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@@ -43,3 +43,4 @@ videos/David_PopUp2Reps.mp4 filter=lfs diff=lfs merge=lfs -text
 videos/Popup1.png filter=lfs diff=lfs merge=lfs -text
 videos/PopUp1Rep.mp4 filter=lfs diff=lfs merge=lfs -text
 videos/PopUp1RepFacingLeft.mp4 filter=lfs diff=lfs merge=lfs -text
+yolo11/v8pose.png filter=lfs diff=lfs merge=lfs -text

yolo11/Trial.ipynb

@@ -0,0 +1,356 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Running on local URL:  http://127.0.0.1:7863\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7863/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "0: 448x640 1 person, 73.4ms\n",
+      "Speed: 6.0ms preprocess, 73.4ms inference, 3.8ms postprocess per image at shape (1, 3, 448, 640)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Traceback (most recent call last):\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\gradio\\queueing.py\", line 625, in process_events\n",
+      "    response = await route_utils.call_process_api(\n",
+      "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "    ...<5 lines>...\n",
+      "    )\n",
+      "    ^\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\gradio\\route_utils.py\", line 322, in call_process_api\n",
+      "    output = await app.get_blocks().process_api(\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "    ...<11 lines>...\n",
+      "    )\n",
+      "    ^\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\gradio\\blocks.py\", line 2098, in process_api\n",
+      "    result = await self.call_function(\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "    ...<8 lines>...\n",
+      "    )\n",
+      "    ^\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\gradio\\blocks.py\", line 1645, in call_function\n",
+      "    prediction = await anyio.to_thread.run_sync(  # type: ignore\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "        fn, *processed_input, limiter=self.limiter\n",
+      "        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "    )\n",
+      "    ^\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
+      "    return await get_async_backend().run_sync_in_worker_thread(\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "        func, args, abandon_on_cancel=abandon_on_cancel, limiter=limiter\n",
+      "        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "    )\n",
+      "    ^\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2461, in run_sync_in_worker_thread\n",
+      "    return await future\n",
+      "           ^^^^^^^^^^^^\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 962, in run\n",
+      "    result = context.run(func, *args)\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\gradio\\utils.py\", line 883, in wrapper\n",
+      "    response = f(*args, **kwargs)\n",
+      "  File \"C:\\Users\\Owner\\AppData\\Local\\Temp\\ipykernel_31136\\1222528933.py\", line 183, in <lambda>\n",
+      "    fn=lambda input_video: process_video(input_video) if input_video else (\"\", []),\n",
+      "                           ~~~~~~~~~~~~~^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\Owner\\AppData\\Local\\Temp\\ipykernel_31136\\1222528933.py\", line 81, in process_video\n",
+      "    writer = imageio.get_writer(output_video_path, fps=fps, codec=\"libx264\")\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\imageio\\v2.py\", line 324, in get_writer\n",
+      "    image_file = imopen(uri, \"w\" + mode, **imopen_args)\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\imageio\\core\\imopen.py\", line 113, in imopen\n",
+      "    request = Request(uri, io_mode, format_hint=format_hint, extension=extension)\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\imageio\\core\\request.py\", line 249, in __init__\n",
+      "    self._parse_uri(uri)\n",
+      "    ~~~~~~~~~~~~~~~^^^^^\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\imageio\\core\\request.py\", line 414, in _parse_uri\n",
+      "    raise FileNotFoundError(\"The directory %r does not exist\" % dn)\n",
+      "FileNotFoundError: The directory 'e:\\\\content\\\\drive\\\\MyDrive\\\\Computer Vision YOLO-Judol Detection' does not exist\n",
+      "Traceback (most recent call last):\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\gradio\\queueing.py\", line 625, in process_events\n",
+      "    response = await route_utils.call_process_api(\n",
+      "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "    ...<5 lines>...\n",
+      "    )\n",
+      "    ^\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\gradio\\route_utils.py\", line 322, in call_process_api\n",
+      "    output = await app.get_blocks().process_api(\n",
+      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "    ...<11 lines>...\n",
+      "    )\n",
+      "    ^\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\gradio\\blocks.py\", line 2108, in process_api\n",
+      "    data = await self.postprocess_data(block_fn, result[\"prediction\"], state)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\gradio\\blocks.py\", line 1914, in postprocess_data\n",
