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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import mediapipe as mp\n",
"import cv2\n",
"import numpy as np\n",
"import pandas as pd\n",
"import datetime\n",
"\n",
"import pickle\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Determine important landmarks for plank\n",
"IMPORTANT_LMS = [\n",
" \"NOSE\",\n",
" \"LEFT_SHOULDER\",\n",
" \"RIGHT_SHOULDER\",\n",
" \"RIGHT_ELBOW\",\n",
" \"LEFT_ELBOW\",\n",
" \"RIGHT_WRIST\",\n",
" \"LEFT_WRIST\",\n",
" \"LEFT_HIP\",\n",
" \"RIGHT_HIP\",\n",
"]\n",
"\n",
"# Generate all columns of the data frame\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\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def rescale_frame(frame, percent=50):\n",
" '''\n",
" Rescale a frame from OpenCV 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)\n",
"\n",
"\n",
"def save_frame_as_image(frame, message: str = None):\n",
" '''\n",
" Save a frame as image to display the error\n",
" '''\n",
" now = datetime.datetime.now()\n",
"\n",
" if message:\n",
" cv2.putText(frame, message, (50, 150), cv2.FONT_HERSHEY_COMPLEX, 0.4, (0, 0, 0), 1, cv2.LINE_AA)\n",
" \n",
" print(\"Saving ...\")\n",
" cv2.imwrite(f\"../data/logs/bicep_{now}.jpg\", frame)\n",
"\n",
"\n",
"def calculate_angle(point1: list, point2: list, point3: list) -> float:\n",
" '''\n",
" Calculate the angle between 3 points\n",
" Unit of the angle will be in Degree\n",
" '''\n",
" point1 = np.array(point1)\n",
" point2 = np.array(point2)\n",
" point3 = np.array(point3)\n",
"\n",
" # Calculate algo\n",
" angleInRad = np.arctan2(point3[1] - point2[1], point3[0] - point2[0]) - np.arctan2(point1[1] - point2[1], point1[0] - point2[0])\n",
" angleInDeg = np.abs(angleInRad * 180.0 / np.pi)\n",
"\n",
" angleInDeg = angleInDeg if angleInDeg <= 180 else 360 - angleInDeg\n",
" return angleInDeg\n",
"\n",
"\n",
"def extract_important_keypoints(results, important_landmarks: list) -> 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_landmarks:\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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. OOP method for Analyze Pose of each arm\n",
"\n",
"To be easier to detect both arm at the same time, I choose to do the calculation for bicep counter and error detection with OOP.\n",
"\n",
"*Note: Every If any joints from an arm is appeared to be in poor visibility according to mediapipe, that arm will be skip*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class BicepPoseAnalysis:\n",
" def __init__(self, side: str, stage_down_threshold: float, stage_up_threshold: float, peak_contraction_threshold: float, loose_upper_arm_angle_threshold: float, visibility_threshold: float):\n",
" # Initialize thresholds\n",
" self.stage_down_threshold = stage_down_threshold\n",
" self.stage_up_threshold = stage_up_threshold\n",
" self.peak_contraction_threshold = peak_contraction_threshold\n",
" self.loose_upper_arm_angle_threshold = loose_upper_arm_angle_threshold\n",
" self.visibility_threshold = visibility_threshold\n",
"\n",
" self.side = side\n",
" self.counter = 0\n",
" self.stage = \"down\"\n",
" self.is_visible = True\n",
" self.detected_errors = {\n",
" \"LOOSE_UPPER_ARM\": 0,\n",
" \"PEAK_CONTRACTION\": 0,\n",
" }\n",
"\n",
" # Params for loose upper arm error detection\n",
" self.loose_upper_arm = False\n",
"\n",
" # Params for peak contraction error detection\n",
" self.peak_contraction_angle = 1000\n",
" self.peak_contraction_frame = None\n",
" \n",
" def get_joints(self, landmarks) -> bool:\n",
" '''\n",
" Check for joints' visibility then get joints coordinate\n",
" '''\n",
" side = self.side.upper()\n",
