faisalhr1997's picture
Duplicate from deepghs/wd14_tagging_online
5a2f941
import os
import re
from typing import Mapping, Tuple, Dict
import cv2
import gradio as gr
import numpy as np
import pandas as pd
from PIL import Image
from huggingface_hub import hf_hub_download
from onnxruntime import InferenceSession
# noinspection PyUnresolvedReferences
def make_square(img, target_size):
old_size = img.shape[:2]
desired_size = max(old_size)
desired_size = max(desired_size, target_size)
delta_w = desired_size - old_size[1]
delta_h = desired_size - old_size[0]
top, bottom = delta_h // 2, delta_h - (delta_h // 2)
left, right = delta_w // 2, delta_w - (delta_w // 2)
color = [255, 255, 255]
return cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
# noinspection PyUnresolvedReferences
def smart_resize(img, size):
# Assumes the image has already gone through make_square
if img.shape[0] > size:
img = cv2.resize(img, (size, size), interpolation=cv2.INTER_AREA)
elif img.shape[0] < size:
img = cv2.resize(img, (size, size), interpolation=cv2.INTER_CUBIC)
else: # just do nothing
pass
return img
class WaifuDiffusionInterrogator:
def __init__(
self,
repo='SmilingWolf/wd-v1-4-vit-tagger',
model_path='model.onnx',
tags_path='selected_tags.csv',
mode: str = "auto"
) -> None:
self.__repo = repo
self.__model_path = model_path
self.__tags_path = tags_path
self._provider_mode = mode
self.__initialized = False
self._model, self._tags = None, None
def _init(self) -> None:
if self.__initialized:
return
model_path = hf_hub_download(self.__repo, filename=self.__model_path)
tags_path = hf_hub_download(self.__repo, filename=self.__tags_path)
self._model = InferenceSession(str(model_path))
self._tags = pd.read_csv(tags_path)
self.__initialized = True
def _calculation(self, image: Image.Image) -> pd.DataFrame:
self._init()
# code for converting the image and running the model is taken from the link below
# thanks, SmilingWolf!
# https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags/blob/main/app.py
# convert an image to fit the model
_, height, _, _ = self._model.get_inputs()[0].shape
# alpha to white
image = image.convert('RGBA')
new_image = Image.new('RGBA', image.size, 'WHITE')
new_image.paste(image, mask=image)
image = new_image.convert('RGB')
image = np.asarray(image)
# PIL RGB to OpenCV BGR
image = image[:, :, ::-1]
image = make_square(image, height)
image = smart_resize(image, height)
image = image.astype(np.float32)
image = np.expand_dims(image, 0)
# evaluate model
input_name = self._model.get_inputs()[0].name
label_name = self._model.get_outputs()[0].name
confidence = self._model.run([label_name], {input_name: image})[0]
full_tags = self._tags[['name', 'category']].copy()
full_tags['confidence'] = confidence[0]
return full_tags
def interrogate(self, image: Image) -> Tuple[Dict[str, float], Dict[str, float]]:
full_tags = self._calculation(image)
# first 4 items are for rating (general, sensitive, questionable, explicit)
ratings = dict(full_tags[full_tags['category'] == 9][['name', 'confidence']].values)
# rest are regular tags
tags = dict(full_tags[full_tags['category'] != 9][['name', 'confidence']].values)
return ratings, tags
WAIFU_MODELS: Mapping[str, WaifuDiffusionInterrogator] = {
'wd14-vit': WaifuDiffusionInterrogator(),
'wd14-convnext': WaifuDiffusionInterrogator(
repo='SmilingWolf/wd-v1-4-convnext-tagger'
),
}
RE_SPECIAL = re.compile(r'([\\()])')
def image_to_wd14_tags(image: Image.Image, model_name: str, threshold: float,
use_spaces: bool, use_escape: bool, include_ranks: bool, score_descend: bool) \
-> Tuple[Mapping[str, float], str, Mapping[str, float]]:
model = WAIFU_MODELS[model_name]
ratings, tags = model.interrogate(image)
filtered_tags = {
tag: score for tag, score in tags.items()
if score >= threshold
}
text_items = []
tags_pairs = filtered_tags.items()
if score_descend:
tags_pairs = sorted(tags_pairs, key=lambda x: (-x[1], x[0]))
for tag, score in tags_pairs:
tag_outformat = tag
if use_spaces:
tag_outformat = tag_outformat.replace('_', ' ')
if use_escape:
tag_outformat = re.sub(RE_SPECIAL, r'\\\1', tag_outformat)
if include_ranks:
tag_outformat = f"({tag_outformat}:{score:.3f})"
text_items.append(tag_outformat)
output_text = ', '.join(text_items)
return ratings, output_text, filtered_tags
if __name__ == '__main__':
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
gr_input_image = gr.Image(type='pil', label='Original Image')
with gr.Row():
gr_model = gr.Radio(list(WAIFU_MODELS.keys()), value='wd14-vit', label='Waifu Model')
gr_threshold = gr.Slider(0.0, 1.0, 0.5, label='Tagging Confidence Threshold')
with gr.Row():
gr_space = gr.Checkbox(value=False, label='Use Space Instead Of _')
gr_escape = gr.Checkbox(value=True, label='Use Text Escape')
gr_confidence = gr.Checkbox(value=False, label='Keep Confidences')
gr_order = gr.Checkbox(value=True, label='Descend By Confidence')
gr_btn_submit = gr.Button(value='Tagging', variant='primary')
with gr.Column():
gr_ratings = gr.Label(label='Ratings')
with gr.Tabs():
with gr.Tab("Tags"):
gr_tags = gr.Label(label='Tags')
with gr.Tab("Exported Text"):
gr_output_text = gr.TextArea(label='Exported Text')
gr_btn_submit.click(
image_to_wd14_tags,
inputs=[gr_input_image, gr_model, gr_threshold, gr_space, gr_escape, gr_confidence, gr_order],
outputs=[gr_ratings, gr_output_text, gr_tags],
)
demo.queue(os.cpu_count()).launch()