class_15_2_Holistic.ipynb

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    "# <center> 👉 class_15_2 » _MediaPipe Holistic_ </center>   \n",
    "\n",
    "https://chuoling.github.io/mediapipe/solutions/holistic.html  \n",
    "    \n",
    "# MediaPipe Holistic  \n",
    "\n",
    "Live perception of simultaneous human pose, face landmarks, and hand tracking in real-time on mobile devices can enable various modern life applications:   \n",
    "- fitness and sport analysis, gesture control and sign language recognition, augmented reality try-on and effects.   \n",
    "- MediaPipe already offers fast and accurate, yet separate, solutions for these tasks.   \n",
    "- Combining them all in real-time into a semantically consistent end-to-end solution is a uniquely difficult problem requiring simultaneous inference of multiple, dependent neural networks.  "
   ]
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    "import cv2\n",
    "import mediapipe as mp\n",
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_drawing_styles = mp.solutions.drawing_styles\n",
    "mp_holistic = mp.solutions.holistic\n",
    "\n",
    "# For static images:\n",
    "# IMAGE_FILES = []\n",
    "# with mp_holistic.Holistic(\n",
    "#     static_image_mode=True,\n",
    "#     model_complexity=2,\n",
    "#     enable_segmentation=True,\n",
    "#     refine_face_landmarks=True) as holistic:\n",
    "#     for idx, file in enumerate(IMAGE_FILES):\n",
    "#         image = cv2.imread(file)\n",
    "#         image_height, image_width, _ = image.shape\n",
    "#         # Convert the BGR image to RGB before processing.\n",
    "#         results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
    "\n",
    "#         if results.pose_landmarks:\n",
    "#             print(\n",
    "#               f'Nose coordinates: ('\n",
    "#               f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, '\n",
    "#               f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_height})'\n",
    "#           )\n",
    "\n",
    "#         annotated_image = image.copy()\n",
    "#         # Draw segmentation on the image.\n",
    "#         # To improve segmentation around boundaries, consider applying a joint\n",
    "#         # bilateral filter to \"results.segmentation_mask\" with \"image\".\n",
    "#         condition = np.stack((results.segmentation_mask,) * 3, axis=-1) > 0.1\n",
    "#         bg_image = np.zeros(image.shape, dtype=np.uint8)\n",
    "#         bg_image[:] = BG_COLOR\n",
    "#         annotated_image = np.where(condition, annotated_image, bg_image)\n",
    "#         # Draw pose, left and right hands, and face landmarks on the image.\n",
    "#         mp_drawing.draw_landmarks(\n",
    "#             annotated_image,\n",
    "#             results.face_landmarks,\n",
    "#             mp_holistic.FACEMESH_TESSELATION,\n",
    "#             landmark_drawing_spec=None,\n",
    "#             connection_drawing_spec=mp_drawing_styles\n",
    "#             .get_default_face_mesh_tesselation_style())\n",
    "#         mp_drawing.draw_landmarks(\n",
    "#             annotated_image,\n",
    "#             results.pose_landmarks,\n",
    "#             mp_holistic.POSE_CONNECTIONS,\n",
    "#             landmark_drawing_spec=mp_drawing_styles.\n",
    "#             get_default_pose_landmarks_style())\n",
    "#         cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)\n",
    "#         # Plot pose world landmarks.\n",
    "#         mp_drawing.plot_landmarks(\n",
    "#             results.pose_world_landmarks, mp_holistic.POSE_CONNECTIONS)\n",
    "\n",
    "# For webcam input:\n",
    "cap = cv2.VideoCapture(0)\n",
    "with mp_holistic.Holistic(\n",
    "    min_detection_confidence=0.5,\n",
    "    min_tracking_confidence=0.5) as holistic:\n",
    "    while cap.isOpened():\n",
    "        success, image = cap.read()\n",
    "        if not success:\n",
    "            print(\"Ignoring empty camera frame.\")\n",
    "          # If loading a video, use 'break' instead of 'continue'.\n",
    "            continue\n",
    "\n",
    "        # To improve performance, optionally mark the image as not writeable to\n",
    "        # pass by reference.\n",
    "        image.flags.writeable = False\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "        results = holistic.process(image)\n",
    "\n",
    "        # Draw landmark annotation on the image.\n",
    "        image.flags.writeable = True\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        mp_drawing.draw_landmarks(\n",
    "            image,\n",
    "            results.face_landmarks,\n",
    "            mp_holistic.FACEMESH_CONTOURS,\n",
    "            landmark_drawing_spec=None,\n",
    "            connection_drawing_spec=mp_drawing_styles\n",
    "            .get_default_face_mesh_contours_style())\n",
    "        mp_drawing.draw_landmarks(\n",
    "            image,\n",
    "            results.pose_landmarks,\n",
    "            mp_holistic.POSE_CONNECTIONS,\n",
    "            landmark_drawing_spec=mp_drawing_styles\n",
    "            .get_default_pose_landmarks_style())\n",
    "        # Flip the image horizontally for a selfie-view display.\n",
    "        cv2.imshow('MediaPipe Holistic', cv2.flip(image, 1))\n",
    "        if cv2.waitKey(5) & 0xFF == 27:\n",
    "            break\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
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