def step_fn(inputs): img_with_shadow, shadow_mask, img_no_shadow, input_pureflash = inputs gray_pureflash = 0.33 * (input_pureflash[..., 0:1] + input_pureflash[..., 1:2] + input_pureflash[..., 2:3]) # bad_mask = detect_shadow(img_with_shadow, input_pureflash) shadow_mask_layer = UNet_SE(tf.concat([img_with_shadow, gray_pureflash], axis=3), output_channel=3, ext='Ref_') no_shadow_layer = UNet_SE(tf.concat([img_with_shadow, shadow_mask_layer], axis=3), ext='Trans_') lossDict["percep_t"] = 0.1 * compute_percep_loss( img_no_shadow, no_shadow_layer, reuse=False) lossDict["percep_r"] = 0.1 * compute_percep_loss( shadow_mask, shadow_mask_layer, reuse=True) lossDict["total"] = lossDict["percep_t"] + lossDict["percep_r"] tf_psnr = tf.image.psnr(img_no_shadow[0], no_shadow_layer[0], 1.0) encoded_concat = encode_jpeg( concat_img((img_with_shadow[0], no_shadow_layer[0], img_no_shadow[0], input_pureflash[0], shadow_mask_layer[0], shadow_mask[0]))) train_vars = tf.trainable_variables() R_vars = [var for var in train_vars if 'Ref_' in var.name] T_vars = [var for var in train_vars if 'Trans_' in var.name] all_vars = [var for var in train_vars if 'g_' in var.name] opt = tf.train.AdamOptimizer(learning_rate=0.0001).minimize( lossDict["total"], var_list=all_vars) with tf.control_dependencies([opt]): return tf.identity(loss), tf_psnr, encoded_concat
iterator = tf.data.Iterator.from_structure(train_ds.output_types, train_ds.output_shapes) img_with_shadow,shadow_mask,img_no_shadow,input_pureflash = iterator.get_next() training_init_op = iterator.make_initializer(train_ds) validation_init_op = iterator.make_initializer(val_ds) with tf.variable_scope(tf.get_variable_scope()): gray_pureflash = 0.33 * (input_pureflash[...,0:1] + input_pureflash[...,1:2] + input_pureflash[...,2:3]) # bad_mask = detect_shadow(img_with_shadow, input_pureflash) shadow_mask_layer = UNet_SE(tf.concat([img_with_shadow, gray_pureflash], axis=3), output_channel = 1, ext='Ref_') no_shadow_layer = UNet_SE(tf.concat([img_with_shadow, shadow_mask_layer], axis=3), ext='Trans_') lossDict["percep_t"] = 0.1 * compute_percep_loss(img_no_shadow, no_shadow_layer, reuse=False) lossDict["percep_r"]=0.1* tf.reduce_mean(tf.math.abs(shadow_mask-shadow_mask_layer)) # lossDict["percep_r"] = 0.1 * compute_percep_loss(shadow_mask, shadow_mask_layer, reuse=True) lossDict["total"] = lossDict["percep_t"] + lossDict["percep_r"] tf_psnr=tf.math.reduce_mean(tf.image.psnr(tf.clip_by_value(img_no_shadow,0,1), tf.clip_by_value(no_shadow_layer,0,1),1.0)) encoded_concat=encode_jpeg( concat_img((img_with_shadow[0],no_shadow_layer[0],img_no_shadow[0], input_pureflash[0],tf.image.grayscale_to_rgb(tf.clip_by_value(shadow_mask_layer[0],0,1)), tf.image.grayscale_to_rgb(shadow_mask[0])))) train_vars = tf.trainable_variables() R_vars = [var for var in train_vars if 'Ref_' in var.name]
