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app.py

@@ -1,183 +1,183 @@
-import shutil
-from pathlib import Path
-from typing import List, Dict, Union, Tuple, Literal, Optional
-
-import numpy as np
-import gradio as gr
-from gradio.components.base import Component
-from ultralytics import YOLO
-
-from utils import download_model, detect_image, detect_video, get_csv_annotate
-
-
-# ======================= MODEL ===================================
-
-MODELS_DIR = Path('models')
-MODELS_DIR.mkdir(exist_ok=True)
-
-MODELS = {
-    'yolov11n.pt': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt',
-    'yolov11s.pt': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt',
-    'yolov11m.pt': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt',
-    'yolov11l.pt': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt',
-    'yolov11x.pt': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt',
-}
-MODEL_NAMES = list(MODELS.keys())
-
-model_path = download_model(MODEL_NAMES[0], MODELS_DIR, MODELS)
-default_model = YOLO(model_path)
-
-IMAGE_EXTENSIONS = ['.jpg', '.jpeg', '.png']
-VIDEO_EXTENSIONS = ['.mp4', '.avi']
-
-
-# =================== ADDITIONAL INTERFACE FUNCTIONS ========================
-
-def change_model(model_state: Dict[str, YOLO], model_name: str):
-    progress = gr.Progress()
-    progress(0.3, desc='Downloading the model')
-    model_path = download_model(model_name)
-    progress(0.7, desc='Model initialization')
-    model_state['model'] = YOLO(model_path)
-    return f'Model {model_name} initialized'
-
-
-def detect(file_path: str, file_link: str, model_state: Dict[str, YOLO], conf: float, iou: float):
-    model = model_state['model']
-    if file_link:
-        file_path = file_link
-
-    file_ext = f'.{file_path.rsplit(".")[-1]}'
-    if file_ext in IMAGE_EXTENSIONS:
-        np_image = detect_image(file_path, model, conf, iou)
-        return np_image, "Detection complete, opening image..."
-    elif file_ext in VIDEO_EXTENSIONS or 'youtube.com' in file_link:
-        video_path = detect_video(file_path, model, conf, iou)
-        return video_path, "Detection complete, converting and opening video..."
-    else:
-        gr.Info('Invalid image or video format...')
-        return None, None
-
-# =================== INTERFACE COMPONENTS ============================
-
-def get_output_media_components(detect_result: Optional[Union[np.ndarray, str, Path]] = None):
-    visible = isinstance(detect_result, np.ndarray)
-    image_output = gr.Image(
-        value=detect_result if visible else None,
-        type="numpy",
-        width=640,
-        height=480,
-        visible=visible,
-        label='Output',
-        )
-    visible = isinstance(detect_result, (str, Path))
-    video_output = gr.Video(
-        value=detect_result if visible else None,
-        width=640,
-        height=480,
-        visible=visible,
-        label='Output',
-        )
-    clear_btn = gr.Button(
-        value='Clear',
-        scale=0,
-        visible=detect_result is not None,
-        )
-    return image_output, video_output, clear_btn
-
-
-def get_download_csv_btn(csv_annotations_path: Optional[Path] = None):
-    download_csv_btn = gr.DownloadButton(
-        label='Download csv annotations for video',
-        value=csv_annotations_path,
-        scale=0,
-        visible=csv_annotations_path is not None,
-        )
-    return download_csv_btn
-
-# =================== APPINTERFACE ==========================
-
-css = '''
-.gradio-container { width: 70% !important }
-'''
-with gr.Blocks(css=css) as demo:
-    gr.HTML("""<h3 style='text-align: center'>YOLOv11 Detector</h3>""")
-    
-    model_state = gr.State({'model': default_model})
-    detect_result = gr.State(None)
-    csv_annotations_path = gr.State(None)
-
-    with gr.Row():
-        with gr.Column():
-            file_path = gr.File(file_types=['image', 'video'], file_count='single', label='Select an image or video')
-            file_link = gr.Textbox(label='Direct link to image or YouTube link')
-            model_name = gr.Radio(choices=MODEL_NAMES, value=MODEL_NAMES[0], label='Select YOLO model')
-            conf = gr.Slider(0, 1, value=0.5, step=0.05, label='Confidence')
-            iou = gr.Slider(0, 1, value=0.7, step=0.1, label='IOU')
-            status_message = gr.Textbox(value='Ready to go', label='Status')
-            detect_btn = gr.Button('Detect', interactive=True)
-
-        with gr.Column():
-            image_output, video_output, clear_btn = get_output_media_components()
-            download_csv_btn = get_download_csv_btn()
-
-    model_name.change(
-        fn=lambda: gr.update(interactive=False),
-        inputs=None,
-        outputs=[detect_btn],
-    ).then(
-        fn=change_model,
-        inputs=[model_state, model_name],
-        outputs=[status_message],
-    ).success(
-        fn=lambda: gr.update(interactive=True),
-        inputs=None,
-        outputs=[detect_btn],
-    )
-
-    detect_btn.click(
-        fn=detect,
-        inputs=[file_path, file_link, model_state, conf, iou],
-        outputs=[detect_result, status_message],
-    ).success(
-        fn=get_output_media_components,
-        inputs=[detect_result],
-        outputs=[image_output, video_output, clear_btn],
-    ).then(
-        fn=lambda: 'Ready to go',
-        inputs=None,
-        outputs=[status_message],
-    ).then(
-        fn=get_csv_annotate,
-        inputs=[detect_result],
-        outputs=[csv_annotations_path],
-    ).success(
-        fn=get_download_csv_btn,
-        inputs=[csv_annotations_path],
-        outputs=[download_csv_btn],
-    )
-
-    def clear_results_dir(detect_result):
-        if isinstance(detect_result, Path):
-            shutil.rmtree(detect_result.parent, ignore_errors=True)
-
-    clear_components = [image_output, video_output, clear_btn, download_csv_btn]
-    clear_btn.click(
-        fn=lambda: [gr.update(visible=False) for _ in range(len(clear_components))],
-        inputs=None,
-        outputs=clear_components,
-    ).then(
-        fn=clear_results_dir,
-        inputs=[detect_result],
-        outputs=None,
-    ).then(
-        fn=lambda: (None, None),
-        inputs=None,
-        outputs=[detect_result, csv_annotations_path]
-        )
-
-    gr.HTML("""<h3 style='text-align: center'>
-    <a href="https://github.com/sergey21000/yolo-detector" target='_blank'>GitHub Page</a></h3>
