(fake_F_npy_path, fake_F)
Save_npy_path_as_knpy(fake_F_npy_path, fake_F_knpy_path)
print("fake_F_npy_path :", fake_F_npy_path)
print("fake_F_knpy_path:", fake_F_knpy_path)
###############################################################################
gather_fake_W_dir = public_write_dir + "/pred_mask/fake_W"
Check_dir_exist_and_build(gather_fake_W_dir)
fake_W = np.zeros(shape=(h, w, 3), dtype=np.float32)
fake_W = np.concatenate((fake_W, pred_mask), axis=-1)
fake_W = fake_W.astype(np.float32)
gather_fake_W_npy_dir = gather_fake_W_dir + "/1 npy"
gather_fake_W_knpy_dir = gather_fake_W_dir + "/2 knpy"
Check_dir_exist_and_build(gather_fake_W_npy_dir)
Check_dir_exist_and_build(gather_fake_W_knpy_dir)
fake_W_npy_path = f"{gather_fake_W_npy_dir}/{fake_name}.npy"
fake_W_knpy_path = f"{gather_fake_W_knpy_dir}/{fake_name}.knpy"
np.save(fake_W_npy_path, fake_W)
Save_npy_path_as_knpy(fake_W_npy_path, fake_W_knpy_path)
print("fake_W_npy_path :", fake_W_npy_path)
print("fake_W_knpy_path:", fake_W_knpy_path)