"\n",
" # Check visibility\n",
" joints_visibility = [ landmarks[mp_pose.PoseLandmark[f\"{side}_SHOULDER\"].value].visibility, landmarks[mp_pose.PoseLandmark[f\"{side}_ELBOW\"].value].visibility, landmarks[mp_pose.PoseLandmark[f\"{side}_WRIST\"].value].visibility ]\n",
"\n",
" is_visible = all([ vis > self.visibility_threshold for vis in joints_visibility ])\n",
" self.is_visible = is_visible\n",
"\n",
" if not is_visible:\n",
" return self.is_visible\n",
" \n",
" # Get joints' coordinates\n",
" self.shoulder = [ landmarks[mp_pose.PoseLandmark[f\"{side}_SHOULDER\"].value].x, landmarks[mp_pose.PoseLandmark[f\"{side}_SHOULDER\"].value].y ]\n",
" self.elbow = [ landmarks[mp_pose.PoseLandmark[f\"{side}_ELBOW\"].value].x, landmarks[mp_pose.PoseLandmark[f\"{side}_ELBOW\"].value].y ]\n",
" self.wrist = [ landmarks[mp_pose.PoseLandmark[f\"{side}_WRIST\"].value].x, landmarks[mp_pose.PoseLandmark[f\"{side}_WRIST\"].value].y ]\n",
"\n",
" return self.is_visible\n",
" \n",
" def analyze_pose(self, landmarks, frame):\n",
" '''\n",
" - Bicep Counter\n",
" - Errors Detection\n",
" '''\n",
" self.get_joints(landmarks)\n",
"\n",
" # Cancel calculation if visibility is poor\n",
" if not self.is_visible:\n",
" return (None, None)\n",
"\n",
" # * Calculate curl angle for counter\n",
" bicep_curl_angle = int(calculate_angle(self.shoulder, self.elbow, self.wrist))\n",
" if bicep_curl_angle > self.stage_down_threshold:\n",
" self.stage = \"down\"\n",
" elif bicep_curl_angle < self.stage_up_threshold and self.stage == \"down\":\n",
" self.stage = \"up\"\n",
" self.counter += 1\n",
" \n",
" # * Calculate the angle between the upper arm (shoulder & joint) and the Y axis\n",
" shoulder_projection = [ self.shoulder[0], 1 ] # Represent the projection of the shoulder to the X axis\n",
" ground_upper_arm_angle = int(calculate_angle(self.elbow, self.shoulder, shoulder_projection))\n",
"\n",
" # * Evaluation for LOOSE UPPER ARM error\n",
" if ground_upper_arm_angle > self.loose_upper_arm_angle_threshold:\n",
" # Limit the saved frame\n",
" if not self.loose_upper_arm:\n",
" self.loose_upper_arm = True\n",
" # save_frame_as_image(frame, f\"Loose upper arm: {ground_upper_arm_angle}\")\n",
" self.detected_errors[\"LOOSE_UPPER_ARM\"] += 1\n",
" else:\n",
" self.loose_upper_arm = False\n",
" \n",
" # * Evaluate PEAK CONTRACTION error\n",
" if self.stage == \"up\" and bicep_curl_angle < self.peak_contraction_angle:\n",
" # Save peaked contraction every rep\n",
" self.peak_contraction_angle = bicep_curl_angle\n",
" self.peak_contraction_frame = frame\n",
" \n",
" elif self.stage == \"down\":\n",
" # * Evaluate if the peak is higher than the threshold if True, marked as an error then saved that frame\n",
" if self.peak_contraction_angle != 1000 and self.peak_contraction_angle >= self.peak_contraction_threshold:\n",
" # save_frame_as_image(self.peak_contraction_frame, f\"{self.side} - Peak Contraction: {self.peak_contraction_angle}\")\n",
" self.detected_errors[\"PEAK_CONTRACTION\"] += 1\n",
" \n",
" # Reset params\n",
" self.peak_contraction_angle = 1000\n",
" self.peak_contraction_frame = None\n",
" \n",
" return (bicep_curl_angle, ground_upper_arm_angle)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Bicep Detection"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"VIDEO_PATH1 = \"../data/db_curl/bc_test_1.mp4\"\n",
"VIDEO_PATH2 = \"../data/db_curl/bc_test_2.mp4\"\n",
"VIDEO_PATH3 = \"../data/db_curl/bc_test_3.mp4\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load input scaler\n",
"with open(\"./model/input_scaler.pkl\", \"rb\") as f:\n",
