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{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "objc[24504]: Class CaptureDelegate is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_videoio.3.4.16.dylib (0x1153c8860) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x2876f6480). One of the two will be used. Which one is undefined.\n",
                        "objc[24504]: Class CVWindow is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x115110a68) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x2876f64d0). One of the two will be used. Which one is undefined.\n",
                        "objc[24504]: Class CVView is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x115110a90) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x2876f64f8). One of the two will be used. Which one is undefined.\n",
                        "objc[24504]: Class CVSlider is implemented in both /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/mediapipe/.dylibs/libopencv_highgui.3.4.16.dylib (0x115110ab8) and /Users/fuixlabsdev1/Programming/PP/graduation-thesis/env/lib/python3.8/site-packages/cv2/cv2.abi3.so (0x2876f6520). One of the two will be used. Which one is undefined.\n"
                    ]
                }
            ],
            "source": [
                "import mediapipe as mp\n",
                "import cv2\n",
                "import numpy as np\n",
                "import pandas as pd\n",
                "import pickle\n",
                "\n",
                "# Drawing helpers\n",
                "mp_drawing = mp.solutions.drawing_utils\n",
                "mp_pose = mp.solutions.pose"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### 1. Set up important functions and variables"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": [
                "IMPORTANT_LMS = [\n",
                "    \"NOSE\",\n",
                "    \"LEFT_SHOULDER\",\n",
                "    \"RIGHT_SHOULDER\",\n",
                "    \"LEFT_HIP\",\n",
                "    \"RIGHT_HIP\",\n",
                "    \"LEFT_KNEE\",\n",
                "    \"RIGHT_KNEE\",\n",
                "    \"LEFT_ANKLE\",\n",
                "    \"RIGHT_ANKLE\"\n",
                "]\n",
                "\n",
                "headers = [\"label\"] # Label column\n",
                "\n",
                "for lm in IMPORTANT_LMS:\n",
                "    headers += [f\"{lm.lower()}_x\", f\"{lm.lower()}_y\", f\"{lm.lower()}_z\", f\"{lm.lower()}_v\"]\n",
                "\n",
                "\n",
                "def extract_important_keypoints(results) -> list:\n",
                "    '''\n",
                "    Extract important keypoints from mediapipe pose detection\n",
                "    '''\n",
                "    landmarks = results.pose_landmarks.landmark\n",
                "\n",
                "    data = []\n",
                "    for lm in IMPORTANT_LMS:\n",
                "        keypoint = landmarks[mp_pose.PoseLandmark[lm].value]\n",
                "        data.append([keypoint.x, keypoint.y, keypoint.z, keypoint.visibility])\n",
                "    \n",
                "    return np.array(data).flatten().tolist()\n",
                "\n",
                "\n",
                "def rescale_frame(frame, percent=50):\n",
                "    '''\n",
                "    Rescale a frame to a certain percentage compare to its original frame\n",
                "    '''\n",
                "    width = int(frame.shape[1] * percent/ 100)\n",
                "    height = int(frame.shape[0] * percent/ 100)\n",
                "    dim = (width, height)\n",
                "    return cv2.resize(frame, dim, interpolation =cv2.INTER_AREA)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### 2. Analyze and detection bad pose\n",
                "\n",
                "Look through [this](./analyze_bad_pose.ipynb) on how we analyze bad foot and knee placement while doing squat."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": [
                "import math \n",
                "\n",
                "\n",
                "def calculate_distance(pointX, pointY) -> float:\n",
                "    '''\n",
                "    Calculate a distance between 2 points\n",
                "    '''\n",
                "\n",
                "    x1, y1 = pointX\n",
                "    x2, y2 = pointY\n",
                "\n",
                "    return math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)\n",
                "\n",
                "\n",
                "def analyze_foot_knee_placement(results, stage: str, foot_shoulder_ratio_thresholds: list, knee_foot_ratio_thresholds: dict, visibility_threshold: int) -> dict:\n",
                "    '''\n",
                "    Calculate the ratio between the foot and shoulder for FOOT PLACEMENT analysis\n",
                "    \n",
