Delete utils/tagger.py

utils/tagger.py

@@ -1,137 +0,0 @@
-# -*- coding: utf-8 -*-
-# https://github.com/kohya-ss/sd-scripts/blob/main/finetune/tag_images_by_wd14_tagger.py
-
-import csv
-import os
-os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
-
-from PIL import Image
-import cv2
-import numpy as np
-from pathlib import Path
-import onnx
-import onnxruntime as ort
-
-# from wd14 tagger
-IMAGE_SIZE = 448
-
-model = None  # Initialize model variable
-
-
-def convert_array_to_bgr(array):
-    """
-    Convert a NumPy array image to BGR format regardless of its original format.
-    
-    Parameters:
-    - array: NumPy array of the image.
-    
-    Returns:
-    - A NumPy array representing the image in BGR format.
-    """
-    # グレースケール画像（2次元配列）
-    if array.ndim == 2:
-        # グレースケールをBGRに変換（3チャンネルに拡張）
-        bgr_array = np.stack((array,) * 3, axis=-1)
-    # RGBAまたはRGB画像（3次元配列）
-    elif array.ndim == 3:
-        # RGBA画像の場合、アルファチャンネルを削除
-        if array.shape[2] == 4:
-            array = array[:, :, :3]
-        # RGBをBGRに変換
-        bgr_array = array[:, :, ::-1]
-    else:
-        raise ValueError("Unsupported array shape.")
-
-    return bgr_array
-
-
-def preprocess_image(image):
-    image = np.array(image)
-    image = convert_array_to_bgr(image)
-
-    size = max(image.shape[0:2])
-    pad_x = size - image.shape[1]
-    pad_y = size - image.shape[0]
-    pad_l = pad_x // 2
-    pad_t = pad_y // 2
-    image = np.pad(image, ((pad_t, pad_y - pad_t), (pad_l, pad_x - pad_l), (0, 0)), mode="constant", constant_values=255)
-
-    interp = cv2.INTER_AREA if size > IMAGE_SIZE else cv2.INTER_LANCZOS4
-    image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE), interpolation=interp)
-
-    image = image.astype(np.float32)
-    return image
-
-def modelLoad(model_dir):
-    onnx_path = os.path.join(model_dir, "model.onnx")
-    # 実行プロバイダーをCPUのみに指定
-    providers = ['CPUExecutionProvider']
-    # InferenceSessionの作成時にプロバイダーのリストを指定
-    ort_session = ort.InferenceSession(onnx_path, providers=providers)
-    input_name = ort_session.get_inputs()[0].name
-    
-    # 実際に使用されているプロバイダーを取得して表示
-    actual_provider = ort_session.get_providers()[0]  # 使用されているプロバイダー
-    print(f"Using provider: {actual_provider}")
-    
-    return [ort_session, input_name]
-
-def analysis(image_path, model_dir, model):
-    ort_session = model[0]
-    input_name = model[1]
-
-    with open(os.path.join(model_dir, "selected_tags.csv"), "r", encoding="utf-8") as f:
-        reader = csv.reader(f)
-        l = [row for row in reader]
-        header = l[0]  # tag_id,name,category,count
-        rows = l[1:]
-    assert header[0] == "tag_id" and header[1] == "name" and header[2] == "category", f"unexpected csv format: {header}"
-
-    general_tags = [row[1] for row in rows[1:] if row[2] == "0"]
-    character_tags = [row[1] for row in rows[1:] if row[2] == "4"]
-
-    tag_freq = {}
-    undesired_tags = ["transparent background"]
-
-    image_pil = Image.open(image_path)
-    image_preprocessed = preprocess_image(image_pil)
-    image_preprocessed = np.expand_dims(image_preprocessed, axis=0)
-
-    # 推論を実行
-    prob = ort_session.run(None, {input_name: image_preprocessed})[0][0]
-    # タグを生成
-    combined_tags = []
-    general_tag_text = ""
-    character_tag_text = ""
-    remove_underscore = True
-    caption_separator = ", "
-    general_threshold = 0.35
-    character_threshold = 0.35
-
-    for i, p in enumerate(prob[4:]):
-        if i < len(general_tags) and p >= general_threshold:
-            tag_name = general_tags[i]
-            if remove_underscore and len(tag_name) > 3:  # ignore emoji tags like >_< and ^_^
-                tag_name = tag_name.replace("_", " ")
-
-            if tag_name not in undesired_tags:
-                tag_freq[tag_name] = tag_freq.get(tag_name, 0) + 1
-                general_tag_text += caption_separator + tag_name
-                combined_tags.append(tag_name)
-        elif i >= len(general_tags) and p >= character_threshold:
-            tag_name = character_tags[i - len(general_tags)]
-            if remove_underscore and len(tag_name) > 3:
-                tag_name = tag_name.replace("_", " ")
-
-            if tag_name not in undesired_tags:
-                tag_freq[tag_name] = tag_freq.get(tag_name, 0) + 1
-                character_tag_text += caption_separator + tag_name
-                combined_tags.append(tag_name)
-
-    # 先頭のカンマを取る
-    if len(general_tag_text) > 0:
-        general_tag_text = general_tag_text[len(caption_separator) :]
-    if len(character_tag_text) > 0:
-        character_tag_text = character_tag_text[len(caption_separator) :]
-    tag_text = caption_separator.join(combined_tags)
-    return tag_text