scaleFactor=1.2,
minNeighbors=3,
minSize=(10, 10))
#facerect = cascade.detectMultiScale(frame_gray, scaleFactor=1.01, minNeighbors=3, minSize=(3, 3))
if len(facerect) > 0:
print('face detected')
color = (255, 255, 255) # 白
for rect in facerect:
# 検出した顔を囲む矩形の作成
#cv2.rectangle(frame, tuple(rect[0:2]), tuple(rect[0:2] + rect[2:4]), color, thickness=2)
x, y = rect[0:2]
width, height = rect[2:4]
image = frame[y - 10:y + height, x:x + width]
result = model.predict(image)
if result == 0: # boss
print('Boss is approaching')
show_image()
else:
print('Not boss')
#10msecキー入力待ち
k = cv2.waitKey(100)
#Escキーを押されたら終了
if k == 27:
break
#キャプチャを終了
cap.release()
cv2.destroyAllWindows()
# 物体認識（顔認識）の実行
facerect = cascade.detectMultiScale(frame_gray, scaleFactor=1.2, minNeighbors=3, minSize=(10, 10))
#facerect = cascade.detectMultiScale(frame_gray, scaleFactor=1.01, minNeighbors=3, minSize=(3, 3))
if len(facerect) > 0:
print('face detected')
color = (255, 255, 255) # 白
for rect in facerect:
# 検出した顔を囲む矩形の作成
#cv2.rectangle(frame, tuple(rect[0:2]), tuple(rect[0:2] + rect[2:4]), color, thickness=2)
x, y = rect[0:2]
width, height = rect[2:4]
image = frame[y - 10: y + height, x: x + width]
result = model.predict(image)
if result == 0: # boss
print('Boss is approaching')
show_image()
else:
print('Not boss')
#10msecキー入力待ち
k = cv2.waitKey(100)
#Escキーを押されたら終了
if k == 27:
break
#キャプチャを終了
cap.release()
cv2.destroyAllWindows()