# Created by MediaPipe
# Modified by Augmented Startups 2021
# Pose-Estimation in 5 Minutes
# Watch 5 Minute Tutorial at www.augmentedstartups.info/YouTube
import cv2
import mediapipe as mp
import time
import os

mp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.pose
mp_holistic = mp.solutions.holistic

# For static images:
with mp_pose.Pose(
        static_image_mode=True,
        # model_complexity=2,
        min_detection_confidence=0.5) as pose:
    image = cv2.imread('4.jpg')  # Insert your Image Here
    image_height, image_width, _ = image.shape
    # Convert the BGR image to RGB before processing.
    results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    # Draw pose landmarks on the image.
    annotated_image = image.copy()
    mp_drawing.draw_landmarks(annotated_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
    cv2.imwrite(r'4.png', annotated_image)

# For webcam input:
# cap = cv2.VideoCapture(0)
cap = cv2.VideoCapture("1.mp4")
# For Video input:
prevTime = 0
with mp_pose.Pose(
        min_detection_confidence=0.5,
        min_tracking_confidence=0.5) as pose:
    while cap.isOpened():
        success, image = cap.read()
        if not success:
            print("Ignoring empty camera frame.")
            # If loading a video, use 'break' instead of 'continue'.
            continue

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

        # Draw the pose annotation on the image.
        image.flags.writeable = True
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        mp_drawing.draw_landmarks(
            image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
        currTime = time.time()
        fps = 1 / (currTime - prevTime)
        prevTime = currTime
        cv2.putText(image, f'FPS: {int(fps)}', (20, 70), cv2.FONT_HERSHEY_PLAIN, 3, (0, 196, 255), 2)
        cv2.imshow('BlazePose', image)
        if cv2.waitKey(5) & 0xFF == 27:
            break
cap.release()
# Learn more AI in Computer Vision by Enrolling in our AI_CV Nano Degree:
# https://bit.ly/AugmentedAICVPRO