{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 0. Install and Import Dependencies" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: mediapipe in .\\.venv\\lib\\site-packages (0.10.21)\n", "Requirement already satisfied: opencv-python in .\\.venv\\lib\\site-packages (4.11.0.86)\n", "Requirement already satisfied: pandas in .\\.venv\\lib\\site-packages (2.3.2)\n", "Requirement already satisfied: scikit-learn in .\\.venv\\lib\\site-packages (1.7.1)\n", "Requirement already satisfied: absl-py in .\\.venv\\lib\\site-packages (from mediapipe) (2.3.1)\n", "Requirement already satisfied: attrs>=19.1.0 in .\\.venv\\lib\\site-packages (from mediapipe) (25.3.0)\n", "Requirement already satisfied: flatbuffers>=2.0 in .\\.venv\\lib\\site-packages (from mediapipe) (25.2.10)\n", "Requirement already satisfied: jax in .\\.venv\\lib\\site-packages (from mediapipe) (0.7.1)\n", "Requirement already satisfied: jaxlib in .\\.venv\\lib\\site-packages (from mediapipe) (0.7.1)\n", "Requirement already satisfied: matplotlib in .\\.venv\\lib\\site-packages (from mediapipe) (3.10.6)\n", "Requirement already satisfied: numpy<2 in .\\.venv\\lib\\site-packages (from mediapipe) (1.26.4)\n", "Requirement already satisfied: opencv-contrib-python in .\\.venv\\lib\\site-packages (from mediapipe) (4.11.0.86)\n", "Requirement already satisfied: protobuf<5,>=4.25.3 in .\\.venv\\lib\\site-packages (from mediapipe) (4.25.8)\n", "Requirement already satisfied: sounddevice>=0.4.4 in .\\.venv\\lib\\site-packages (from mediapipe) (0.5.2)\n", "Requirement already satisfied: sentencepiece in .\\.venv\\lib\\site-packages (from mediapipe) (0.2.1)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in .\\.venv\\lib\\site-packages (from pandas) (2.9.0.post0)\n", "Requirement already satisfied: pytz>=2020.1 in .\\.venv\\lib\\site-packages (from pandas) (2025.2)\n", "Requirement already satisfied: tzdata>=2022.7 in .\\.venv\\lib\\site-packages (from pandas) (2025.2)\n", "Requirement already satisfied: scipy>=1.8.0 in .\\.venv\\lib\\site-packages (from scikit-learn) (1.16.1)\n", "Requirement already satisfied: joblib>=1.2.0 in .\\.venv\\lib\\site-packages (from scikit-learn) (1.5.2)\n", "Requirement already satisfied: threadpoolctl>=3.1.0 in .\\.venv\\lib\\site-packages (from scikit-learn) (3.6.0)\n", "Requirement already satisfied: six>=1.5 in .\\.venv\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n", "Requirement already satisfied: CFFI>=1.0 in .\\.venv\\lib\\site-packages (from sounddevice>=0.4.4->mediapipe) (1.17.1)\n", "Requirement already satisfied: pycparser in .\\.venv\\lib\\site-packages (from CFFI>=1.0->sounddevice>=0.4.4->mediapipe) (2.22)\n", "Requirement already satisfied: ml_dtypes>=0.5.0 in .\\.venv\\lib\\site-packages (from jax->mediapipe) (0.5.3)\n", "Requirement already satisfied: opt_einsum in .\\.venv\\lib\\site-packages (from jax->mediapipe) (3.4.0)\n", "Requirement already satisfied: contourpy>=1.0.1 in .\\.venv\\lib\\site-packages (from matplotlib->mediapipe) (1.3.3)\n", "Requirement already satisfied: cycler>=0.10 in .\\.venv\\lib\\site-packages (from matplotlib->mediapipe) (0.12.1)\n", "Requirement already satisfied: fonttools>=4.22.0 in .\\.venv\\lib\\site-packages (from matplotlib->mediapipe) (4.59.2)\n", "Requirement already satisfied: kiwisolver>=1.3.1 in .\\.venv\\lib\\site-packages (from matplotlib->mediapipe) (1.4.9)\n", "Requirement already satisfied: packaging>=20.0 in .\\.venv\\lib\\site-packages (from matplotlib->mediapipe) (25.0)\n", "Requirement already satisfied: pillow>=8 in .\\.venv\\lib\\site-packages (from matplotlib->mediapipe) (11.3.0)\n", "Requirement already satisfied: pyparsing>=2.3.1 in .\\.venv\\lib\\site-packages (from matplotlib->mediapipe) (3.2.3)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install mediapipe opencv-python pandas scikit-learn" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import mediapipe as mp # Import mediapipe\n", "import cv2 # Import opencv\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "mp_drawing = mp.solutions.drawing_utils # Drawing helpers\n", "mp_holistic = mp.solutions.holistic # Mediapipe Solutions\n", "mp_face_mesh = mp.solutions.face_mesh\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Make Some Detections" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 13\u001b[39m\n\u001b[32m 10\u001b[39m image.flags.writeable = \u001b[38;5;28;01mFalse\u001b[39;00m \n\u001b[32m 12\u001b[39m \u001b[38;5;66;03m# Make Detections\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m13\u001b[39m results = \u001b[43mholistic\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 14\u001b[39m \u001b[38;5;66;03m#print(results.face_landmarks)\u001b[39;00m\n\u001b[32m 15\u001b[39m \n\u001b[32m 16\u001b[39m \u001b[38;5;66;03m#face_landmarks, pose_landmarks, left_hand_landmarks, right_hand_landmarks\u001b[39;00m\n\u001b[32m 17\u001b[39m \n\u001b[32m 18\u001b[39m \u001b[38;5;66;03m# Recolor image back to BGR for rendering\u001b[39;00m\n\u001b[32m 19\u001b[39m image.flags.writeable = \u001b[38;5;28;01mTrue\u001b[39;00m \n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\human_action_recognizer\\human_action_recognizer\\.venv\\Lib\\site-packages\\mediapipe\\python\\solutions\\holistic.py:160\u001b[39m, in \u001b[36mHolistic.process\u001b[39m\u001b[34m(self, image)\u001b[39m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mprocess\u001b[39m(\u001b[38;5;28mself\u001b[39m, image: np.ndarray) -> NamedTuple:\n\u001b[32m 137\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Processes an RGB image and returns the pose landmarks, left and right hand landmarks, and face landmarks on the most prominent person detected.