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"""
Some preprocessing utilities have been taken from:
https://github.com/google-research/maxim/blob/main/maxim/run_eval.py
"""
import gradio as gr
import numpy as np
import tensorflow as tf
from huggingface_hub.keras_mixin import from_pretrained_keras
from PIL import Image
from create_maxim_model import Model
from maxim.configs import MAXIM_CONFIGS
CKPT = "google/maxim-s2-enhancement-fivek"
VARIANT = CKPT.split("/")[-1].split("_")[0]
VARIANT = VARIANT.upper()
_MODEL = from_pretrained_keras(CKPT)
def mod_padding_symmetric(image, factor=64):
"""Padding the image to be divided by factor."""
height, width = image.shape[0], image.shape[1]
height_pad, width_pad = ((height + factor) // factor) * factor, (
(width + factor) // factor
) * factor
padh = height_pad - height if height % factor != 0 else 0
padw = width_pad - width if width % factor != 0 else 0
image = tf.pad(
image, [(padh // 2, padh // 2), (padw // 2, padw // 2), (0, 0)], mode="REFLECT"
)
return image
def make_shape_even(image):
"""Pad the image to have even shapes."""
height, width = image.shape[0], image.shape[1]
padh = 1 if height % 2 != 0 else 0
padw = 1 if width % 2 != 0 else 0
image = tf.pad(image, [(0, padh), (0, padw), (0, 0)], mode="REFLECT")
return image
def process_image(image: Image):
input_img = np.asarray(image) / 255.0
height, width = input_img.shape[0], input_img.shape[1]
# Padding images to have even shapes
input_img = make_shape_even(input_img)
height_even, width_even = input_img.shape[0], input_img.shape[1]
# padding images to be multiplies of 64
input_img = mod_padding_symmetric(input_img, factor=64)
input_img = tf.expand_dims(input_img, axis=0)
return input_img, height, width, height_even, width_even
def init_new_model(input_img):
configs = MAXIM_CONFIGS.get(VARIANT)
configs.update(
{
"variant": VARIANT,
"dropout_rate": 0.0,
"num_outputs": 3,
"use_bias": True,
"num_supervision_scales": 3,
}
)
configs.update({"input_resolution": (input_img.shape[1], input_img.shape[2])})
new_model = Model(**configs)
new_model.set_weights(_MODEL.get_weights())
return new_model
def infer(image):
preprocessed_image, height, width, height_even, width_even = process_image(image)
new_model = init_new_model(preprocessed_image)
preds = new_model.predict(preprocessed_image)
if isinstance(preds, list):
preds = preds[-1]
if isinstance(preds, list):
preds = preds[-1]
preds = np.array(preds[0], np.float32)
new_height, new_width = preds.shape[0], preds.shape[1]
h_start = new_height // 2 - height_even // 2
h_end = h_start + height
w_start = new_width // 2 - width_even // 2
w_end = w_start + width
preds = preds[h_start:h_end, w_start:w_end, :]
return Image.fromarray(np.array((np.clip(preds, 0.0, 1.0) * 255.0).astype(np.uint8)))
title = "Retouch images."
description = f"The underlying model is [this](https://huggingface.co/{CKPT}). You can use the model for image retouching useful for image editing applications. To quickly try out the model, you can choose from the available sample images below, or you can submit your own image. Not that, internally, the model is re-initialized based on the spatial dimensions of the input image and this process is time-consuming."
iface = gr.Interface(
infer,
inputs="image",
outputs=gr.Image().style(height=242),
title=title,
description=description,
allow_flagging="never",
examples=[["1.png"], ["111.png"], ["748.png"], ["a4541-DSC_0040-2.png"]],
)
iface.launch(debug=True)
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