import cv2
import mediapipe as mp
import numpy as np
import os
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_pose = mp.solutions.pose


#pip install mediapipe
# For static images:
IMAGE_FILES = [os.path.join('/home/ok/Desktop/vps-nginx-php/yoga_images/png/', filename) for filename in os.listdir('/home/ok/Desktop/vps-nginx-php/yoga_images/png/') if filename.endswith(".png")]
BG_COLOR = (192, 192, 192) # gray
with mp_pose.Pose(
    static_image_mode=True,
    model_complexity=2,
    enable_segmentation=True,
    min_detection_confidence=0.5) as pose:
  for idx, file in enumerate(IMAGE_FILES):
    image = cv2.imread(file)
    basename = os.path.basename(file).replace('.', '_');
    image_height, image_width, _ = image.shape
    # Convert the BGR image to RGB before processing.
    results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

    if not results.pose_landmarks:
      continue
    print(
        f'Nose coordinates: ('
        f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].x * image_width}, '
        f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].y * image_height})'
    )

    annotated_image = image.copy()
    # Draw segmentation on the image.
    # To improve segmentation around boundaries, consider applying a joint
    # bilateral filter to "results.segmentation_mask" with "image".
    condition = np.stack((results.segmentation_mask,) * 3, axis=-1) > 0.1
    bg_image = np.zeros(image.shape, dtype=np.uint8)
    bg_image[:] = BG_COLOR
    annotated_image = np.where(condition, annotated_image, bg_image)
    # Draw pose landmarks on the image.
    mp_drawing.draw_landmarks(
        annotated_image,
        results.pose_landmarks,
        mp_pose.POSE_CONNECTIONS,
        landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
    #cv2.imwrite('./tmp/mediapipe_pose_' + str(idx) + '.png', annotated_image)
    cv2.imwrite('./tmp/'  + basename + '.png', annotated_image)
    # Plot pose world landmarks.
    #mp_drawing.plot_landmarks(
    #    results.pose_world_landmarks, mp_pose.POSE_CONNECTIONS)