extract_features.py

import csv
import numpy as np
import os
import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_holistic = mp.solutions.holistic


actions = ['እንደገና','አውሮፕላን']


with mp_holistic.Holistic(
        static_image_mode=True,
        min_detection_confidence=0.5,
        min_tracking_confidence=0.5) as holistic:
    for action in actions:
        frames = os.listdir('frames-processed/{}'.format(action))
        for frame in frames:

            cap = cv2.VideoCapture(
                '/home/mekbibtarekegn/ETHSL-MediaPipe/frames-processed/{}/{}'.format(action, frame))
            print(frame)
            while cap.isOpened():
                success, image = cap.read()
                if not success:
                    print("Ignoring empty camera frame.")
                # If loading a video, use 'break' instead of 'continue'.
                    break

            # To improve performance, optionally mark the image as not writeable to
            # pass by reference.
                image.flags.writeable = False
                image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
                results = holistic.process(image)

            # Draw landmark annotation on the image.
                image.flags.writeable = True
                image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
            # mp_drawing.draw_landmarks(
            #     image,
            #     results.face_landmarks,
            #     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,
                    results.pose_landmarks,
                    mp_holistic.POSE_CONNECTIONS,
                    landmark_drawing_spec=mp_drawing_styles
                    .get_default_pose_landmarks_style())
                mp_drawing.draw_landmarks(
                    image,
                    results.left_hand_landmarks,
                    mp_holistic.HAND_CONNECTIONS,
                    landmark_drawing_spec=mp_drawing_styles
                    .get_default_pose_landmarks_style())
                mp_drawing.draw_landmarks(
                    image,
                    results.right_hand_landmarks,
                    mp_holistic.HAND_CONNECTIONS,
                    landmark_drawing_spec=mp_drawing_styles
                    .get_default_pose_landmarks_style())
            # Flip the image horizontally for a selfie-view display.
                try:
                    # Extract Pose landmarks
                    pose = list(np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten(
                    ) if results.pose_landmarks else np.zeros(33*4))
                    lh = list(np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten(
                    ) if results.left_hand_landmarks else np.zeros(21*3))
                    rh = list(np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten(
                    ) if results.right_hand_landmarks else np.zeros(21*3))

                    # Concate rows
                    row = pose + lh+rh

                    # Append class name
                    row.insert(0, action)

                    # Export to CSV
                    with open('coords.csv', mode='a', newline='') as f:
                        csv_writer = csv.writer(
                            f, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
                        csv_writer.writerow(row)

                except:
                    pass
                cv2.imshow('MediaPipe Holistic', cv2.flip(image, 1))
                if cv2.waitKey(5) & 0xFF == 27:
                    break
        cap.release()