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test.py
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test.py
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import cv2
import mediapipe as mp
import numpy as np
from tensorflow.keras.models import load_model
actions = ['zero', 'one', 'two', 'three', 'four', 'five']
seq_length = 30
model = load_model('models/model.h5')
# MediaPipe hands model (초기화)
mp_hands = mp.solutions.hands
mp_drawing = mp.solutions.drawing_utils
hands = mp_hands.Hands(
max_num_hands = 1,
min_detection_confidence=0.5,
min_tracking_confidence=0.5)
# 웹캠 열기
cap = cv2.VideoCapture(0)
seq = []
action_seq = []
while cap.isOpened():
ret, img = cap.read()
img0 = img.copy()
img = cv2.flip(img, 1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
result = hands.process(img)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
if result.multi_hand_landmarks is not None:
for res in result.multi_hand_landmarks:
joint = np.zeros((21,4))
for j, lm in enumerate(res.landmark):
joint[j] = [lm.x, lm.y, lm.z, lm.visibility]
# 점들 간의 각도 계산하기
v1 = joint[[0,1,2,3,0,5,6,7,0,9,10,11,0,13,14,15,0,17,18,19], :3] # Parent joint
v2 = joint[[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], :3] # Child joint
v = v2 - v1 # v2와 v1 사이의 벡터 구하기
# 점곱을 구한 다음 arccos으로 각도 구하기
v = v / np.linalg.norm(v, axis=1)[:, np.newaxis]
# Get angle using arcos of dot product
angle = np.arccos(np.einsum('nt,nt->n',
v[[0,1,2,4,5,6,8,9,10,12,13,14,16,17,18],:],
v[[1,2,3,5,6,7,9,10,11,13,14,15,17,18,19],:])) # [15,]
angle = np.degrees(angle) # 라디안을 각도로 바꾸기
d = np.concatenate([joint.flatten(), angle])
seq.append(d)
mp_drawing.draw_landmarks(img, res, mp_hands.HAND_CONNECTIONS)
if len(seq) < seq_length:
continue
input_data = np.expand_dims(np.array(seq[-seq_length:], dtype=np.float32), axis=0)
# 모델 예측
y_pred = model.predict(input_data).squeeze()
# 예측한 값의 인덱스 구하기
i_pred = int(np.argmax(y_pred))
conf = y_pred[i_pred]
# confidence가 0.9보다 작으면
if conf < 0.9:
continue # 제스쳐 인식 못 한 상황으로 판단
action = actions[i_pred]
action_seq.append(action) # action_seq에 action을 저장
#print(action_seq)
# 보인 제스쳐의 횟수가 3 미만인 경우에는 계속
if len(action_seq) < 3:
continue
# 제스쳐 판단 불가이면 this_action은 ?
this_action = '?'
# 만약 마지막 3개의 제스쳐가 같으면 제스쳐가 제대로 취해졌다고 판단
if action_seq[-1] == action_seq[-2] == action_seq[-3]:
this_action = action
print(this_action)
# 텍스트 출력
cv2.putText(img, f'{this_action.upper()}', org=(int(res.landmark[0].x * img.shape[1]), int(res.landmark[0].y * img.shape[0] + 20)), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=(255, 255, 255), thickness=2)
# out.write(img0)
# out2.write(img)
cv2.imshow('img', img)
if cv2.waitKey(1) == ord('q'):
break