Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9a893cbd-ba71-4466-8ad4-62dd178232a8",
   "metadata": {},
   "source": [
    "Take the Frame, Flow and Speech models and make a prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c75293c8-7f07-4aee-b393-f8154004f08a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aacf743a-ac76-420c-bd98-141f62629f37",
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   "source": [
    "from display import pltDisplay\n",
    "from datetime import datetime\n",
    "from pathlib import Path\n",
    "from sorting import human_sort\n",
    "# from reload import reload_module\n",
    "import dlib\n",
    "# from pyflow import pyflow\n",
    "\n",
    "# import IPython.display as ipd\n",
    "# from IPython.display import clear_output\n",
    "import constants as const\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "# import mtcnn  # https://pypi.org/project/mtcnn/\n",
    "import librosa\n",
    "import librosa.display\n",
    "import numpy as np\n",
    "import progressbar as pb\n",
    "import subprocess\n",
    "import tensorflow as tf\n",
    "import utils\n",
    "import utils_flow as ut_fl\n",
    "import utils_audio as ut_au\n",
    "\n",
    "from pympler import asizeof"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e7841cc8-6d36-4618-8f6f-e384fd75919e",
   "metadata": {
    "execution": {
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   },
   "source": [
    "from reload import reload_module\n",
    "reload_module(ut_fl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f3e4bcd3-ffcf-47b6-bb7f-4b1c436f2fad",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "SEED = 42\n",
    "SEQUENCE_LENGTH = const.MAX_SPEECH_SEQUENCE_LENGTH  # max\n",
    "SAMPLE_RATE = const.RAVDESS_SAMPLE_RATE\n",
    "\n",
    "# Dimensions of generated images\n",
    "IMG_HEIGHT = IMG_WIDTH = 64\n",
    "MAX_WIDTH = MAX_HEIGHT = const.MAX_WIDTH\n",
    "label_names = const.EMOTIONS_LABELS_SORTED"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e441e6cb-2d8f-4244-940f-1fb28fa773eb",
   "metadata": {
    "execution": {
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   },
   "source": [
    "# MTCNN TEST\n",
    "det_mtcnn = MTCNN()\n",
    "test_img = Path(const.frames_path, 'calm/01-01-02-01-02-02-20-034-01.jpg')\n",
    "img = cv2.imread(str(test_img))\n",
    "# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "rects = det_mtcnn.detect_faces(img)\n",
    "# clear_output()\n",
    "pltDisplay(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c700aa2-a4b0-4495-bb48-18caae8e93ec",
   "metadata": {},
   "source": [
    "# Selection of random videos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [],
   "source": [
    "# Array of array for every emotion\n",
    "emotion_arrays = [\n",
    "    list(\n",
    "        Path(const.dataset_path)\n",
    "        .glob(f'*/*/01-01-{i:02d}-*-*-*-*.mp4')\n",
    "    ) for i in range(1, 9)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
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   "id": "84fcc453-7463-4da9-81e0-019cbefe13ef",
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   "outputs": [],
   "source": [
    "rnd_gen = np.random.default_rng(seed=SEED)\n",
    "rnd_idxs = [rnd_gen.integers(len(em_list)) for em_list in emotion_arrays]"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "rnd_videos = [\n",
    "    em_list[rnd_idxs[idx]]\n",
    "    for idx, em_list in enumerate(emotion_arrays)\n",
    "]"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Video 1\n",
      "Modality: Full-AV\n",
      "Channel: Speech\n",
      "Emotion: Neutral\n",
      "Intensity: Normal\n",
      "Statement: Kids are talking by the door\n",
      "Repetition: 1st repetition\n",
      "Actor: 3 (Male)\n",
      "\n",
      "\n",
      "Video 2\n",
      "Modality: Full-AV\n",
      "Channel: Speech\n",
      "Emotion: Calm\n",
      "Intensity: Strong\n",
      "Statement: Kids are talking by the door\n",
      "Repetition: 1st repetition\n",
      "Actor: 19 (Male)\n",
      "\n",
      "\n",
      "Video 3\n",
      "Modality: Full-AV\n",
      "Channel: Speech\n",
      "Emotion: Happy\n",
      "Intensity: Strong\n",
      "Statement: Kids are talking by the door\n",
      "Repetition: 2nd repetition\n",
      "Actor: 16 (Female)\n",
      "\n",
      "\n",
      "Video 4\n",
      "Modality: Full-AV\n",
      "Channel: Speech\n",
      "Emotion: Sad\n",
      "Intensity: Strong\n",
      "Statement: Kids are talking by the door\n",
      "Repetition: 1st repetition\n",
      "Actor: 11 (Male)\n",
      "\n",
      "\n",
      "Video 5\n",
      "Modality: Full-AV\n",
      "Channel: Speech\n",
      "Emotion: Angry\n",
      "Intensity: Normal\n",
      "Statement: Dogs are sitting by the door\n",
      "Repetition: 2nd repetition\n",
      "Actor: 11 (Male)\n",
      "\n",
      "\n",
      "Video 6\n",
      "Modality: Full-AV\n",
      "Channel: Speech\n",
      "Emotion: Fearful\n",
      "Intensity: Strong\n",
      "Statement: Kids are talking by the door\n",
      "Repetition: 1st repetition\n",
      "Actor: 21 (Male)\n",
      "\n",
      "\n",
      "Video 7\n",
      "Modality: Full-AV\n",
      "Channel: Speech\n",
      "Emotion: Disgust\n",
      "Intensity: Normal\n",
      "Statement: Kids are talking by the door\n",
      "Repetition: 1st repetition\n",
      "Actor: 3 (Male)\n",
      "\n",
      "\n",
      "Video 8\n",
      "Modality: Full-AV\n",
      "Channel: Speech\n",
      "Emotion: Surprised\n",
      "Intensity: Strong\n",
      "Statement: Kids are talking by the door\n",
      "Repetition: 2nd repetition\n",
      "Actor: 17 (Male)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for idx, video in enumerate(rnd_videos):\n",
    "    print()\n",
    "    print(f'Video {idx + 1}')\n",
    "    utils.filename_info(str(video))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   },
   "outputs": [],
   "source": [
    "# Select single video\n",
    "video_path = rnd_videos[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9aba707e-342e-4c36-9186-cfa7892573c7",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modality: Full-AV\n",
      "Channel: Speech\n",
      "Emotion: Sad\n",
      "Intensity: Strong\n",
      "Statement: Kids are talking by the door\n",
      "Repetition: 1st repetition\n",
      "Actor: 11 (Male)\n"
     ]
    }
   ],
   "source": [
    "utils.filename_info(str(video_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ded7b19-2a2e-45b4-a506-7fd84e446313",
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "correct_label = utils.get_class_string(str(video_path))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27fe3604-8a3c-421d-8fd9-fc0142b5e57e",
   "metadata": {},
   "source": [
    "# Model Prediction - Frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a143af9e-f102-4584-9f0d-f549a2a2099f",
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "model_frame = tf.saved_model.load(\n",
    "    Path('Models/Frame/Simple_MNIST/Run_6/E2E/')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6aaae8cf-27d5-4834-8760-fce005555185",
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    "execution": {
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   "outputs": [],
   "source": [
    "def standard_face_detector(img):\n",
    "    '''\n",
    "        Return dlib standard bounding box\n",
    "    '''\n",
    "\n",
    "    # if (len(img.shape) != 2 and img.shape[2] != 1):\n",
    "    #     raise ValueError('Image must be in grayscale')\n",
    "\n",
    "    # Initialize dlib's face detector\n",
    "    detector = dlib.get_frontal_face_detector()\n",
    "\n",
    "    # Detecting faces in the grayscale image\n",
    "    faces = detector(img)\n",
    "\n",
    "    bounding_box = []\n",
    "    # Creating a for loop to extract specific coordinates (x1,x2,y1,y2)\n",
    "    for face in faces:\n",
    "        x1 = face.left()\n",
    "        y1 = face.top()\n",
    "        x2 = face.right()\n",
    "        y2 = face.bottom()\n",
    "\n",
    "        bounding_box.append([(x1, y1), (x2, y2)])\n",
    "        # Drawing a rectangle around the face\n",
    "        # cv2.rectangle(image_rgb, (x1, y1), (x2, y2), (0, 255, 0), 3)\n",
    "\n",
    "    return faces"
   ]
  },
  {
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   "execution_count": 19,
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   "source": [
    "def improved_face_detector(img, face):\n",
    "    '''\n",
    "        Return dlib bounding box not considering jaws landmarks\n",
    "    '''\n",
    "\n",
    "    # if (len(img.shape) != 2 and img.shape[2] != 1):\n",
    "    #     raise ValueError('Image must be in grayscale')\n",
    "\n",
    "    p = 'OTHER/shape_predictor_68_face_landmarks.dat'\n",
    "\n",
    "    # Initialize dlib's shape predictor\n",
    "    predictor = dlib.shape_predictor(p)\n",
    "\n",
    "    # Get the shape using the predictor\n",
    "    landmarks = predictor(img, face)\n",
    "\n",
    "    # for n in range(0, 68):\n",
    "    #     x = landmarks.part(n).x\n",
    "    #     y = landmarks.part(n).y\n",
    "    #     cv2.circle(image_rgb, (x, y), 5, (0, 0, 255), -1)\n",
    "    parts = landmarks.parts()[17:]\n",
    "\n",
    "    points_x = []\n",
    "    points_y = []\n",
    "    for point in parts:\n",
    "        x = point.x\n",
    "        y = point.y\n",
    "        points_x.append(x)\n",
    "        points_y.append(y)\n",
    "\n",
    "    x_min = min(points_x)\n",
    "    x_max = max(points_x)\n",
    "    y_min = min(points_y)\n",
    "    y_max = max(points_y)\n",
    "\n",
    "    # min_point = x_min, y_min\n",
    "    # max_point = x_max, y_max\n",
    "\n",
    "\n",
    "    # Calculate the width and height of the bounding box\n",
    "    w = abs(x_max - x_min)\n",
    "    h = abs(y_max - y_min)\n",
    "    # w = max((x_max - x_min), MAX_WIDTH)\n",
    "    # h = max((y_max - y_min), MAX_HEIGHT)\n",
    "\n",
    "    # Calculate the maximum to make a squared bounding box\n",
    "    m = max(w, h)\n",
    "\n",
    "    # bounding_box_max = (x_min + m, y_min + m)\n",
    "\n",
    "    return [x_min, y_min, m, m]"
   ]
  },
  {
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   "source": [
    "def frames_from_video(path, channels=1, size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                      start=0, end=400):\n",
    "\n",
    "    assert start >= 0, 'start cannot be negative'\n",
    "    assert end >= 0, 'end cannot be negative'\n",
    "    assert start < end, 'start cannot be lower than end'\n",
    "\n",
    "    # frames = []\n",
    "\n",
    "\n",
    "    frame_path = Path(folder_path, 'Frame')\n",
    "    frame_path.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    vidcap = cv2.VideoCapture(str(path))  # open video\n",
    "    # det_cascade = cv2.CascadeClassifier(\n",
    "    #     'OTHER/haarcascade_frontalface_default.xml'\n",
    "    # )\n",
    "    # det_mtcnn = mtcnn.MTCNN()\n",
    "\n",
    "    # standard_detector = dlib.get_frontal_face_detector()\n",
    "    # p = 'OTHER/shape_predictor_68_face_landmarks.dat'\n",
    "    # predictor = dlib.shape_predictor(p)\n",
    "\n",
    "    video_frames = utils.get_video_frames(vidcap)\n",
    "    n_frames = min(end, video_frames)\n",
    "\n",
    "    p1 = pb.Progressbar('p1', n_frames - start)\n",
    "    vidcap.set(cv2.CAP_PROP_POS_FRAMES, start)\n",
    "\n",
    "    for frame_number in range(start, n_frames):\n",
    "\n",
    "        success, image = vidcap.read()\n",
    "\n",
    "        # # MTCNN\n",
    "        # rects = det_mtcnn.detect_faces(image)\n",
    "        # # clear_output()\n",
