def validation():
img_names = sorted(glob("./data/real_world/val/others" + '/*'))
psnr = []
txt_path = "./result/%s/%04d/psnr_ssim.txt" % (model, epoch)
f = open(txt_path, 'w')
sess.run(validation_init_op)
for id in range(val_num):
fetch_list = [
transmission_layer, reflection_layer, input_ambient, target,
reflection, input_pureflash, target, lossDict
]
pred_image_t, pred_image_r, gt_input_ambient, gt_target, gt_reflection, tmp_pureflash, tmp_T, crtDict = sess.run(
fetch_list)
print("Epc: %3d, shape of outputs: " % epoch, pred_image_t.shape,
pred_image_r.shape)
tmp_psnr = calculate_psnr(pred_image_t[0], tmp_T[0])
psnr.append(tmp_psnr)
f.writelines('%s: %.6f\n' % (img_names[0], tmp_psnr))
utils.save_concat_img(
gt_input_ambient, gt_target, gt_reflection, tmp_pureflash,
pred_image_t, pred_image_r,
"./result/%s/%04d/val_%06d.jpg" % (model, epoch, id))
mean_psnr = np.mean(psnr)
f.writelines('%s: %.6f\n' % ("average", mean_psnr))
f.close()
return mean_psnr
def validation():
img_names = sorted(glob("./data/real_world/val/others" + '/*'))
psnr = []
txt_path = "./result/%s/%04d/psnr_ssim.txt"%(model, epoch)
f = open(txt_path,'w')
for id in range(len(img_names) // 5):
tmp_pureflash, tmp_ambient, tmp_flash, tmp_T, tmp_R = load_paired_data(img_names, id)
h,w = tmp_T.shape[1:3]
h = h // 32 * 32
w = w // 32 * 32
# tmp_T, tmp_R, tmp_ambient, tmp_pureflash, tmp_flash = tmp_T[:,:h:2,:w:2,:], tmp_R[:,:h:2,:w:2,:], tmp_ambient[:,:h:2,:w:2,:], tmp_pureflash[:,:h:2,:w:2,:], tmp_flash[:,:h:2,:w:2,:]
tmp_T, tmp_R, tmp_ambient, tmp_pureflash, tmp_flash = tmp_T[:,:h,:w,:], tmp_R[:,:h,:w,:], tmp_ambient[:,:h,:w,:], tmp_pureflash[:,:h,:w,:], tmp_flash[:,:h,:w,:]
fetch_list=[transmission_layer, reflection_layer, input_ambient, target, reflection, lossDict]
pred_image_t, pred_image_r, gt_input_ambient, gt_target, gt_reflection, crtDict=sess.run(fetch_list,
feed_dict={input_ambient:tmp_ambient, reflection:tmp_R, target:tmp_T, input_pureflash:tmp_pureflash})
print("Epc: %3d, shape of outputs: "%epoch, pred_image_t.shape, pred_image_r.shape)
tmp_psnr = calculate_psnr(pred_image_t[0], tmp_T[0])
psnr.append(tmp_psnr)
f.writelines('%s: %.6f\n'%(img_names[0], tmp_psnr))
utils.save_concat_img(gt_input_ambient, gt_target, gt_reflection, tmp_pureflash, pred_image_t,pred_image_r, "./result/%s/%04d/val_%06d.jpg"%(model, epoch, id))
mean_psnr = np.mean(psnr)
f.writelines('%s: %.6f\n'%("average", mean_psnr))
f.close()
return mean_psnr
def validation():
img_names = sorted(glob("./data/real_world/val/others" + '/*'))
psnr = []
txt_path = "./result/%s/%04d/psnr_ssim.txt" % (model, epoch)
f = open(txt_path, 'w')
sess.run(validation_init_op)
id = 0
while True:
try:
fetch_list = [
transmission_layer, shadow_mask_layer, img_with_shadow,
img_no_shadow, shadow_mask, input_pureflash, img_no_shadow,
lossDict
]