\u001b[39;00m\n\u001b[32m 138\u001b[39m \n\u001b[32m 139\u001b[39m \u001b[33;03m Args:\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 157\u001b[39m \u001b[33;03m \"enable_segmentation\" is set to true.\u001b[39;00m\n\u001b[32m 158\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m160\u001b[39m results = \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_data\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mimage\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mimage\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 161\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m results.pose_landmarks: \u001b[38;5;66;03m# pytype: disable=attribute-error\u001b[39;00m\n\u001b[32m 162\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m landmark \u001b[38;5;129;01min\u001b[39;00m results.pose_landmarks.landmark: \u001b[38;5;66;03m# pytype: disable=attribute-error\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\human_action_recognizer\\human_action_recognizer\\.venv\\Lib\\site-packages\\mediapipe\\python\\solution_base.py:340\u001b[39m, in \u001b[36mSolutionBase.process\u001b[39m\u001b[34m(self, input_data)\u001b[39m\n\u001b[32m 334\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 335\u001b[39m \u001b[38;5;28mself\u001b[39m._graph.add_packet_to_input_stream(\n\u001b[32m 336\u001b[39m stream=stream_name,\n\u001b[32m 337\u001b[39m packet=\u001b[38;5;28mself\u001b[39m._make_packet(input_stream_type,\n\u001b[32m 338\u001b[39m data).at(\u001b[38;5;28mself\u001b[39m._simulated_timestamp))\n\u001b[32m--> \u001b[39m\u001b[32m340\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_graph\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwait_until_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 341\u001b[39m \u001b[38;5;66;03m# Create a NamedTuple object where the field names are mapping to the graph\u001b[39;00m\n\u001b[32m 342\u001b[39m \u001b[38;5;66;03m# output stream names.\u001b[39;00m\n\u001b[32m 343\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._output_stream_type_info \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "\u001b[31mKeyboardInterrupt\u001b[39m: " ] } ], "source": [ "cap = cv2.VideoCapture(0)\n", "# Initiate holistic model\n", "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n", " \n", " while cap.isOpened():\n", " ret, frame = cap.read()\n", " \n", " # Recolor Feed\n", " image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n", " image.flags.writeable = False \n", " \n", " # Make Detections\n", " results = holistic.process(image)\n", " #print(results.face_landmarks)\n", " \n", " #face_landmarks, pose_landmarks, left_hand_landmarks, right_hand_landmarks\n", " \n", " # Recolor image back to BGR for rendering\n", " image.flags.writeable = True \n", " image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n", " \n", " \n", " # 1. Right hand\n", " mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n", " mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n", " mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)\n", " )\n", "\n", " # 2 Left Hand\n", " mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n", " mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4),\n", " mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n", " )\n", "\n", " # 3. Pose Detections\n", " mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, \n", " mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4),\n", " mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\n", " )\n", " # Export coordinates\n", " try:\n", " # Extract Pose landmarks\n", " pose = results.pose_landmarks.landmark\n", " pose_row = list(np.array([[landmark.x, landmark.y, landmark.z, landmark.visibility] for landmark in pose]).flatten())\n", " \n", " # Extract Face landmarks\n", " face = results.face_landmarks.landmark\n", " face_row = list(np.array([[landmark.x, landmark.y, landmark.z, landmark.visibility] for landmark in face]).flatten())\n", " \n", " # Concate rows\n", " row = pose_row+face_row\n", " \n", " # Append class name \n", " row.insert(0, class_name)\n", " \n", " # Export to CSV\n", " with open('coords.csv', mode='a', newline='') as f:\n", " csv_writer = csv.writer(f, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n", " csv_writer.writerow(row) \n", " \n", " except:\n", " pass\n", " \n", " cv2.imshow('Raw Webcam Feed', image)\n", "\n", " if cv2.waitKey(10) & 0xFF == ord('q'):\n", " break\n", "\n", "cap.release()\n", "cv2.destroyAllWindows()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[4], line 44\u001b[0m\n\u001b[0;32m 37\u001b[0m mp_drawing\u001b[38;5;241m.\u001b[39mdraw_landmarks(image, results\u001b[38;5;241m.\u001b[39mpose_landmarks, mp_holistic\u001b[38;5;241m.\u001b[39mPOSE_CONNECTIONS, \n\u001b[0;32m 38\u001b[0m mp_drawing\u001b[38;5;241m.\u001b[39mDrawingSpec(color\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m245\u001b[39m,\u001b[38;5;241m117\u001b[39m,\u001b[38;5;241m66\u001b[39m), thickness\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, circle_radius\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m),\n\u001b[0;32m 39\u001b[0m mp_drawing\u001b[38;5;241m.\u001b[39mDrawingSpec(color\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m245\u001b[39m,\u001b[38;5;241m66\u001b[39m,\u001b[38;5;241m230\u001b[39m), thickness\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, circle_radius\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m 40\u001b[0m )\n\u001b[0;32m 42\u001b[0m cv2\u001b[38;5;241m.\u001b[39mimshow(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRaw Webcam Feed\u001b[39m\u001b[38;5;124m'\u001b[39m, image)\n\u001b[1;32m---> 44\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwaitKey\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;241m&\u001b[39m \u001b[38;5;241m0xFF\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mord\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mq\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m 45\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m 47\u001b[0m cap\u001b[38;5;241m.