{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19730,
     "status": "ok",
     "timestamp": 1744098140927,
     "user": {
      "displayName": "Lan Hoang",
      "userId": "15367629887304430933"
     },
     "user_tz": -420
    },
    "id": "Vnowuw1FWR6Y",
    "outputId": "18eb608c-528b-4a68-84c1-f83e2d1b5091"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Collecting git+https://github.com/tensorflow/docs\n",
      "  Cloning https://github.com/tensorflow/docs to c:\\users\\pc\\appdata\\local\\temp\\pip-req-build-cktlp7ez\n",
      "  Resolved https://github.com/tensorflow/docs to commit 18c3a45517af27c8513e165b8a52e6bbc1204b99\n",
      "  Preparing metadata (setup.py): started\n",
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      "Requirement already satisfied: platformdirs>=2.5 in c:\\users\\pc\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from jupyter-core!=5.0.*,>=4.12->nbformat->tensorflow-docs==2025.3.6.10029) (4.2.2)\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  Running command git clone --filter=blob:none --quiet https://github.com/tensorflow/docs 'C:\\Users\\PC\\AppData\\Local\\Temp\\pip-req-build-cktlp7ez'\n"
     ]
    }
   ],
   "source": [
    "%pip install gradio\n",
    "%pip install git+https://github.com/tensorflow/docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 18553,
     "status": "ok",
     "timestamp": 1744098159483,
     "user": {
      "displayName": "Lan Hoang",
      "userId": "15367629887304430933"
     },
     "user_tz": -420
    },
    "id": "nBrcqD-WktG-"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\PC\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"🎬 Keras Video Classification CNN-RNN model\n",
    "\n",
    "Spaces for showing the model usage.\n",
    "\n",
    "Author:\n",
    "    - Thomas Chaigneau @ChainYo\n",
    "\"\"\"\n",
    "import os\n",
    "import cv2\n",
    "\n",
    "import gradio as gr\n",
    "import numpy as np\n",
    "\n",
    "from tensorflow import keras\n",
    "\n",
    "from tensorflow_docs.vis import embed\n",
    "\n",
    "from huggingface_hub import from_pretrained_keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 109,
     "status": "ok",
     "timestamp": 1744098315598,
     "user": {
      "displayName": "Lan Hoang",
      "userId": "15367629887304430933"
     },
     "user_tz": -420
    },
    "id": "2l8ZXA4-XD_-"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A subdirectory or file Samples already exists.\n"
     ]
    }
   ],
   "source": [
    "%mkdir Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677,
     "referenced_widgets": [
      "7953decbcc66485d9e477a9d45777f99",
      "f8709807dedf4e349dd9d463db6abac7",
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      "5b22eb49678f44c09cf7e5a6007133de",
      "32364e76989b40b39659c3f93e727fde",
      "cb23ba857ca548e88967a0ef2d3f32e4",
      "70331cb723934c37abd7a1815a969841",
      "7c618cdc320f4cb3909fe11d4124cd85",
      "d9fb30527f9a4bed9896bf89cbd4eb54",
      "fd8b65aab251427bac9380d843ad02e1",
      "9a3b710001334adc8684c2227e2b556b"
     ]
    },
    "executionInfo": {
     "elapsed": 9546,
     "status": "ok",
     "timestamp": 1744103445447,
     "user": {
      "displayName": "Lan Hoang",
      "userId": "15367629887304430933"
     },
     "user_tz": -420
    },
    "id": "G_1_pfzxlXSo",
    "outputId": "f1b2fb37-45b1-450b-ff8e-09fcb6f8ee55"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7953decbcc66485d9e477a9d45777f99",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Fetching 11 files:   0%|          | 0/11 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Gradio in a Colab notebook requires sharing enabled. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
      "\n",
      "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
      "* Running on public URL: https://06f7718827d3f617a0.gradio.live\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://06f7718827d3f617a0.gradio.live\" 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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Kích thước ảnh đầu vào và số lượng đặc trưng\n",
    "IMG_SIZE = 224\n",
    "NUM_FEATURES = 2048\n",
    "\n",
    "# Tải mô hình CNN-RNN từ HuggingFace\n",
    "model = from_pretrained_keras(\"keras-io/video-classification-cnn-rnn\")\n",