pred_image_t, pred_image_r, gt_img_with_shadow, gt_img_no_shadow, gt_shadow_mask, tmp_pureflash, tmp_T, crtDict = sess.run(
fetch_list)
tmp_psnr = calculate_psnr(pred_image_t[0], tmp_T[0])
psnr.append(tmp_psnr)
f.writelines('%s: %.6f\n' % (img_names[0], tmp_psnr))
if id % 100 == 0:
print("Epc: %3d, shape of outputs: " % epoch,
pred_image_t.shape, pred_image_r.shape)
utils.save_concat_img(
gt_img_with_shadow, gt_img_no_shadow, gt_shadow_mask,
tmp_pureflash, pred_image_t, pred_image_r,
"./result/%s/%04d/val_%06d.jpg" % (model, epoch, id))
id += 1
except tf.errors.OutOfRangeError:
break
mean_psnr = np.mean(psnr)
print('%s: %.6f\n' % ("average", mean_psnr))
f.writelines('%s: %.6f\n' % ("average", mean_psnr))
f.close()
return mean_psnr
target: tmp_T,
input_pureflash: tmp_pureflash,
input_flash: tmp_flash
})
step += 1
if step % 10 == 0:
crtLoss_str = " ".join([
"{}: {:.3f}".format(key, value)
for key, value in crtDict.items()
])
print("Epc:{:03d}-{:04d} | {} time:{:.3f}".format(
epoch, id, crtLoss_str,
time.time() - st))
if step % 100 == 0:
utils.save_concat_img(
gt_input_ambient, gt_target, gt_reflection, tmp_pureflash,
pred_image_t, pred_image_r,
"./result/%s/%04d/train_%06d.jpg" % (model, epoch, id))
mean_psnr = validation()
if mean_psnr > best_psnr:
best_psnr = mean_psnr
print("mean: {:.2f}".format(mean_psnr))
print("best: {:.2f}".format(best_psnr))
saver.save(sess, "./result/%s/model.ckpt" % model)
saver.save(sess, "./result/%s/%04d/model.ckpt" % (model, epoch - 1))
if (is_test or (epoch % save_model_freq == 0 and epoch < 1000)):
saver.save(sess, "./result/%s/model.ckpt" % model)
saver.save(sess, "./result/%s/%04d/model.ckpt" % (model, epoch - 1))
img_names = sorted(
glob("./data/synthetic/with_corrn_reflection/test/others" +
_, pred_image_t, pred_image_r, gt_img_with_shadow, gt_img_no_shadow, gt_shadow_mask, tmp_pureflash, crtDict = sess.run(
fetch_list)
step += 1
if step % 100 == 0:
crtLoss_str = " ".join([
"{}: {:.3f}".format(key, value)
for key, value in crtDict.items()
])
print("Epc:{:03d}-{:04d} | {} time:{:.3f}".format(
epoch, id, crtLoss_str,
time.time() - st))
st = time.time()
if step % 100 == 0:
utils.save_concat_img(
gt_img_with_shadow, gt_img_no_shadow, gt_shadow_mask,
tmp_pureflash, pred_image_t, pred_image_r,
"./result/%s/%04d/train_%06d.jpg" % (model, epoch, id))
id += 1
except tf.errors.OutOfRangeError:
break
print("Epc:{:03d} | time:{:.3f}".format(epoch, time.time() - ept))
mean_psnr = validation()
if mean_psnr > best_psnr:
best_psnr = mean_psnr
print("mean: {:.2f}".format(mean_psnr))
print("best: {:.2f}".format(best_psnr))
saver.save(sess, "./result/%s/model.ckpt" % model)
saver.save(sess, "./result/%s/%04d/model.ckpt" % (model, epoch - 1))
if (False): #(is_test or (epoch % save_model_freq == 0 and epoch < 1000)):
saver.save(sess, "./result/%s/model.ckpt" % model)