\u001b[39mrelease()\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "cap = cv2.VideoCapture(0)\n", "# Initiate holistic model\n", "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n", " \n", " while cap.isOpened():\n", " ret, frame = cap.read()\n", " \n", " # Recolor Feed\n", " image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n", " image.flags.writeable = True \n", " \n", " # Make Detections\n", " results = holistic.process(image)\n", " # print(results.face_landmarks)\n", " \n", " # face_landmarks, pose_landmarks, left_hand_landmarks, right_hand_landmarks\n", " \n", " # Recolor image back to BGR for rendering\n", " image.flags.writeable = True \n", " image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n", " \n", " \n", " \n", " # 1. Right hand\n", " mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n", " mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n", " mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)\n", " )\n", "\n", " # 2. Left Hand\n", " mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n", " mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4),\n", " mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n", " )\n", "\n", " # 3. Pose Detections\n", " mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, \n", " mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4),\n", " mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\n", " )\n", " \n", " cv2.imshow('Raw Webcam Feed', image)\n", "\n", " if cv2.waitKey(10) & 0xFF == ord('q'):\n", " break\n", "\n", "cap.release()\n", "cv2.destroyAllWindows()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results.face_landmarks.landmark[0].visibility" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Capture Landmarks & Export to CSV\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: numpy in c:\\users\\deepa\\downloads\\body-language-decoder-main\\myenv\\lib\\site-packages (1.26.4)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install numpy\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import csv\n", "import os\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "501" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num_coords = len(results.pose_landmarks.landmark)+len(results.face_landmarks.landmark)\n", "num_coords" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "landmarks = ['class']\n", "for val in range(1, num_coords+1):\n", " landmarks += ['x{}'.format(val), 'y{}'.format(val), 'z{}'.format(val), 'v{}'.format(val)]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['class',\n", " 'x1',\n", " 'y1',\n", " 'z1',\n", " 'v1',\n", " 'x2',\n", " 'y2',\n", " 'z2',\n", " 'v2',\n", " 'x3',\n", " 'y3',\n", " 'z3',\n", " 'v3',\n", " 'x4',\n", " 'y4',\n", " 'z4',\n", " 'v4',\n", " 'x5',\n", " 'y5',\n", " 'z5',\n", " 'v5',\n", " 'x6',\n", " 'y6',\n", " 'z6',\n", " 'v6',\n", " 'x7',\n", " 'y7',\n", " 'z7',\n", " 'v7',\n", " 'x8',\n", " 'y8',\n", " 'z8',\n", " 'v8',\n", " 'x9',\n", " 'y9',\n", " 'z9',\n", " 'v9',\n", " 'x10',\n", " 'y10',\n", " 'z10',\n", " 'v10',\n", " 'x11',\n", " 'y11',\n", " 'z11',\n", " 'v11',\n", " 'x12',\n", " 'y12',\n", " 'z12',\n", " 'v12',\n", " 'x13',\n", " 'y13',\n", " 'z13',\n", " 'v13',\n", " 'x14',\n", " 'y14',\n", " 'z14',\n", " 'v14',\n", " 'x15',\n", " 'y15',\n", " 'z15',\n", " 'v15',\n", " 'x16',\n", " 'y16',\n", " 'z16',\n", " 'v16',\n", " 'x17',\n", " 'y17',\n", " 'z17',\n", " 'v17',\n", " 'x18',\n", " 'y18',\n", " 'z18',\n", " 'v18',\n", " 'x19',\n", " 'y19',\n", " 'z19',\n", " 'v19',\n", " 'x20',\n", " 'y20',\n", " 'z20',\n", " 'v20',\n", " 'x21',\n", " 'y21',\n", " 'z21',\n", " 'v21',\n", " 'x22',\n", " 'y22',\n", " 'z22',\n", " 'v22',\n", " 'x23',\n", " 'y23',\n", " 'z23',\n", " 'v23',\n", " 'x24',\n", " 'y24',\n", " 'z24',\n", " 'v24',\n", " 'x25',\n", " 'y25',\n", " 'z25',\n", " 'v25',\n", " 'x26',\n", " 'y26',\n", " 'z26',\n", " 'v26',\n", " 'x27',\n", " 'y27',\n", " 'z27',\n", " 'v27',\n", " 'x28',\n", " 'y28',\n", " 'z28',\n", " 'v28',\n", " 'x29',\n", " 'y29',\n", " 'z29',\n", " 'v29',\n", " 'x30',\n", " 'y30',\n", " 'z30',\n", " 'v30',\n", " 'x31',\n", " 'y31',\n", " 'z31',\n", " 'v31',\n", " 'x32',\n", " 'y32',\n", " 'z32',\n", " 'v32',\n", " 'x33',\n", " 'y33',\n", " 'z33',\n", " 'v33',\n", " 'x34',\n", " 'y34',\n", " 'z34',\n", " 'v34',\n", 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"code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "with open('coords.csv', mode='w', newline='') as f:\n", " csv_writer = csv.writer(f, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n", " csv_writer.writerow(landmarks)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class_name = \"THINKING\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%pip install --upgrade pip setuptools wheel\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[14], line 13\u001b[0m\n\u001b[0;32m 10\u001b[0m image\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m \n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# Make Detections\u001b[39;00m\n\u001b[1;32m---> 13\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mholistic\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 14\u001b[0m \u001b[38;5;66;03m# print(results.face_landmarks)\u001b[39;00m\n\u001b[0;32m 15\u001b[0m \n\u001b[0;32m 16\u001b[0m \u001b[38;5;66;03m# face_landmarks, pose_landmarks, left_hand_landmarks, right_hand_landmarks\u001b[39;00m\n\u001b[0;32m 17\u001b[0m \n\u001b[0;32m 18\u001b[0m \u001b[38;5;66;03m# Recolor image back to BGR for rendering\u001b[39;00m\n\u001b[0;32m 19\u001b[0m image\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m \n", "File \u001b[1;32mc:\\Users\\deepa\\Downloads\\Body-Language-Decoder-main\\myenv\\Lib\\site-packages\\mediapipe\\python\\solutions\\holistic.py:160\u001b[0m, in \u001b[0;36mHolistic.process\u001b[1;34m(self, image)\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprocess\u001b[39m(\u001b[38;5;28mself\u001b[39m, image: np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NamedTuple:\n\u001b[0;32m 137\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Processes an RGB image and returns the pose landmarks, left and right hand landmarks, and face landmarks on the most prominent person detected.