    "\n",
    "# Tạo danh sách video ví dụ từ thư mục Samples\n",
    "samples = []\n",
    "for file in os.listdir(\"Samples\"):\n",
    "    tag = file.split(\"_\")[1]\n",
    "    samples.append([f\"samples/{file}\"])\n",
    "\n",
    "# Cắt phần hình vuông ở trung tâm frame\n",
    "def crop_center_square(frame):\n",
    "    y, x = frame.shape[0:2]\n",
    "    min_dim = min(y, x)\n",
    "    start_x = (x // 2) - (min_dim // 2)\n",
    "    start_y = (y // 2) - (min_dim // 2)\n",
    "    return frame[start_y : start_y + min_dim, start_x : start_x + min_dim]\n",
    "\n",
    "# Đọc video và xử lý từng frame\n",
    "def load_video(path, max_frames=0, resize=(IMG_SIZE, IMG_SIZE)):\n",
    "    cap = cv2.VideoCapture(path)\n",
    "    frames = []\n",
    "    try:\n",
    "        while True:\n",
    "            ret, frame = cap.read()\n",
    "            if not ret:\n",
    "                break\n",
    "            frame = crop_center_square(frame)\n",
    "            frame = cv2.resize(frame, resize)\n",
    "            frame = frame[:, :, [2, 1, 0]]\n",
    "            frames.append(frame)\n",
    "\n",
    "            if len(frames) == max_frames:\n",
    "                break\n",
    "    finally:\n",
    "        cap.release()\n",
    "    return np.array(frames)\n",
    "\n",
    "# Xây dựng mô hình trích xuất đặc trưng (InceptionV3)\n",
    "def build_feature_extractor():\n",
    "    feature_extractor = keras.applications.InceptionV3(\n",
    "        weights=\"imagenet\",\n",
    "        include_top=False,\n",
    "        pooling=\"avg\",\n",
    "        input_shape=(IMG_SIZE, IMG_SIZE, 3),\n",
    "    )\n",
    "    preprocess_input = keras.applications.inception_v3.preprocess_input\n",
    "\n",
    "    inputs = keras.Input((IMG_SIZE, IMG_SIZE, 3))\n",
    "    preprocessed = preprocess_input(inputs)\n",
    "\n",
    "    outputs = feature_extractor(preprocessed)\n",
    "    return keras.Model(inputs, outputs, name=\"feature_extractor\")\n",
    "\n",
    "# Tạo feature extractor 1 lần\n",
    "feature_extractor = build_feature_extractor()\n",
    "\n",
    "# Trích xuất đặc trưng cho từng frame của video\n",
    "def prepare_video(frames, max_seq_length: int = 20):\n",
    "    frames = frames[None, ...]\n",
    "    frame_mask = np.zeros(shape=(1, max_seq_length,), dtype=\"bool\")\n",
    "    frame_features = np.zeros(shape=(1, max_seq_length, NUM_FEATURES), dtype=\"float32\")\n",
    "\n",
    "    for i, batch in enumerate(frames):\n",
    "        video_length = batch.shape[0]\n",
    "        length = min(max_seq_length, video_length)\n",
    "        for j in range(length):\n",
    "            frame_features[i, j, :] = feature_extractor.predict(batch[None, j, :])\n",
    "        frame_mask[i, :length] = 1  # 1 = not masked, 0 = masked\n",
    "\n",
    "    return frame_features, frame_mask\n",
    "\n",
    "# Dự đoán hành động từ video\n",
    "def sequence_prediction(path):\n",
    "    class_vocab = [\"CricketShot\", \"PlayingCello\", \"Punch\", \"ShavingBeard\", \"TennisSwing\"]\n",
    "\n",
    "    frames = load_video(path)\n",
    "    frame_features, frame_mask = prepare_video(frames)\n",
    "    probabilities = model.predict([frame_features, frame_mask])[0]\n",
    "\n",
    "    preds = {}\n",
    "    for i in np.argsort(probabilities)[::-1]:\n",
    "        preds[class_vocab[i]] = float(probabilities[i])\n",
    "    return preds\n",
    "\n",
    "# HTML mô tả bên dưới app\n",
    "article = article = \"<div style='text-align: center;'><a href='https://github.com/ChainYo' target='_blank'>Space by Thomas Chaigneau</a><br><a href='https://keras.io/examples/vision/video_classification/' target='_blank'>Keras example by Sayak Paul</a></div>\"\n",
    "\n",
    "# Tạo giao diện Gradio\n",
    "app = gr.Interface(\n",
    "    fn=sequence_prediction,\n",
    "    inputs=[gr.Video(label=\"Video\")],\n",
    "    outputs=gr.Label(label=\"Prediction\"),\n",
    "    title=\"Keras Video Classification with CNN-RNN\",\n",
    "    description=\"Video classification demo using CNN-RNN based model.\",\n",
    "    article=article,\n",
    "    examples=samples\n",
    ")\n",
    "\n",
    "# Khởi chạy ứng dụng\n",
    "app.launch()\n",
    "\n"
   ]
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