- 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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