                "    Calculate the ratio between the knee and foot for KNEE PLACEMENT analysis\n",
                "\n",
                "    Return result explanation:\n",
                "        -1: Unknown result due to poor visibility\n",
                "        0: Correct knee placement\n",
                "        1: Placement too tight\n",
                "        2: Placement too wide\n",
                "    '''\n",
                "    analyzed_results = {\n",
                "        \"foot_placement\": -1,\n",
                "        \"knee_placement\": -1,\n",
                "    }\n",
                "\n",
                "    landmarks = results.pose_landmarks.landmark\n",
                "\n",
                "    # * Visibility check of important landmarks for foot placement analysis\n",
                "    left_foot_index_vis = landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].visibility\n",
                "    right_foot_index_vis = landmarks[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value].visibility\n",
                "\n",
                "    left_knee_vis = landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].visibility\n",
                "    right_knee_vis = landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].visibility\n",
                "\n",
                "    # If visibility of any keypoints is low cancel the analysis\n",
                "    if (left_foot_index_vis < visibility_threshold or right_foot_index_vis < visibility_threshold or left_knee_vis < visibility_threshold or right_knee_vis < visibility_threshold):\n",
                "        return analyzed_results\n",
                "    \n",
                "    # * Calculate shoulder width\n",
                "    left_shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]\n",
                "    right_shoulder = [landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y]\n",
                "    shoulder_width = calculate_distance(left_shoulder, right_shoulder)\n",
                "\n",
                "    # * Calculate 2-foot width\n",
                "    left_foot_index = [landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].x, landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].y]\n",
                "    right_foot_index = [landmarks[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value].y]\n",
                "    foot_width = calculate_distance(left_foot_index, right_foot_index)\n",
                "\n",
                "    # * Calculate foot and shoulder ratio\n",
                "    foot_shoulder_ratio = round(foot_width / shoulder_width, 1)\n",
                "\n",
                "    # * Analyze FOOT PLACEMENT\n",
                "    min_ratio_foot_shoulder, max_ratio_foot_shoulder = foot_shoulder_ratio_thresholds\n",
                "    if min_ratio_foot_shoulder <= foot_shoulder_ratio <= max_ratio_foot_shoulder:\n",
                "        analyzed_results[\"foot_placement\"] = 0\n",
                "    elif foot_shoulder_ratio < min_ratio_foot_shoulder:\n",
                "        analyzed_results[\"foot_placement\"] = 1\n",
                "    elif foot_shoulder_ratio > max_ratio_foot_shoulder:\n",
                "        analyzed_results[\"foot_placement\"] = 2\n",
                "    \n",
                "    # * Visibility check of important landmarks for knee placement analysis\n",
                "    left_knee_vis = landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].visibility\n",
                "    right_knee_vis = landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].visibility\n",
                "\n",
                "    # If visibility of any keypoints is low cancel the analysis\n",
                "    if (left_knee_vis < visibility_threshold or right_knee_vis < visibility_threshold):\n",
                "        print(\"Cannot see foot\")\n",
                "        return analyzed_results\n",
                "\n",
                "    # * Calculate 2 knee width\n",
                "    left_knee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x, landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]\n",
                "    right_knee = [landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y]\n",
                "    knee_width = calculate_distance(left_knee, right_knee)\n",
                "\n",
                "    # * Calculate foot and shoulder ratio\n",
                "    knee_foot_ratio = round(knee_width / foot_width, 1)\n",
                "\n",
                "    # * Analyze KNEE placement\n",
                "    up_min_ratio_knee_foot, up_max_ratio_knee_foot = knee_foot_ratio_thresholds.get(\"up\")\n",
                "    middle_min_ratio_knee_foot, middle_max_ratio_knee_foot = knee_foot_ratio_thresholds.get(\"middle\")\n",
                "    down_min_ratio_knee_foot, down_max_ratio_knee_foot = knee_foot_ratio_thresholds.get(\"down\")\n",