\u001b[39;00m\n\u001b[0;32m 138\u001b[0m \n\u001b[0;32m 139\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 157\u001b[0m \u001b[38;5;124;03m \"enable_segmentation\" is set to true.\u001b[39;00m\n\u001b[0;32m 158\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 160\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mimage\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mimage\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m results\u001b[38;5;241m.\u001b[39mpose_landmarks: \u001b[38;5;66;03m# pytype: disable=attribute-error\u001b[39;00m\n\u001b[0;32m 162\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m landmark \u001b[38;5;129;01min\u001b[39;00m results\u001b[38;5;241m.\u001b[39mpose_landmarks\u001b[38;5;241m.\u001b[39mlandmark: \u001b[38;5;66;03m# pytype: disable=attribute-error\u001b[39;00m\n", "File \u001b[1;32mc:\\Users\\deepa\\Downloads\\Body-Language-Decoder-main\\myenv\\Lib\\site-packages\\mediapipe\\python\\solution_base.py:340\u001b[0m, in \u001b[0;36mSolutionBase.process\u001b[1;34m(self, input_data)\u001b[0m\n\u001b[0;32m 334\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 335\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_graph\u001b[38;5;241m.\u001b[39madd_packet_to_input_stream(\n\u001b[0;32m 336\u001b[0m stream\u001b[38;5;241m=\u001b[39mstream_name,\n\u001b[0;32m 337\u001b[0m packet\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_packet(input_stream_type,\n\u001b[0;32m 338\u001b[0m data)\u001b[38;5;241m.\u001b[39mat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_simulated_timestamp))\n\u001b[1;32m--> 340\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_graph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait_until_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 341\u001b[0m \u001b[38;5;66;03m# Create a NamedTuple object where the field names are mapping to the graph\u001b[39;00m\n\u001b[0;32m 342\u001b[0m \u001b[38;5;66;03m# output stream names.\u001b[39;00m\n\u001b[0;32m 343\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_stream_type_info \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "cap = cv2.VideoCapture(0)\n", "# Initiate holistic model\n", "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n", " \n", " while cap.isOpened():\n", " ret, frame = cap.read()\n", " \n", " # Recolor Feed\n", " image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n", " image.flags.writeable = False \n", " \n", " # Make Detections\n", " results = holistic.process(image)\n", " # print(results.face_landmarks)\n", " \n", " # face_landmarks, pose_landmarks, left_hand_landmarks, right_hand_landmarks\n", " \n", " # Recolor image back to BGR for rendering\n", " image.flags.writeable = True \n", " image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n", " \n", " \n", " # 1. Right hand\n", " mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n", " mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n", " mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)\n", " )\n", "\n", " # 2 Left Hand\n", " mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n", " mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4),\n", " mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n", " )\n", "\n", " # 3. Pose Detections\n", " mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, \n", " mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4),\n", " mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\n", " )\n", " # Export coordinates\n", " try:\n", " # Extract Pose landmarks\n", " pose = results.pose_landmarks.landmark\n", " pose_row = list(np.array([[landmark.x, landmark.y, landmark.z, landmark.visibility] for landmark in pose]).flatten())\n", " \n", " # Extract Face landmarks\n", " face = results.face_landmarks.landmark\n", " face_row = list(np.array([[landmark.x, landmark.y, landmark.z, landmark.visibility] for landmark in face]).flatten())\n", " \n", " # Concate rows\n", " row = pose_row+face_row\n", " \n", " # Append class name \n", " row.insert(0, class_name)\n", " \n", " # Export to CSV\n", " with open('coords.csv', mode='a', newline='') as f:\n", " csv_writer = csv.writer(f, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n", " csv_writer.writerow(row) \n", " \n", " except:\n", " pass\n", " \n", " cv2.imshow('Raw Webcam Feed', image)\n", "\n", " if cv2.waitKey(10) & 0xFF == ord('q'):\n", " break\n", "\n", "cap.release()\n", "cv2.destroyAllWindows()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Train Custom Model Using Scikit Learn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3.1 Read in Collected Data and Process" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: pandas in c:\\users\\deepa\\downloads\\body-language-decoder-main\\myenv\\lib\\site-packages (2.2.3)\n", "Requirement already satisfied: numpy>=1.23.2 in c:\\users\\deepa\\downloads\\body-language-decoder-main\\myenv\\lib\\site-packages (from pandas) (1.26.4)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\deepa\\downloads\\body-language-decoder-main\\myenv\\lib\\site-packages (from pandas) (2.9.0.post0)\n", "Requirement already satisfied: pytz>=2020.1 in c:\\users\\deepa\\downloads\\body-language-decoder-main\\myenv\\lib\\site-packages (from pandas) (2024.2)\n", "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\deepa\\downloads\\body-language-decoder-main\\myenv\\lib\\site-packages (from pandas) (2024.2)\n", "Requirement already satisfied: six>=1.5 in c:\\users\\deepa\\downloads\\body-language-decoder-main\\myenv\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install pandas" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: scikit-learn in c:\\users\\deepa\\downloads\\body-language-decoder-main\\myenv\\lib\\site-packages (1.5.2)\n", "Requirement already satisfied: numpy>=1.19.5 in c:\\users\\deepa\\downloads\\body-language-decoder-main\\myenv\\lib\\site-packages (from scikit-learn) (1.26.4)\n", "Requirement already satisfied: scipy>=1.6.0 in c:\\users\\deepa\\downloads\\body-language-decoder-main\\myenv\\lib\\site-packages (from scikit-learn) (1.14.1)\n", "Requirement already satisfied: joblib>=1.2.0 in c:\\users\\deepa\\downloads\\body-language-decoder-main\\myenv\\lib\\site-packages (from scikit-learn) (1.4.2)\n", "Requirement already satisfied: threadpoolctl>=3.1.0 in c:\\users\\deepa\\downloads\\body-language-decoder-main\\myenv\\lib\\site-packages (from scikit-learn) (3.5.0)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install scikit-learn" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "\n", "import pandas as pd\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('coords.csv')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [class, x1, y1, z1, v1, x2, y2, z2, v2, x3, y3, z3, v3, x4, y4, z4, v4, x5, y5, z5, v5, x6, y6, z6, v6, x7, y7, z7, v7, x8, y8, z8, v8, x9, y9, z9, v9, x10, y10, z10, v10, x11, y11, z11, v11, x12, y12, z12, v12, x13, y13, z13, v13, x14, y14, z14, v14, x15, y15, z15, v15, x16, y16, z16, v16, x17, y17, z17, v17, x18, y18, z18, v18, x19, y19, z19, v19, x20, y20, z20, v20, x21, y21, z21, v21, x22, y22, z22, v22, x23, y23, z23, v23, x24, y24, z24, v24, x25, y25, z25, ...]\n", "Index: []\n", "\n", "[0 rows x 2005 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['class']=='sad']" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "X = df.drop('class', axis=1) # features\n", "y = df['class'] # target value" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X shape: (884, 2004), y shape: 884\n" ] } ], "source": [ "print(f\"X shape: {X.shape}, y shape: {len(y)}\")\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(884, 2004)\n", "(884,)\n" ] } ], "source": [ "print(X.shape)\n", "print(y.shape)\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "297 HELLO!\n", "375 HELLO!\n", "571 HELLO!\n", "603 HELLO!\n", "21 NORMAL\n", " ... \n", "474 HELLO!\n", "99 NORMAL\n", "125 NORMAL\n", "511 HELLO!\n", "230 NORMAL\n", "Name: class, Length: 177, dtype: object" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3.2 Train Machine Learning Classification Model" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "from sklearn.pipeline import make_pipeline \n", "from sklearn.preprocessing import StandardScaler \n", "\n", "from sklearn.linear_model import LogisticRegression, RidgeClassifier\n", "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "pipelines = {\n", " 'lr':make_pipeline(StandardScaler(), LogisticRegression()),\n", " 'rc':make_pipeline(StandardScaler(), RidgeClassifier()),\n", " 'rf':make_pipeline(StandardScaler(), RandomForestClassifier()),\n", " 'gb':make_pipeline(StandardScaler(), GradientBoostingClassifier()),\n", "}" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "fit_models = {}\n", "for algo, pipeline in pipelines.items():\n", " model = pipeline.fit(X_train, y_train)\n", " fit_models[algo] = model" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'lr': Pipeline(steps=[('standardscaler', StandardScaler()),\n", " ('logisticregression', LogisticRegression())]),\n", " 'rc': Pipeline(steps=[('standardscaler', StandardScaler()),\n", " ('ridgeclassifier', RidgeClassifier())]),\n", " 'rf': Pipeline(steps=[('standardscaler', StandardScaler()),\n", " ('randomforestclassifier', RandomForestClassifier())]),\n", " 'gb': Pipeline(steps=[('standardscaler', StandardScaler()),\n", " ('gradientboostingclassifier', GradientBoostingClassifier())])}" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fit_models" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['HELLO!', 'HELLO!', 'HELLO!', 'HELLO!', 'NORMAL', 'HELLO!',\n", " 'HAPPY', 'NORMAL', 'HELLO!', 'HAPPY', 'NORMAL', 'THINKING',\n", " 'THINKING', 'HELLO!', 'NORMAL', 'HELLO!', 'HELLO!', 'THINKING',\n", " 'HELLO!', 'HELLO!', 'HAPPY', 'HELLO!', 'NORMAL', 'HELLO!',\n", " 'THINKING', 'HELLO!', 'HAPPY', 'THINKING', 'NORMAL', 'THINKING',\n", " 'NORMAL', 'THINKING', 'NORMAL', 'HELLO!', 'THINKING', 'NORMAL',\n", " 'THINKING', 'THINKING', 'THINKING', 'NORMAL', 'HELLO!', 'HELLO!',\n", " 'NORMAL', 'THINKING', 'HAPPY', 'NORMAL', 'HELLO!', 'HELLO!',\n", " 'HELLO!', 'HAPPY', 'NORMAL', 'NORMAL', 'NORMAL', 'HELLO!',\n", " 'NORMAL', 'NORMAL', 'HELLO!', 'THINKING', 'NORMAL', 'NORMAL',\n", " 'NORMAL', 'THINKING', 'HAPPY', 'HELLO!', 'NORMAL', 'NORMAL',\n", " 'NORMAL', 'NORMAL', 'THINKING', 'NORMAL', 'HELLO!', 'NORMAL',\n", " 'HELLO!', 'HELLO!', 'HELLO!', 'NORMAL', 'THINKING', 'NORMAL',\n", " 'HELLO!', 'NORMAL', 'HELLO!', 'HELLO!', 'HELLO!', 'HELLO!',\n", " 'HELLO!', 'HELLO!', 'NORMAL', 'HAPPY', 'HAPPY', 'HELLO!', 'HELLO!',\n", " 'HELLO!', 'NORMAL', 'THINKING', 'NORMAL', 'THINKING', 'HELLO!',\n", " 'HELLO!', 'NORMAL', 'NORMAL', 'HAPPY', 'NORMAL', 'HELLO!',\n", " 'HELLO!', 'NORMAL', 'HELLO!', 'HELLO!', 'HELLO!', 'NORMAL',\n", " 'HAPPY', 'HELLO!', 'NORMAL', 'NORMAL', 'HELLO!', 'HELLO!',\n", " 'HELLO!', 'HELLO!', 'HELLO!', 'HELLO!', 'NORMAL', 'NORMAL',\n", " 'THINKING', 'HAPPY', 'NORMAL', 'NORMAL', 'NORMAL', 'HELLO!',\n", " 'THINKING', 'THINKING', 'NORMAL', 'THINKING', 'NORMAL', 