-    """)
-    
+import shutil
+from pathlib import Path
+from typing import List, Dict, Union, Tuple, Literal, Optional
+
+import numpy as np
+import gradio as gr
+from gradio.components.base import Component
+from ultralytics import YOLO
+
+from utils import download_model, detect_image, detect_video, get_csv_annotate
+
+
+# ======================= MODEL ===================================
+
+MODELS_DIR = Path('models')
+MODELS_DIR.mkdir(exist_ok=True)
+
+MODELS = {
+    'yolov11n.pt': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt',
+    'yolov11s.pt': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt',
+    'yolov11m.pt': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt',
+    'yolov11l.pt': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt',
+    'yolov11x.pt': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt',
+}
+MODEL_NAMES = list(MODELS.keys())
+
+model_path = download_model(MODEL_NAMES[0], MODELS_DIR, MODELS)
+default_model = YOLO(model_path)
+
+IMAGE_EXTENSIONS = ['.jpg', '.jpeg', '.png']
+VIDEO_EXTENSIONS = ['.mp4', '.avi']
+
+
+# =================== ADDITIONAL INTERFACE FUNCTIONS ========================
+
+def change_model(model_state: Dict[str, YOLO], model_name: str):
+    progress = gr.Progress()
+    progress(0.3, desc='Downloading the model')
+    model_path = download_model(model_name)
+    progress(0.7, desc='Model initialization')
+    model_state['model'] = YOLO(model_path)
+    return f'Model {model_name} initialized'
+
+
+def detect(file_path: str, file_link: str, model_state: Dict[str, YOLO], conf: float, iou: float):
+    model = model_state['model']
+    if file_link:
+        file_path = file_link
+
+    file_ext = f'.{file_path.rsplit(".")[-1]}'
+    if file_ext in IMAGE_EXTENSIONS:
+        np_image = detect_image(file_path, model, conf, iou)
+        return np_image, "Detection complete, opening image..."
+    elif file_ext in VIDEO_EXTENSIONS or 'youtube.com' in file_link:
+        video_path = detect_video(file_path, model, conf, iou)
+        return video_path, "Detection complete, converting and opening video..."
+    else:
+        gr.Info('Invalid image or video format...')
+        return None, None
+
+# =================== INTERFACE COMPONENTS ============================
+
+def get_output_media_components(detect_result: Optional[Union[np.ndarray, str, Path]] = None):
+    visible = isinstance(detect_result, np.ndarray)
+    image_output = gr.Image(
+        value=detect_result if visible else None,
+        type="numpy",
+        width=640,
+        height=480,
+        visible=visible,
+        label='Output',
+        )
+    visible = isinstance(detect_result, (str, Path))
+    video_output = gr.Video(
+        value=detect_result if visible else None,
+        width=640,
+        height=480,
+        visible=visible,
+        label='Output',
+        )
+    clear_btn = gr.Button(
+        value='Clear',
+        scale=0,
+        visible=detect_result is not None,
+        )
+    return image_output, video_output, clear_btn
+
+
+def get_download_csv_btn(csv_annotations_path: Optional[Path] = None):
+    download_csv_btn = gr.DownloadButton(
+        label='Download csv annotations for video',
+        value=csv_annotations_path,
+        scale=0,
+        visible=csv_annotations_path is not None,
+        )
+    return download_csv_btn
+
+# =================== APPINTERFACE ==========================
+
+css = '''
+.gradio-container { width: 70% !important }
+'''
+with gr.Blocks(css=css) as demo:
+    gr.HTML("""<h3 style='text-align: center'>YOLOv11 Detector</h3>""")
+    
+    model_state = gr.State({'model': default_model})
+    detect_result = gr.State(None)
+    csv_annotations_path = gr.State(None)
+
+    with gr.Row():
+        with gr.Column():
+            file_path = gr.File(file_types=['image', 'video'], file_count='single', label='Select an image or video')
+            file_link = gr.Textbox(label='Direct link to image or YouTube link')
+            model_name = gr.Radio(choices=MODEL_NAMES, value=MODEL_NAMES[0], label='Select YOLO model')
+            conf = gr.Slider(0, 1, value=0.5, step=0.05, label='Confidence')
+            iou = gr.Slider(0, 1, value=0.7, step=0.1, label='IOU')
+            status_message = gr.Textbox(value='Ready to go', label='Status')
+            detect_btn = gr.Button('Detect', interactive=True)
+
+        with gr.Column():
+            image_output, video_output, clear_btn = get_output_media_components()
+            download_csv_btn = get_download_csv_btn()
+
+    model_name.change(
+        fn=lambda: gr.update(interactive=False),
+        inputs=None,
+        outputs=[detect_btn],
+    ).then(
+        fn=change_model,
+        inputs=[model_state, model_name],
+        outputs=[status_message],
+    ).success(
+        fn=lambda: gr.update(interactive=True),
+        inputs=None,
+        outputs=[detect_btn],
+    )
+
+    detect_btn.click(
+        fn=detect,
+        inputs=[file_path, file_link, model_state, conf, iou],
+        outputs=[detect_result, status_message],
+    ).success(
+        fn=get_output_media_components,
+        inputs=[detect_result],
+        outputs=[image_output, video_output, clear_btn],
+    ).then(
+        fn=lambda: 'Ready to go',
+        inputs=None,
+        outputs=[status_message],
+    ).then(
+        fn=get_csv_annotate,
+        inputs=[detect_result],
+        outputs=[csv_annotations_path],
+    ).success(
+        fn=get_download_csv_btn,
+        inputs=[csv_annotations_path],
+        outputs=[download_csv_btn],
+    )
+
+    def clear_results_dir(detect_result):
+        if isinstance(detect_result, Path):
+            shutil.rmtree(detect_result.parent, ignore_errors=True)
+
+    clear_components = [image_output, video_output, clear_btn, download_csv_btn]
+    clear_btn.click(
+        fn=lambda: [gr.update(visible=False) for _ in range(len(clear_components))],
+        inputs=None,
+        outputs=clear_components,
+    ).then(
+        fn=clear_results_dir,
+        inputs=[detect_result],
+        outputs=None,
+    ).then(
+        fn=lambda: (None, None),
+        inputs=None,
+        outputs=[detect_result, csv_annotations_path]
+        )
+
+    gr.HTML("""<h3 style='text-align: center'>
+    <a href="https://github.com/sergey21000/yolo-detector" target='_blank'>GitHub Page</a></h3>
+    """)
+    
 demo.launch(server_name='0.0.0.0')  # debug=True