                "\n",
                "    if stage == \"up\":\n",
                "        if up_min_ratio_knee_foot <= knee_foot_ratio <= up_max_ratio_knee_foot:\n",
                "            analyzed_results[\"knee_placement\"] = 0\n",
                "        elif knee_foot_ratio < up_min_ratio_knee_foot:\n",
                "            analyzed_results[\"knee_placement\"] = 1\n",
                "        elif knee_foot_ratio > up_max_ratio_knee_foot:\n",
                "            analyzed_results[\"knee_placement\"] = 2\n",
                "    elif stage == \"middle\":\n",
                "        if middle_min_ratio_knee_foot <= knee_foot_ratio <= middle_max_ratio_knee_foot:\n",
                "            analyzed_results[\"knee_placement\"] = 0\n",
                "        elif knee_foot_ratio < middle_min_ratio_knee_foot:\n",
                "            analyzed_results[\"knee_placement\"] = 1\n",
                "        elif knee_foot_ratio > middle_max_ratio_knee_foot:\n",
                "            analyzed_results[\"knee_placement\"] = 2\n",
                "    elif stage == \"down\":\n",
                "        if down_min_ratio_knee_foot <= knee_foot_ratio <= down_max_ratio_knee_foot:\n",
                "            analyzed_results[\"knee_placement\"] = 0\n",
                "        elif knee_foot_ratio < down_min_ratio_knee_foot:\n",
                "            analyzed_results[\"knee_placement\"] = 1\n",
                "        elif knee_foot_ratio > down_max_ratio_knee_foot:\n",
                "            analyzed_results[\"knee_placement\"] = 2\n",
                "    \n",
                "    return analyzed_results\n",
                "\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### 3. Make detection"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [],
            "source": [
                "VIDEO_PATH1 = \"../data/squat/squat_test_1.mov\"\n",
                "VIDEO_PATH2 = \"../data/squat/squat_test_2.mov\"\n",
                "VIDEO_PATH3 = \"../data/squat/squat_test_3.mp4\"\n",
                "VIDEO_PATH4 = \"../data/squat/squat_right_2.mp4\""
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Load model for counter\n",
                "with open(\"./model/squat_model.pkl\", \"rb\") as f:\n",
                "    count_model = pickle.load(f)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [],
            "source": [
                "cap = cv2.VideoCapture(VIDEO_PATH3)\n",
                "\n",
                "# Counter vars\n",
                "counter = 0\n",
                "current_stage = \"\"\n",
                "PREDICTION_PROB_THRESHOLD = 0.7\n",
                "\n",
                "# Error vars\n",
                "VISIBILITY_THRESHOLD = 0.6\n",
                "FOOT_SHOULDER_RATIO_THRESHOLDS = [1.2, 2.8]\n",
                "KNEE_FOOT_RATIO_THRESHOLDS = {\n",
                "    \"up\": [0.5, 1.0],\n",
                "    \"middle\": [0.7, 1.0],\n",
                "    \"down\": [0.7, 1.1],\n",
                "}\n",
                "\n",
                "\n",
                "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
                "    while cap.isOpened():\n",
                "        ret, image = cap.read()\n",
                "\n",
                "        if not ret:\n",
                "            break\n",
                "        \n",
                "        # Reduce size of a frame\n",
                "        image = rescale_frame(image, 100)\n",
                "\n",
                "        # Recolor image from BGR to RGB for mediapipe\n",
                "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
                "        image.flags.writeable = False\n",
                "\n",
                "        results = pose.process(image)\n",
                "        if not results.pose_landmarks:\n",
                "            continue\n",
                "\n",
                "        # Recolor image from BGR to RGB for mediapipe\n",
                "        image.flags.writeable = True\n",
                "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
                "\n",
                "        # Draw landmarks and connections\n",
                "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(244, 117, 66), thickness=2, circle_radius=2), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=1))\n",
                "\n",
                "        # Make detection\n",
                "        try:\n",
                "            # * Model prediction for SQUAT counter\n",
                "            # Extract keypoints from frame for the input\n",
                "            row = extract_important_keypoints(results)\n",
                "            X = pd.DataFrame([row], columns=headers[1:])\n",
                "\n",
                "            # Make prediction and its probability\n",