'HAPPY',\n", " 'NORMAL', 'NORMAL', 'NORMAL', 'NORMAL', 'HAPPY', 'NORMAL',\n", " 'HELLO!', 'HELLO!', 'HELLO!', 'THINKING', 'NORMAL', 'HELLO!',\n", " 'HELLO!', 'HELLO!', 'HELLO!', 'THINKING', 'HELLO!', 'HELLO!',\n", " 'NORMAL', 'NORMAL', 'HAPPY', 'HAPPY', 'HELLO!', 'THINKING',\n", " 'HELLO!', 'NORMAL', 'NORMAL', 'HELLO!', 'HELLO!', 'THINKING',\n", " 'HELLO!', 'NORMAL', 'NORMAL', 'NORMAL', 'HELLO!', 'HELLO!',\n", " 'NORMAL', 'HELLO!', 'THINKING', 'HELLO!', 'NORMAL', 'NORMAL',\n", " 'HELLO!', 'NORMAL'], dtype='#sk-container-id-1 {\n", " /* Definition of color scheme common for light and dark mode */\n", " --sklearn-color-text: black;\n", " --sklearn-color-line: gray;\n", " /* Definition of color scheme for unfitted estimators */\n", " --sklearn-color-unfitted-level-0: #fff5e6;\n", " --sklearn-color-unfitted-level-1: #f6e4d2;\n", " --sklearn-color-unfitted-level-2: #ffe0b3;\n", " --sklearn-color-unfitted-level-3: chocolate;\n", " /* Definition of color scheme for fitted estimators */\n", " --sklearn-color-fitted-level-0: #f0f8ff;\n", " --sklearn-color-fitted-level-1: #d4ebff;\n", " --sklearn-color-fitted-level-2: #b3dbfd;\n", " --sklearn-color-fitted-level-3: cornflowerblue;\n", "\n", " /* Specific color for light theme */\n", " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n", " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", " --sklearn-color-icon: #696969;\n", "\n", " @media (prefers-color-scheme: dark) {\n", " /* Redefinition of color scheme for dark theme */\n", " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n", " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", " --sklearn-color-icon: #878787;\n", " }\n", "}\n", "\n", "#sk-container-id-1 {\n", " color: var(--sklearn-color-text);\n", "}\n", "\n", "#sk-container-id-1 pre {\n", " padding: 0;\n", "}\n", "\n", "#sk-container-id-1 input.sk-hidden--visually {\n", " border: 0;\n", " clip: rect(1px 1px 1px 1px);\n", " clip: rect(1px, 1px, 1px, 1px);\n", " height: 1px;\n", " margin: -1px;\n", " overflow: hidden;\n", " padding: 0;\n", " position: absolute;\n", " width: 1px;\n", "}\n", "\n", "#sk-container-id-1 div.sk-dashed-wrapped {\n", " border: 1px dashed var(--sklearn-color-line);\n", " margin: 0 0.4em 0.5em 0.4em;\n", " box-sizing: border-box;\n", " padding-bottom: 0.4em;\n", " background-color: var(--sklearn-color-background);\n", "}\n", "\n", "#sk-container-id-1 div.sk-container {\n", " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n", " but bootstrap.min.css set `[hidden] { display: none !important; }`\n", " so we also need the `!important` here to be able to override the\n", " default hidden behavior on the sphinx rendered scikit-learn.org.\n", " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n", " display: inline-block !important;\n", " position: relative;\n", "}\n", "\n", "#sk-container-id-1 div.sk-text-repr-fallback {\n", " display: none;\n", "}\n", "\n", "div.sk-parallel-item,\n", "div.sk-serial,\n", "div.sk-item {\n", " /* draw centered vertical line to link estimators */\n", " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n", " background-size: 2px 100%;\n", " background-repeat: no-repeat;\n", " background-position: center center;\n", "}\n", "\n", "/* Parallel-specific style estimator block */\n", "\n", "#sk-container-id-1 div.sk-parallel-item::after {\n", " content: \"\";\n", " width: 100%;\n", " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n", " flex-grow: 1;\n", "}\n", "\n", "#sk-container-id-1 div.sk-parallel {\n", " display: flex;\n", " align-items: stretch;\n", " justify-content: center;\n", " background-color: var(--sklearn-color-background);\n", " position: relative;\n", "}\n", "\n", "#sk-container-id-1 div.sk-parallel-item {\n", " display: flex;\n", " flex-direction: column;\n", "}\n", "\n", "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n", " align-self: flex-end;\n", " width: 50%;\n", "}\n", "\n", "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n", " align-self: flex-start;\n", " width: 50%;\n", "}\n", "\n", "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n", " width: 0;\n", "}\n", "\n", "/* Serial-specific style estimator block */\n", "\n", "#sk-container-id-1 div.sk-serial {\n", " display: flex;\n", " flex-direction: column;\n", " align-items: center;\n", " background-color: var(--sklearn-color-background);\n", " padding-right: 1em;\n", " padding-left: 1em;\n", "}\n", "\n", "\n", "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n", "clickable and can be expanded/collapsed.\n", "- Pipeline and ColumnTransformer use this feature and define the default style\n", "- Estimators will overwrite some part of the style using the `sk-estimator` class\n", "*/\n", "\n", "/* Pipeline and ColumnTransformer style (default) */\n", "\n", "#sk-container-id-1 div.sk-toggleable {\n", " /* Default theme specific background. It is overwritten whether we have a\n", " specific estimator or a Pipeline/ColumnTransformer */\n", " background-color: var(--sklearn-color-background);\n", "}\n", "\n", "/* Toggleable label */\n", "#sk-container-id-1 label.sk-toggleable__label {\n", " cursor: pointer;\n", " display: block;\n", " width: 100%;\n", " margin-bottom: 0;\n", " padding: 0.5em;\n", " box-sizing: border-box;\n", " text-align: center;\n", "}\n", "\n", "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n", " /* Arrow on the left of the label */\n", " content: \"▸\";\n", " float: left;\n", " margin-right: 0.25em;\n", " color: var(--sklearn-color-icon);\n", "}\n", "\n", "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n", " color: var(--sklearn-color-text);\n", "}\n", "\n", "/* Toggleable content - dropdown */\n", "\n", "#sk-container-id-1 div.sk-toggleable__content {\n", " max-height: 0;\n", " max-width: 0;\n", " overflow: hidden;\n", " text-align: left;\n", " /* unfitted */\n", " background-color: var(--sklearn-color-unfitted-level-0);\n", "}\n", "\n", "#sk-container-id-1 div.sk-toggleable__content.fitted {\n", " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-0);\n", "}\n", "\n", "#sk-container-id-1 div.sk-toggleable__content pre {\n", " margin: 0.2em;\n", " border-radius: 0.25em;\n", " color: var(--sklearn-color-text);\n", " /* unfitted */\n", " background-color: var(--sklearn-color-unfitted-level-0);\n", "}\n", "\n", "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n", " /* unfitted */\n", " background-color: var(--sklearn-color-fitted-level-0);\n", "}\n", "\n", "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n", " /* Expand drop-down */\n", " max-height: 200px;\n", " max-width: 100%;\n", " overflow: auto;\n", "}\n", "\n", "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n", " content: \"▾\";\n", "}\n", "\n", "/* Pipeline/ColumnTransformer-specific style */\n", "\n", "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", " color: var(--sklearn-color-text);\n", " background-color: var(--sklearn-color-unfitted-level-2);\n", "}\n", "\n", "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", " background-color: var(--sklearn-color-fitted-level-2);\n", "}\n", "\n", "/* Estimator-specific style */\n", "\n", "/* Colorize estimator box */\n", "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", " /* unfitted */\n", " background-color: var(--sklearn-color-unfitted-level-2);\n", "}\n", "\n", "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-2);\n", "}\n", "\n", "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n", "#sk-container-id-1 div.sk-label label {\n", " /* The background is the default theme color */\n", " color: var(--sklearn-color-text-on-default-background);\n", "}\n", "\n", "/* On hover, darken the color of the background */\n", "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n", " color: var(--sklearn-color-text);\n", " background-color: var(--sklearn-color-unfitted-level-2);\n", "}\n", "\n", "/* Label box, darken color on hover, fitted */\n", "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n", " color: var(--sklearn-color-text);\n", " background-color: var(--sklearn-color-fitted-level-2);\n", "}\n", "\n", "/* Estimator label */\n", "\n", "#sk-container-id-1 div.sk-label label {\n", " font-family: monospace;\n", " font-weight: bold;\n", " display: inline-block;\n", " line-height: 1.2em;\n", "}\n", "\n", "#sk-container-id-1 div.sk-label-container {\n", " text-align: center;\n", "}\n", "\n", "/* Estimator-specific */\n", "#sk-container-id-1 div.sk-estimator {\n", " font-family: monospace;\n", " border: 1px dotted var(--sklearn-color-border-box);\n", " border-radius: 0.25em;\n", " box-sizing: border-box;\n", " margin-bottom: 0.5em;\n", " /* unfitted */\n", " background-color: var(--sklearn-color-unfitted-level-0);\n", "}\n", "\n", "#sk-container-id-1 div.sk-estimator.fitted {\n", " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-0);\n", "}\n", "\n", "/* on hover */\n", "#sk-container-id-1 div.sk-estimator:hover {\n", " /* unfitted */\n", " background-color: var(--sklearn-color-unfitted-level-2);\n", "}\n", "\n", "#sk-container-id-1 div.sk-estimator.fitted:hover {\n", " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-2);\n", "}\n", "\n", "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n", "\n", "/* Common style for \"i\" and \"?\" */\n", "\n", ".sk-estimator-doc-link,\n", "a:link.sk-estimator-doc-link,\n", "a:visited.sk-estimator-doc-link {\n", " float: right;\n", " font-size: smaller;\n", " line-height: 1em;\n", " font-family: monospace;\n", " background-color: var(--sklearn-color-background);\n", " border-radius: 1em;\n", " height: 1em;\n", " width: 1em;\n", " text-decoration: none !important;\n", " margin-left: 1ex;\n", " /* unfitted */\n", " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", " color: var(--sklearn-color-unfitted-level-1);\n", "}\n", "\n", ".sk-estimator-doc-link.fitted,\n", "a:link.sk-estimator-doc-link.fitted,\n", "a:visited.sk-estimator-doc-link.fitted {\n", " /* fitted */\n", " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", " color: var(--sklearn-color-fitted-level-1);\n", "}\n", "\n", "/* On hover */\n", "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n", ".sk-estimator-doc-link:hover,\n", "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n", ".sk-estimator-doc-link:hover {\n", " /* unfitted */\n", " background-color: var(--sklearn-color-unfitted-level-3);\n", " color: var(--sklearn-color-background);\n", " text-decoration: none;\n", "}\n", "\n", "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n", ".sk-estimator-doc-link.fitted:hover,\n", "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n", ".sk-estimator-doc-link.fitted:hover {\n", " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", " color: var(--sklearn-color-background);\n", " text-decoration: none;\n", "}\n", "\n", "/* Span, style for the box shown on hovering the info icon */\n", ".sk-estimator-doc-link span {\n", " display: none;\n", " z-index: 9999;\n", " position: relative;\n", " font-weight: normal;\n", " right: .2ex;\n", " padding: .5ex;\n", " margin: .5ex;\n", " width: min-content;\n", " min-width: 20ex;\n", " max-width: 50ex;\n", " color: var(--sklearn-color-text);\n", " box-shadow: 2pt 2pt 4pt #999;\n", " /* unfitted */\n", " background: var(--sklearn-color-unfitted-level-0);\n", " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n", "}\n", "\n", ".sk-estimator-doc-link.fitted span {\n", " /* fitted */\n", " background: var(--sklearn-color-fitted-level-0);\n", " border: var(--sklearn-color-fitted-level-3);\n", "}\n", "\n", ".sk-estimator-doc-link:hover span {\n", " display: block;\n", "}\n", "\n", "/* \"?