counter = 0
while True:
counter += 1
_, image = cam.read()
if counter <= 10: # 放弃前10帧
continue
image = cv2.flip(image, 1)
if error == 1:
end_time = time.time()
difference = end_time - start_time
print(difference)
if difference >= limit_time:
error = 0
face_location_list = faceutil.get_face_location(image)
for (left, top, right, bottom) in face_location_list:
cv2.rectangle(image, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.imshow('Collecting Faces', image) # show the image
# Press 'ESC' for exiting video
k = cv2.waitKey(100) & 0xff
if k == 27:
break
face_count = len(face_location_list)
if error == 0 and face_count == 0: # 没有检测到人脸
print('[WARNING] 没有检测到人脸')
audioplayer.play_audio(os.path.join(audio_dir, 'no_face_detected.mp3'))
error = 1
start_time = time.time()
# grab the current frame
(grabbed, frame) = camera.read()
# if we are viewing a video and we did not grab a frame, then we
# have reached the end of the video
if input_video and not grabbed:
break
if not input_video:
frame = cv2.flip(frame, 1)
# resize the frame, convert it to grayscale, and then clone the
# original frame so we can draw on it later in the program
frame = imutils.resize(frame, width=600)
face_location_list = faceutil.get_face_location(frame)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# loop over the face bounding boxes
for (left, top, right, bottom) in face_location_list:
# extract the ROI of the face from the grayscale image,
# resize it to a fixed 28x28 pixels, and then prepare the
# ROI for classification via the CNN
roi = gray[top:bottom, left:right]
roi = cv2.resize(
roi,
(FACIAL_EXPRESSION_TARGET_WIDTH, FACIAL_EXPRESSION_TARGET_HEIGHT))
roi = roi.astype("float") / 255.0
roi = img_to_array(roi)
roi = np.expand_dims(roi, axis=0)
prediction = sess.run(logits, feed_dict={x: image_arr})
max_index = np.argmax(prediction)
print('预测的标签为：', max_index, lists[max_index])
print('预测的结果为：', prediction)
return lists[max_index]
if __name__ == '__main__':
import time
from oldcare.facial import FaceUtil
imgs = os.listdir(img_dir)
for img in imgs:
print(img)
image = cv2.imread(img_dir + '/' + img)
faceutil = FaceUtil()
face_location_list = faceutil.get_face_location(image)
# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# print(gray)
#
# faceCascade = cv2.CascadeClassifier(facePath)
# faces = faceCascade.detectMultiScale(
# image,
# scaleFactor=1.1,
# minNeighbors=8,
# minSize=(55, 55),
# flags=cv2.CASCADE_SCALE_IMAGE
# )
print(len(face_location_list))
for (x, y, w, h) in face_location_list:
face = image[y:h, x:w]
face = cv2.resize(face, (48, 48))