" input_scaler = pickle.load(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3.1. Detection with SKLearn model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load model\n",
"with open(\"./model/KNN_model.pkl\", \"rb\") as f:\n",
" sklearn_model = pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cap = cv2.VideoCapture(VIDEO_PATH3)\n",
"\n",
"VISIBILITY_THRESHOLD = 0.65\n",
"\n",
"# Params for counter\n",
"STAGE_UP_THRESHOLD = 90\n",
"STAGE_DOWN_THRESHOLD = 120\n",
"\n",
"# Params to catch FULL RANGE OF MOTION error\n",
"PEAK_CONTRACTION_THRESHOLD = 60\n",
"\n",
"# LOOSE UPPER ARM error detection\n",
"LOOSE_UPPER_ARM = False\n",
"LOOSE_UPPER_ARM_ANGLE_THRESHOLD = 40\n",
"\n",
"# STANDING POSTURE error detection\n",
"POSTURE_ERROR_THRESHOLD = 0.7\n",
"posture = \"C\"\n",
"\n",
"# Init analysis class\n",
"left_arm_analysis = BicepPoseAnalysis(side=\"left\", stage_down_threshold=STAGE_DOWN_THRESHOLD, stage_up_threshold=STAGE_UP_THRESHOLD, peak_contraction_threshold=PEAK_CONTRACTION_THRESHOLD, loose_upper_arm_angle_threshold=LOOSE_UPPER_ARM_ANGLE_THRESHOLD, visibility_threshold=VISIBILITY_THRESHOLD)\n",
"\n",
"right_arm_analysis = BicepPoseAnalysis(side=\"right\", stage_down_threshold=STAGE_DOWN_THRESHOLD, stage_up_threshold=STAGE_UP_THRESHOLD, peak_contraction_threshold=PEAK_CONTRACTION_THRESHOLD, loose_upper_arm_angle_threshold=LOOSE_UPPER_ARM_ANGLE_THRESHOLD, visibility_threshold=VISIBILITY_THRESHOLD)\n",
"\n",
"with mp_pose.Pose(min_detection_confidence=0.8, min_tracking_confidence=0.8) 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, 50)\n",
" # image = cv2.flip(image, 1)\n",
" \n",
" video_dimensions = [image.shape[1], image.shape[0]]\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",
"\n",
" if not results.pose_landmarks:\n",
" print(\"No human found\")\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",
" landmarks = results.pose_landmarks.landmark\n",
" \n",
" (left_bicep_curl_angle, left_ground_upper_arm_angle) = left_arm_analysis.analyze_pose(landmarks=landmarks, frame=image)\n",
" (right_bicep_curl_angle, right_ground_upper_arm_angle) = right_arm_analysis.analyze_pose(landmarks=landmarks, frame=image)\n",
"\n",
" # Extract keypoints from frame for the input\n",
" row = extract_important_keypoints(results, IMPORTANT_LMS)\n",
" X = pd.DataFrame([row], columns=HEADERS[1:])\n",
" X = pd.DataFrame(input_scaler.transform(X))\n",
"\n",
"\n",
" # Make prediction and its probability\n",
" predicted_class = sklearn_model.predict(X)[0]\n",
" prediction_probabilities = sklearn_model.predict_proba(X)[0]\n",
" class_prediction_probability = round(prediction_probabilities[np.argmax(prediction_probabilities)], 2)\n",
"\n",
" if class_prediction_probability >= POSTURE_ERROR_THRESHOLD:\n",
" posture = predicted_class\n",
"\n",
" # Visualization\n",
" # Status box\n",
" cv2.rectangle(image, (0, 0), (500, 40), (245, 117, 16), -1)\n",
"\n",
" # Display probability\n",
" cv2.putText(image, \"RIGHT\", (15, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(right_arm_analysis.counter) if right_arm_analysis.is_visible else \"UNK\", (10, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
"\n",
" # Display Left Counter\n",
" cv2.putText(image, \"LEFT\", (95, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(left_arm_analysis.counter) if left_arm_analysis.is_visible else \"UNK\", (100, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
"\n",
" # * Display error\n",
" # Right arm error\n",
" cv2.putText(image, \"R_PC\", (165, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(right_arm_analysis.detected_errors[\"PEAK_CONTRACTION\"]), (160, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