) training_init_op = iterator.make_initializer(train_ds) validation_init_op = iterator.make_initializer(val_ds) with tf.variable_scope(tf.get_variable_scope()): gray_pureflash = 0.33 * (input_pureflash[..., 0:1] + input_pureflash[..., 1:2] + input_pureflash[..., 2:3]) # bad_mask = detect_shadow(img_with_shadow, input_pureflash) shadow_mask_layer = UNet_SE(img_with_shadow, output_channel=3, ext='Ref_') no_shadow_layer = UNet_SE(tf.concat([img_with_shadow, shadow_mask_layer], axis=3), ext='Trans_') lossDict["percep_t"] = 0.1 * compute_percep_loss( img_no_shadow, no_shadow_layer, reuse=False) lossDict["percep_r"] = 0.1 * compute_percep_loss( shadow_mask, shadow_mask_layer, reuse=True) lossDict["total"] = lossDict["percep_t"] + lossDict["percep_r"] tf_psnr = tf.image.psnr(img_no_shadow[0], no_shadow_layer[0], 1.0) encoded_concat = encode_jpeg( concat_img((img_with_shadow[0], no_shadow_layer[0], img_no_shadow[0], input_pureflash[0], shadow_mask_layer[0], shadow_mask[0]))) train_vars = tf.trainable_variables() R_vars = [var for var in train_vars if 'Ref_' in var.name] T_vars = [var for var in train_vars if 'Trans_' in var.name] all_vars = [var for var in train_vars if 'g_' in var.name] for var in R_vars:
# mask_shadow = tf.cast(tf.greater(input_pureflash, 0.02), tf.float32) # mask_highlight = tf.cast(tf.less(input_flash, 0.96), tf.float32) # mask_shadow_highlight = mask_shadow * mask_highlight gray_pureflash = 0.33 * (input_pureflash[..., 0:1] + input_pureflash[..., 1:2] + input_pureflash[..., 2:3]) bad_mask = detect_shadow(input_ambient, input_pureflash) reflection_layer = UNet_SE(tf.concat( [input_ambient, gray_pureflash, (-bad_mask + 1)], axis=3), output_channel=3, ext='Ref_') transmission_layer = UNet_SE(tf.concat( [input_ambient, reflection_layer, (-bad_mask + 1)], axis=3), ext='Trans_') lossDict["percep_t"] = 0.1 * compute_percep_loss( target, transmission_layer, reuse=False) lossDict["percep_r"] = 0.1 * compute_percep_loss( reflection, reflection_layer, reuse=True) lossDict["total"] = lossDict["percep_t"] + lossDict["percep_r"] train_vars = tf.trainable_variables() R_vars = [var for var in train_vars if 'Ref_' in var.name] T_vars = [var for var in train_vars if 'Trans_' in var.name] all_vars = [var for var in train_vars if 'g_' in var.name] for var in R_vars: print(var) for var in T_vars: print(var) opt = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(lossDict["total"],
# img_no_shadow=tf.placeholder(tf.float32,shape=[None,None,None,3]) # mask_shadow = tf.cast(tf.greater(input_pureflash, 0.02), tf.float32) # mask_highlight = tf.cast(tf.less(input_flash, 0.96), tf.float32) # mask_shadow_highlight = mask_shadow * mask_highlight gray_pureflash = 0.33 * (input_pureflash[..., 0:1] + input_pureflash[..., 1:2] + input_pureflash[..., 2:3]) # bad_mask = detect_shadow(img_with_shadow, input_pureflash) shadow_mask_layer = shadow_mask # shadow_mask_layer = UNet_SE(tf.concat([img_with_shadow, gray_pureflash], axis=3), output_channel = 3, ext='Ref_') transmission_layer = UNet_SE(tf.concat( [img_with_shadow, shadow_mask_layer], axis=3), ext='Trans_') lossDict["percep_t"] = 0.1 * compute_percep_loss( img_no_shadow, transmission_layer, reuse=False) lossDict["percep_r"] = tf.constant(0) # lossDict["percep_r"] = 0.1 * compute_percep_loss(shadow_mask, shadow_mask_layer, reuse=True) lossDict["total"] = lossDict["percep_t"] # + lossDict["percep_r"] tf_psnr = tf.image.psnr(img_no_shadow, transmission_layer, 1) train_vars = tf.trainable_variables() R_vars = [var for var in train_vars if 'Ref_' in var.name] T_vars = [var for var in train_vars if 'Trans_' in var.name] all_vars = [var for var in train_vars if 'g_' in var.name] for var in R_vars: print(var) for var in T_vars: print(var)