    "        # x, y, w, h = rects[0]['box']\n",
    "\n",
    "        # # CASCADE\n",
    "\n",
    "        # rects = det_cascade.detectMultiScale(\n",
    "        #     image, scaleFactor=1.05, minNeighbors=5,\n",
    "        #     minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE\n",
    "        # )\n",
    "        # x, y, w, h = rects[0]\n",
    "\n",
    "        # # DLIB\n",
    "        bounding_box = standard_face_detector(image)[0]\n",
    "        x, y, w, h = improved_face_detector(image, bounding_box)\n",
    "\n",
    "        if channels == 1:\n",
    "            image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "        image = image[y:y + h, x:x + w]\n",
    "        image = cv2.resize(image, dsize=size)\n",
    "        # frames.append(image)\n",
    "        save_path = Path(frame_path, f'{frame_number:02d}.jpg')\n",
    "        cv2.imwrite(str(save_path), image)\n",
    "        p1.add_to_progress(1)\n",
    "\n",
    "    vidcap.release()\n",
    "    p1.update_progress(n_frames - start)\n",
    "    # return frames\n",
    "    # return folder_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "655a1350-6e71-4bd6-893e-35ed9068b99d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:08:32.963763Z",
     "iopub.status.busy": "2024-04-22T07:08:32.962754Z",
     "iopub.status.idle": "2024-04-22T07:08:32.971692Z",
     "shell.execute_reply": "2024-04-22T07:08:32.970502Z",
     "shell.execute_reply.started": "2024-04-22T07:08:32.963763Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create average image\n",
    "def calc_avg_image(images):\n",
    "\n",
    "    img = cv2.imread(str(images[0]))\n",
    "    avg_image = np.zeros_like(img)\n",
    "    for idx, img_path in enumerate(images):\n",
    "        img = cv2.imread(str(img_path))\n",
    "        alpha = 1.0/(idx + 2)\n",
    "        beta = 1.0 - alpha\n",
    "        avg_image = cv2.addWeighted(img, alpha, avg_image, beta, 0.0)\n",
    "    return avg_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "59434521-2db3-4fbb-83c0-d129a360bfef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:08:33.379636Z",
     "iopub.status.busy": "2024-04-22T07:08:33.378635Z",
     "iopub.status.idle": "2024-04-22T07:08:33.390229Z",
     "shell.execute_reply": "2024-04-22T07:08:33.389077Z",
     "shell.execute_reply.started": "2024-04-22T07:08:33.379636Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def softmax(x):\n",
    "\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum(axis=0)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f363900b-32c2-4ce9-ba80-464d32177855",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-04T07:50:42.677302Z",
     "iopub.status.busy": "2024-04-04T07:50:42.676320Z",
     "iopub.status.idle": "2024-04-04T07:50:49.478297Z",
     "shell.execute_reply": "2024-04-04T07:50:49.477298Z",
     "shell.execute_reply.started": "2024-04-04T07:50:42.677302Z"
    }
   },
   "source": [
    "# Show multiple faces\n",
    "path = str(Path('OTHER/faces_test.jpg'))\n",
    "original = cv2.imread(path)\n",
    "image_rgb = original.copy()\n",
    "# image = cv2.cvtColor(image_rgb, cv2.COLOR_BGR2GRAY)\n",
    "image = image_rgb\n",
    "cut_faces = []\n",
    "\n",
    "for face in standard_face_detector(image):\n",
    "    bb = improved_face_detector(image, face)\n",
    "    x_min, y_min, w, h = bb\n",
    "    cv2.rectangle(\n",
    "        image_rgb, (x_min, y_min), (x_min + w, y_min + h), (255, 0, 0), 3\n",
    "    )\n",
    "    frame = original[y_min:y_min + h, x_min:x_min + w]\n",
    "    frame = cv2.resize(frame, dsize=(MAX_HEIGHT, MAX_WIDTH))\n",
    "    cut_faces.append(frame)\n",
    "\n",
    "cut_faces.append(image_rgb)\n",
    "utils.show_images(cut_faces)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "9db72dc0-b64b-40bd-9a46-e84618d47e5e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-04T07:51:33.618283Z",
     "iopub.status.busy": "2024-04-04T07:51:33.618283Z",
     "iopub.status.idle": "2024-04-04T07:51:36.666277Z",
     "shell.execute_reply": "2024-04-04T07:51:36.665301Z",
     "shell.execute_reply.started": "2024-04-04T07:51:33.618283Z"
    }
   },
   "source": [
    "# Test haarcascade and dlib to mimic ravdess\n",
    "# https://datahacker.rs/009-how-to-detect-facial-landmarks-using-dlib-and-opencv/\n",
    "\n",
    "\n",
    "path = str(Path('test/50.jpg'))\n",
    "# path = str(Path('OTHER/face_test.jpg'))\n",
    "original = cv2.imread(path)\n",
    "image_rgb = original.copy()\n",
    "# image = cv2.cvtColor(image_rgb, cv2.COLOR_BGR2GRAY)\n",
    "image = image_rgb\n",
    "\n",
    "for face in standard_face_detector(image):\n",
    "    bb = improved_face_detector(image, face)\n",
    "    x_min, y_min, w, h = bb\n",
    "    cv2.rectangle(\n",
    "        image_rgb, (x_min, y_min), (x_min + w, y_min + h), (255, 0, 0), 3\n",
    "    )\n",
    "    frame = original[x_min:x_min + w, y_min:y_min + h]\n",
    "    frame = cv2.resize(frame, dsize=(MAX_HEIGHT, MAX_WIDTH))\n",
    "\n",
    "utils.show_images(\n",
    "    [original, image_rgb, frame], cols=3, titles=['Original', 'Draw', 'Face']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "09460ede-f354-4b20-8df9-e05d9fe69514",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:08:35.426316Z",
     "iopub.status.busy": "2024-04-22T07:08:35.426316Z",
     "iopub.status.idle": "2024-04-22T07:08:35.431941Z",
     "shell.execute_reply": "2024-04-22T07:08:35.431283Z",
     "shell.execute_reply.started": "2024-04-22T07:08:35.426316Z"
    }
   },
   "outputs": [],
   "source": [
    "# CHECK POSSIBLE MEMORY LEAK IN MTCNN, TRY WITH CASCADE\n",
    "# UPDATE:memory leak should be in predict methods (mtcnn)\n",
    "# CHECK IF COULD USE GENERATOR, AVOIDING LOADING ALL LIST IN RAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6a11c550-916a-4687-8063-2789176b17df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:08:37.675756Z",
     "iopub.status.busy": "2024-04-22T07:08:37.675756Z",
     "iopub.status.idle": "2024-04-22T07:08:37.688095Z",
     "shell.execute_reply": "2024-04-22T07:08:37.687103Z",
     "shell.execute_reply.started": "2024-04-22T07:08:37.675756Z"
    }
   },
   "outputs": [],
   "source": [
    "date_str = datetime.now().strftime('%Y%m%d_%H%M%S')\n",
    "folder_path = Path('test', date_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9d1a9c97-d226-4c8f-bb34-cd726f5b9679",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:09:34.496713Z",
     "iopub.status.busy": "2024-04-22T07:09:34.496713Z",
     "iopub.status.idle": "2024-04-22T07:10:00.778931Z",
     "shell.execute_reply": "2024-04-22T07:10:00.778931Z",
     "shell.execute_reply.started": "2024-04-22T07:09:34.496713Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Progress: [####################] 100%             34/34'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frames_from_video(video_path, size=(64, 64), channels=1, start=50, end=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "22afa0d1-e01b-404b-8b75-56b8e9a954f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:10:14.582772Z",
     "iopub.status.busy": "2024-04-22T07:10:14.582772Z",
     "iopub.status.idle": "2024-04-22T07:10:14.599331Z",
     "shell.execute_reply": "2024-04-22T07:10:14.598324Z",
     "shell.execute_reply.started": "2024-04-22T07:10:14.582772Z"
    }
   },
   "outputs": [],
   "source": [
    "frames = [str(img) for img in folder_path.glob('Frame/*')]\n",
    "# Order list\n",
    "human_sort(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1fa5b387-264d-4fcc-8095-49014b123a72",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:10:15.211056Z",
     "iopub.status.busy": "2024-04-22T07:10:15.211056Z",
     "iopub.status.idle": "2024-04-22T07:10:15.244457Z",
     "shell.execute_reply": "2024-04-22T07:10:15.242813Z",
     "shell.execute_reply.started": "2024-04-22T07:10:15.211056Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.3 KB'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.sizeof_fmt(asizeof.asizeof(frames))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "29117781-f12e-410a-a3ff-eb8675f0cbcd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:10:15.844687Z",
     "iopub.status.busy": "2024-04-22T07:10:15.844687Z",
     "iopub.status.idle": "2024-04-22T07:10:16.044420Z",
     "shell.execute_reply": "2024-04-22T07:10:16.042847Z",
     "shell.execute_reply.started": "2024-04-22T07:10:15.844687Z"
    }
   },
   "outputs": [],
   "source": [
    "avg_image = calc_avg_image(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1aded738-f1c3-4e5f-9baf-529ee2a3c9c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:10:16.393878Z",
     "iopub.status.busy": "2024-04-22T07:10:16.393878Z",
     "iopub.status.idle": "2024-04-22T07:10:16.614396Z",
     "shell.execute_reply": "2024-04-22T07:10:16.614396Z",
     "shell.execute_reply.started": "2024-04-22T07:10:16.393878Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.show_images([avg_image])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8bd22471-da9b-474f-a06f-04a3edc061ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:10:21.561825Z",
     "iopub.status.busy": "2024-04-22T07:10:21.560829Z",
     "iopub.status.idle": "2024-04-22T07:10:21.845025Z",
     "shell.execute_reply": "2024-04-22T07:10:21.843021Z",
     "shell.execute_reply.started": "2024-04-22T07:10:21.561825Z"
    }
   },
   "outputs": [],
   "source": [
    "model_frame_preds = {'class_ids': [], 'max_class_pred': [],\n",
    "                     'predictions': [], 'avg_pred': []}\n",
    "# res_list = []\n",
    "\n",
    "for frame_path in frames:\n",
    "    frame = cv2.imread(str(frame_path))\n",
    "    frame = cv2.resize(frame, dsize=(IMG_HEIGHT, IMG_WIDTH))\n",
    "    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "    frame = frame / 255\n",
    "    frame = tf.constant(frame, dtype=tf.float32)\n",
    "    frame = utils.extend_tensor(frame, 0)\n",
    "    frame = utils.extend_tensor(frame)\n",
    "    res = model_frame(frame)\n",
    "    # res_list.append(res)\n",
    "\n",
    "    model_frame_preds['class_ids'].append(\n",
    "        res['class_ids'].numpy()[0]\n",
    "    )\n",
    "    model_frame_preds['max_class_pred'].append(\n",
    "        np.max(res['predictions'].numpy()[0])\n",
    "    )\n",
    "    model_frame_preds['predictions'].append(\n",
    "        res['predictions'].numpy()[0]\n",
    "    )"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a440e50c-f7a1-4b4e-b4e4-2796adca834b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-15T09:53:18.629533Z",
     "iopub.status.busy": "2024-03-15T09:53:18.618651Z",
     "iopub.status.idle": "2024-03-15T09:53:18.747179Z",
     "shell.execute_reply": "2024-03-15T09:53:18.745176Z",
     "shell.execute_reply.started": "2024-03-15T09:53:18.628531Z"
    }
   },
   "source": [
    "np.unique(model_frame_preds['class_ids'], return_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004e3447-9619-441f-ad57-76213e45ffdb",
   "metadata": {},
   "source": [
    "softmax is not scale invariant, so if the input were [0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3] (which sums to 1.6) the softmax would be [0.125, 0.138, 0.153, 0.169, 0.125, 0.138, 0.153]. This shows that for values between 0 and 1 softmax, in fact, de-emphasizes the maximum value (note that 0.169 is not only less than 0.475, it is also less than the initial proportion of 0.4/1.6=0.25).\n",
    "\n",
    "https://en.wikipedia.org/wiki/Softmax_function"
   ]
  },
  {
   "cell_type": "raw",
   "id": "378b28f1-5c12-4105-87f4-4989d02f04e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T09:10:34.808891Z",
     "iopub.status.busy": "2024-04-02T09:10:34.807890Z",
