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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
_MODEL = from_pretrained_keras("sayakpaul/S-2_enhancement_lol")
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("S-2")
configs.update(
{
"variant": "S-2",
"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 = "Enhance low-light images."
article = "Model based on [this](https://huggingface.co/sayakpaul/S-2_enhancement_lol)."
iface = gr.Interface(
infer,
inputs="image",
outputs="image",
title=title,
article=article,
allow_flagging="never",
examples=[["1.png"], ["111.png"], ["748.png"], ["a4541-DSC_0040-2.png"]],
)
iface.launch(debug=True)
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