simple script to load in a video (or connect to a webcam), annotate it with `mediapipe` data, and save it out in the same directory as the original video

simple_mediapipe_video_annotator.py

#to install dependencies:
# pip install opencv-python 
# pip install mediapipe

import cv2
import mediapipe as mp
from pathlib import Path


run_mediapipe = True
save_video=True

## use webcam
# camera_number = 0
# cap = cv2.VideoCapture(camera_number)

# use video file
input_video_folder = Path(r"C:\Users\jonma\Dropbox\FreeMoCapProject\FreeMocap_Data\sesh_2022-04-20_07_41_59_paul_tiktok_ayub_0\SyncedVideos")
input_video_filename = 'sesh_2022-04-20_07_41_59_paul_tiktok_ayub_0_synced_Cam5.mp4'
cap = cv2.VideoCapture(str(input_video_folder / input_video_filename))

video_frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
video_frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
framerate = cap.get(cv2.CAP_PROP_FPS)

### mediapipe stuff
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_holistic = mp.solutions.holistic

body_drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=.2)
hand_drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=.2)
face_drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=.2)


def annotate_image_with_mediapipe_data(image, mediapipe_results):
    mp_drawing.draw_landmarks(image=image,
                              landmark_list=mediapipe_results.face_landmarks,
                              connections=mp_holistic.FACEMESH_CONTOURS,
                              landmark_drawing_spec=None,
                              connection_drawing_spec=mp_drawing_styles
                              .get_default_face_mesh_contours_style())
    mp_drawing.draw_landmarks(image=image,
                              landmark_list=mediapipe_results.face_landmarks,
                            #   connections=mp_holistic.FACEMESH_TESSELATION,
                              connections=mp_holistic.FACEMESH_CONTOURS,
                              landmark_drawing_spec=None,
                              connection_drawing_spec=mp_drawing_styles
                              .get_default_face_mesh_tesselation_style())
    mp_drawing.draw_landmarks(image=image,
                              landmark_list=mediapipe_results.pose_landmarks,
                              connections=mp_holistic.POSE_CONNECTIONS,
                              landmark_drawing_spec=mp_drawing_styles
                              .get_default_pose_landmarks_style())
    mp_drawing.draw_landmarks(
                          image=image,
                          landmark_list=mediapipe_results.left_hand_landmarks,
                          connections=mp_holistic.HAND_CONNECTIONS,
                          landmark_drawing_spec=None,
                          connection_drawing_spec=mp_drawing_styles
                          .get_default_hand_connections_style())
    mp_drawing.draw_landmarks(
                          image=image,
                          landmark_list=mediapipe_results.right_hand_landmarks,
                          connections=mp_holistic.HAND_CONNECTIONS,
                          landmark_drawing_spec = None,
                          connection_drawing_spec=mp_drawing_styles
                          .get_default_hand_connections_style())
    return image

drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)


### webcam stuff



if save_video:
    writer = cv2.VideoWriter(input_video_filename+'_annotated.mp4',
                            cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'),
                            framerate,
                            (video_frame_width, video_frame_height))

with mp_holistic.Holistic(
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5,
    model_complexity=2) as holistic:
    success = True
    while success:
        success, raw_image = cap.read()
        
        if not success:
            break

        if run_mediapipe:
            mediapipe_results = holistic.process(raw_image)
            annotated_image = annotate_image_with_mediapipe_data(raw_image, mediapipe_results)
        else:
            annotated_image = raw_image
        if save_video:
            writer.write(annotated_image)
        cv2.imshow(f'Camera: {camera_number}, press Q to quit', annotated_image)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break