     "iopub.status.idle": "2024-04-02T09:10:34.835890Z",
     "shell.execute_reply": "2024-04-02T09:10:34.834889Z",
     "shell.execute_reply.started": "2024-04-02T09:10:34.808891Z"
    }
   },
   "source": [
    "# Difference average / softmax of sum\n",
    "rng = np.random.default_rng(0)\n",
    "ar = rng.integers(1, 100, (2, 5))\n",
    "prob_ar = []\n",
    "for idx, vals in enumerate(ar):\n",
    "    print(idx, vals)\n",
    "    print(vals / sum(vals))\n",
    "    prob_ar.append(vals / sum(vals))\n",
    "\n",
    "a = np.average(model_frame_preds['predictions'], axis=0)\n",
    "b = softmax(np.sum(model_frame_preds['predictions'], axis=0))\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    nrows=1, ncols=2, figsize=(20, 10)\n",
    ")\n",
    "\n",
    "\n",
    "ax[0].bar(label_names, a)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "\n",
    "ax[1].bar(label_names, b)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54eebd3b-a712-4856-9b16-62b70708226a",
   "metadata": {},
   "source": [
    "### Hard Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "61f9bbbd-4881-4d80-8d89-4b46995a5cd4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:10:29.020550Z",
     "iopub.status.busy": "2024-04-22T07:10:29.020550Z",
     "iopub.status.idle": "2024-04-22T07:10:29.031033Z",
     "shell.execute_reply": "2024-04-22T07:10:29.030026Z",
     "shell.execute_reply.started": "2024-04-22T07:10:29.020550Z"
    }
   },
   "outputs": [],
   "source": [
    "frame_max_idxs = [np.argmax(ar) for ar in model_frame_preds['predictions']]\n",
    "frame_count_idxs = [frame_max_idxs.count(i) for i in range(len(label_names))]\n",
    "frame_max_class = np.argmax(frame_count_idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "47af8a3f-f3a7-488e-94bf-5ef25af0f339",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:10:29.931085Z",
     "iopub.status.busy": "2024-04-22T07:10:29.931085Z",
     "iopub.status.idle": "2024-04-22T07:10:30.175468Z",
     "shell.execute_reply": "2024-04-22T07:10:30.175468Z",
     "shell.execute_reply.started": "2024-04-22T07:10:29.931085Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5s7z/yt/b3NjYmK9//esZOXJkevbsma997Wv58pe/nKqqqlx++eWtPt/WW2+d7t27569//WuHvVds3F577bV85StfyejRo1NTU5MBAwZkt912y3e/+90sWrQoSfLSSy9lypQp2WOPPTJo0KD06tUr/1979x4VRf3/D/y5LHIVBQEhRVYuAgYpICCa3EQUyhuicjG5JKl9yUteKSsFPRpxBNI0E5TSylIJFYxE5eISoKQfSkNFQClABREUkIvI/P7w7PxYd1cXdk0ur8c5ng7vmXnPzPbe175m5j3vt4GBAQIDA1FcXCz1vjq34aamJqxatQojRoyAqqoqbG1thb4rR44cgYODA9TV1aGnp4fly5ezx0P6j6SkJDg4OEBVVRV6enoIDAxEVVWV2HUlzTFy9epVLFy4ECYmJlBRUYGuri6sra2xcuVK3L59W2jdrsblrKwscDgcBAcHiz0mSeO5/vvvvwgLC4O5uTnU1NQwZMgQWFpaYsmSJbh+/TqAp+PTGhkZAQCys7PB4XDYf5L2R3o+wbjDmZmZAAAjIyP2/6ugnbS1teHLL7+Evb09NDQ0oK6uDgcHB+zbtw8Mw4jUyefz8cEHH2DMmDHQ0tKCqqoqLCwsEB4ejvr6epH1O7fbO3fuIDQ0FAYGBlBUVERcXBw4HA5cXFwAAN999x17fIJ5IzrnPeKI+66Q/qe5uRnh4eHg8XhQVlaGqakpoqKiRNqwLO339u3bCA4Ohp6eHptLHDhwQOzxCMapb2trw8aNG9nfBGNjY3z22WdoaWkRWt/KygocDkdinnPr1i0oKChg1KhRYr+XpPfpnKdK234BoKamBmvWrIG5uTlUVFSgpaUFLy8vsW/bdTV+SpsLdG7fkZGRsLCwgLKyMmbPng0AaGlpwb59+zBr1iwYGxtDVVUVmpqacHZ2xk8//STT50b6B2nyaYZhcOjQIfj5+cHMzAzq6urQ0NCAg4MDdu/ejY6ODrF1NzU1Yf369TA0NISKigosLCwQExNDsbUfkKZddSfvfNG9M0B4LpDz589j2rRp0NTUxKBBg+Dh4YH8/HyRfb0oh3623s6ampoQFRUFa2traGpqYuDAgTAxMcG8efNw6tQpkX01NjYiMjISb7zxBtTU1DBo0CC4uLiwb52L05VrZ9Kz0Rsj5JVbtGgRmpqaYGlpiTfeeAMPHz7EhQsXsGHDBpw9exbp6engcrlC29y6dQv29vZQUVHBpEmTcPfuXeTl5WH27NlIS0vD1KlThdZfuXIlduzYAWVlZbi7u0NVVRUHDx5Ebm4uFBUlfw2am5vh4uKC8vJyuLi4wNbWFlpaWpgzZw52796N+Ph4vPfeeyLbZWRkoKysDJ6enuDxePL5oEivdu7cOcycORMPHjyAsbExZs2ahaamJhQVFWHDhg0ICAjAyJEjkZCQgKioKLz++uuws7ODiooKioqKcPDgQRw/fhx8Ph9jxoyRer9tbW1wd3dHaWkpHB0d0djYiHPnzsHb2xu//fYbLl++jHXr1sHe3h5Tp04Fn8/Hzp07UVtbix9++OElfiKkJ/nqq6+wbNkycLlcuLi4QEdHB2fOnIGjoyPGjh0rVR2XLl3CpEmT0NLSAgcHBzg4OKChoQFlZWX48ssvMXv2bKHXnLsbl7uioqICtra2uHfvHsaMGYMZM2agpaUF5eXliI+Px4QJE2Bubg5ra2v4+PggKSkJenp68PT0ZOuYNGmSXI6F/Pesra0RFBSE3377DXfv3oWPjw8GDhwIANDX10dTUxO8vLzA5/Oho6ODSZMmQUFBAXl5eQgNDUVBQQH27NkjVOfatWtRWFgIKysrTJ48Ga2trbh06RKioqKQmpqK/Px8dh+d1dTUwN7eHu3t7ez3RE1NDUFBQbhz5w5OnToFExMTtr31pDlQSM/W1taGqVOn4u+//4aDgwNGjx6N7OxshIeHo6GhAVu2bGHX7W77vX//PhwdHdHa2gpXV1fU1dUhMzMTQUFBuHnzJjZu3CiyDcMwmDt3Ls6cOQN3d3dYW1vj7Nmz2Lx5M3Jzc3Hq1Ck2v1+yZAmWL1+OhIQEfPHFFyJ1CR5UhoaGin0oT3qvrrTfa9euYcqUKaisrISJiQneeust1NbWIiMjA+np6Th48CACAgK6fSxdyQU6Ojowe/ZsnDt3Di4uLhgzZgy0tbUBPL1ODQ0NhZ6eHiwsLODg4IA7d+4gNzcXfD4f165dk3jTkRBp8+nW1lYEBARAS0sLr7/+Opvv5uXlISwsDBcuXBC5Wdza2oqpU6ciNzcXOjo6mDFjBhoaGhAeHo7S0tJXc8LkP9HV67TukHTvrLPc3FwsWbIEpqam8PLyQklJCc6cOYNz584hNTUVHh4eIvVKyqElefLkCdvODQwM4OrqCiUlJVRUVCA1NRXq6uqYNm0au/7du3cxefJkFBUVYfjw4fDw8MCjR4+Ql5cHb29vbNu2DeHh4UL7kMe1M+lBGEJeseTkZKaxsVGo7OHDh8z06dMZAMx3333HlicmJjIAGADMsmXLmMePH7PL4uLiGACMk5OTUF18Pp8BwOjo6DBFRUVs+f3795lx48ax9d28eZNddvPmTbZ8woQJTF1dnchxT5w4kQHAFBYWiizz9fVlADBJSUld/ThIH3T//n1GV1eXAcDExsYyHR0dQsuzs7OZ+vp6hmEYJi8vjykpKRGpY//+/QwAxs3NTWQZj8djng3nnduwq6src//+fXaZ4HtkamrKDBkyhDl37hy7rLKykhk6dCgDgCktLZXpvEnvcPPmTUZZWZlRVlZmMjMz2fKmpibGw8ODbUedl4lrc0FBQRLjXlFREVNVVcX+3Z24nJmZyQBggoKCxJ6HYP+dj3Pjxo0MAGb79u0i69+6dUvouyb4zri4uIitn/ReLi4uIu2JYRjm/fffZwAwCxcuZBoaGtjy6upqZvz48QwAJjU1VWibkydPCsVThmGYlpYWZvHixQwAJiIiQmiZoN0CYLy9vZnm5maR43te2xbE640bN3bp3AAwPB5P7Dakb+j8O+/k5MTU1NSwywoKChhFRUVGTU1NqG3L0n49PDyE8vULFy4wAwcOZBQUFJj//e9/QtsJtjEwMBDKJaqrqxkrKysGAPPll1+y5fX19YyamhozdOhQpq2tTaiu9vZ2Zvjw4YyioiJz586drn9QpEfqavttb28Xajudc+lLly4x2trajLq6OnP37l22vDvxU5pcQHDcpqamTEVFhcjye/fuMadOnWKePHkiVF5WVsaMHDmSUVBQEInZgnwlMTFR4n5J/yBtPv348WMmKSmJaW1tFVqnurqasbOzYwAw2dnZQsu2bt3KAGAcHBzYa0+GYZiLFy8ygwYNotyhD5O2XckSN59370wQ4wAwH3/8sVAM3717NwOAGTZsmFCeLE0OLS52CrabNWuWSByur69n/vjjD6EyLy8vBgCzbt06oRyktLSUMTExYbhcLvPnn38KnW9Xr51Jz0ZDaZFXbvbs2VBXVxcq09DQQGxsLADg+PHjItsYGxtj+/btQr2Kw8LCoKWlhfz8fLS1tbHlgt6eq1evxujRo9lyLS0tREdHv/D4duzYAU1NTZHyJUuWAAASEhKEymtra3Hs2DHo6elhxowZL6yf9H3x8fGoqanB9OnTsXLlSpHejs7Ozhg8eDAAwNHRUez8DSEhIXjzzTeRlZWFBw8eSL1vLpeL+Ph4od4agYGB0NXVRUlJCT744AM4OTmxy4YNG4YFCxYAAE0C3E/s378fra2tCAwMZIfvAQA1NTXs3LlT6t651dXVACB2cuvRo0cL9UKSNS5L63nHxOPxaK6Ufqy6uhoJCQkwMjJCfHy8UC95XV1dfPPNNwDA/lfgrbfeEun9pqysjLi4OCgqKorNWQTr7Ny5EyoqKnI+E9LfKSgoICEhQWiMbTs7O3h5eeHRo0f4448/2PLutl8Oh4OdO3cK5ev29vYICwtDR0cHvv76a7HbffbZZ0Jz5+jq6rIxfteuXWz54MGD4evri+rqapw4cUKojrS0NFRWVmLmzJnQ09N70cdBehlp229KSgquXLkCf39/LF++XCg3sbGxwaeffoqmpiZ8//33/9mxb9u2DcOHDxcp19bWxtSpU6GgIHyrxcjICBs2bEBHR4fE4Z8JkTafVlRUxJw5c6CkpCS0jq6uLrZt2wZA9D6KIFbHxsay154AYGtri7CwMPmdBOlxunKdJgtJ984EeDweIiIihGL4+++/j/Hjx6OqqgrJycki23Q1hxacq6urq0gcHjx4MMaNG8f+XVhYiLS0NEycOBGff/45BgwYwC4T3HN88uSJ0D0/eV07k56DhtIiPcKNGzfw66+/oqSkBE1NTejo6GDHubxx44bI+q6urkJBC3iaHBgbG+PixYuora1lg3tubi4AYN68eSL1uLm5QVtbG7W1tWKP67XXXoOdnZ3YZfPnz8eHH36I77//Hl988QVUVVUBAAcOHEBrayuCg4NFjpH0T2fOnAHw/x+mvUhjYyNSUlJQWFiI+/fv4/HjxwCA27dvg2EYlJaWwtbWVqq6Ro4cCVNTU6EyBQUF8Hg81NTUiH1dVXCz+Nk5IUjflJOTA+BpTHuWubk5bGxscOnSpRfWM27cOKSlpSEwMBCffPIJ7OzsRJJRAVniclcIEt+wsDBs2bIFTk5Ochumi/Ru2dnZePz4MTw9PaGsrCyyfOzYsdDQ0EBBQYHIssrKSqSkpODatWt4+PAhO463kpKS2JwFeHrTQdwNNEJkNXLkSJiZmYmUC8qe/S3vTvu1sbGBubm5SLm/vz+ioqLY35Fn+fn5iZR5enpCS0sLxcXFqKmpga6uLgBg6dKlSExMRHx8PHx8fNj1BfP5iRu6lvR+0rbf06dPAwA7j8ezBENdiYvZLwOHw3lhB7icnBxkZWWhsrISLS0tYBiGPR9J3zVCupJPA09v7Kanp6O8vByPHj0CwzBoaGgAINzO/vnnH/z7778YPnw4Jk6cKFKPv78/+0CF9D1dbVfd8bx7ZwI+Pj5ir8X8/f1x/vx55OTkwN/fX2hZV3Noa2trKCgoIDo6Gvr6+nj77behoaEhdl3Bb8usWbPEPtAQ99sir2tn0nPQ3QHySjEMgzVr1iA2NlbihF+CH/bODAwMxK4r6PHZedL2qqoqcDgcidsYGhpKvAFnaGgo8dhVVFQQGBiIuLg4HD16FAsXLgTw9A0SDoeDRYsWSdyW9C///vsvAEjVOz0jIwN+fn6oqamRuI6474QkkpIIQa9PccsFyzp/j0jfJZgkTlK8MzQ0lCq5W7t2LXJycpCSkoKUlBQMHjwY48ePx/Tp0xEcHCyUkMoSl7siODgY6enpOHz4MCZPngw1NTW2J+q7776LoUOHyrwP0jsJJo38+uuvJfZ2B56Ol9xZTEwMPvroI6E3U6XxvHyCEFl0JSfubvuVNF/eyJEjAUDsZKNaWloSb0TweDzU1dWhqqqKfTDi4OAAGxsbnD59GuXl5eDxeLh9+zZ+/fVXGBoaiswfSPoGaduvIGb7+vrC19dXYn337t2T7wFKMHToULEP1QHgwYMHmDNnDjIyMiRu35VcnvQv0ubTbW1tCA4OxqFDhyTW1bmdSZPvk76rK9dp3SVNG+pOPtHVtmlmZobo6GiEh4fD398fXC4XVlZWmDJlCkJCQmBpacmuK/htWb9+PdavXy+xzs6/LfK6diY9Bz0YIa/Uzz//jJiYGBgYGCAuLg4TJkyArq4uBgwYgLa2NigrK4t9YCLP19MkPZAB8MLX9ZYuXYq4uDgkJCRg4cKFyM3NRVFREdzc3DBq1Ci5HSPpG17UbhsbGzF//nzU1tbi008/hb+/P3g8HlRVVcHhcBAQEIBDhw49t812dZ/0qicRtCdZ28KgQYOQkZGB33//HSkpKcjKysLZs2eRnp6Obdu2gc/nSz10VVfauICg13NnXC4XP//8M8LDw3H8+HFkZmYiPz8f586dw7Zt23Dq1Ck4Ojp2eV+k93vy5AmApz3hx4wZI9U2+fn5WL16NQYPHoy9e/fC1dUV+vr67M2xYcOGSXzT7mUNoSWu3ZP+RdrYLUv7lTdJMX7JkiVYunQp9u/fj4iICCQmJqK9vR2LFi2Se89W0jNI234FMdvLy+u5nRosLCyk3rcs8fN5MX39+vXIyMiAs7MzIiMjYWVlBU1NTXC5XKSnp2PatGndynNI/yBtPh0TE4NDhw7BysoK0dHR7ETXAwYMQHFxMczNzYXa2Yvyfbom7NvkdZ32vLgpS64ryz05cVatWoV58+bh2LFjOH36NPh8PrZv347Y2Fjs2LGDHTpO8Nvi5OQkNPTnszoP9yiva2fSc9CDEfJKCcYQ/PrrrzF9+nShZWVlZXLZx2uvvYZbt26hoqJCbLCvqKjodt3m5uZwdXVFVlYWrl+/Tq/7E7FGjBiBa9euoaSk5LkXbHw+H7W1tfDx8UFkZKTIcnl9JwjpbNiwYSguLkZ5ebnYB7r//POP1HVxOBxMmjSJfe24pqYGK1aswKFDh/Dxxx/j559/BtC9uCwYQ7mxsVHsvgVvZoljY2MDGxsbbNq0CQ8fPkRERARiYmKwYsUKnD9/XurzI32HoJeyq6srYmJipNpGkLNs2bIFQUFBQsuam5tx584d+R4kZGv3hHQmS/stLy9/bvmwYcNEltXV1aGhoUFsL1TB78qzY5ovWLAAa9euxf79+/Hpp59i3759UFBQwLvvvvucMyP9gSBmL126FDNnzpRqm1cVP5OTk8HlcnHixAmheRwAyuWJdKTJpwUxXfBwpDNx7UwQp18Uz0nfJU27etlxU1I7E+QF4vKJ7hoxYgSWLVuGZcuWob29HT/99BNCQkKwatUqLFiwAJqamuxvy9y5c7F8+XKp6pXntTPpGajrDXml6urqADwNWs86fPiwXPYhGEPz6NGjIsuysrJkfuVaMG9ETEwMDh8+jCFDhmDOnDky1Un6lilTpgAA9u7d+9z1nvd9KCkpoVcyyUshSI6PHDkisqy4uBiFhYXdrltXVxebNm0CAFy+fJkt705cFtxAKy4uFllWW1sr9fdj0KBB2Lp1KzgcjtAxCS4E2tvbpaqH9G5ubm7gcrlITU1le4u9yPNi9JEjR15KD+Dntfvr16/TxReRmiztt7CwUGwbFAzh8uabb4rdTvAwvLNTp06hrq4Oo0aNEun5P3DgQAQEBKCiogJr165FWVkZvLy8JA63RPoPQS597NgxqbfpTvyURy5QV1cHDQ0NkYcigPyub0n/Ii6f7up9FB6PBwMDA1RWViIvL09k+U8//STHIya9gbh29bLzzqSkJLF5t6D9SconZKWoqIh33nkH9vb2aGtrY8+vO78tL/Pambwa9GCEvFKCifX27t0rdEHG5/MRHR0tl30sXrwYALB9+3Zcv36dLa+vr8e6detkrn/OnDnQ1dXF3r178ejRIwQGBkocc5b0T6GhodDR0UFKSgq++uorkZsPfD4fDx48YL8Pv/zyi9AcI/X19Vi0aBE7CTsh8hQSEgIlJSUcOHAAfD6fLW9ubsaKFSukHmpiz549uHnzpkh5WloaAOFxWLsTl42MjGBoaIjLly/j+PHjbHlTUxPee+89PHz4UGSbgwcP4sqVKyLlv/32GxiGETomHR0dDBgwAKWlpVLfKCe91/DhwxEcHIwbN25g4cKFYh/G5ebm4tdff2X/FsToffv2CcXjoqKi545LLAt7e3uoqakhLS0NFy9eZMtramqwaNEiGkqLSE2W9tvR0YHly5fj0aNHbNnFixexa9cuKCgosJ2EnhUZGcmO3w08HaNbEOP/7//+T+w2S5cuBQDExcUBoLewyVNz586FhYUFvv32W0RFRYnkxG1tbfjll1+EOjx0J37KIxcwMzNDfX29yIPB2NhYZGZmdqtO0n9Im08LYvqePXuE1jt69CgOHDggtm5BrF69erVQ3lxYWIhdu3bJfvCkx5K2Xb3svLO8vBwRERFCZXv37kVeXh709fXh7e0tU/0AkJmZiTNnzogca3l5Oa5evSo0z6WjoyPc3d2RmZmJDz/8UORNmY6ODqSnp7MTrgPyu3YmPQc9GCGv1PLly6Guro7du3fDysoK/v7+cHZ2houLC3thJCsXFxeEhYWhpqYG1tbWmD59OubNmwdTU1N0dHSw48sLegh1lZKSEkJCQti/Q0ND5XLcpO8YMmQIDh8+DA0NDSxbtgyjRo2Cr68vZsyYAWNjYzg7O6Ourg52dnbw8PDAP//8AzMzM3h7e8Pb2xtGRkaoqqrCrFmzXvWpkD7I2NgYUVFRaGlpgZubG6ZMmQI/Pz+YmpriypUrIsMcSrJnzx4YGxvD0tISc+fOhZ+fH2xsbLBy5Uqoqqpi48aN7LrdjcuCXk0+Pj6YPHkyZs6cCRMTE1y5ckXs0BpJSUl44403YGpqCm9vbwQEBGDixInw9vYGl8vF1q1b2XWVlJTg6emJO3fuYOzYsQgMDERoaCgSExO7+pGSXmLHjh1wc3PDoUOH2Fjs5+cHV1dXGBgY4M0330R6ejq7fkhICPT19ZGSkgJzc3P4+vrCw8MD1tbWcHJykjihpCwGDhyINWvWoL29HZMmTYKXlxe8vLxgZmYGhmEwYcIEue+T9E2ytN/p06fj6tWrMDExga+vLzw9PTFhwgQ0NDTg448/xrhx40S2MTQ0xNixY2FpaYmZM2fCx8cHo0aNwl9//QU3Nzd88MEHYvdlbW0NBwcHAE97rr799tvy+QBIr6aoqIjk5GSMGDEC4eHh4PF48PT0xPz58zFhwgTo6enBx8cHpaWl7DbdiZ/yyAU++ugjAICfnx+cnZ0REBAAS0tLrFmzBh9++KHsHwbp06TNp9etWwcul4vw8HDY2dkhICAA9vb2mDdvnsR2tnbtWowfPx55eXkwMTHB/Pnz4eXlhfHjxyMgIOC/PE3yH5O2Xb3svPO9997D559/DisrKwQEBMDBwQFLlizBgAEDkJiYCFVVVZnP9c8//4SHhwf09fXh5eWFd955B9OmTYOFhQXq6+uxYsUKoSG7fvjhB4wZMwZxcXHg8Xhwd3eHn58fnJycoK+vj2nTpuGPP/5g15fXtTPpOejBCHmlzMzMUFBQgBkzZuDevXs4ceIEGhsb8c0338jtjREA2LlzJ3bu3AkjIyOcPn0aubm58PPzQ0ZGBu7duwcOhwMtLa1u1+/u7g7g6fAwlpaW8jps0oe4ubmhsLAQixcvRnt7O44dO4b8/HwMHToU27Ztg76+PgDg+PHj2LBhA3R1ddmeGn5+fsjPz4empuarPQnSZ61cuRKHDx+GtbU1cnJycPbsWbi6uiI/Px/a2tpS1bF582a8++674HA4OHv2LFJSUvDo0SMsXrwYf/31l0gi3Z24HBISgsTERIwePRq///47Lly4gBkzZiAvL09sDF+1ahXCwsKgoaEBPp+P5ORkVFdXw9/fHwUFBSLDHiYkJGDhwoWora3Fjz/+iH379iE7O7uLnybpLdTU1JCeno6EhATY2triypUrSE5ORmlpKUxMTPDFF19gzZo17Pra2tooKChAQEAA2tracOLECVRWViIyMpIdUuhl2LRpE6Kjo2FgYICzZ8/iypUrWLRoEU6fPt3tTh2k/5Gl/WprayMvLw9TpkxBZmYmsrKy8PrrryMxMRGbN28Wuw2Hw8HRo0excuVKXL58GampqRg8eDA2bNiAkydPQlFR8lSXgrw6JCTkueuR/sXCwgKFhYXYtGkThg4dipycHJw8eRI1NTVwdnZGYmIiOyyKQHfip6y5wIIFC3Dy5Ek4OjqisLAQaWlpGDZsGDIyMqSeH4X0X9Lm087OzsjJycHkyZNRVlaG1NRUKCkpISkpiZ1Y+lnKyso4c+YM1qxZA2VlZRw/fhxlZWXYsmULvvrqq//yNMl/rCvXaS8z75w4cSKys7Ohr6+P1NRUXL16Fe7u7sjKyoKnp6espwngaWeOTz75BGZmZvjzzz9x5MgR/P3333ByckJycjJiY2OF1tfT00N+fj5iYmIwatQoFBQU4NixY6ioqICNjQ127dqFd955R2gbeVw7k56Dw7yMAZEJ6SUqKysxcuRImJqa4urVq92uZ/HixYiPj0diYiKCg4Pld4CEENLPyCsuE0IIkU1WVhbc3NwQFBSEb7/9VurtOBwOeDye0DBa0mIYBhYWFrhx4wZKSkpgbGzc5ToIIYQQ0nNs2rQJERERdL+M9Ej0xgjpF65du4bm5mahspqaGoSEhKC9vV2mV0fLy8vx/fffQ0dHB76+vrIeKiGE9AsvMy4TQgjpnY4ePYri4mK89dZb9FCEEEIIIYS8VPRuMukX4uLi8OOPP8LGxgavvfYa7t69i0uXLuHhw4ewtbUVGipDWtHR0fjrr79w+vRpNDc34/PPP5fLmIiEENIfvIy4TAghpHcKDQ1FfX09UlNTweVyERkZ+aoPiRBCCCGE9HH0YIT0C3PmzEFVVRUuXbqE8+fPg8vlwsTEBD4+Pli9enW3HmicPHkS2dnZGD58OCIiIrBs2bKXcOSEENI3vYy4TAghpHfat28fFBUVYWZmhs2bN8PW1vZVHxIhhBBCCOnjaI4RQgghhBBCCCGEEEIIIYT0GzTHCCGEEEIIIYQQQgghhBBC+g16MEIIIYQQQgghhBBCCCGEkH6DHowQQgghhBBCCCGEEEIIIaTfoAcjhBBCCCGEEEIIIYQQQgjpN+jBCCGEEEIIIYQQQgghhBBC+g16MEIIIYQQQgghhBBCCCGEkH6DHowQQgghhBBCCCGEEEIIIaTfoAcjhBBCCCGEEEIIIYQQQgjpN+jBCCGEEEIIIYQQQgghhBBC+o3/B5l6cCu8OxK/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valued_arr = []\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    nrows=1, ncols=1, figsize=(20, 10)\n",
