import os
import cv2
import gradio as gr
import numpy as np
import sys
import io
class Logger:
def __init__(self):
self.terminal = sys.stdout
self.log = io.BytesIO()
def write(self, message):
self.terminal.write(message)
self.log.write(bytes(message, encoding='utf-8'))
def flush(self):
self.terminal.flush()
self.log.flush()
def isatty(self):
return False
log = Logger()
sys.stdout = log
def read_logs():
out = log.log.getvalue().decode()
if out.count("\n") >= 30:
log.log = io.BytesIO()
sys.stdout.flush()
return out
with gr.Blocks(css=".output-image, .input-image, .image-preview {height: 600px !important}") as app:
gr.Markdown("""
# HINet (or INR-Harmonization) - A novel image Harmonization method based on Implicit neural Networks
## Harmonize any image you want! Arbitrary resolution, and arbitrary aspect ratio!
### Official Gradio Demo. See here for [**How to play with this Space**](https://github.com/WindVChen/INR-Harmonization/blob/main/assets/demo.gif)
**Since Gradio Space only support CPU, the speed may kind of slow. You may better download the code to run locally with a GPU.**
* Official Repo: [INR-Harmonization](https://github.com/WindVChen/INR-Harmonization)
""")
gr.HTML("""
(Notice: Sometimes it will encounter CONFLICTs when multiple users access this space at the same time, so we highly recommend you to duplicate this space and run in your private space for no queue on your own hardware
""")
gr.Markdown("""
## Quick Start
1. Select desired `Pretrained Model`.
2. Select a composite image, and then a mask with the same size.
3. Select the inference mode (for non-square image, only `Arbitrary Image` support). Also note that `Square Image` mode will be much faster than `Arbitrary Image` mode.
4. Set `Split Resolution` (Patches' resolution) or `Split Number` (How many patches, about N*N) according to the inference mode.
5. Click `Start` and enjoy it!
6. Click `Stop` if you want to stop the current process. You can also click `Reset` button any time to reinitialize the GUI.
""")
valid_checkpoints_dict = {"Resolution_256_iHarmony4": "Resolution_256_iHarmony4.pth",
"Resolution_1024_HAdobe5K": "Resolution_1024_HAdobe5K.pth",
"Resolution_2048_HAdobe5K": "Resolution_2048_HAdobe5K.pth",
"Resolution_RAW_HAdobe5K": "Resolution_RAW_HAdobe5K.pth",
"Resolution_RAW_iHarmony4": "Resolution_RAW_iHarmony4.pth"}
global_state = gr.State({
'pretrained_weight': valid_checkpoints_dict["Resolution_RAW_iHarmony4"],
})
with gr.Row():
with gr.Column():
form_composite_image = gr.Image(label='Input Composite image', type='pil').style(height=512)
gr.Examples(examples=sorted([os.path.join("demo", i) for i in os.listdir("demo") if "composite" in i]),
label="Composite Examples", inputs=form_composite_image, cache_examples=False)
with gr.Column():
form_mask_image = gr.Image(label='Input Mask image', type='pil', interactive=False).style(height=512)
gr.Examples(examples=sorted([os.path.join("demo", i) for i in os.listdir("demo") if "mask" in i]),
label="Mask Examples", inputs=form_mask_image, cache_examples=False)
with gr.Row():
with gr.Column(scale=4):
with gr.Row():
with gr.Column(scale=2, min_width=10):
gr.Markdown(value='Model Selection', show_label=False)
with gr.Column(scale=4, min_width=10):
form_pretrained_dropdown = gr.Dropdown(
choices=list(valid_checkpoints_dict.values()),
label="Pretrained Model",
value=valid_checkpoints_dict["Resolution_RAW_iHarmony4"],
interactive=True
)
with gr.Row():
with gr.Column(scale=2, min_width=10):
gr.Markdown(value='Inference Mode', show_label=False)
with gr.Column(scale=4, min_width=10):
form_inference_mode = gr.Radio(
['Square Image', 'Arbitrary Image'],
value='Arbitrary Image',
interactive=False,
label='Mode',
)
with gr.Row():
with gr.Column(scale=2, min_width=10):
gr.Markdown(value='Split Parameter', show_label=False)
with gr.Column(scale=4, min_width=10):
form_split_res = gr.Slider(
minimum=0,
maximum=2048,
step=128,
value=256,
interactive=False,
label="Split Resolution",
)
form_split_num = gr.Number(
value=2,
interactive=False,
label="Split Number")
with gr.Row():
form_log = gr.Textbox(read_logs, label="Logs", interactive=False, type="text", every=1)
with gr.Column(scale=4):
form_harmonized_image = gr.Image(label='Harmonized Result', type='numpy', interactive=False).style(height=512)
form_start_btn = gr.Button("Start Harmonization", interactive=False)
form_reset_btn = gr.Button("Reset", interactive=True)
form_stop_btn = gr.Button("Stop", interactive=True)
def on_change_form_composite_image(form_composite_image):
if form_composite_image is None:
return gr.update(interactive=False, value=None), gr.update(value=None)
return gr.update(interactive=True, value=None), gr.update(value=None)
def on_change_form_mask_image(form_composite_image, form_mask_image):
if form_mask_image is None:
return gr.update(interactive=False), gr.update(
interactive=False if form_composite_image is None else True), gr.update(interactive=False), gr.update(
interactive=False), gr.update(interactive=False), gr.update(value=None)
if form_composite_image.size[:2] != form_mask_image.size[:2]:
raise gr.Error("Composite image and mask image should have the same resolution!")
