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# 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
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