    ")\n",
    "\n",
    "bar_plot = plt.bar(label_names, frame_count_idxs)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.title(f'Predicted label: {label_names[frame_max_class]}', fontsize=20)\n",
    "plt.bar_label(\n",
    "    bar_plot, label_type='center', fontsize=25, color='black', padding=15\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1965aee5-65a3-4678-8aad-1ac5095008ab",
   "metadata": {},
   "source": [
    "### Soft Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c8bc0c44-9823-4b6a-aa36-2d9a3952b218",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2024-04-22T07:10:50.755735Z",
     "iopub.status.busy": "2024-04-22T07:10:50.755735Z",
     "iopub.status.idle": "2024-04-22T07:10:50.775519Z",
     "shell.execute_reply": "2024-04-22T07:10:50.774168Z",
     "shell.execute_reply.started": "2024-04-22T07:10:50.755735Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pred_prob = model_frame_preds['predictions']\n",
    "if len(frames) > 1:\n",
    "    pred_prob = np.average(model_frame_preds['predictions'], axis=0)\n",
    "    # pred_prob = softmax(\n",
    "    #     np.sum(model_frame_preds['predictions'], axis=0)\n",
    "    # )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8f8ec6b3-162d-46fb-af8b-4f11f50383ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:10:50.979046Z",
     "iopub.status.busy": "2024-04-22T07:10:50.978045Z",
     "iopub.status.idle": "2024-04-22T07:10:50.996767Z",
     "shell.execute_reply": "2024-04-22T07:10:50.995777Z",
     "shell.execute_reply.started": "2024-04-22T07:10:50.979046Z"
    }
   },
   "outputs": [],
   "source": [
    "model_frame_preds['avg_pred'].append(pred_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8b44c5f1-2ff9-45c0-9457-3f7fc301b465",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:10:51.497077Z",
     "iopub.status.busy": "2024-04-22T07:10:51.497077Z",
     "iopub.status.idle": "2024-04-22T07:10:51.824740Z",
     "shell.execute_reply": "2024-04-22T07:10:51.823743Z",
     "shell.execute_reply.started": "2024-04-22T07:10:51.497077Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valued_arr = []\n",
    "\n",
    "for label, pred in zip(label_names, pred_prob):\n",
    "    # print(label, pred)\n",
    "    valued_arr.append(f'{label}\\n{pred:.2%}')\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    nrows=1, ncols=2, width_ratios=[0.4, 0.6], figsize=(20, 10)\n",
    ")\n",
    "\n",
    "pltDisplay(avg_image, ax=ax[0])\n",
    "bar_plot = ax[1].bar(valued_arr, pred_prob)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.title(f'Predicted label: {label_names[np.argmax(pred_prob)]}', fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a01b68fd-0f1c-49d1-a8d8-3621bc0fb969",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-12T15:19:27.701549Z",
     "iopub.status.busy": "2024-04-12T15:19:27.700553Z",
     "iopub.status.idle": "2024-04-12T15:19:28.755550Z",
     "shell.execute_reply": "2024-04-12T15:19:28.754574Z",
     "shell.execute_reply.started": "2024-04-12T15:19:27.701549Z"
    }
   },
   "source": [
    "plt.style.use('_mpl-gallery')\n",
    "\n",
    "# make data\n",
    "x = 0.5 + np.arange(len(frames))\n",
    "y = rnd_gen.integers(0, len(label_names), len(frames))\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "ax.scatter(x, y)\n",
    "\n",
    "ax.set(xlim=(0, len(frames)), xticks=np.arange(len(frames)),\n",
    "       ylim=(0, 8), yticks=np.arange(len(label_names)), yticklabels=label_names)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "414f14ac-01f1-46fd-a457-f6c52942a8a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-14T10:26:49.508339Z",
     "iopub.status.busy": "2024-03-14T10:26:49.507380Z",
     "iopub.status.idle": "2024-03-14T10:26:49.527138Z",
     "shell.execute_reply": "2024-03-14T10:26:49.524729Z",
     "shell.execute_reply.started": "2024-03-14T10:26:49.508339Z"
    }
   },
   "source": [
    "# Model Prediction - Flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "873f2067-1f2c-4ee0-b464-3cff2dad43db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:12:15.354862Z",
     "iopub.status.busy": "2024-04-22T07:12:15.354862Z",
     "iopub.status.idle": "2024-04-22T07:12:15.787782Z",
     "shell.execute_reply": "2024-04-22T07:12:15.787782Z",
     "shell.execute_reply.started": "2024-04-22T07:12:15.354862Z"
    }
   },
   "outputs": [],
   "source": [
    "model_flow = tf.saved_model.load(\n",
    "    Path('Models/Flow/Simple_MNIST/Run_6/E2E/')\n",
    ")"
   ]
  },
  {
   "cell_type": "raw",
   "id": "cc6760fe-b560-4831-8b2d-7e86b8e5e78d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-12T14:32:09.342210Z",
     "iopub.status.busy": "2024-04-12T14:32:09.341205Z",
     "iopub.status.idle": "2024-04-12T14:32:09.350202Z",
     "shell.execute_reply": "2024-04-12T14:32:09.348200Z",
     "shell.execute_reply.started": "2024-04-12T14:32:09.342210Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "source": [
    "parameters = {\n",
    "    'WINSIZE': 7,\n",
    "    'PYR_SCALE': 0.3,\n",
    "    'POLY_SIGMA': 1.5,\n",
    "    'POLY_N': 1,\n",
    "    'LEVELS': 10,\n",
    "    'ITERATIONS': 1,\n",
    "    'FLAGS': 0\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1b7e560a-2646-4d3c-ad64-321974ba7220",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:12:16.880977Z",
     "iopub.status.busy": "2024-04-22T07:12:16.879979Z",
     "iopub.status.idle": "2024-04-22T07:12:16.886970Z",
     "shell.execute_reply": "2024-04-22T07:12:16.885975Z",
     "shell.execute_reply.started": "2024-04-22T07:12:16.880977Z"
    }
   },
   "outputs": [],
   "source": [
    "# TVL1\n",
    "parameters = {\n",
    "    'warps': 2,\n",
    "    'useInitialFlow': False,\n",
    "    'theta': 0.3,\n",
    "    'tau': 0.25,\n",
    "    'scaleStep': 0.8,\n",
    "    'outerIterations': 3,\n",
    "    'nscales': 3,\n",
    "    'medianFiltering': 5,\n",
    "    'lambda_': 0.15,\n",
    "    'innerIterations': 3,\n",
    "    'gamma': 0.0,\n",
    "    'epsilon': 0.01\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "34f15b94-1ff2-4a62-a7f4-b240a485ca8d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:12:23.605041Z",
     "iopub.status.busy": "2024-04-22T07:12:23.605041Z",
     "iopub.status.idle": "2024-04-22T07:12:23.631895Z",
     "shell.execute_reply": "2024-04-22T07:12:23.631895Z",
     "shell.execute_reply.started": "2024-04-22T07:12:23.605041Z"
    }
   },
   "outputs": [],
   "source": [
    "def parse_image(filename):\n",
    "    image = tf.io.read_file(filename)\n",
    "    image = tf.image.decode_jpeg(image, channels=1)  # Farneback\n",
    "    # image = tf.image.decode_jpeg(image, channels=3)  # TVL\n",
    "    image = tf.image.resize(image, [IMG_HEIGHT, IMG_WIDTH])\n",
    "    image = image / 255\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "ab623886-ddb8-4cb1-9965-9201a6b93737",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-12T17:33:52.031818Z",
     "iopub.status.busy": "2024-04-12T17:33:52.031818Z",
     "iopub.status.idle": "2024-04-12T17:33:52.057818Z",
     "shell.execute_reply": "2024-04-12T17:33:52.056841Z",
     "shell.execute_reply.started": "2024-04-12T17:33:52.031818Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module <module 'utils_flow' from 'C:\\\\Users\\\\mic-c\\\\Documents\\\\GitHubRepo\\\\CNN-emotion-recognition\\\\utils_flow.py'> reloaded\n"
     ]
    }
   ],
   "source": [
    "reload_module(ut_fl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "efc5ceda-47f9-4fb0-bf65-e10607b958f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:12:35.204516Z",
     "iopub.status.busy": "2024-04-22T07:12:35.204516Z",
     "iopub.status.idle": "2024-04-22T07:12:35.869937Z",
     "shell.execute_reply": "2024-04-22T07:12:35.868936Z",
     "shell.execute_reply.started": "2024-04-22T07:12:35.204516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Progress: [####################] 100%             34/34'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save = True\n",
    "# test = True  # for choosing the right saving path\n",
    "view = False  # True if you want to view images while generating\n",
    "of_mode = 1  # 0: farneback, 1: TVL, 2: pyflow\n",
    "\n",
    "gen_flows = []\n",
    "# flows = []\n",
    "\n",
    "folder_flow_path = Path(folder_path, 'Flow')\n",
    "folder_flow_path.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "total_elements = len(frames)\n",
    "frame_number = 0\n",
    "\n",
    "p1 = pb.Progressbar('p1', elements=total_elements)\n",
    "\n",
    "for prvs_path, next_path in zip(frames[:-1], frames[1:]):\n",
    "\n",
    "    # prvs = cv2.imread(str(prvs_path))\n",
    "    prvs = parse_image(str(prvs_path))\n",
    "    # next = cv2.imread(str(next_path))\n",
    "    next = parse_image(str(next_path))\n",
    "    p1.add_to_progress(1)\n",
    "    frame_number += 1\n",
    "    # folder_flow_path = Path('test')\n",
    "\n",
    "    # if (save):\n",
    "    #     # folder_frame_path.mkdir(parents=True, exist_ok=True)\n",
    "    #     folder_flow_path.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "\n",
    "    flow_string = f'{frame_number:03d}'\n",
    "\n",
    "    save_string = f'{flow_string}.jpg'\n",
    "\n",
    "    prvs_rgb = prvs\n",
    "    # prvs = cv2.cvtColor(prvs, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "\n",
    "    next_rgb = next\n",
    "    # next = cv2.cvtColor(next, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    if (of_mode == 0):\n",
    "        gr1 = ut_fl.calc_flow_farneback(prvs, next, parameters)\n",
    "    elif (of_mode == 1):\n",
    "        gr1 = ut_fl.calc_flow_TVL1(prvs.numpy(), next.numpy(), parameters)\n",
    "    elif (of_mode == 2):\n",
    "        gr1 = ut_fl.calc_flow_pyflow(prvs_rgb, next_rgb, parameters)\n",
    "\n",
    "    # if (save):\n",
    "    #     # gr1_res = cv2.resize(gr1, dsize=(IMG_HEIGHT, IMG_WIDTH))\n",
    "    #     cv2.imwrite(str(Path(folder_flow_path, save_string)), gr1)\n",
    "    cv2.imwrite(str(Path(folder_flow_path, save_string)), gr1)\n",
    "    gen_flows.append(gr1)\n",
    "    # flows.append(gr1)\n",
    "\n",
    "    if (view):\n",
    "        # cv2.imshow('Vector Flow', draw_flow(next, flow))\n",
    "        cv2.imshow('Video', prvs)\n",
    "        # cv2.imshow('BGR Flow', bgr)\n",
    "        cv2.imshow('GR1 BGRTOGRAY', gr1)\n",