else:
w, h = form_composite_image.size[:2]
if h != w or (h % 16 != 0):
return gr.update(value='Arbitrary Image', interactive=False), gr.update(interactive=True), gr.update(
interactive=True), gr.update(interactive=True, visible=True), gr.update(interactive=False,
value=-1, visible=False), gr.update(value=None)
else:
return gr.update(value='Square Image', interactive=True), gr.update(interactive=True), gr.update(
interactive=True), gr.update(interactive=False, visible=False), gr.update(interactive=True,
value=h // 2,
maximum=h,
minimum=h // 16,
step=h // 16, visible=True), gr.update(value=None)
form_composite_image.change(
on_change_form_composite_image,
inputs=[form_composite_image],
outputs=[form_mask_image, form_harmonized_image]
)
form_mask_image.change(
on_change_form_mask_image,
inputs=[form_composite_image, form_mask_image],
outputs=[form_inference_mode, form_mask_image, form_start_btn, form_split_num, form_split_res,
form_harmonized_image]
)
def on_change_form_split_num(form_composite_image, form_split_num):
w, h = form_composite_image.size[:2]
if form_split_num < 1:
return gr.update(value=1)
elif form_split_num > min(w, h):
return gr.update(value=min(w, h))
else:
return gr.update(value=form_split_num)
form_split_num.change(
on_change_form_split_num,
inputs=[form_composite_image, form_split_num],
outputs=[form_split_num]
)
def on_change_form_inference_mode(form_inference_mode):
if form_inference_mode == "Square Image":
return gr.update(interactive=True, visible=True), gr.update(interactive=False, visible=False)
else:
return gr.update(interactive=False, visible=False), gr.update(interactive=True, visible=True)
form_inference_mode.change(on_change_form_inference_mode, inputs=[form_inference_mode],
outputs=[form_split_res, form_split_num])
def on_click_form_start_btn(form_composite_image, form_mask_image, form_pretrained_dropdown, form_inference_mode,
form_split_res, form_split_num):
log.log = io.BytesIO()
print(f"Harmonizing image with {form_composite_image.size[1]}*{form_composite_image.size[0]}...")
if form_inference_mode == "Square Image":
from efficient_inference_for_square_image import parse_args, main_process, global_state
global_state[0] = 1
opt = parse_args()
opt.transform_mean = [.5, .5, .5]
opt.transform_var = [.5, .5, .5]
opt.pretrained = os.path.join("./pretrained_models", form_pretrained_dropdown)
opt.split_resolution = form_split_res
opt.save_path = None
opt.workers = 0
opt.device = "cpu"
composite_image = np.asarray(form_composite_image)
mask = np.asarray(form_mask_image)
try:
return cv2.cvtColor(
main_process(opt, composite_image=composite_image, mask=mask),
cv2.COLOR_BGR2RGB)
except:
raise gr.Error("Patches too big. Try to reduce the `split_res`!")
else:
from inference_for_arbitrary_resolution_image import parse_args, main_process, global_state
global_state[0] = 1
opt = parse_args()
opt.transform_mean = [.5, .5, .5]
opt.transform_var = [.5, .5, .5]
opt.pretrained = os.path.join("./pretrained_models", form_pretrained_dropdown)
opt.split_num = int(form_split_num)
opt.save_path = None
opt.workers = 0
opt.device = "cpu"
composite_image = np.asarray(form_composite_image)
mask = np.asarray(form_mask_image)
try:
return cv2.cvtColor(
main_process(opt, composite_image=composite_image, mask=mask),
cv2.COLOR_BGR2RGB)
except:
raise gr.Error("Patches too big. Try to increase the `split_num`!")
generate = form_start_btn.click(on_click_form_start_btn,
inputs=[form_composite_image, form_mask_image, form_pretrained_dropdown,
form_inference_mode,
form_split_res, form_split_num], outputs=[form_harmonized_image])
def on_click_form_reset_btn(form_inference_mode):
if form_inference_mode == "Square Image":
from efficient_inference_for_square_image import global_state
global_state[0] = 0
else:
from inference_for_arbitrary_resolution_image import global_state
global_state[0] = 0
log.log = io.BytesIO()
return gr.update(value=None), gr.update(value=None, interactive=True), gr.update(value=None,
interactive=False), gr.update(
interactive=False)
form_reset_btn.click(on_click_form_reset_btn,
inputs=[form_inference_mode],
outputs=[form_log, form_composite_image, form_mask_image, form_start_btn], cancels=generate)
def on_click_form_stop(form_inference_mode):
if form_inference_mode == "Square Image":
from efficient_inference_for_square_image import global_state
global_state[0] = 0
else:
from inference_for_arbitrary_resolution_image import global_state
global_state[0] = 0
log.log = io.BytesIO()
return gr.update(value=None), gr.update(value=None, interactive=True), gr.update(value=None,
interactive=False), gr.update(
interactive=False)
form_stop_btn.click(on_click_form_stop,
inputs=[form_inference_mode],
outputs=[form_log, form_composite_image, form_mask_image, form_start_btn], cancels=generate)
gr.HTML("""
""")
gr.close_all()
app.queue(concurrency_count=1, max_size=1, api_open=False)
app.launch(show_api=False)