fix: bug

.gitignore

@@ -1,6 +1,7 @@
 .DS_Store
 yolov5s.pt
 # __pycache__
-*.jpg
 gradio_queue.db
+__pycache__
+.venv
 __pycache__

README.md

@@ -4,7 +4,7 @@ emoji: 🐢
 colorFrom: indigo
 colorTo: red
 sdk: gradio
-sdk_version: 3.1.3
+sdk_version: 4.31.4
 app_file: app.py
 pinned: false
 ---

app.py

@@ -3,26 +3,16 @@ import torch
 from PIL import Image
 import json
 
-# Images
-torch.hub.download_url_to_file(
-    'https://iiif.dl.itc.u-tokyo.ac.jp/iiif/genji/TIFF/A00_6587/01/01_0004.tif/full/1024,/0/default.jpg', '『源氏物語』(東京大学総合図書館所蔵).jpg')
-torch.hub.download_url_to_file(
-    'https://rmda.kulib.kyoto-u.ac.jp/iiif/RB00007030/01/RB00007030_00003_0.ptif/full/1024,/0/default.jpg', '『源氏物語』(京都大学所蔵).jpg')
-torch.hub.download_url_to_file(
-    'https://kotenseki.nijl.ac.jp/api/iiif/100312034/v4/HRSM/HRSM-00396/HRSM-00396-00012.tif/full/1024,/0/default.jpg', '『平家物語』(国文学研究資料館提供).jpg')
-
 # Model
-# model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # force_reload=True to update
-model = torch.hub.load('ultralytics/yolov5', 'custom',
-                       path='best.pt', source="local")
-
+model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt', source="local")
 
 def yolo(im, size=1024):
     g = (size / max(im.size))  # gain
     im = im.resize((int(x * g) for x in im.size), resample=Image.Resampling.LANCZOS)  # resize
 
     results = model(im)  # inference
-    results.render()  # updates results.imgs with boxes and labels
+
+    results.render()
 
     df = results.pandas().xyxy[0].to_json(orient="records")
     res = json.loads(df)
@@ -33,10 +23,10 @@ def yolo(im, size=1024):
     ]
 
 
-inputs = gr.inputs.Image(type='pil', label="Original Image")
+inputs = gr.Image(type='pil', label="Original Image")
 outputs = [
-    gr.outputs.Image(type="pil", label="Output Image"),
-    gr.outputs.JSON(label="Output JSON")
+    gr.Image(type="pil", label="Output Image"),
+    gr.JSON(label="Output JSON")
 ]
 
 title = "YOLOv5 NDL-DocL Datasets"
@@ -44,5 +34,5 @@ description = "YOLOv5 NDL-DocL Datasets Gradio demo for object detection. Upload
 article = "<p style='text-align: center'>YOLOv5 NDL-DocL Datasets is an object detection model trained on the <a href=\"https://github.com/ndl-lab/layout-dataset\">NDL-DocL Datasets</a>.</p>"
 
 examples = [['『源氏物語』(東京大学総合図書館所蔵).jpg'], ['『源氏物語』(京都大学所蔵).jpg'], ['『平家物語』(国文学研究資料館提供).jpg']]
-gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article,
-             examples=examples, theme="huggingface").launch(enable_queue=True)  # cache_examples=True,
+demo = gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article,examples=examples)
+demo.launch()

init.sh

@@ -1,2 +1,8 @@
 rm best.pt
-gdown https://drive.google.com/uc?id=1DduqMfElGLPYWZTbrEO8F3qn6VPOZDPM
+gdown https://drive.google.com/uc?id=1DduqMfElGLPYWZTbrEO8F3qn6VPOZDPM
+
+wget https://iiif.dl.itc.u-tokyo.ac.jp/iiif/genji/TIFF/A00_6587/01/01_0004.tif/full/1024,/0/default.jpg -O "『源氏物語』(東京大学総合図書館所蔵).jpg"
+
+wget https://rmda.kulib.kyoto-u.ac.jp/iiif/RB00007030/01/RB00007030_00003_0.ptif/full/1024,/0/default.jpg -O "『源氏物語』(京都大学所蔵).jpg"
+
+wget https://kotenseki.nijl.ac.jp/api/iiif/100312034/v4/HRSM/HRSM-00396/HRSM-00396-00012.tif/full/1024,/0/default.jpg -O "『平家物語』(国文学研究資料館提供).jpg"

requirements-dev.txt

@@ -0,0 +1 @@
+gradio

ultralytics/yolov5/utils/plots.py

@@ -79,7 +79,7 @@ class Annotator:
             self.font = check_pil_font(font='Arial.Unicode.ttf' if is_chinese(example) else font,
                                        size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
         else:  # use cv2
-            self.im = im
+            self.im = np.ascontiguousarray(im.copy())  # Ensure the image array is writable
         self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2)  # line width
 
     def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
@@ -93,9 +93,8 @@ class Annotator:
                                      box[1] - h if outside else box[1],
                                      box[0] + w + 1,
                                      box[1] + 1 if outside else box[1] + h + 1), fill=color)
-                # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls')  # for PIL>8.0
                 self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
-        else:  # cv2
+        else:  # use cv2
             p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
             cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
             if label: