dev(narugo): code upgrade

README.md

@@ -4,7 +4,7 @@ emoji: 📈
 colorFrom: gray
 colorTo: green
 sdk: gradio
-sdk_version: 3.18.0
+sdk_version: 4.44.0
 app_file: app.py
 pinned: false
 license: mit

app2.py

@@ -0,0 +1,19 @@
+import os
+
+import gradio as gr
+
+from detection import EyesDetection
+
+_GLOBAL_CSS = """
+.limit-height {
+    max-height: 55vh;
+}
+"""
+
+if __name__ == '__main__':
+    with gr.Blocks(css=_GLOBAL_CSS) as demo:
+        with gr.Tabs():
+            with gr.Tab('Eyes Detection'):
+                EyesDetection().make_ui()
+
+    demo.queue(os.cpu_count()).launch()

detection/__init__.py

@@ -0,0 +1,2 @@
+from .base import ObjectDetection, DeepGHSObjectDetection
+from .eyes import EyesDetection

detection/base.py

@@ -0,0 +1,117 @@
+import os.path
+from functools import lru_cache
+from typing import List, Tuple
+
+import gradio as gr
+from hbutils.color import rnd_colors
+from hfutils.operate import get_hf_fs
+from hfutils.utils import hf_fs_path, parse_hf_fs_path
+from imgutils.data import ImageTyping
+
+
+class ObjectDetection:
+    @lru_cache()
+    def get_default_model(self) -> str:
+        return self._get_default_model()
+
+    def _get_default_model(self) -> str:
+        raise NotImplementedError
+
+    @lru_cache()
+    def list_models(self) -> List[str]:
+        return self._list_models()
+
+    def _list_models(self) -> List[str]:
+        raise NotImplementedError
+
+    @lru_cache()
+    def get_default_iou_and_score(self, model_name: str) -> Tuple[float, float]:
+        return self._get_default_iou_and_score(model_name)
+
+    def _get_default_iou_and_score(self, model_name: str) -> Tuple[float, float]:
+        raise NotImplementedError
+
+    @lru_cache()
+    def get_labels(self, model_name: str) -> List[str]:
+        return self._get_labels(model_name)
+
+    def _get_labels(self, model_name: str) -> List[str]:
+        raise NotImplementedError
+
+    def detect(self, image: ImageTyping, model_name: str,
+               iou_threshold: float = 0.7, score_threshold: float = 0.25) \
+            -> List[Tuple[Tuple[float, float, float, float], str, float]]:
+        raise NotImplementedError
+
+    def _gr_detect(self, image: ImageTyping, model_name: str,
+                   iou_threshold: float = 0.7, score_threshold: float = 0.25) \
+            -> gr.AnnotatedImage:
+        labels = self.get_labels(model_name=model_name)
+        _colors = list(map(str, rnd_colors(len(labels))))
+        _color_map = dict(zip(labels, _colors))
+        return gr.AnnotatedImage(
+            value=(image, [
+                (bbox, label) for bbox, label, _ in
+                self.detect(image, model_name, iou_threshold, score_threshold)
+            ]),
+            color_map=_color_map,
+            label='Labeled',
+        )
+
+    def make_ui(self):
+        with gr.Row():
+            with gr.Column():
+                default_model_name = self.get_default_model()
+                model_list = self.list_models()
+                gr_input_image = gr.Image(type='pil', label='Original Image')
+                gr_model = gr.Dropdown(model_list, value=default_model_name, label='Model')
+                with gr.Row():
+                    iou, score = self.get_default_iou_and_score(default_model_name)
+                    gr_iou_threshold = gr.Slider(0.0, 1.0, iou, label='IOU Threshold')
+                    gr_score_threshold = gr.Slider(0.0, 1.0, score, label='Score Threshold')
+
+                gr_submit = gr.Button(value='Submit', variant='primary')
+
+            with gr.Column():
+                gr_output_image = gr.AnnotatedImage(label="Labeled")
+
+            gr_submit.click(
+                self._gr_detect,
+                inputs=[
+                    gr_input_image,
+                    gr_model,
+                    gr_iou_threshold,
+                    gr_score_threshold,
+                ],
+                outputs=[gr_output_image],
+            )
+
+
+class DeepGHSObjectDetection(ObjectDetection):
+    def __init__(self, repo_id: str):
+        self._repo_id = repo_id
+
+    def _get_default_model(self) -> str:
+        raise NotImplementedError
+
+    def _list_models(self) -> List[str]:
+        hf_fs = get_hf_fs()
+        return [
+            os.path.dirname(parse_hf_fs_path(path).filename)
+            for path in hf_fs.glob(hf_fs_path(
+                repo_id=self._repo_id,
+                repo_type='model',
+                filename='*/model.onnx'
+            ))
+        ]
+
+    def _get_default_iou_and_score(self, model_name: str) -> Tuple[float, float]:
+        raise NotImplementedError
+
+    def _get_labels(self, model_name: str) -> List[str]:
+        raise NotImplementedError
+
+    def detect(self, image: ImageTyping, model_name: str,
+               iou_threshold: float = 0.7, score_threshold: float = 0.25) \
+            -> List[Tuple[Tuple[float, float, float, float], str, float]]:
+        raise NotImplementedError

detection/eyes.py

@@ -0,0 +1,33 @@
+import re
+from typing import List, Tuple
+
+from imgutils.data import ImageTyping
+from imgutils.detect.eye import detect_eyes, _LABELS
+
+from .base import DeepGHSObjectDetection
+
+
+def _parse_model_name(model_name: str):
+    matching = re.fullmatch(r'^eye_detect_(?P<version>[\s\S]+?)_(?P<level>[\s\S]+?)$', model_name)
+    return matching.group('version'), matching.group('level')
+
+
+class EyesDetection(DeepGHSObjectDetection):
+    def __init__(self):
+        DeepGHSObjectDetection.__init__(self, repo_id='deepghs/anime_eye_detection')
+
+    def _get_default_model(self) -> str:
+        return 'eye_detect_v1.0_s'
+
+    def _get_default_iou_and_score(self, model_name: str) -> Tuple[float, float]:
+        return 0.3, 0.3
+
+    def _get_labels(self, model_name: str) -> List[str]:
+        return _LABELS
+
+    def detect(self, image: ImageTyping, model_name: str,
+               iou_threshold: float = 0.7, score_threshold: float = 0.25) \
+            -> List[Tuple[Tuple[float, float, float, float], str, float]]:
+        version, level = _parse_model_name(model_name)
+        return detect_eyes(image, level=level, version=version,
+                           conf_threshold=score_threshold, iou_threshold=iou_threshold)

requirements.txt

@@ -1,4 +1,4 @@
-gradio==3.18.0
+gradio>=4.44.0
 numpy
 pillow
 onnxruntime
@@ -7,5 +7,5 @@ scikit-image
 pandas
 opencv-python>=4.6.0
 hbutils>=0.9.0
-dghs-imgutils>=0.0.1
-httpx==0.23.0
+dghs-imgutils>=0.5.0
+httpx