    "        # cv2.imshow('GR2 SPLIT', gr2)\n",
    "\n",
    "        k = cv2.waitKey(200) & 0xff\n",
    "        if k == 27:\n",
    "            break\n",
    "\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "p1.update_progress(total_elements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "adda36aa-0a24-494a-b78b-c8aa5e7208fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:12:40.041357Z",
     "iopub.status.busy": "2024-04-22T07:12:40.041357Z",
     "iopub.status.idle": "2024-04-22T07:12:40.058331Z",
     "shell.execute_reply": "2024-04-22T07:12:40.057338Z",
     "shell.execute_reply.started": "2024-04-22T07:12:40.041357Z"
    }
   },
   "outputs": [],
   "source": [
    "flows = [str(img) for img in folder_path.glob('Flow/*')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "85a23c77-6f1e-412e-a7fe-68d81b96f895",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:12:40.701517Z",
     "iopub.status.busy": "2024-04-22T07:12:40.700528Z",
     "iopub.status.idle": "2024-04-22T07:12:40.870627Z",
     "shell.execute_reply": "2024-04-22T07:12:40.870627Z",
     "shell.execute_reply.started": "2024-04-22T07:12:40.701517Z"
    }
   },
   "outputs": [],
   "source": [
    "flow_avg_img = calc_avg_image(flows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "43c1a8e0-c573-4718-9ef8-f4f1fb5d8aec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:12:41.124326Z",
     "iopub.status.busy": "2024-04-22T07:12:41.124326Z",
     "iopub.status.idle": "2024-04-22T07:12:41.274913Z",
     "shell.execute_reply": "2024-04-22T07:12:41.274913Z",
     "shell.execute_reply.started": "2024-04-22T07:12:41.124326Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.show_images([flow_avg_img])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a8c462bc-099b-4160-80c5-c36e529bb822",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:13:15.456741Z",
     "iopub.status.busy": "2024-04-22T07:13:15.456741Z",
     "iopub.status.idle": "2024-04-22T07:13:15.654126Z",
     "shell.execute_reply": "2024-04-22T07:13:15.653127Z",
     "shell.execute_reply.started": "2024-04-22T07:13:15.456741Z"
    }
   },
   "outputs": [],
   "source": [
    "model_flows_preds = {'class_ids': [], 'max_class_pred': [],\n",
    "                     'predictions': [], 'avg_pred': []}\n",
    "\n",
    "for frame_path in flows:\n",
    "    frame = cv2.imread(str(frame_path), cv2.IMREAD_GRAYSCALE)\n",
    "    frame = cv2.resize(frame, dsize=(IMG_HEIGHT, IMG_WIDTH))\n",
    "    # frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "    frame = frame / 255\n",
    "    frame = tf.constant(frame, dtype=tf.float32)\n",
    "    frame = utils.extend_tensor(frame, 0)\n",
    "    frame = utils.extend_tensor(frame)\n",
    "    res = model_flow(frame)\n",
    "\n",
    "    model_flows_preds['class_ids'].append(\n",
    "        res['class_ids'].numpy()[0]\n",
    "    )\n",
    "    model_flows_preds['max_class_pred'].append(\n",
    "        np.max(res['predictions'].numpy()[0])\n",
    "    )\n",
    "    model_flows_preds['predictions'].append(\n",
    "        res['predictions'].numpy()[0]\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50480d82-b36d-46e9-b345-d64b818cdf7d",
   "metadata": {},
   "source": [
    "### Hard Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5324e5fd-e125-41b0-a476-c9f347a4ee99",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:13:16.782587Z",
     "iopub.status.busy": "2024-04-22T07:13:16.782587Z",
     "iopub.status.idle": "2024-04-22T07:13:16.794940Z",
     "shell.execute_reply": "2024-04-22T07:13:16.793941Z",
     "shell.execute_reply.started": "2024-04-22T07:13:16.782587Z"
    }
   },
   "outputs": [],
   "source": [
    "flow_max_idxs = [np.argmax(ar) for ar in model_flows_preds['predictions']]\n",
    "flow_count_idxs = [flow_max_idxs.count(i) for i in range(len(label_names))]\n",
    "flow_max_class = np.argmax(flow_count_idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c54ce49f-ea11-4921-9675-855e07e92574",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:13:17.323480Z",
     "iopub.status.busy": "2024-04-22T07:13:17.323480Z",
     "iopub.status.idle": "2024-04-22T07:13:17.569450Z",
     "shell.execute_reply": "2024-04-22T07:13:17.568044Z",
     "shell.execute_reply.started": "2024-04-22T07:13:17.323480Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+nHzyydluu+3Sp0+fNDY2Zsstt8wZZ5yRKVOmLLH9ouftpEmTctxxx2WDDTZIXV1dLrnkkpRKpYwePTpJ8rOf/awy38L7Riz6v3va0tafFYpn9uzZOeOMM7LR/9fencfVlP9/AH/dboqIUimkS6VCqFSytEmUsSW0WCqyzLexbw1jKB5ID9XYR9F8MWOfUERoUVMRvlkmW0WmLCVFRSWd3x8e9/y67r3cutdoeT//6dHnnPM559zH577v+5zP53wOjwdFRUXo6+sjKChIqA1L036fPXsGb29vaGpqsrnE/v37RR4Pf5766upqrFmzhv1N0NXVxc8//4zKykqB9Y2NjcHhcMTmOY8fP4acnBx69uwp8ntJmp66eaqk7RcAioqKsHTpUhgaGqJ169ZQVVWFs7OzyKft6hs/Jc0F6rbvwMBAGBkZQVFREePHjwcAVFZWYu/evRg3bhx0dXXRpk0bqKiowMbGBocPH5bqcyMtgyT5NMMwOHToENzd3WFgYIC2bdtCWVkZlpaW2LlzJ2pra0XWXVFRgRUrVkBHRwetW7eGkZERQkJCKLa2AJK0q4bknV+6dwYIvgvkypUrGDlyJFRUVNC+fXs4OjoiPT1daF9fyqE/rbeuiooKBAUFwcTEBCoqKmjXrh309PQwadIknD9/Xmhf5eXlCAwMRN++faGkpIT27dvD1taWfepclPpcO5PGjZ4YId/czJkzUVFRgT59+qBv37548+YNrl69ilWrVuHSpUuIi4sDl8sV2Obx48ewsLBA69atMXToULx48QJpaWkYP348YmNjMWLECIH1Fy5ciK1bt0JRUREODg5o06YNDhw4gNTUVMjLi/8avHv3Dra2tsjLy4OtrS3MzMygqqqKCRMmYOfOnQgPD8esWbOEtouPj0dubi6cnJzA4/Fk80GRJu3y5csYO3YsXr9+DV1dXYwbNw4VFRXIysrCqlWr4Onpie7duyMiIgJBQUHo3bs3zM3N0bp1a2RlZeHAgQM4deoUkpOT0a9fP4n3W11dDQcHB+Tk5MDKygrl5eW4fPkyXFxccO7cOdy+fRvLly+HhYUFRowYgeTkZGzbtg3FxcX4/fffv+InQhqT7du3Y968eeByubC1tYW6ujouXrwIKysr9O/fX6I6bty4gaFDh6KyshKWlpawtLREWVkZcnNz8csvv2D8+PECjzk3NC7XR35+PszMzPDy5Uv069cPY8aMQWVlJfLy8hAeHo5BgwbB0NAQJiYmcHV1xYkTJ6CpqQknJye2jqFDh8rkWMi/z8TEBF5eXjh37hxevHgBV1dXtGvXDgCgpaWFiooKODs7Izk5Gerq6hg6dCjk5OSQlpYGX19fZGRkYPfu3QJ1Llu2DJmZmTA2NsawYcNQVVWFGzduICgoCDExMUhPT2f3UVdRUREsLCxQU1PDfk+UlJTg5eWF58+f4/z589DT02PbW2N6Bwpp3KqrqzFixAj8/fffsLS0RK9evZCUlAR/f3+UlZVh/fr17LoNbb+vXr2ClZUVqqqqYGdnh5KSEiQkJMDLywuPHj3CmjVrhLZhGAYTJ07ExYsX4eDgABMTE1y6dAnr1q1Damoqzp8/z+b3c+bMwfz58xEREYHNmzcL1cXvqPT19RXZKU+arvq033v37mH48OEoKCiAnp4eRo0aheLiYsTHxyMuLg4HDhyAp6dng4+lPrlAbW0txo8fj8uXL8PW1hb9+vWDmpoagI/Xqb6+vtDU1ISRkREsLS3x/PlzpKamIjk5Gffu3RN705EQSfPpqqoqeHp6QlVVFb1792bz3bS0NPj5+eHq1atCN4urqqowYsQIpKamQl1dHWPGjEFZWRn8/f2Rk5PzbU6Y/Cvqe53WEOLundWVmpqKOXPmQF9fH87OzsjOzsbFixdx+fJlxMTEwNHRUahecTm0OB8+fGDbuba2Nuzs7KCgoID8/HzExMSgbdu2GDlyJLv+ixcvMGzYMGRlZaFr165wdHTE27dvkZaWBhcXF2zcuBH+/v4C+5DFtTNpRBhCvrGoqCimvLxcoOzNmzfM6NGjGQDMf//7X7Y8MjKSAcAAYObNm8e8f/+eXRYWFsYAYKytrQXqSk5OZgAw6urqTFZWFlv+6tUrZsCAAWx9jx49Ypc9evSILR80aBBTUlIidNyDBw9mADCZmZlCy9zc3BgAzIkTJ+r7cZBm6NWrV4yGhgYDgAkNDWVqa2sFliclJTGlpaUMwzBMWloak52dLVTHvn37GACMvb290DIej8d8Gs7rtmE7Ozvm1atX7DL+90hfX5/p2LEjc/nyZXZZQUEB06lTJwYAk5OTI9V5k6bh0aNHjKKiIqOoqMgkJCSw5RUVFYyjoyPbjuouE9XmvLy8xMa9rKws5unTp+z/DYnLCQkJDADGy8tL5Hnw91/3ONesWcMAYLZs2SK0/uPHjwW+a/zvjK2trcj6SdNla2sr1J4YhmG+//57BgAzbdo0pqysjC0vLCxkBg4cyABgYmJiBLY5c+aMQDxlGIaprKxkZs+ezQBgAgICBJbx2y0AxsXFhXn37p3Q8X2ubfPj9Zo1a+p1bgAYHo8nchvSPNT9nbe2tmaKiorYZRkZGYy8vDyjpKQk0Lalab+Ojo4C+frVq1eZdu3aMXJycsz//vc/ge3422hrawvkEoWFhYyxsTEDgPnll1/Y8tLSUkZJSYnp1KkTU11dLVBXTU0N07VrV0ZeXp55/vx5/T8o0ijVt/3W1NQItJ26ufSNGzcYNTU1pm3btsyLFy/Y8obET0lyAf5x6+vrM/n5+ULLX758yZw/f5758OGDQHlubi7TvXt3Rk5OTihm8/OVyMhIsfslLYOk+fT79++ZEydOMFVVVQLrFBYWMubm5gwAJikpSWDZhg0bGACMpaUle+3JMAxz/fp1pn379pQ7NGOStitp4ubn7p3xYxwAZuXKlQIxfOfOnQwApkuXLgJ5siQ5tKjYyd9u3LhxQnG4tLSUuXbtmkCZs7MzA4BZvny5QA6Sk5PD6OnpMVwul7l586bA+db32pk0bjSVFvnmxo8fj7Zt2wqUKSsrIzQ0FABw6tQpoW10dXWxZcsWgVHFfn5+UFVVRXp6Oqqrq9ly/mjPJUuWoFevXmy5qqoqgoODv3h8W7duhYqKilD5nDlzAAAREREC5cXFxTh58iQ0NTUxZsyYL9ZPmr/w8HAUFRVh9OjRWLhwodBoRxsbG3To0AEAYGVlJfL9DT4+PhgyZAgSExPx+vVriffN5XIRHh4uMFpj+vTp0NDQQHZ2Nn744QdYW1uzy7p06YIpU6YAAL0EuIXYt28fqqqqMH36dHb6HgBQUlLCtm3bJB6dW1hYCAAiX27dq1cvgVFI0sZlSX3umHg8Hr0rpQUrLCxEREQEevTogfDwcIFR8hoaGvj1118BgP3LN2rUKKHRb4qKiggLC4O8vLzInIW/zrZt29C6dWsZnwlp6eTk5BARESEwx7a5uTmcnZ3x9u1bXLt2jS1vaPvlcDjYtm2bQL5uYWEBPz8/1NbWYteuXSK3+/nnnwXenaOhocHG+B07drDlHTp0gJubGwoLC3H69GmBOmJjY1FQUICxY8dCU1PzSx8HaWIkbb/R0dG4c+cOPDw8MH/+fIHcxNTUFKtXr0ZFRQUOHjz4rx37xo0b0bVrV6FyNTU1jBgxAnJygrdaevTogVWrVqG2tlbs9M+ESJpPy8vLY8KECVBQUBBYR0NDAxs3bgQgfB+FH6tDQ0PZa08AMDMzg5+fn+xOgjQ69blOk4a4e2d8PB4PAQEBAjH8+++/x8CBA/H06VNERUUJbVPfHJp/rnZ2dkJxuEOHDhgwYAD7f2ZmJmJjYzF48GBs2rQJrVq1Ypfx7zl++PBB4J6frK6dSeNBU2mRRuHhw4c4e/YssrOzUVFRgdraWnaey4cPHwqtb2dnJxC0gI/Jga6uLq5fv47i4mI2uKempgIAJk2aJFSPvb091NTUUFxcLPK4OnfuDHNzc5HLJk+ejEWLFuHgwYPYvHkz2rRpAwDYv38/qqqq4O3tLXSMpGW6ePEigP/vTPuS8vJyREdHIzMzE69evcL79+8BAM+ePQPDMMjJyYGZmZlEdXXv3h36+voCZXJycuDxeCgqKhL5uCr/ZvGn74QgzVNKSgqAjzHtU4aGhjA1NcWNGze+WM+AAQMQGxuL6dOn46effoK5ublQMsonTVyuD37i6+fnh/Xr18Pa2lpm03SRpi0pKQnv37+Hk5MTFBUVhZb3798fysrKyMjIEFpWUFCA6Oho3Lt3D2/evGHn8VZQUBCZswAfbzqIuoFGiLS6d+8OAwMDoXJ+2ae/5Q1pv6ampjA0NBQq9/DwQFBQEPs78il3d3ehMicnJ6iqquLBgwcoKiqChoYGAGDu3LmIjIxEeHg4XF1d2fX57/MTNXUtafokbb8XLlwAAPY9Hp/iT3UlKmZ/DRwO54sD4FJSUpCYmIiCggJUVlaCYRj2fMR91wipTz4NfLyxGxcXh7y8PLx9+xYMw6CsrAyAYDt78uQJ/vnnH3Tt2hWDBw8WqsfDw4PtUCHNT33bVUN87t4Zn6urq8hrMQ8PD1y5cgUpKSnw8PAQWFbfHNrExARycnIIDg6GlpYWvvvuOygrK4tcl//bMm7cOJEdGqJ+W2R17UwaD7o7QL4phmGwdOlShIaGin3hF/+HvS5tbW2R6/JHfNZ9afvTp0/B4XDEbqOjoyP2BpyOjo7YY2/dujWmT5+OsLAwHD9+HNOmTQPw8QkSDoeDmTNnit2WtCz//PMPAEg0Oj0+Ph7u7u4oKioSu46o74Q44pII/qhPUcv5y+p+j0jzxX9JnLh4p6OjI1Fyt2zZMqSkpCA6OhrR0dHo0KEDBg4ciNGjR8Pb21sgIZUmLteHt7c34uLicPToUQwbNgxKSkrsSNQZM2agU6dOUu+DNE38l0bu2rVL7Gh34ON8yXWFhITgxx9/FHgyVRKfyycIkUZ9cuKGtl9x78vr3r07AIh82aiqqqrYGxE8Hg8lJSV4+vQp2zFiaWkJU1NTXLhwAXl5eeDxeHj27BnOnj0LHR0dofcHkuZB0vbLj9lubm5wc3MTW9/Lly9le4BidOrUSWSnOgC8fv0aEyZMQHx8vNjt65PLk5ZF0ny6uroa3t7eOHTokNi66rYzSfJ90nzV5zqtoSRpQw3JJ+rbNg0MDBAcHAx/f394eHiAy+XC2NgYw4cPh4+PD/r06cOuy/9tWbFiBVasWCG2zrq/LbK6diaNB3WMkG/qyJEjCAkJgba2NsLCwjBo0CBoaGigVatWqK6uhqKiosgOE1k+niauQwbAFx/Xmzt3LsLCwhAREYFp06YhNTUVWVlZsLe3R8+ePWV2jKR5+FK7LS8vx+TJk1FcXIzVq1fDw8MDPB4Pbdq0AYfDgaenJw4dOvTZNlvffdKjnoTfnqRtC+3bt0d8fDz++usvREdHIzExEZcuXUJcXBw2btyI5ORkiaeuqk8b5+OPeq6Ly+XiyJEj8Pf3x6lTp5CQkID09HRcvnwZGzduxPnz52FlZVXvfZGm78OHDwA+joTv16+fRNukp6djyZIl6NChA/bs2QM7OztoaWmxN8e6dOki9km7rzWFlqh2T1oWSWO3NO1X1sTF+Dlz5mDu3LnYt28fAgICEBkZiZqaGsycOVPmI1tJ4yBp++XHbGdn588OajAyMpJ439LEz8/F9BUrViA+Ph42NjYIDAyEsbExVFRUwOVyERcXh5EjRzYozyEtg6T5dEhICA4dOgRjY2MEBwezL7pu1aoVHjx4AENDQ4F29qV8n64JmzdZXad9Lm5Kk+tKc09OlMWLF2PSpEk4efIkLly4gOTkZGzZsgWhoaHYunUrO3Uc/7fF2tpaYOrPT9Wd7lFW186k8aCOEfJN8ecQ3LVrF0aPHi2wLDc3Vyb76Ny5Mx4/foz8/HyRwT4/P7/BdRsaGsLOzg6JiYm4f/8+Pe5PROrWrRvu3buH7Ozsz16wJScno7i4GK6urggMDBRaLqvvBCF1denSBQ8ePEBeXp7IDt0nT55IXBeHw8HQoUPZx46LioqwYMECHDp0CCtXrsSRI0cANCwu8+dQLi8vF7lv/pNZopiamsLU1BRr167FmzdvEBAQgJCQECxYsABXrlyR+PxI88EfpWxnZ4eQkBCJtuHnLOvXr4eXl5fAsnfv3uH58+eyPUhI1+4JqUua9puXl/fZ8i5duggtKykpQVlZmchRqPzflU/nNJ8yZQqWLVuGffv2YfXq1di7dy/k5OQwY8aMz5wZaQn4MXvu3LkYO3asRNt8q/gZFRUFLpeL06dPC7zHAaBcnkhGknyaH9P5nSN1iWpn/Dj9pXhOmi9J2tXXjpvi2hk/LxCVTzRUt27dMG/ePMybNw81NTU4fPgwfHx8sHjxYkyZMgUqKirsb8vEiRMxf/58ieqV5bUzaRxo6A35pkpKSgB8DFqfOnr0qEz2wZ9D8/jx40LLEhMTpX7kmv/eiJCQEBw9ehQdO3bEhAkTpKqTNC/Dhw8HAOzZs+ez633u+5CdnU2PZJKvgp8cHzt2TGjZgwcPkJmZ2eC6NTQ0sHbtWgDA7du32fKGxGX+DbQHDx4ILSsuLpb4+9G+fXts2LABHA5H4Jj4FwI1NTUS1UOaNnt7e3C5XMTExLCjxb7kczH62LFjX2UE8Ofa/f379+nii0hMmvabmZkpsg3yp3AZMmSIyO34neF1nT9/HiUlJejZs6fQyP927drB09MT+fn5WLZsGXJzc+Hs7Cx2uiXScvBz6ZMnT0q8TUPipyxygZKSEigrKwt1igCyu74lLYuofLq+91F4PB60tbVRUFCAtLQ0oeWHDx+W4RGTpkBUu/raeeeJEydE5t389icun5CWvLw8pk6dCgsLC1RXV7Pn15Dflq957Uy+DeoYId8U/8V6e/bsEbggS05ORnBwsEz2MXv2bADAli1bcP/+fba8tLQUy5cvl7r+CRMmQENDA3v27MHbt28xffp0sXPOkpbJ19cX6urqiI6Oxvbt24VuPiQnJ+P169fs9+HPP/8UeMdIaWkpZs6cyb6EnRBZ8vHxgYKCAvbv34/k5GS2/N27d1iwYIHEU03s3r0bjx49EiqPjY0FIDgPa0Pico8ePaCjo4Pbt2/j1KlTbHlFRQVmzZqFN2/eCG1z4MAB3LlzR6j83LlzYBhG4JjU1dXRqlUr5OTkSHyjnDRdXbt2hbe3Nx4+fIhp06aJ7IxLTU3F2bNn2f/5MXrv3r0C8TgrK+uz8xJLw8LCAkpKSoiNjcX169fZ8qKiIsycOZOm0iISk6b91tbWYv78+Xj79i1bdv36dezYsQNycnLsIKFPBQYGsvN3Ax/n6ObH+P/85z8it5k7dy4AICwsDAA9hU0+mjhxIoyMjPDbb78hKChIKCeurq7Gn3/+KTDgoSHxUxa5gIGBAUpLS4U6BkNDQ5GQkNCgOknLIWk+zY/pu3fvFljv+PHj2L9/v8i6+bF6yZIlAnlzZmYmduzYIf3Bk0ZL0nb1tfPOvLw8BAQECJTt2bMHaWlp0NLSgouLi1T1A0BCQgIuXrwodKx5eXm4e/euwHsurays4ODggISEBCxatEjoSZna2lrExcWxL1wHZHftTBoP6hgh39T8+fPRtm1b7Ny5E8bGxvDw8ICNjQ1sbW3ZCyNp2draws/PD0VFRTAxMcHo0aMxadIk6Ovro7a2lp1fnj9CqL4UFBTg4+PD/u/r6yuT4ybNR8eOHXH06FEoKytj3rx56NmzJ9zc3DBmzBjo6urCxsYGJSUlMDc3h6OjI548eQIDAwO4uLjAxcUFPXr0wNOnTzFu3LhvfSqkGdLV1UVQUBAqKythb2+P4cOHw93dHfr6+rhz547QNIfi7N69G7q6uujTpw8mTpwId3d3mJqaYuHChWjTpg3WrFnDrtvQuMwf1eTq6ophw4Zh7Nix0NPTw507d0ROrXHixAn07dsX+vr6cHFxgaenJwYPHgwXFxdwuVxs2LCBXVdBQQFOTk54/vw5+vfvj+nTp8PX1xeRkZH1/UhJE7F161bY29vj0KFDbCx2d3eHnZ0dtLW1MWTIEMTFxbHr+/j4QEtLC9HR0TA0NISbmxscHR1hYmICa2trsS+UlEa7du2wdOlS1NTUYOjQoXB2doazszMMDAzAMAwGDRok832S5kma9jt69GjcvXsXenp6cHNzg5OTEwYNGoSysjKsXLkSAwYMENpGR0cH/fv3R58+fTB27Fi4urqiZ8+euHXrFuzt7fHDDz+I3JeJiQksLS0BfBy5+t1338nmAyBNmry8PKKiotCtWzf4+/uDx+PByckJkydPxqBBg6CpqQlXV1fk5OSw2zQkfsoiF/jxxx8BAO7u7rCxsYGnpyf69OmDpUuXYtGiRdJ/GKRZkzSfXr58ObhcLvz9/WFubg5PT09YWFhg0qRJYtvZsmXLMHDgQKSlpUFPTw+TJ0+Gs7MzBg4cCE9Pz3/zNMm/TNJ29bXzzlmzZmHTpk0wNjaGp6cnLC0tMWfOHLRq1QqRkZFo06aN1Od68+ZNODo6QktLC87Ozpg6dSpGjhwJIyMjlJaWYsGCBQJTdv3+++/o168fwsLCwOPx4ODgAHd3d1hbW0NLSwsjR47EtWvX2PVlde1MGg/qGCHflIGBATIyMjBmzBi8fPkSp0+fRnl5OX799VeZPTECANu2bcO2bdvQo0cPXLhwAampqXB3d0d8fDxevnwJDocDVVXVBtfv4OAA4OP0MH369JHVYZNmxN7eHpmZmZg9ezZqampw8uRJpKeno1OnTti4cSO0tLQAAKdOncKqVaugoaHBjtRwd3dHeno6VFRUvu1JkGZr4cKFOHr0KExMTJCSkoJLly7Bzs4O6enpUFNTk6iOdevWYcaMGeBwOLh06RKio6Px9u1bzJ49G7du3RJKpBsSl318fBAZGYlevXrhr7/+wtWrVzFmzBikpaWJjOGLFy+Gn58flJWVkZycjKioKBQWFsLDwwMZGRlC0x5GRERg2rRpKC4uxh9//IG9e/ciKSmpnp8maSqUlJQQFxeHiIgImJmZ4c6dO4iKikJOTg709PSwefNmLF26lF1fTU0NGRkZ8PT0RHV1NU6fPo2CggIEBgayUwp9DWvXrkVwcDC0tbVx6dIl3LlzBzNnzsSFCxcaPKiDtDzStF81NTWkpaVh+PDhSEhIQGJiInr37o3IyEisW7dO5DYcDgfHjx/HwoULcfv2bcTExKBDhw5YtWoVzpw5A3l58a+65OfVPj4+n12PtCxGRkbIzMzE2rVr0alTJ6SkpODMmTMoKiqCjY0NIiMj2WlR+BoSP6XNBaZMmYIzZ87AysoKmZmZiI2NRZcuXRAfHy/x+1FIyyVpPm1jY4OUlBQMGzYMubm5iImJgYKCAk6cOMG+WPpTioqKuHjxIpYuXQpFRUWcOnUKubm5WL9+PbZv3/5vnib5l9XnOu1r5p2DBw9GUlIStLS0EBMTg7t378LBwQGJiYlwcnKS9jQBfBzM8dNPP8HAwAA3b97EsWPH8Pfff8Pa2hpRUVEIDQ0VWF9TUxPp6ekICQlBz549kZGRgZMnTyI/Px+mpqbYsWMHpk6dKrCNLK6dSePBYb7GhMiENBEFBQXo3r079PX1cffu3QbXM3v2bISHhyMyMhLe3t6yO0BCCGlhZBWXCSGESCcxMRH29vbw8vLCb7/9JvF2HA4HPB5PYBotSTEMAyMjIzx8+BDZ2dnQ1dWtdx2EEEIIaTzWrl2LgIAAul9GGiV6YoS0CPfu3cO7d+8EyoqKiuDj44OamhqpHh3Ny8vDwYMHoa6uDjc3N2kPlRBCWoSvGZcJIYQ0TcePH8eDBw8watQo6hQhhBBCCCFfFT2bTFqEsLAw/PHHHzA1NUXnzp3x4sUL3LhxA2/evIGZmZnAVBmSCg4Oxq1bt3DhwgW8e/cOmzZtksmciIQQ0hJ8jbhMCCGkafL19UVpaSliYmLA5XIRGBj4rQ+JEEIIIYQ0c9QxQlqECRMm4OnTp7hx4wauXLkCLpcLPT09uLq6YsmSJQ3q0Dhz5gySkpLQtWtXBAQEYN68eV/hyAkhpHn6GnGZEEJI07R3717Iy8vDwMAA69atg5mZ2bc+JEIIIYQQ0szRO0YIIYQQQgghhBBCCCGEENJi0DtGCCGEEEIIIYQQQgghhBDSYlDHCCGEEEIIIYQQQgghhBBCWgzqGCGEEEIIIYQQQgghhBBCSItBHSOEEEIIIYQQQgghhBBCCGkxqGOEEEIIIYQQQgghhBBCCCEtBnWMEEIIIYQQQgghhBBCCCGkxaCOEUIIIYQQQgghhBBCCCGEtBjUMUIIIYQQQgghhBBCCCGEkBaDOkYIIYQQQgghhBBCCCGEENJi/B9bb/ov75hLXwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valued_arr = []\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    nrows=1, ncols=1, figsize=(20, 10)\n",
    ")\n",
    "\n",
    "bar_plot = plt.bar(label_names, flow_count_idxs)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.title(f'Predicted label: {label_names[flow_max_class]}', fontsize=20)\n",
    "plt.bar_label(\n",
    "    bar_plot, label_type='center', fontsize=25, color='black', padding=15\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee6d6e3-246c-4f32-8dab-d79812e27e1c",
   "metadata": {},
   "source": [
    "### Soft Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c1d86232-350f-408c-b702-5a415d0f0c5a",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2024-04-22T07:13:25.057105Z",
     "iopub.status.busy": "2024-04-22T07:13:25.056099Z",
     "iopub.status.idle": "2024-04-22T07:13:25.068653Z",
     "shell.execute_reply": "2024-04-22T07:13:25.067351Z",
     "shell.execute_reply.started": "2024-04-22T07:13:25.057105Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pred_prob = model_flows_preds['predictions']\n",
    "if len(frames) > 1:\n",
    "    pred_prob = np.average(model_flows_preds['predictions'], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "91d0042f-24a6-4d9f-b87c-33e53328e434",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:13:25.889032Z",
     "iopub.status.busy": "2024-04-22T07:13:25.889032Z",
     "iopub.status.idle": "2024-04-22T07:13:25.905530Z",
     "shell.execute_reply": "2024-04-22T07:13:25.904519Z",
     "shell.execute_reply.started": "2024-04-22T07:13:25.889032Z"
    }
   },
   "outputs": [],
   "source": [
    "model_flows_preds['avg_pred'].append(\n",
    "        pred_prob\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "455d2488-fe5b-4a40-8d5d-2ad2130ffbc2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:13:26.906847Z",
     "iopub.status.busy": "2024-04-22T07:13:26.906847Z",
     "iopub.status.idle": "2024-04-22T07:13:27.251187Z",
     "shell.execute_reply": "2024-04-22T07:13:27.250052Z",
     "shell.execute_reply.started": "2024-04-22T07:13:26.906847Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valued_arr = []\n",
    "\n",
    "for label, pred in zip(label_names, pred_prob):\n",
    "    # print(label, pred)\n",
    "    valued_arr.append(f'{label}\\n{pred:.2%}')\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    nrows=1, ncols=2, width_ratios=[0.4, 0.6], figsize=(20, 10)\n",
    ")\n",
    "\n",
    "pltDisplay(flow_avg_img, ax=ax[0])\n",
    "ax[1].bar(valued_arr, pred_prob)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.title(f'Predicted label: {label_names[np.argmax(pred_prob)]}', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf442a3-4d91-4f0f-abb8-52c0b69119f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-14T10:26:49.508339Z",
     "iopub.status.busy": "2024-03-14T10:26:49.507380Z",
     "iopub.status.idle": "2024-03-14T10:26:49.527138Z",
     "shell.execute_reply": "2024-03-14T10:26:49.524729Z",
     "shell.execute_reply.started": "2024-03-14T10:26:49.508339Z"
    }
   },
   "source": [
    "# Model Prediction - Audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "9e3f333e-d608-455b-b3f1-dfe3f0a1f2d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:39:54.472966Z",
     "iopub.status.busy": "2024-04-22T09:39:54.472966Z",
     "iopub.status.idle": "2024-04-22T09:39:55.171074Z",
     "shell.execute_reply": "2024-04-22T09:39:55.170086Z",
     "shell.execute_reply.started": "2024-04-22T09:39:54.472966Z"
    }
   },
   "outputs": [],
   "source": [
    "model_speech = tf.saved_model.load(\n",
    "    Path('Models/Speech/tensorflow_tutorial_model/Run_4/E2E/')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "7aae6367-3fa4-4584-9c26-beb66824efa9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:40:03.783722Z",
     "iopub.status.busy": "2024-04-22T09:40:03.783722Z",
     "iopub.status.idle": "2024-04-22T09:40:03.801492Z",
     "shell.execute_reply": "2024-04-22T09:40:03.800779Z",
     "shell.execute_reply.started": "2024-04-22T09:40:03.783722Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File test\\20240422_090837\\Audio\\01-01-04-02-01-01-11.wav already exists\n"
     ]
    }
   ],
   "source": [
    "# Extract wav from mp4\n",
    "folder_audio_path = Path(folder_path, 'Audio')\n",
    "folder_audio_path.mkdir(parents=True, exist_ok=True)\n",
    "filename = f'{utils.remove_extension(video_path.name)}.wav'\n",
    "audio_path = str(Path(folder_audio_path, filename))\n",
    "if not Path(audio_path).is_file():\n",
    "\n",
    "    command = f'ffmpeg -i {video_path}\\\n",
    "        -ab 160k -ac 2 -ar {SAMPLE_RATE}\\\n",
    "        -vn {audio_path}'\n",
    "\n",
    "    ret_code = subprocess.call(command, shell=True)\n",
    "    if ret_code == 0:\n",
    "        print(f'{audio_path} generated correctly')\n",
    "    else:\n",
    "        print(f'ERROR: ret_code: {ret_code}')\n",
    "else:\n",
    "    print(f'File {audio_path} already exists')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "5aa7a5d2-13b7-48d0-a225-38f59a1cadb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T07:34:36.345788Z",
     "iopub.status.busy": "2024-04-22T07:34:36.345788Z",
     "iopub.status.idle": "2024-04-22T07:34:36.360786Z",
     "shell.execute_reply": "2024-04-22T07:34:36.359834Z",
     "shell.execute_reply.started": "2024-04-22T07:34:36.345788Z"
    }
   },
   "source": [
    "fn_input, sample_rate = librosa.load(audio_path, sr=SAMPLE_RATE)\n",
    "ipd.display(ipd.Audio(data=fn_input, rate=sample_rate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "1e3f9678-5618-4d14-85a2-569048f4aef5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:51:11.773587Z",
     "iopub.status.busy": "2024-04-22T09:51:11.773587Z",
     "iopub.status.idle": "2024-04-22T09:51:11.787984Z",
     "shell.execute_reply": "2024-04-22T09:51:11.786679Z",
     "shell.execute_reply.started": "2024-04-22T09:51:11.773587Z"
    }
   },
   "outputs": [],
   "source": [
    "audio_file = tf.io.read_file(audio_path)\n",
    "audio_file, sample_rate = tf.audio.decode_wav(audio_file, desired_channels=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "792b48c6-cb80-48b5-9657-bab40c0870e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:51:14.248904Z",
     "iopub.status.busy": "2024-04-22T09:51:14.248904Z",
     "iopub.status.idle": "2024-04-22T09:51:14.252905Z",
     "shell.execute_reply": "2024-04-22T09:51:14.252905Z",
     "shell.execute_reply.started": "2024-04-22T09:51:14.248904Z"
    }
   },
   "outputs": [],
   "source": [
    "frame_len = 48000\n",
    "hop_len = 12000\n",
    "model_speech_preds = {\n",
    "    'class_ids': [], 'max_class_pred': [], 'predictions': [], 'avg_pred': []\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "b1ec18f7-0548-45a4-9e05-ee2d6c8e4af8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:51:14.610358Z",
     "iopub.status.busy": "2024-04-22T09:51:14.610358Z",
     "iopub.status.idle": "2024-04-22T09:51:14.625228Z",
     "shell.execute_reply": "2024-04-22T09:51:14.624242Z",
     "shell.execute_reply.started": "2024-04-22T09:51:14.610358Z"
    }
   },
   "outputs": [],
   "source": [
    "# padded model\n",
    "res = model_speech(audio_path)\n",
    "model_speech_preds['class_ids'].append(\n",
    "    res['class_ids'].numpy()[0]\n",
    ")\n",
    "model_speech_preds['max_class_pred'].append(\n",
    "    np.max(res['predictions'].numpy()[0])\n",
    ")\n",
    "model_speech_preds['predictions'].append(\n",
    "    res['predictions'].numpy()[0]\n",
    ")"
   ]
  },
  {
   "cell_type": "raw",
   "id": "aab404f9-9e71-4284-9875-d6e4da938259",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T08:52:47.917985Z",
     "iopub.status.busy": "2024-04-22T08:52:47.917985Z",
     "iopub.status.idle": "2024-04-22T08:52:48.038720Z",
     "shell.execute_reply": "2024-04-22T08:52:48.037686Z",
     "shell.execute_reply.started": "2024-04-22T08:52:47.917985Z"
    }
   },
   "source": [
    "# chunked model\n",
    "audio_file = tf.io.read_file(audio_path)\n",
    "audio_file, sample_rate = tf.audio.decode_wav(audio_file, desired_channels=1)\n",
    "fn_input = utils.squeeze_tensor(audio_file)\n",
    "audio_len = fn_input.shape[0]\n",
    "\n",
    "int_div = int(np.ceil(audio_len / frame_len))\n",
    "end = int_div * frame_len\n",
    "if (audio_len < end):\n",
    "    paddings = [[0, end - audio_len]]\n",
    "    fn_input = tf.pad(fn_input, paddings, 'CONSTANT', constant_values=0)\n",
    "\n",
    "audio_len = fn_input.shape[0]\n",
    "frames = tf.signal.frame(fn_input, frame_len, hop_len, axis=0)\n",
    "\n",
    "for frame in frames:\n",
    "    pad = np.count_nonzero(frame == 0, axis=0)\n",
    "    not_pad = len(frame) - pad\n",