                "            predicted_class = count_model.predict(X)[0]\n",
                "            prediction_probabilities = count_model.predict_proba(X)[0]\n",
                "            prediction_probability = round(prediction_probabilities[prediction_probabilities.argmax()], 2)\n",
                "\n",
                "            # Evaluate model prediction\n",
                "            if predicted_class == \"down\" and prediction_probability >= PREDICTION_PROB_THRESHOLD:\n",
                "                current_stage = \"down\"\n",
                "            elif current_stage == \"down\" and predicted_class == \"up\" and prediction_probability >= PREDICTION_PROB_THRESHOLD: \n",
                "                current_stage = \"up\"\n",
                "                counter += 1\n",
                "\n",
                "            # Analyze squat pose\n",
                "            analyzed_results = analyze_foot_knee_placement(results=results, stage=current_stage, foot_shoulder_ratio_thresholds=FOOT_SHOULDER_RATIO_THRESHOLDS, knee_foot_ratio_thresholds=KNEE_FOOT_RATIO_THRESHOLDS, visibility_threshold=VISIBILITY_THRESHOLD)\n",
                "\n",
                "            foot_placement_evaluation = analyzed_results[\"foot_placement\"]\n",
                "            knee_placement_evaluation = analyzed_results[\"knee_placement\"]\n",
                "            \n",
                "            # * Evaluate FOOT PLACEMENT error\n",
                "            if foot_placement_evaluation == -1:\n",
                "                foot_placement = \"UNK\"\n",
                "            elif foot_placement_evaluation == 0:\n",
                "                foot_placement = \"Correct\"\n",
                "            elif foot_placement_evaluation == 1:\n",
                "                foot_placement = \"Too tight\"\n",
                "            elif foot_placement_evaluation == 2:\n",
                "                foot_placement = \"Too wide\"\n",
                "            \n",
                "            # * Evaluate KNEE PLACEMENT error\n",
                "            if knee_placement_evaluation == -1:\n",
                "                knee_placement = \"UNK\"\n",
                "            elif knee_placement_evaluation == 0:\n",
                "                knee_placement = \"Correct\"\n",
                "            elif knee_placement_evaluation == 1:\n",
                "                knee_placement = \"Too tight\"\n",
                "            elif knee_placement_evaluation == 2:\n",
                "                knee_placement = \"Too wide\"\n",
                "            \n",
                "            # Visualization\n",
                "            # Status box\n",
                "            cv2.rectangle(image, (0, 0), (500, 60), (245, 117, 16), -1)\n",
                "\n",
                "            # Display class\n",
                "            cv2.putText(image, \"COUNT\", (10, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
                "            cv2.putText(image, f'{str(counter)}, {predicted_class.split(\" \")[0]}, {str(prediction_probability)}', (5, 40), cv2.FONT_HERSHEY_COMPLEX, .7, (255, 255, 255), 2, cv2.LINE_AA)\n",
                "\n",
                "            # Display Foot and Shoulder width ratio\n",
                "            cv2.putText(image, \"FOOT\", (200, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
                "            cv2.putText(image, foot_placement, (195, 40), cv2.FONT_HERSHEY_COMPLEX, .7, (255, 255, 255), 2, cv2.LINE_AA)\n",
                "\n",
                "            # Display knee and Shoulder width ratio\n",
                "            cv2.putText(image, \"KNEE\", (330, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
                "            cv2.putText(image, knee_placement, (325, 40), cv2.FONT_HERSHEY_COMPLEX, .7, (255, 255, 255), 2, cv2.LINE_AA)\n",
                "\n",
                "        except Exception as e:\n",
                "            print(f\"Error: {e}\")\n",
                "        \n",
                "        cv2.imshow(\"CV2\", image)\n",
                "        \n",
                "        # Press Q to close cv2 window\n",
                "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
                "            break\n",
                "\n",
                "    cap.release()\n",
                "    cv2.destroyAllWindows()\n",
                "\n",
                "    # (Optional)Fix bugs cannot close windows in MacOS (https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv)\n",
                "    for i in range (1, 5):\n",
                "        cv2.waitKey(1)\n",
                "  "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": []
        }
    ],
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