\"-specific style due to the `` HTML tag */\n", "\n", "#sk-container-id-1 a.estimator_doc_link {\n", " float: right;\n", " font-size: 1rem;\n", " line-height: 1em;\n", " font-family: monospace;\n", " background-color: var(--sklearn-color-background);\n", " border-radius: 1rem;\n", " height: 1rem;\n", " width: 1rem;\n", " text-decoration: none;\n", " /* unfitted */\n", " color: var(--sklearn-color-unfitted-level-1);\n", " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", "}\n", "\n", "#sk-container-id-1 a.estimator_doc_link.fitted {\n", " /* fitted */\n", " border: var(--sklearn-color-fitted-level-1) 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[0.05 0.52 0.33 0.1 ]\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[35], line 102\u001b[0m\n\u001b[0;32m 98\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m 100\u001b[0m cv2\u001b[38;5;241m.\u001b[39mimshow(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRaw Webcam Feed\u001b[39m\u001b[38;5;124m'\u001b[39m, image)\n\u001b[1;32m--> 102\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwaitKey\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;241m&\u001b[39m \u001b[38;5;241m0xFF\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mord\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mq\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m 103\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m 105\u001b[0m cap\u001b[38;5;241m.\u001b[39mrelease()\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] }, { "ename": "", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", "\u001b[1;31mClick here for more info. \n", "\u001b[1;31mView Jupyter log for further details." ] } ], "source": [ "cap = cv2.VideoCapture(0)\n", "# Initiate holistic model\n", "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n", " \n", " while cap.isOpened():\n", " ret, frame = cap.read()\n", " \n", " # Recolor Feed\n", " image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n", " image.flags.writeable = False \n", " \n", " # Make Detections\n", " results = holistic.process(image)\n", " # print(results.face_landmarks)\n", " \n", " # face_landmarks, pose_landmarks, left_hand_landmarks, right_hand_landmarks\n", " \n", " # Recolor image back to BGR for rendering\n", " image.flags.writeable = True \n", " image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n", " \n", " \n", " \n", " # 2. Right hand\n", " mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n", " mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n", " mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)\n", " )\n", "\n", " # 3. Left Hand\n", " mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n", " mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4),\n", " mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n", " )\n", "\n", " # 4. Pose Detections\n", " mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, \n", " mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4),\n", " mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\n", " )\n", " # Export coordinates\n", " try:\n", " # Extract Pose landmarks\n", " pose = results.pose_landmarks.landmark\n", " pose_row = list(np.array([[landmark.x, landmark.y, landmark.z, landmark.visibility] for landmark in pose]).flatten())\n", " \n", " # Extract Face landmarks\n", " face = results.face_landmarks.landmark\n", " face_row = list(np.array([[landmark.x, landmark.y, landmark.z, landmark.visibility] for landmark in face]).flatten())\n", " \n", " # Concate rows\n", " row = pose_row+face_row\n", " \n", "# # Append class name \n", "# row.insert(0, class_name)\n", " \n", "# # Export to CSV\n", "# with open('coords.csv', mode='a', newline='') as f:\n", "# csv_writer = csv.writer(f, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n", "# csv_writer.writerow(row) \n", "\n", " # Make Detections\n", " X = pd.DataFrame([row])\n", " body_language_class = model.predict(X)[0]\n", " body_language_prob = model.predict_proba(X)[0]\n", " print(body_language_class, body_language_prob)\n", " \n", " # Grab ear coords\n", " coords = tuple(np.multiply(\n", " np.array(\n", " (results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].x, \n", " results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].y))\n", " , [640,480]).astype(int))\n", " \n", " cv2.rectangle(image, \n", " (coords[0], coords[1]+5), \n", " (coords[0]+len(body_language_class)*20, coords[1]-30), \n", " (245, 117, 16), -1)\n", " cv2.putText(image, body_language_class, coords, \n", " cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n", " \n", " # Get status box\n", " cv2.rectangle(image, (0,0), (250, 60), (245, 117, 16), -1)\n", " \n", " # Display Class\n", " cv2.putText(image, 'CLASS'\n", " , (95,12), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n", " cv2.putText(image, body_language_class.split(' ')[0]\n", " , (90,40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n", " \n", " # Display Probability\n", " cv2.putText(image, 'PROB'\n", " , (15,12), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n", " cv2.putText(image, str(round(body_language_prob[np.argmax(body_language_prob)],2))\n", " , (10,40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n", " \n", " except:\n", " pass\n", " \n", " cv2.imshow('Raw Webcam Feed', image)\n", "\n", " if cv2.waitKey(10) & 0xFF == ord('q'):\n", " break\n", "\n", "cap.release()\n", "cv2.destroyAllWindows()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "mp_holistic = mp.solutions.holistic\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tuple(np.multiply(np.array((results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].x, \n", "results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].y)), [640,480]).astype(int))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 2 }