utils.py

@@ -25,7 +25,7 @@ def download_model(model_name: str, models_dir: Path, models: dict) -> str:
 
 
 def detect_image(image_path: str, model: YOLO, conf: float, iou: float) -> np.ndarray:
-    gr.Progress()(0.5, desc='Детекция изображения...')
+    gr.Progress()(0.5, desc='Image detection...')
     detections = model.predict(source=image_path, conf=conf, iou=iou)
     np_image = detections[0].plot()
     np_image = cv2.cvtColor(np_image, cv2.COLOR_BGR2RGB)
@@ -36,7 +36,7 @@ def detect_video(video_path_or_url: str, model: YOLO, conf: float, iou: float) -
     progress = gr.Progress()
     video_path = video_path_or_url
     if 'youtube.com' in video_path_or_url or 'youtu.be' in video_path_or_url:
-        progress(0.001, desc='Загрузка видео с YouTube...')
+        progress(0.001, desc='Downloading videos from YouTube...')
         ydl_opts = {'format': 'bestvideo[height<=720]'}
         with yt_dlp.YoutubeDL(ydl_opts) as ydl:
             video_info_dict = ydl.extract_info(video_path_or_url, download=True)
@@ -60,7 +60,7 @@ def detect_video(video_path_or_url: str, model: YOLO, conf: float, iou: float) -
     frames_count = 0
     for result in generator:
         frames_count += 1
-        progress((frames_count, num_frames), desc=f'Детекция видео, шаг {frames_count}/{num_frames}')
+        progress((frames_count, num_frames), desc=f'Video detection, step {frames_count}/{num_frames}')
 
     file_name = Path(result.path).with_suffix('.avi').name
     result_video_path = Path(result.save_dir) / file_name