    "    if (pad > not_pad):\n",
    "        print('pad')\n",
    "        continue\n",
    "\n",
    "    model_input = utils.extend_tensor(frame, 0)\n",
    "\n",
    "    res = model_speech(model_input)\n",
    "    model_speech_preds['class_ids'].append(\n",
    "        res['class_ids'].numpy()[0]\n",
    "    )\n",
    "    model_speech_preds['max_class_pred'].append(\n",
    "        np.max(res['predictions'].numpy()[0])\n",
    "    )\n",
    "    model_speech_preds['predictions'].append(\n",
    "        res['predictions'].numpy()[0]\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c79e11-14d7-4982-95bd-aa7d66d4efa1",
   "metadata": {},
   "source": [
    "### Hard Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "881315ec-9f24-4c4c-b47a-cb5a7ebdf34e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:52:07.316365Z",
     "iopub.status.busy": "2024-04-22T09:52:07.316365Z",
     "iopub.status.idle": "2024-04-22T09:52:07.335250Z",
     "shell.execute_reply": "2024-04-22T09:52:07.334338Z",
     "shell.execute_reply.started": "2024-04-22T09:52:07.316365Z"
    }
   },
   "outputs": [],
   "source": [
    "speech_max_idxs = [np.argmax(ar) for ar in model_speech_preds['predictions']]\n",
    "speech_count_idxs = [speech_max_idxs.count(i) for i in range(len(label_names))]\n",
    "speech_max_class = np.argmax(speech_count_idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "1dd271f8-6015-46b1-87f0-4e4bc4cba7b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:52:07.569196Z",
     "iopub.status.busy": "2024-04-22T09:52:07.569196Z",
     "iopub.status.idle": "2024-04-22T09:52:07.586236Z",
     "shell.execute_reply": "2024-04-22T09:52:07.584822Z",
     "shell.execute_reply.started": "2024-04-22T09:52:07.569196Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([3], 3, [0, 0, 0, 1, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "speech_max_idxs, speech_max_class, speech_count_idxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "1c2964de-f08c-4138-a7c4-f4ba0994c1ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:52:08.007453Z",
     "iopub.status.busy": "2024-04-22T09:52:08.007453Z",
     "iopub.status.idle": "2024-04-22T09:52:08.273722Z",
     "shell.execute_reply": "2024-04-22T09:52:08.272741Z",
     "shell.execute_reply.started": "2024-04-22T09:52:08.007453Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valued_arr = []\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    nrows=1, ncols=1, figsize=(20, 10)\n",
    ")\n",
    "\n",
    "bar_plot = plt.bar(label_names, speech_count_idxs)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.title(f'Predicted label: {label_names[speech_max_class]}', fontsize=20)\n",
    "plt.bar_label(\n",
    "    bar_plot, label_type='center', fontsize=25, color='black', padding=15\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6307a09-9fb0-4319-b34b-83693c6fa1c6",
   "metadata": {},
   "source": [
    "### Soft Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "691c340b-809f-4c5c-988b-a032d2511147",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:52:11.235032Z",
     "iopub.status.busy": "2024-04-22T09:52:11.235032Z",
     "iopub.status.idle": "2024-04-22T09:52:11.247269Z",
     "shell.execute_reply": "2024-04-22T09:52:11.246253Z",
     "shell.execute_reply.started": "2024-04-22T09:52:11.235032Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_prob = softmax(model_speech_preds['predictions'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "7ccfb210-1b7e-4d82-ba6c-bf92f4771854",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:52:11.536982Z",
     "iopub.status.busy": "2024-04-22T09:52:11.536982Z",
     "iopub.status.idle": "2024-04-22T09:52:11.557642Z",
     "shell.execute_reply": "2024-04-22T09:52:11.555979Z",
     "shell.execute_reply.started": "2024-04-22T09:52:11.536982Z"
    }
   },
   "outputs": [],
   "source": [
    "model_speech_preds['avg_pred'].append(\n",
    "        pred_prob\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "8f75462b-7894-46b9-ae2b-953ff44b2670",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:52:11.973764Z",
     "iopub.status.busy": "2024-04-22T09:52:11.973764Z",
     "iopub.status.idle": "2024-04-22T09:52:12.335756Z",
     "shell.execute_reply": "2024-04-22T09:52:12.334739Z",
     "shell.execute_reply.started": "2024-04-22T09:52:11.973764Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valued_arr = []\n",
    "\n",
    "for label, pred in zip(label_names, pred_prob):\n",
    "    # print(label, pred)\n",
    "    valued_arr.append(f'{label}\\n{pred:.2%}')\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    nrows=1, ncols=2, width_ratios=[0.4, 0.6], figsize=(20, 10)\n",
    ")\n",
    "\n",
    "ax[0].plot(audio_file)\n",
    "ax[1].bar(valued_arr, pred_prob)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.title(f'Predicted label: {label_names[np.argmax(pred_prob)]}', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47219444-f9ec-4640-8bec-0092fb857d6c",
   "metadata": {},
   "source": [
    "# Average of the three models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "bada046c-0d84-4526-a9fa-d11dd0f742bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:52:13.074540Z",
     "iopub.status.busy": "2024-04-22T09:52:13.074540Z",
     "iopub.status.idle": "2024-04-22T09:52:13.090338Z",
     "shell.execute_reply": "2024-04-22T09:52:13.089331Z",
     "shell.execute_reply.started": "2024-04-22T09:52:13.074540Z"
    }
   },
   "outputs": [],
   "source": [
    "avg_frame = model_frame_preds['avg_pred'][0]\n",
    "avg_flow = model_flows_preds['avg_pred'][0]\n",
    "avg_audio = model_speech_preds['avg_pred'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad84261-3dea-456d-91c5-770e39681934",
   "metadata": {},
   "source": [
    "## Simple Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "ea2fa7ef-8ae7-4a30-bfa2-a0f780c09eef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:52:15.500336Z",
     "iopub.status.busy": "2024-04-22T09:52:15.499343Z",
     "iopub.status.idle": "2024-04-22T09:52:15.510325Z",
     "shell.execute_reply": "2024-04-22T09:52:15.509315Z",
     "shell.execute_reply.started": "2024-04-22T09:52:15.500336Z"
    }
   },
   "outputs": [],
   "source": [
    "#  Simple Average\n",
    "simple_average = (\n",
    "    np.average([avg_frame, avg_flow, avg_audio], axis=0)\n",
    ")"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d2ee046e-92b6-402e-8744-099d0e02fa1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-18T17:06:02.617505Z",
     "iopub.status.busy": "2024-03-18T17:06:02.616505Z",
     "iopub.status.idle": "2024-03-18T17:06:03.409133Z",
     "shell.execute_reply": "2024-03-18T17:06:03.408953Z",
     "shell.execute_reply.started": "2024-03-18T17:06:02.617505Z"
    }
   },
   "source": [
    "# Test matplotlib subplot positioning\n",
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
    "\n",
    "\n",
    "t1 = np.arange(0.0, 3.0, 0.01)\n",
    "\n",
    "ax1 = plt.subplot(122)\n",
    "# ax1.margins(0.05)           # Default margin is 0.05, value 0 means fit\n",
    "ax1.plot(t1, f(t1))\n",
    "\n",
    "ax2 = plt.subplot(321)\n",
    "# ax2.margins(2, 2)           # Values >0.0 zoom out\n",
    "# ax2.plot(t1, f(t1))\n",
    "ax2.set_title('1')\n",
    "\n",
    "ax3 = plt.subplot(323)\n",
    "# ax3.margins(x=0, y=-0.25)   # Values in (-0.5, 0.0) zooms in to center\n",
    "ax3.plot(t1, f(t1))\n",
    "ax3.set_title('2')\n",
    "\n",
    "ax3 = plt.subplot(325)\n",
    "# ax3.margins(x=0, y=-0.25)   # Values in (-0.5, 0.0) zooms in to center\n",
    "ax3.plot(t1, f(t1))\n",
    "ax3.set_title('3')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "926307d6-c62f-449a-9070-1907bb3b42c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:52:16.357982Z",
     "iopub.status.busy": "2024-04-22T09:52:16.357982Z",
     "iopub.status.idle": "2024-04-22T09:52:17.383422Z",
     "shell.execute_reply": "2024-04-22T09:52:17.382425Z",
     "shell.execute_reply.started": "2024-04-22T09:52:16.357982Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valued_arr = []\n",
    "\n",
    "for label, pred in zip(label_names, simple_average):\n",
    "    # print(label, pred)\n",
    "    valued_arr.append(f'{label}\\n{pred:.2%}')\n",
    "\n",
    "\n",
    "# fig, ax = plt.subplots(\n",
    "#     figsize=(20, 10)\n",
    "# )\n",
    "fig = plt.figure(figsize=(20, 10))\n",
    "fig.suptitle(f'Predicted label: {label_names[np.argmax(simple_average)]}')\n",
    "\n",
    "ax1 = fig.add_subplot(122)\n",
    "ax1.bar(valued_arr, simple_average)\n",
    "\n",
    "ax2 = fig.add_subplot(321)\n",
    "pltDisplay(avg_image, ax=ax2)\n",
    "\n",
    "ax3 = fig.add_subplot(323)\n",
    "pltDisplay(flow_avg_img, ax=ax3)\n",
    "\n",
    "ax4 = fig.add_subplot(325)\n",
    "ax4.plot(audio_file)\n",
    "# ax[0, 1].bar(valued_arr, final_model_preds)\n",
    "# plt.xticks(fontsize=15)\n",
    "# plt.yticks(fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa1eecbd-14a7-4b3b-b04c-34cfe6660668",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-18T16:17:42.179016Z",
     "iopub.status.busy": "2024-03-18T16:17:42.178019Z",
     "iopub.status.idle": "2024-03-18T16:17:42.196015Z",
     "shell.execute_reply": "2024-03-18T16:17:42.195014Z",
     "shell.execute_reply.started": "2024-03-18T16:17:42.179016Z"
    }
   },
   "source": [
    "## Weighted Average (TEST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "471e63fd-b74c-48f7-8d43-e24f44c65ff6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:52:25.380904Z",
     "iopub.status.busy": "2024-04-22T09:52:25.379921Z",
     "iopub.status.idle": "2024-04-22T09:52:25.394733Z",
     "shell.execute_reply": "2024-04-22T09:52:25.393727Z",
     "shell.execute_reply.started": "2024-04-22T09:52:25.380904Z"
    }
   },
   "outputs": [],
   "source": [
    "#  Weighted Average\n",
    "weights = [0.6, 0.2, 0.2]\n",
    "\n",
    "weighted_average = (\n",
    "    np.average(a=[avg_frame, avg_flow, avg_audio], axis=0, weights=weights)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "3909ffad-e397-4100-b439-4edfb9d3fd56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:52:25.718411Z",
     "iopub.status.busy": "2024-04-22T09:52:25.718411Z",
     "iopub.status.idle": "2024-04-22T09:52:26.238311Z",
     "shell.execute_reply": "2024-04-22T09:52:26.237313Z",
     "shell.execute_reply.started": "2024-04-22T09:52:25.718411Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valued_arr = []\n",
    "\n",
    "for label, pred in zip(label_names, weighted_average):\n",
    "    # print(label, pred)\n",
    "    valued_arr.append(f'{label}\\n{pred:.2%}')\n",
    "\n",
    "\n",
    "# fig, ax = plt.subplots(\n",
    "#     figsize=(20, 10)\n",
    "# )\n",
    "fig = plt.figure(figsize=(20, 10))\n",
    "fig.suptitle(f'Predicted label: {label_names[np.argmax(weighted_average)]}')\n",
    "\n",
    "ax1 = fig.add_subplot(122)\n",
    "ax1.bar(valued_arr, weighted_average)\n",
    "\n",
    "ax2 = fig.add_subplot(321)\n",
    "pltDisplay(avg_image, ax=ax2)\n",
    "\n",
    "ax3 = fig.add_subplot(323)\n",
    "pltDisplay(flow_avg_img, ax=ax3)\n",
    "\n",
    "ax4 = fig.add_subplot(325)\n",
    "ax4.plot(audio_file)\n",
    "# ax[0, 1].bar(valued_arr, final_model_preds)\n",
    "# plt.xticks(fontsize=15)\n",
    "# plt.yticks(fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4f9c527-362f-42af-9feb-d65fa415a8dc",
   "metadata": {},
   "source": [
    "# Max Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "2ba825ec-ca27-4def-9cdb-80031b53f05f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:52:30.142464Z",
     "iopub.status.busy": "2024-04-22T09:52:30.142464Z",
     "iopub.status.idle": "2024-04-22T09:52:30.152229Z",
