제작 도전

파이썬으로 미디어 파이프 튜토리얼 해보기

영성 2021. 7. 27. 19:04

- 미디어파이프 설치 : !pip install mediapipe opencv-python

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

 

# 웹캠실행
cap = cv2.VideoCapture(0)
while cap.isOpened():
    ret, frame = cap.read()
    cv2.imshow('Mediapipe Feed', frame)
    
    if cv2.waitKey(10) & 0xFF == 27: # esc 키를 누르면 닫음
        break
        
cap.release()
cv2.destroyAllWindows()

- 기본 웹캠

 

1. 감지

cap = cv2.VideoCapture(0)
## 미디어 파이프 instance 결정
with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
    while cap.isOpened():
        ret, frame = cap.read()
        
        # 이미지를 다시 RGB형식으로 칠함 (먼저는 프레임을 잡아줘야한다)
        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        image.flags.writeable = False # 이미지 다시쓰기
      
        # 탐지하기
        results = pose.process(image)
    
        # 이미지를 RGB로 나타냄
        image.flags.writeable = True
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        
        # Render detections
        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,
                                mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2), 
                                mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2) 
                                 )               
        
        cv2.imshow('Mediapipe Feed', image)

        if cv2.waitKey(10) & 0xFF == 27:
            break

    cap.release()
    cv2.destroyAllWindows()

- 사람을 인식하고 색있는 바로 표시한다

 

2. 관절 결정

cap = cv2.VideoCapture(0)
## Setup mediapipe instance
with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
    while cap.isOpened():
        ret, frame = cap.read()
        
        # Recolor image to RGB
        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        image.flags.writeable = False
      
        # Make detection
        results = pose.process(image)
    
        # Recolor back to BGR
        image.flags.writeable = True
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        
        # Extract landmarks
        try:
            landmarks = results.pose_landmarks.landmark
            print(landmarks)
        except:
            pass
        
        
        # Render detections
        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,
                                mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2), 
                                mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2) 
                                 )               
        
        cv2.imshow('Mediapipe Feed', image)

        if cv2.waitKey(10) & 0xFF == 27:
            break

    cap.release()
    cv2.destroyAllWindows()

- 관절을 결정하고 나눈 것을 저장한다

 

 

3. 각도 계산

def calculate_angle(a,b,c):
    a = np.array(a) # First
    b = np.array(b) # Mid
    c = np.array(c) # End
    
    radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0])
    angle = np.abs(radians*180.0/np.pi)
    
    if angle >180.0:
        angle = 360-angle
        
    return angle
shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]
elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]
wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]
shoulder, elbow, wrist

calculate_angle(shoulder, elbow, wrist)

tuple(np.multiply(elbow, [640, 480]).astype(int))

cap = cv2.VideoCapture(0)
## Setup mediapipe instance
with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
    while cap.isOpened():
        ret, frame = cap.read()
        
        # Recolor image to RGB
        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        image.flags.writeable = False
      
        # Make detection
        results = pose.process(image)
    
        # Recolor back to BGR
        image.flags.writeable = True
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        
        # Extract landmarks
        try:
            landmarks = results.pose_landmarks.landmark
            
            # Get coordinates
            shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]
            elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]
            wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]
            
            # Calculate angle
            angle = calculate_angle(shoulder, elbow, wrist)
            
            # Visualize angle
            cv2.putText(image, str(angle), 
                           tuple(np.multiply(elbow, [640, 480]).astype(int)), 
                           cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA
                                )
                       
        except:
            pass
        
        
        # Render detections
        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,
                                mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2), 
                                mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2) 
                                 )               
        
        cv2.imshow('Mediapipe Feed', image)

        if cv2.waitKey(10) & 0xFF == 27:
            break

    cap.release()
    cv2.destroyAllWindows()

- 각도를 계산한다

 

4. 동작 카운트

cap = cv2.VideoCapture(0)

# Curl counter variables
counter = 0 
stage = None

## Setup mediapipe instance
with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
    while cap.isOpened():
        ret, frame = cap.read()
        
        # Recolor image to RGB
        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        image.flags.writeable = False
      
        # Make detection
        results = pose.process(image)
    
        # Recolor back to BGR
        image.flags.writeable = True
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        
        # Extract landmarks
        try:
            landmarks = results.pose_landmarks.landmark
            
            # Get coordinates
            shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]
            elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]
            wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]
            
            # Calculate angle
            angle = calculate_angle(shoulder, elbow, wrist)
            
            # Visualize angle
            cv2.putText(image, str(angle), 
                           tuple(np.multiply(elbow, [640, 480]).astype(int)), 
                           cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA
                                )
            
            # Curl counter logic
            if angle > 160:
                stage = "down"
            if angle < 30 and stage =='down':
                stage="up"
                counter +=1
                print(counter)
                       
        except:
            pass
        
        # Render curl counter
        # Setup status box
        cv2.rectangle(image, (0,0), (225,73), (245,117,16), -1)
        
        # Rep data
        cv2.putText(image, 'REPS', (15,12), 
                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA)
        cv2.putText(image, str(counter), 
                    (10,60), 
                    cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 2, cv2.LINE_AA)
        
        # Stage data
        cv2.putText(image, 'STAGE', (65,12), 
                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA)
        cv2.putText(image, stage, 
                    (60,60), 
                    cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 2, cv2.LINE_AA)
        
        
        # Render detections
        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,
                                mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2), 
                                mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2) 
                                 )               
        
        cv2.imshow('Mediapipe Feed', image)

        if cv2.waitKey(10) & 0xFF == 27:
            break

    cap.release()
    cv2.destroyAllWindows()

- 계산한 각도에 따라 변화하는 동작마다 카운트 한다

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