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app (2).py

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+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()

requirements (2).txt

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+gradio==3.16.1
+numpy
+pillow
+onnxruntime
+huggingface_hub
+scikit-image
+pandas
+opencv-python>=4.6.0