" cv2.putText(image, \"R_LUA\", (225, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(right_arm_analysis.detected_errors[\"LOOSE_UPPER_ARM\"]), (220, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
"\n",
" # Left arm error\n",
" cv2.putText(image, \"L_PC\", (300, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(left_arm_analysis.detected_errors[\"PEAK_CONTRACTION\"]), (295, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
" cv2.putText(image, \"L_LUA\", (380, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(left_arm_analysis.detected_errors[\"LOOSE_UPPER_ARM\"]), (375, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
"\n",
" # Lean back error\n",
" cv2.putText(image, \"LB\", (460, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(f\"{posture}, {predicted_class}, {class_prediction_probability}\"), (440, 30), cv2.FONT_HERSHEY_COMPLEX, 0.3, (255, 255, 255), 1, cv2.LINE_AA)\n",
"\n",
"\n",
" # * Visualize angles\n",
" # Visualize LEFT arm calculated angles\n",
" if left_arm_analysis.is_visible:\n",
" cv2.putText(image, str(left_bicep_curl_angle), tuple(np.multiply(left_arm_analysis.elbow, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(left_ground_upper_arm_angle), tuple(np.multiply(left_arm_analysis.shoulder, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
"\n",
"\n",
" # Visualize RIGHT arm calculated angles\n",
" if right_arm_analysis.is_visible:\n",
" cv2.putText(image, str(right_bicep_curl_angle), tuple(np.multiply(right_arm_analysis.elbow, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(right_ground_upper_arm_angle), tuple(np.multiply(right_arm_analysis.shoulder, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 0), 1, cv2.LINE_AA)\n",
" \n",
" except Exception as e:\n",
" print(f\"Error: {e}\")\n",
" \n",
" cv2.imshow(\"CV2\", image)\n",
"\n",
" # if left_arm_analysis.loose_upper_arm:\n",
" # save_frame_as_image(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",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3.2. Detection with Deep Learning model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load model\n",
"with open(\"./model/bicep_model_deep_learning.pkl\", \"rb\") as f:\n",
" DL_model = pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cap = cv2.VideoCapture(VIDEO_PATH3)\n",
"\n",
"VISIBILITY_THRESHOLD = 0.65\n",
"\n",
"# Params for counter\n",
"STAGE_UP_THRESHOLD = 90\n",
"STAGE_DOWN_THRESHOLD = 120\n",
"\n",
"# Params to catch FULL RANGE OF MOTION error\n",
"PEAK_CONTRACTION_THRESHOLD = 60\n",
"\n",
"# LOOSE UPPER ARM error detection\n",
"LOOSE_UPPER_ARM = False\n",
"LOOSE_UPPER_ARM_ANGLE_THRESHOLD = 40\n",
"\n",
"# STANDING POSTURE error detection\n",
"POSTURE_ERROR_THRESHOLD = 0.95\n",
"posture = 0\n",
"\n",
"# Init analysis class\n",
"left_arm_analysis = BicepPoseAnalysis(side=\"left\", stage_down_threshold=STAGE_DOWN_THRESHOLD, stage_up_threshold=STAGE_UP_THRESHOLD, peak_contraction_threshold=PEAK_CONTRACTION_THRESHOLD, loose_upper_arm_angle_threshold=LOOSE_UPPER_ARM_ANGLE_THRESHOLD, visibility_threshold=VISIBILITY_THRESHOLD)\n",
"\n",
"right_arm_analysis = BicepPoseAnalysis(side=\"right\", stage_down_threshold=STAGE_DOWN_THRESHOLD, stage_up_threshold=STAGE_UP_THRESHOLD, peak_contraction_threshold=PEAK_CONTRACTION_THRESHOLD, loose_upper_arm_angle_threshold=LOOSE_UPPER_ARM_ANGLE_THRESHOLD, visibility_threshold=VISIBILITY_THRESHOLD)\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, 50)\n",
" # image = cv2.flip(image, 1)\n",
" \n",
" video_dimensions = [image.shape[1], image.shape[0]]\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",
"\n",
" if not results.pose_landmarks:\n",
" print(\"No human found\")\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",
