cv2.imshow("preview", frame)
#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:
# 创建围绕检测的面部的矩形
#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键输入等待
k = cv2.waitKey(100)
#Esc退出按下该键
if k == 27:
break
#退出拍摄
cap.release()
cv2.destroyAllWindows()
cv2.imshow('Recognizing', buffer)
#cv2.namedWindow('Recognizing', cv2.WINDOW_AUTOSIZE | cv2.WINDOW_KEEPRATIO | cv2.WINDOW_GUI_EXPANDED)
for rect in facerect:
[x, y, width, height] = extendFaceRect(rect)
# Crop the face
if GRAY_MODE == True:
img_predict = frame_gray[y:y + height, x:x + width]
else:
img_predict = frame[y:y + height, x:x + width]
# Predict face
if GRAY_MODE == True:
result = model.predict(img_predict, img_channels=1)
else:
result = model.predict(img_predict)
if DEBUG_OUTPUT == True:
outimg = frame[y:y + height, x:x + width]
if result == 0:
write_image(
'./output/isme/' + str(random.randint(1, 999999)) +
'.jpg', outimg)
else:
write_image(
'./output/notme/' +
str(random.randint(1, 999999)) + '.jpg', outimg)
if result == 0: # Is me
# Recognize faces
facerect = cascade.detectMultiScale(frame_gray, 1.3, 5)
recStatus = 0
if len(facerect) > 0:
print(timestamp(), 'Cara detectada.')
color = (255, 255, 255) # 白
for rect in facerect:
[x, y, width, height] = extendFaceRect(rect)
# Crop the face
img_predict = frame[y:y + height, x:x + width]
# Predict face
result = model.predict(img_predict)
if result == 0: # Is me
print(timestamp(), "!Eres tu Raul! :)")
isme += 1
recStatus = 1
else:
print(timestamp(), 'No eres Raul >:(')
notme += 1
if recStatus == 0:
recStatus = -1
print(timestamp(), 'yo', isme, 'otro', notme)
# End if Face Detected
if recStatus == -1 or (recStatus == 0 and