+      "    prediction_value = block.postprocess(prediction_value)\n",
+      "  File \"e:\\Steven's Files\\Work\\Reserves\\Python Coding\\Pop-Up Trainer\\Pop_Up_Trainer\\2025014_PopUp_Clean\\.venv\\Lib\\site-packages\\gradio\\components\\video.py\", line 293, in postprocess\n",
+      "    raise TypeError(\n",
+      "        f\"If a tuple is provided, both elements must be strings or Path objects. Received: {value}\"\n",
+      "    )\n",
+      "TypeError: If a tuple is provided, both elements must be strings or Path objects. Received: ('', [])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# -*- coding: utf-8 -*-\n",
+    "\"\"\"Judol Gradio YOLO11.ipynb\n",
+    "Automatically generated by Colab.\n",
+    "Original file is located at\n",
+    "    https://colab.research.google.com/drive/1oiuTAi-cys1ydtUhSDJSRdeA02mAmZQH\n",
+    "    https://huggingface.co/JrEasy/Judol-Detection-YOLO11/blob/main/judol_gradio_yolo11.py\n",
+    "\"\"\"\n",
+    "\n",
+    "#!pip install ultralytics\n",
+    "#!pip install gradio\n",
+    "\n",
+    "import cv2\n",
+    "from ultralytics import YOLO\n",
+    "import gradio as gr\n",
+    "import imageio\n",
+    "#from google.colab import drive\n",
+    "\n",
+    "#model = YOLO('https://huggingface.co/JrEasy/Judol-Detection-YOLO11/resolve/main/best.pt')\n",
+    "model = YOLO(\"yolo11/yolo11n-pose.pt\")\n",
+    "\n",
+    "confidence_threshold = 0.5\n",
+    "\n",
+    "class_names = {\n",
+    "    0: \"BK8\",\n",
+    "    1: \"Gate of Olympus\",\n",
+    "    2: \"Princess\",\n",
+    "    3: \"Starlight Princess\",\n",
+    "    4: \"Zeus\",\n",
+    "}\n",
+    "\n",
+    "class_colors = {\n",
+    "    0: (0, 255, 0),       # Green for BK8\n",
+    "    1: (255, 0, 0),       # Blue for Gate of Olympus\n",
+    "    2: (0, 0, 255),       # Red for Princess\n",
+    "    3: (255, 255, 0),     # Cyan for Starlight Princess\n",
+    "    4: (255, 0, 255),     # Magenta for Zeus\n",
+    "}\n",
+    "\n",
+    "def format_time_ranges(timestamps, classes):\n",
+    "\n",
+    "    if not timestamps:\n",
+    "        return \"\"\n",
+    "\n",
+    "\n",
+    "    class_timestamps = {}\n",
+    "\n",
+    "    for timestamp, class_id in zip(timestamps, classes):\n",
+    "        class_name = class_names.get(class_id, 'Unknown')\n",
+    "        if class_name not in class_timestamps:\n",
+    "            class_timestamps[class_name] = []\n",
+    "        class_timestamps[class_name].append(timestamp)\n",
+    "\n",
+    "\n",
+    "    formatted_ranges = []\n",
+    "\n",
+    "    for class_name, timestamps in class_timestamps.items():\n",
+    "        timestamps = sorted(timestamps)\n",
+    "        ranges = []\n",
+    "        start = timestamps[0]\n",
+    "        for i in range(1, len(timestamps)):\n",
+    "            if timestamps[i] - timestamps[i - 1] <= 1:\n",
+    "                continue\n",
+    "            else:\n",
+    "                ranges.append(f\"{int(start)}-{int(timestamps[i - 1])}\")\n",
+    "                start = timestamps[i]\n",
+    "\n",
+    "        ranges.append(f\"{int(start)}-{int(timestamps[-1])}\")\n",
+    "\n",
+    "        formatted_ranges.append(f\"{class_name} = {', '.join(ranges)}\")\n",
+    "\n",
+    "    return \", \".join(formatted_ranges)\n",
+    "\n",
+    "def process_video(input_video):\n",
+    "    cap = cv2.VideoCapture(input_video)\n",
+    "    if not cap.isOpened():\n",
+    "        print(\"Error: Could not open input video.\")\n",
+    "        return None, []\n",
+    "\n",
+    "    fps = cap.get(cv2.CAP_PROP_FPS)\n",
+    "    output_video_path = \"/content/drive/MyDrive/Computer Vision YOLO-Judol Detection/processed_video.mp4\"\n",
+    "    writer = imageio.get_writer(output_video_path, fps=fps, codec=\"libx264\")\n",
+    "\n",
+    "    frame_count = 0\n",
+    "    timestamps = []\n",
+    "    classes_detected = []\n",
+    "\n",
+    "    while cap.isOpened():\n",
+    "        ret, frame = cap.read()\n",
+    "        if not ret:\n",
+    "            break\n",
+    "\n",
+    "        timestamp = frame_count / fps\n",
+    "        frame_count += 1\n",
+    "\n",
+    "        gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
+    "        input_frame = cv2.merge([gray_frame, gray_frame, gray_frame])\n",
+    "\n",
+    "        results = model.predict(input_frame)\n",
+    "\n",
+    "        for result in results:\n",
+    "            for box in result.boxes:\n",
+    "                if box.conf[0] >= confidence_threshold:\n",
+    "                    x1, y1, x2, y2 = map(int, box.xyxy[0])\n",
+    "                    class_id = int(box.cls[0])\n",
+    "                    class_name = class_names.get(class_id, f\"Class {class_id}\")\n",
+    "                    color = class_colors.get(class_id, (0, 255, 0))\n",
+    "                    cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)\n",
+    "                    text = f'{class_name}, Conf: {box.conf[0]:.2f}'\n",