     "shell.execute_reply": "2024-04-22T09:52:30.150921Z",
     "shell.execute_reply.started": "2024-04-22T09:52:30.142464Z"
    }
   },
   "outputs": [],
   "source": [
    "count_idx = np.sum(\n",
    "    [frame_count_idxs, flow_count_idxs, speech_count_idxs],\n",
    "    axis=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "c44361ac-0c75-4584-ad70-b60cb8454417",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-22T09:52:30.606621Z",
     "iopub.status.busy": "2024-04-22T09:52:30.606621Z",
     "iopub.status.idle": "2024-04-22T09:52:31.087520Z",
     "shell.execute_reply": "2024-04-22T09:52:31.086520Z",
     "shell.execute_reply.started": "2024-04-22T09:52:30.606621Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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55YGfVa+++qrp27eviY2NNZ06dQr8rjDGlHu/uX+Hf/LJJ+bYY481CQkJJjk52fTr189s3LgxsH/OnDmmR48eJjY21mRkZAR+vxerza+LHStWalB+fr6WLVumIUOGOLYPGTJEixYtClNUNS8nJ0eS1KRJE0nShg0blJ2d7RiX2NhYDRgwIDAuy5YtU0FBgaNNWlqaOnfuHGizePFiJScnq3fv3oE2ffr0UXJysqNN586dHZ/WPfXUU5WXl+dIDxVprr32Wg0dOlSDBg1ybGfsKjZnzhz16tVLF1xwgVq0aKHu3bvr6aefDuxn/Cp2/PHH66OPPtL3338vSfrmm2/0+eef64wzzpDE+FVFpI3V4sWLNWDAAMXGxjra/Prrr9q4cWPwByDIcnJyZFlWYPUZY1cxv9+vESNG6JZbbtHRRx9daj/jVz6/3693331XRx55pE499VS1aNFCvXv3dqQLY/yAyLRnzx6NHTtWS5cu1UcffSSPx6Nzzz3XkdJ0/Pjxuvnmm5WZmakjjzxSl1xySeBTwF988YVGjRqlG2+8UZmZmRo8eLDuvffeUv388MMPev311/Xmm28qMzNTF154oXw+n+bMmRNos23bNr3zzjv6v//7v9BfeIiNGzdO999/v+666y6tWbNGr7zyilJSUiRJiYmJev7557VmzRo9+uijevrpp/XII49UeL6PP/5Yv/76qz777DM9/PDDmjhxooYNG6bGjRvryy+/1KhRozRq1Cht2bKlJi4vaG655RZ98sknmj17tubNm6dPP/203L9Z33jjDT3yyCN66qmntH79er399tvq0qVLYH9xCtRPP/1Ub775pmbMmKGtW7dWKZ7i1+v999/X2rVrNX36dDVr1kyS9NVXX0mSPvzwQ2VlZemtt946xKsOjUcffVSTJk1Sq1atlJWVpaVLl+rOO+/UzJkzNX36dK1evVo33XSTLrvsMi1YsEBS0e/vVq1a6fXXX9eaNWv097//XXfccYdef/11x7k/+ugjrV27VvPnz9c777yjt956SyNHjlTfvn0jciyC5YUXXlCDBg305Zdf6oEHHtCkSZM0f/58SZLH49Fjjz2mVatW6YUXXtDHH3+sW2+91XH83r17de+99+qFF17QF198odzcXF188cWONsU/G//3v/8FVupee+21kqQTTzxR7dq107///e9A+8LCQr300ksR/3OyvLEzxmjo0KHKzs7We++9p2XLlqlHjx465ZRT9Mcff1Tq3I8++qj69u2rkSNHKisrS1lZWUpPTw/sv/XWWzVlyhStXbtWXbt21e7du3XGGWfoww8/1IoVK3TqqafqzDPPrHMZcbKysnTJJZfoqquu0tq1a/Xpp5/qvPPOkzFGu3bt0hVXXKGFCxdqyZIlat++vc4444xAVgi/36/zzjtPXq9XS5Ys0ZNPPqnbbrstzFdUORVdd2X985//VOfOnbVs2TLdddddge233HKL/va3v2nFihXq16+fzjrrLG3fvt1xrPt+syssLNQ555yjAQMG6Ntvv9XixYv15z//ObDC74MPPtBll12mG264QWvWrNFTTz2l559/PvB3VG1+XdyCv34S5dq2bZt8Pl/gD89iKSkpys7ODlNUNcsYo7Fjx+r4448P5EUtvvayxmXTpk2BNjExMWrcuHGpNsXHZ2dnq0WLFqX6bNGihaONu5/GjRsrJiYmYl+DWbNmafny5Vq6dGmpfYxdxX766SdNnz5dY8eO1R133KGvvvpKN9xwg2JjY3X55Zczfgdx2223KScnRx06dJDX65XP59O9996rSy65RBL3X1VE2lhlZ2erbdu2pfop3peRkXEol1kj9u/fr9tvv13Dhw9XUlKSJMbuYO6//35FRUXphhtuKHM/41e+rVu3avfu3brvvvt0zz336P7779fcuXN13nnn6ZNPPtGAAQMYPyBCnX/++Y7nzz77rFq0aKE1a9aoYcOGkoryhQ8dOlSSdPfdd+voo4/WDz/8oA4dOujxxx/X6aefrptvvlmSdOSRR2rRokV65513HOfNz8/Xv//9bzVv3jywbfjw4Zo5c6YuuOACSdLLL7+sVq1aaeDAgaG63Bqxa9cuPfroo5o2bZquuOIKSdLhhx+u448/XpJ05513Btq2bdtWf/vb3/Taa6+VelPWrkmTJnrsscfk8Xh01FFH6YEHHtDevXt1xx13SCqayLnvvvv0xRdflHrjNlLt3r1bzz77rF588UUNHjxYUtGbsa1atSqz/ebNm5WamqpBgwYpOjparVu31nHHHSdJ+u677/Thhx9q6dKl6tWrlyTpmWeeUfv27asU0+bNm9W9e/fAOey/S4rv3aZNmyo1NbVK560JxfXNvF6vUlNTtWfPHj388MP6+OOP1bdvX0lSu3bt9Pnnn+upp57SgAEDFB0drbvvvjtwjoyMDC1atEivv/66LrzwwsD2Bg0a6JlnnlFMTExgW0JCgmJiYiJyLIKla9eumjBhgiSpffv2mjZtmj766CMNHjzYUd8jIyND//jHP/TXv/5VTzzxRGB7QUGBpk2bFvjAewFMJwAAG0RJREFUyAsvvKCOHTvqq6++Cty7+/fvd9z3jz/+uIYOHaqHHnpIqampuvrqqzVz5kzdcsstkqR3331Xe/fudbw+kai8sfN6vVq5cqW2bt0a+ADMgw8+qLfffltvvPFGpWoqJScnKyYmRgkJCWXef5MmTQr8TJGKvme7desWeH7PPfdo9uzZmjNnjq677rrqXmrEyMrKUmFhoc477zy1adNGkgKTzyeffLKj7VNPPaXGjRtrwYIFGjZsmD788EOtXbtWGzduDNyLkydP1umnn16zF3EIKrruyjr55JMDf8dICnwQ67rrrgv8nTR9+nTNnTtXzz77rOP3tft+s8vNzVVOTo6GDRumww8/XJLUsWPHwP57771Xt99+e+BvhXbt2ukf//iHbr31Vk2YMKFWvy5urFgJA3eOTmNMvcnbed111+nbb7/Vq6++WmrfoYyLu01Z7Q+lTaTYsmWLbrzxRr300kuOXO5ujF3Z/H6/evToocmTJ6t79+76y1/+opEjR2r69OmOdoxf2V577TW99NJLeuWVV7R8+XK98MILevDBB/XCCy842jF+lRdJY1VWLOUdGykKCgp08cUXy+/3O/5xVx7Grmg1xaOPPqrnn3++yvExfgp8sv3ss8/WTTfdpGOOOUa33367hg0bpieffLLCYxk/ILx+/PFHDR8+XO3atVNSUlJg4tH+SV77JzBbtmwpSYGVAOvWrQu8QVjM/VyS2rRp45hUkaSRI0dq3rx5+uWXXyRJM2fO1JVXXlnrv0/Xrl2rvLw8nXLKKWXuf+ONN3T88ccrNTVVDRs21F133XXQT04fffTR8nhK3pZISUlxvHHk9XrVtGnTKq/QCKcff/xR+fn5gTf9paIJpKOOOqrM9hdccIH27dundu3aaeTIkZo9e3Zg5dS6desUFRWlHj16BNofccQRpSbzD+avf/2rZs2apWOOOUa33nprrc6YsWbNGu3fv1+DBw9Ww4YNA48XX3xRP/74Y6Ddk08+qV69eql58+Zq2LChnn766VL3Y5cuXRyTKvWF+9PnLVu2DHyPffLJJxo8eLAOO+wwJSYm6vLLL9f27dsdNXKjoqICk3SS1KFDBzVq1Ehr164NbGvdurVjMrFv377y+/1at26dJOnKK6/UDz/8oCVLlkiSnnvuOV144YVq0KBB8C84iMobu2XLlmn37t1q2rSp477csGGD476sDvuYS0UrM2+99VZ16tRJjRo1UsOGDfXdd9/VuRUr3bp10ymnnKIuXbroggsu0NNPP60dO3ZIKvqdPWrUKB155JFKTk5WcnKydu/eHRiDtWvXlnkv1gYVXXdlue+ZYvYxKP5+tn//VnSsVPQ77corrwysknr00UeVlZUV2L9s2TJNmjTJ8b1QvBJr7969tfp1cWNipQY1a9ZMXq+31Kert27dWuoTinXR9ddfrzlz5uiTTz5xfPMUz8RXNC6pqanKz88v9UPE3ea3334r1e/vv//uaOPuZ8eOHSooKIjI12DZsmXaunWrevbsqaioKEVFRWnBggV67LHHFBUV5fiUqh1jV6Rly5bq1KmTY1vHjh0Dv2S59yp2yy236Pbbb9fFF1+sLl26aMSIEbrppps0ZcoUSYxfVUTaWJXVpvgfU5E6ngUFBbrwwgu1YcMGzZ8/P7BaRWLsKrJw4UJt3bpVrVu3Dvwe2bRpk/72t78FPq3K+JWvWbNmioqKOujvEsYPiDxnnnmmtm/frqefflpffvmlvvzyS0nO4q3R0dGBr4snPYonVMv68ENZ6TfKehOwe/fu6tatm1588UUtX75cK1eu1JVXXlntawq3+Pj4cvctWbJEF198sU4//XS98847WrFihcaPH3/QYrn210Aqeh3K2mZP4RbpqpKmRZLS09O1bt06/etf/1J8fLxGjx6tE088UQUFBeWey769eGLKvq2goMDR/vTTT9emTZs0ZswY/frrrzrllFMcn2KuTYrvhXfffVeZmZmBx5o1a/TGG29Ikl5//XXddNNNuuqqqzRv3jxlZmbq//7v/0rdj5V9E9+yrFKvhXuMa5Pyvsc2bdqkM844Q507d9abb76pZcuW6V//+pek0tdb1kRxRZPHxfuK/9+iRQudeeaZmjlzprZu3ar33ntPV111VbWuqyaUN3Z+v18tW7Z03JOZmZlat25dYFWOx+Op1n3kvl9vueUWvfnmm7r33nu1cOFCZWZmqkuXLodcpDxSeb1ezZ8/X++//746deqkxx9/XEcddZQ2bNigK6+8UsuWLdPUqVO1aNEiZWZmqmnTpoExKOtnaG35kENF113Ze6kqE5XucTnYsTNnztTixYvVr18/vfbaazryyCMDE6V+v193332343th5cqVWr9+veLi4mr16+LGxEoNiomJUc+ePQO5K4vNnz9f/fr1C1NUoWeM0XXXXae33npLH3/8cak0FRkZGUpNTXWMS35+vhYsWBAYl549eyo6OtrRJisrS6tWrQq06du3r3JycgI5YiXpyy+/VE5OjqPNqlWrHDOp8+bNU2xsrHr27Bn8i6+mU045RStXrnT8MOrVq5cuvfRSZWZmql27doxdBfr37x/4REyx77//PrCMknuvYnv37nV8glAq+uVe/I8Zxq/yIm2s+vbtq88++8zxR/e8efOUlpZWKs1QJCieVFm/fr0+/PBDNW3a1LGfsSvfiBEj9O233zp+j6SlpemWW27RBx98IInxq0hMTIyOPfbYCn+XMH5A5Nm+fbvWrl2rO++8U6eccoo6duxY5U95dujQwfE9K0lff/11pY+/5pprNHPmTD333HMaNGiQI09+bdW+fXvFx8fro48+KrXviy++UJs2bTR+/Hj16tVL7du3D6Q7rW+OOOIIRUdHB95gkoomyovrFpYlPj5eZ511lh577DF9+umnWrx4sVauXKkOHTqosLBQK1asCLT94YcftHPnzsDz4hVT9t8fmZmZpfpo3ry5rrzySr300kuaOnWqZsyYIUmBFRs+n++QrremderUSbGxsdq8ebOOOOIIx6P4+2zhwoXq16+fRo8ere7du+uII46o1qqB5s2bO8Z3/fr12rt3b7WvJdJ8/fXXKiws1EMPPaQ+ffroyCOP1K+//lqqXWFhoePn4bp167Rz50516NAhsG3z5s2OYxcvXiyPx6MjjzwysO2aa67RrFmz9NRTT+nwww9X//79Q3RlodejRw9lZ2crKiqq1H1ZXM/IfR9Jpb9XY2JiKv29uHDhQl155ZU699xz1aVLF6WmptbZmnuWZal///66++67tWLFCsXExGj27NlauHChbrjhBp1xxhk6+uijFRsbq23btgWO69SpU5n3Ym1R3nW77yWfz6dVq1ZV+rz230+FhYVatmyZ4/u3srp3765x48Zp0aJF6ty5s1555RVJRd8P69atK/W9cMQRR8jj8dT618WhGoXvcQhmzZploqOjzbPPPmvWrFljxowZYxo0aGA2btwY7tBC5q9//atJTk42n376qcnKygo89u7dG2hz3333meTkZPPWW2+ZlStXmksuucS0bNnS5ObmBtqMGjXKtGrVynz44Ydm+fLl5uSTTzbdunUzhYWFgTannXaa6dq1q1m8eLFZvHix6dKlixk2bFhgf2FhoencubM55ZRTzPLly82HH35oWrVqZa677rqaGYwgGDBggLnxxhsDzxm78n311VcmKirK3HvvvWb9+vXm5ZdfNgkJCeall14KtGH8ynfFFVeYww47zLzzzjtmw4YN5q233jLNmjUzt956a6AN41di165dZsWKFWbFihVGknn44YfNihUrzKZNm4wxkTVWO3fuNCkpKeaSSy4xK1euNG+99ZZJSkoyDz74YA2MVGkVjV1BQYE566yzTKtWrUxmZqbj90heXl7gHPV17Iw5+L3n1qZNG/PII484tjF+5Y/fW2+9ZaKjo82MGTPM+vXrzeOPP268Xq9ZuHBh4Bz1efyASOTz+UzTpk3NZZddZtavX28++ugjc+yxxxpJZvbs2WbDhg1GklmxYkXgmB07dhhJ5pNPPjHGGPP5558bj8djHnroIfP999+bJ5980jRt2tQ0atQocMyECRNMt27dyowhJyfHJCQkmJiYGDNr1qwQXm3NmjhxomncuLF54YUXzA8//GAWL15snnnmGfP222+bqKgo8+qrr5offvjBPProo6ZJkyYmOTk5cKx7vK644gpz9tlnO87v/reOMWX/3op0o0aNMq1btzYffvihWblypTnrrLNMw4YNA9dmv6aZM2eaZ555xqxcudL8+OOPZvz48SY+Pt5s27bNGGPMoEGDTI8ePcyXX35pli9fbk466SQTHx9vpk6dGuivT58+5oQTTjCrV682CxYsMMcdd5zjfr7rrrvM22+/bdavX29WrVplhg0bZo477jhjjDEFBQUmPj7e3HPPPSY7O9vs3Lmzxsapsh555BHTpk2bwPPx48ebpk2bmueff9788MMPZvny5WbatGnm+eefN8YYM3XqVJOUlGTmzp1r1q1bZ+68806TlJR00PvPGGNuvPFGM2DAAMe2iy++2HTs2NEsW7bMLF261Jx88skmOjrazJw50xhjyvyZEqnK+h47++yzzRVXXBH4W2jq1Knmxx9/NC+++KI57LDDjCSzY8cOY0zR/RodHW2OO+44s2TJErNs2TLTt29f06dPn8D5JkyYYBo0aGAGDRpkMjMzzWeffWaOPPJIc/HFFzv69fl8Jj093cTExJj77rsv1JdebRWNnd/vN8cff7zp1q2bmTt3rtmwYYP54osvzPjx483SpUuNMcbMnTvXWJZlXnjhBfP999+bv//97yYpKclxv40cOdIce+yxZsOGDeb33383Pp/PfPLJJ47XoNg555xjjjnmGLNixQqTmZlpzjzzTJOYmOiIsTb+/HRbsmSJuffee83SpUvNpk2bzOuvv25iYmLMe++9Z4455hgzePBgs2bNGrNkyRJzwgknmPj4+MA1+3w+06lTJ3PKKacE7sWePXsG/h6IZBVd95NPPmkSEhLMO++8Y9auXWv+/Oc/m6SkJHPFFVcEji/rtS/+WdW6dWvz1ltvBY5t2LCh+f33340xptz7zf47/KeffjK33367WbRokdm4caP54IMPTJMmTcwTTzxhjCm616OiosyECRPMqlWrzJo1a8ysWbPM+PHjjTG1+3VxY2IlDP71r3+ZNm3amJiYGNOjRw+zYMGCcIcUUpLKfBT/EWKMMX6/30yYMMGkpqaa2NhYc+KJJ5qVK1c6zrNv3z5z3XXXmSZNmpj4+HgzbNgws3nzZkeb7du3m0svvdQkJiaaxMREc+mll5b6YbBp0yYzdOhQEx8fb5o0aWKuu+46s3///lBdftC5f5kzdhX73//+Zzp37mxiY2NNhw4dzIwZMxz7Gb/y5ebmmhtvvNG0bt3axMXFmXbt2pnx48c73sz+//buPiar8o/j+Ifk6Y5u7wYJiiFqCdFMGWIgQ8pUiFFBtmbLnOZ05dCFbrYeprNJUaylGSNHMyyU9bAS7QmBCLK5kvmQbhZlWopi1jQTV1Ph+/vjN/mFmN6SPw8n36/t/MHFOfc+5+K6uc/O976uQ//9z5kLkLO3Mxc3va2vduzYYWPHjrWQkBDr37+/LV682Do6Oi55v/jjfH135uLvXNuZmwVmV27fmV147J3tXBfZ9N/5+2/lypV24403WmhoqI0cOdKqqqq6vMaV3H9Ab1VbW2sJCQkWEhJiI0aMsIaGhosqrJiZlZWV2cCBA83j8VheXp4VFhZa//79O39/vsKKmdnUqVMtPDy8V12v/FPt7e1WWFhosbGxFhQUZIMGDbLnnnvOzMwWLFhgERERds0119jkyZNt6dKlV2xh5fjx4/bQQw/Z1VdfbVFRUVZcXNzl3P56TmvXrrWUlBTr27evhYWFWWpqqtXV1XW+1sGDBy07O9tCQkIsNjbWKisrLTIy0lasWNG5z65duyw1NdU8Ho8lJiZaTU1Nl/G8ZMkSS0hI6Px8yc3NtT179nQe/9prr1lMTIxdddVV3YoKvcHZhZWOjg57+eWXLT4+3oKCgqxfv36WlZXVeW/lzz//tOnTp5vP57Nrr73WZs+ebU888USPCysHDhywzMxMCwsLs2HDhtnHH39sPp/vX1dYMTN76aWXbMCAAebxeCwrK8vefPPNboUVn89n7733ng0dOtSCg4Ptjjvu6PJl4TPv9dLSUouOjrbQ0FCbNGmSHTlypFuehQsXWp8+fezgwYP/r1O+ZC7Ud7///rvNnTvXoqOjLSgoyGJiYmzKlCldrgkXLVpkUVFR5vP5bN68eTZnzpwu4625ubnzvSzJ9u7d+7c3uvfu3dtZaI2JibGSkpJuGd34//Nsu3btsqysLOvXr5+FhIRYXFycvfLKK2ZmtnXrVktOTraQkBAbNmyYvfvuu93Oubm52dLT0y04ONji4uKsurraFTfwz3feJ0+etNmzZ1t4eLhFRkZaUVFRl7Fodv7CSmVlpaWkpFhwcLAlJCTYp59+2rmPP4WVQ4cOWV5eng0YMMCCg4MtNjbWFi1aZO3t7Z37V1dXW1pamnk8Huvbt6/deuutXe7HufXvcrYAs4tcABQAAAAAgCvMrFmz9O2332rjxo1+7T9x4kQlJCRo+fLl/+dkuJK0tLQoJiZGdXV1Gj9+vNNxcIVZtWqVCgoKuixHd7bFixerqqrqnEvSnW3WrFn6+eeftX79+ksXEsA5/fjjjxoyZIi2bdumxMREp+P8KwQ6HQAAAAAAgN7mxRdf1MSJExUWFqZPPvlEb7zxhkpLSy943JEjR1RTU6P6+nqVlJRchqT4N6uvr1dbW5tuueUWtba26vHHH9fgwYOVkZHhdDSgx44dO6ampiatWbNG69atczoOAPQIhRUAAAAAAM6yefNmFRcX6/jx4xo6dKiWL1+umTNnXvC4pKQkHT16VC+88ILi4+MvQ1L8m506dUpPPfWU9uzZI6/Xq7S0NK1Zs0ZBQUFORwN6LDc3V5s3b9YjjzyiiRMnOh0HAHqEpcAAAAAAAAAAAAD8dJXTAQAAAAAAAAAAANyCwgoAAAAAAAAAAICfKKwAAAAAAPAXn3/+ue6++25FR0crICBAVVVVTkdyJfrx0iktLdWQIUMUGhqqUaNGaePGjU5Hcp3jx4+roKBAsbGx8ng8SktLU1NTk9OxXGfw4MEKCAjotuXn5zsdzVWKioo0evRoeb1eRUZGKi8vT83NzU7HcpWioiIFBASooKCgS/s333yje+65Rz6fT16vV6mpqdq3b58zIV2C8dgzFFYAAAAAAPiLEydOaOTIkSopKXE6iqvRj5fG22+/rYKCAj399NPatm2bxo4dq+zsbG4UXqSZM2eqtrZWFRUV2rlzpzIzMzVhwgQdOHDA6Wiu0tTUpNbW1s6ttrZWknT//fc7nMx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      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valued_arr = []\n",
    "\n",
    "for label, pred in zip(label_names, count_idx):\n",
    "    # print(label, pred)\n",
    "    valued_arr.append(f'{label}\\n{pred}')\n",
    "\n",
    "\n",
    "# fig, ax = plt.subplots(\n",
    "#     figsize=(20, 10)\n",
    "# )\n",
    "fig = plt.figure(figsize=(20, 10))\n",
    "fig.suptitle(f'Predicted label: {label_names[np.argmax(count_idx)]}')\n",
    "\n",
    "ax1 = fig.add_subplot(122)\n",
    "ax1.bar(valued_arr, count_idx)\n",
    "\n",
    "ax2 = fig.add_subplot(321)\n",
    "pltDisplay(avg_image, ax=ax2)\n",
    "\n",
    "ax3 = fig.add_subplot(323)\n",
    "pltDisplay(flow_avg_img, ax=ax3)\n",
    "\n",
    "ax4 = fig.add_subplot(325)\n",
    "ax4.plot(audio_file)\n",
    "# ax[0, 1].bar(valued_arr, final_model_preds)\n",
    "# plt.xticks(fontsize=15)\n",
    "# plt.yticks(fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}