" landmarks = results.pose_landmarks.landmark\n",
" \n",
" (left_bicep_curl_angle, left_ground_upper_arm_angle) = left_arm_analysis.analyze_pose(landmarks=landmarks, frame=image)\n",
" (right_bicep_curl_angle, right_ground_upper_arm_angle) = right_arm_analysis.analyze_pose(landmarks=landmarks, frame=image)\n",
"\n",
" # Extract keypoints from frame for the input\n",
" row = extract_important_keypoints(results, IMPORTANT_LMS)\n",
" X = pd.DataFrame([row, ], columns=HEADERS[1:])\n",
" X = pd.DataFrame(input_scaler.transform(X))\n",
"\n",
" # Make prediction and its probability\n",
" prediction = DL_model.predict(X)\n",
" predicted_class = np.argmax(prediction, axis=1)[0]\n",
" prediction_probability = round(max(prediction.tolist()[0]), 2)\n",
"\n",
" if prediction_probability >= POSTURE_ERROR_THRESHOLD:\n",
" posture = predicted_class\n",
"\n",
" # Visualization\n",
" # Status box\n",
" cv2.rectangle(image, (0, 0), (500, 40), (245, 117, 16), -1)\n",
"\n",
" # Display probability\n",
" cv2.putText(image, \"RIGHT\", (15, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(right_arm_analysis.counter) if right_arm_analysis.is_visible else \"UNK\", (10, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
"\n",
" # Display Left Counter\n",
" cv2.putText(image, \"LEFT\", (95, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(left_arm_analysis.counter) if left_arm_analysis.is_visible else \"UNK\", (100, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
"\n",
" # * Display error\n",
" # Right arm error\n",
" cv2.putText(image, \"R_PC\", (165, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(right_arm_analysis.detected_errors[\"PEAK_CONTRACTION\"]), (160, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
" cv2.putText(image, \"R_LUA\", (225, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(right_arm_analysis.detected_errors[\"LOOSE_UPPER_ARM\"]), (220, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
"\n",
" # Left arm error\n",
" cv2.putText(image, \"L_PC\", (300, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(left_arm_analysis.detected_errors[\"PEAK_CONTRACTION\"]), (295, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
" cv2.putText(image, \"L_LUA\", (380, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(left_arm_analysis.detected_errors[\"LOOSE_UPPER_ARM\"]), (375, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)\n",
"\n",
" # Lean back error\n",
" cv2.putText(image, \"LB\", (460, 12), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(\"C\" if posture == 0 else \"L\") + f\" ,{predicted_class}, {prediction_probability}\", (440, 30), cv2.FONT_HERSHEY_COMPLEX, 0.3, (255, 255, 255), 1, cv2.LINE_AA)\n",
"\n",
"\n",
" # * Visualize angles\n",
" # Visualize LEFT arm calculated angles\n",
" if left_arm_analysis.is_visible:\n",
" cv2.putText(image, str(left_bicep_curl_angle), tuple(np.multiply(left_arm_analysis.elbow, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(left_ground_upper_arm_angle), tuple(np.multiply(left_arm_analysis.shoulder, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)\n",
"\n",
"\n",
" # Visualize RIGHT arm calculated angles\n",
" if right_arm_analysis.is_visible:\n",
" cv2.putText(image, str(right_bicep_curl_angle), tuple(np.multiply(right_arm_analysis.elbow, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(right_ground_upper_arm_angle), tuple(np.multiply(right_arm_analysis.shoulder, video_dimensions).astype(int)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 0), 1, 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",
" \n"
]
},
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|