def run_eval_mend():
img = cv2.imread('road-car.png')[np.newaxis, :, :, :]
img = np.pad(img, ((0, 0), (32, 32), (32, 32), (0, 0)), 'reflect')
# mask = cv2.imread('road-label.png')[np.newaxis, :, :, :]
mask = cv2.imread('road-cloud0.png')[np.newaxis, :, :, :]
mask = np.pad(mask, ((0, 0), (32, 32), (32, 32), (0, 0)), 'reflect')[:, :, :, 0:1]
threshold = 244
mask[mask < threshold] = 0
mask[mask >= threshold] = 255
# cv2.imshow('', mask[0])
# cv2.waitKey(5432)
eval_list = [img, mask, img, mask]
from mod_mend_dila import init_train
C = Config('mod_mend_dila')
from mod_mend_nres import init_train
C = Config('mod_mend_nres')
inp_ground, inp_mask01, inp_grdbuf, inp_mskbuf, fetch, eval_fetch = init_train()
C.size = img.shape[1]
sess = mod_util.get_sess(C)
mod_util.get_saver_logger(C, sess)
print("||Training Check")
eval_feed_dict = {inp_ground: eval_list[0],
inp_mask01: eval_list[1],
inp_grdbuf: eval_list[2],
inp_mskbuf: eval_list[3], }
img_util.get_eval_img(mat_list=sess.run(eval_fetch, eval_feed_dict), channel=3,
img_path="%s/eval-%08d.jpg" % ('temp', 0))
def run_eval_haze():
img = cv2.imread('road-thin.png')[np.newaxis, :, :, :]
img = np.pad(img, ((0, 0), (32, 32), (32, 32), (0, 0)), 'reflect')
eval_list = [img, np.zeros_like(img[:, :, :, 0:1])]
from mod_haze_unet import init_train
inp_ground, inp_mask01, train_fetch, eval_fetch = init_train()
C = Config('mod_haze_unet')
C.size = img.shape[1]
sess = mod_util.get_sess(C)
mod_util.get_saver_logger(C, sess)
print("||Training Check")
eval_feed_dict = {inp_ground: eval_list[0],
inp_mask01: eval_list[1], }
img_util.get_eval_img(mat_list=sess.run(eval_fetch, eval_feed_dict), channel=3,
img_path="%s/eval-%08d.jpg" % ('temp', 0))