0filterImage.py

import numpy as np
import os, re, cv2
from typing import *
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:
        # print(image) todo: figure out what to do if URL
        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
        print(image)
        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] = {
    'chen-vit': WaifuDiffusionInterrogator(),
    'chen-convnext': WaifuDiffusionInterrogator(
        repo='SmilingWolf/wd-v1-4-convnext-tagger'
    ),
    'chen-convnext2': WaifuDiffusionInterrogator(
        repo="SmilingWolf/wd-v1-4-convnextv2-tagger-v2"
    ),
    'chen-swinv2': WaifuDiffusionInterrogator(
        repo='SmilingWolf/wd-v1-4-swinv2-tagger-v2'
    ),
    'chen-moat2': WaifuDiffusionInterrogator(
        repo='SmilingWolf/wd-v1-4-moat-tagger-v2'
    ),
    'chen-convnext3': WaifuDiffusionInterrogator(
        repo='SmilingWolf/wd-convnext-tagger-v3'
    ),
    'chen-vit3': WaifuDiffusionInterrogator(
        repo='SmilingWolf/wd-vit-tagger-v3'
    ),
    'chen-swinv3': WaifuDiffusionInterrogator(
        repo='SmilingWolf/wd-swinv2-tagger-v3'
    ),
}
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=False, score_descend=True) \
        -> 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('_', '-')
        else:
            tag_outformat = tag_outformat.replace(' ', ', ')
            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)
    if use_spaces:
        output_text = ' '.join(text_items)
    else:
        output_text = ', '.join(text_items)

    return ratings, output_text, filtered_tags



if __name__ == '__main__':
    # 获取当前目录的子目录的路径
    img_path = 'manga'
    subdir_path = os.path.join(os.getcwd(), img_path)

    # 图片素材获取（包含子目录下所有图片）
    image_files = []
    for root, dirs, files in os.walk(subdir_path):
        for file in files:
            if file.endswith(".jpg") or file.endswith(".png"):
                image_files.append(os.path.relpath(os.path.join(root, file)))
    for image_path in image_files:
        # 打开并读取图像文件
        image_data = Image.open(image_path)
        result = image_to_wd14_tags(image_data, 'chen-moat2', 0.5, True, True)#传入数据判断标签,然后只看rating tag就行，即第[0]个
        # 从 result 中提取第一个元素(rating)
        rating_dict = result[0]
        # 找到占比最大的元素
        max_proportion_key = max(rating_dict, key=rating_dict.get)
        max_proportion_value = rating_dict[max_proportion_key]

        # 输出占比最大的元素
        print(f"占比最大的元素为：{max_proportion_key}，占比为：{max_proportion_value}")
        if max_proportion_key=="questionable" or max_proportion_key=="explicit":
            print("图片不合格，开始删除")
            os.remove(image_path)
            print("成功删除不合格图片")
        else:
            print("图片合格")