+    "                    text_position = (x1, y1 - 10 if y1 > 20 else y1 + 20)\n",
+    "                    cv2.putText(frame, text, text_position, cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)\n",
+    "\n",
+    "                    timestamps.append(timestamp)\n",
+    "                    classes_detected.append(class_id)\n",
+    "\n",
+    "        writer.append_data(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
+    "\n",
+    "    cap.release()\n",
+    "    writer.close()\n",
+    "\n",
+    "\n",
+    "    formatted_time_ranges = format_time_ranges(timestamps, classes_detected)\n",
+    "\n",
+    "    print(f\"Processed video saved at: {output_video_path}\")\n",
+    "\n",
+    "    return output_video_path, formatted_time_ranges\n",
+    "\n",
+    "\n",
+    "def process_image(input_image):\n",
+    "    # Convert image from RGB to BGR for OpenCV processing\n",
+    "    bgr_frame = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "    # Convert to grayscale and create a 3-channel grayscale image\n",
+    "    gray_frame = cv2.cvtColor(bgr_frame, cv2.COLOR_BGR2GRAY)\n",
+    "    input_frame = cv2.merge([gray_frame, gray_frame, gray_frame])\n",
+    "\n",
+    "    # Run the model on the processed input\n",
+    "    results = model.predict(input_frame)\n",
+    "\n",
+    "    detections_log = []  # Store detection logs\n",
+    "    classes_detected = []  # Track detected class IDs\n",
+    "\n",
+    "    for result in results:\n",
+    "        for box in result.boxes:\n",
+    "            if box.conf[0] >= confidence_threshold:  # Filter by confidence\n",
+    "                x1, y1, x2, y2 = map(int, box.xyxy[0])  # Bounding box coordinates\n",
+    "                class_id = int(box.cls[0])  # Class ID\n",
+    "                class_name = class_names.get(class_id, f\"Class {class_id}\")\n",
+    "                color = class_colors.get(class_id, (0, 255, 0))  # Default green color\n",
+    "\n",
+    "                # Draw bounding box and class text on the frame\n",
+    "                cv2.rectangle(bgr_frame, (x1, y1), (x2, y2), color, 2)\n",
+    "                text = f'{class_name}, Conf: {box.conf[0]:.2f}'\n",
+    "                text_position = (x1, y1 - 10 if y1 > 20 else y1 + 20)\n",
+    "                cv2.putText(bgr_frame, text, text_position, cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)\n",
+    "\n",
+    "                # Log detection information\n",
+    "                detections_log.append({\n",
+    "                    \"class\": class_name,\n",
+    "                    \"confidence\": box.conf[0]\n",
+    "                })\n",
+    "                classes_detected.append(class_id)\n",
+    "\n",
+    "    # Count occurrences of each class detected\n",
+    "    class_count = {class_names.get(cls, f\"Class {cls}\"): classes_detected.count(cls) for cls in set(classes_detected)}\n",
+    "\n",
+    "    # Format the detections as 'Class = Count' pairs\n",
+    "    formatted_log = \", \".join([f\"{class_name} = {count}\" for class_name, count in class_count.items()])\n",
+    "\n",
+    "    # Convert the output frame back to RGB\n",
+    "    output_image = cv2.cvtColor(bgr_frame, cv2.COLOR_BGR2RGB)\n",
+    "    return output_image, formatted_log\n",
+    "\n",
+    "with gr.Blocks() as app:\n",
+    "    gr.Markdown(\"## Judol Detection using YOLOv11\")\n",
+    "\n",
+    "    with gr.Tab(\"Video Detection\"):\n",
+    "        with gr.Row():\n",
+    "            input_video = gr.Video(label=\"Upload a video\")\n",
+    "            output_video = gr.Video(label=\"Processed Video\")\n",
+    "            detections_log = gr.Textbox(label=\"Detections Log\", lines=10)\n",
+    "\n",
+    "        input_video.change(\n",
+    "            fn=lambda input_video: process_video(input_video) if input_video else (\"\", []),\n",
+    "            inputs=input_video,\n",
+    "            outputs=[output_video],\n",
+    "        )\n",
+    "\n",
+    "    with gr.Tab(\"Image Detection\"):\n",
+    "        with gr.Row():\n",
+    "            input_image = gr.Image(label=\"Upload an image\")\n",
+    "            output_image = gr.Image(label=\"Processed Image\")\n",
+    "            image_detections_log = gr.Textbox(label=\"Detections Log\", lines=10)\n",
+    "\n",
+    "        input_image.change(\n",
+    "            fn=process_image,\n",
+    "            inputs=input_image,\n",
+    "            outputs=[output_image, image_detections_log],\n",
+    "        )\n",
+    "\n",
+    "app.launch()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

yolo11/yolo11m-pose.pt

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yolo11/yolo11n-pose.onnx

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yolo11/yolo11n-pose.pt

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yolo11/yolo11n.pt

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yolo11/yolov5s.pt

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yolo11/yolov8n-pose.pt

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