Python OpenCV断言失败，值为负值
我正在用OpenCV和Keras制作一个人脸活力检测程序。 我被这个错误所困扰： OpenCV断言失败，值为负值 我尝试了所有建议的答案，但都没有解决我的问题 我已经尝试了StackOverflow和Github问题上的所有解决方案，但在我的案例中没有一个成功Python OpenCV断言失败，值为负值,python,opencv,facial-identification,Python,Opencv,Facial Identification,我正在用OpenCV和Keras制作一个人脸活力检测程序。 我被这个错误所困扰： OpenCV断言失败，值为负值 我尝试了所有建议的答案，但都没有解决我的问题 我已经尝试了StackOverflow和Github问题上的所有解决方案，但在我的案例中没有一个成功 video_capture = cv2.VideoCapture(0) model = load_model() print("[LOG] COLLECTING images.....") images = [
video_capture = cv2.VideoCapture(0)
model = load_model()
print("[LOG] COLLECTING images.....")
images = []
for direc, _, files in tqdm(os.walk(dataset)):
for file in files:
if file.endswith("jpg"):
images.append(os.path.join(direc, file))
return model, face_detector, open_eyes_detector, left_eye__detector, right_eye_detector, video_capture, images
def process_and_encode(images):
known_encodings = []
known_names = []
print("[LOG] Encoding faces....")
for image_path in tqdm(images):
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
boxes = face_recognition.face_locations(image, model='hog')
encoding = face_recognition.face_encodings(image, boxes)
name = image_path.split(os.path.sep)[-2]
if len(encoding) > 0:
known_encodings.append(encoding[0])
known_names.append(name)
return {"encodings": known_encodings, "names": known_names}
这可能是因为您尝试使用imshow，但以前没有imwite。这不完全是一个解决方案，而是一个有效的例子。看一看:
import cv2 # pip install opencv-python
import datetime
from cv2.data import haarcascades as hc
import requests
cap = cv2.VideoCapture(0)
faceCascade = cv2.CascadeClassifier("%s/haarcascade_frontalface_default.xml" % hc)
eye_cascade = cv2.CascadeClassifier('%s/haarcascade_eye.xml' % hc)
profile_cascade = cv2.CascadeClassifier('%s/haarcascade_profileface.xml' % hc)
fullbody_cascade = cv2.CascadeClassifier('%s/haarcascade_fullbody.xml' % hc)
smile_cascade = cv2.CascadeClassifier('%s/haarcascade_smile.xml' % hc)
eyesglasses_cascade = cv2.CascadeClassifier('%s/haarcascade_eye_tree_eyeglasses.xml' % hc)
mouth_cascade = cv2.CascadeClassifier('%s/haarcascade_mcs_mouth.xml' % hc)
filename = 'output/'+datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
def recognizer():
while True:
ret, frame = cap.read()
profile_count = recognize_profile(frame)
face_count, eye_count = recognize_face(frame, True)
cv2.imwrite('%s.png' % filename, frame)
image = cv2.imread('%s.png' % filename)
cv2.imshow('image', image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def recognize_profile(frame):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
profiles = profile_cascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30)
# flags = cv2.CV_HAAR_SCALE_IMAGE
)
for (x, y, w, h) in profiles:
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
return len(profiles)
def recognize_face(frame, recognize_eyes=None):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30)
# flags = cv2.CV_HAAR_SCALE_IMAGE
)
eyes = []
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
if recognize_eyes:
roi_gray = gray[y:y + h, x:x + w]
roi_color = frame[y:y + h, x:x + w]
eyes = eye_cascade.detectMultiScale(roi_gray)
# draw bounding boxes around detected features
for (ex, ey, ew, eh) in eyes:
eye_center = (ex + ew // 2, ey + eh // 2)
radius = int(round((ew + eh) * 0.25))
cv2.circle(roi_color, eye_center, radius, (0, 0, 255), 4)
return len(faces), len(eyes)
def snapshot():
while True:
# Capture frame-by-frame
ret, frame = cap.read()
recognize_profile(frame)
recognize_face(frame, True)
cv2.imwrite('%s.png' % filename, frame)
# if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def live_video_recognizer():
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))
out = cv2.VideoWriter('%s.avi' % filename, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 10, (frame_width, frame_height))
while True:
ret, frame = cap.read()
recognize_profile(frame)
recognize_face(frame, True)
if ret is True:
out.write(frame)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cap.release()
out.release()
cv2.destroyAllWindows()
recognizer()
# snapshot()
# live_video_recognizer()
未创建帧0x0。如果detect_和_display仅包含frame=video_capture.read，会发生什么情况？如果它能工作，你会逐步在detect_和_display中添加一些进程，直到它再次崩溃。不，它不工作。嗨！现在我得到了这个错误：-ValueError：设置一个数组元素和一个序列。你能帮帮我吗？你从哪里得到这个错误的？把你的堆栈跟踪发给我这里是我的堆栈跟踪：-请帮帮我。回溯最近一次调用：文件C:/Users/Saksham Dubey/PycharmProjects/FacePay/FaceLive.py，第192行，在右眼检测器中，数据，眼睛检测到的文件C:/Users/Saksham Dubey/PycharmProjects/FacePay/FaceLive.py，第81行，在检测和显示框中=cv2.resizenp.float32frame，0，0，fx=0.6，fy=0.6 ValueError:设置带有序列的数组元素。嘿！你看过我的堆栈跟踪了吗？
import cv2 # pip install opencv-python
import datetime
from cv2.data import haarcascades as hc
import requests
cap = cv2.VideoCapture(0)
faceCascade = cv2.CascadeClassifier("%s/haarcascade_frontalface_default.xml" % hc)
eye_cascade = cv2.CascadeClassifier('%s/haarcascade_eye.xml' % hc)
profile_cascade = cv2.CascadeClassifier('%s/haarcascade_profileface.xml' % hc)
fullbody_cascade = cv2.CascadeClassifier('%s/haarcascade_fullbody.xml' % hc)
smile_cascade = cv2.CascadeClassifier('%s/haarcascade_smile.xml' % hc)
eyesglasses_cascade = cv2.CascadeClassifier('%s/haarcascade_eye_tree_eyeglasses.xml' % hc)
mouth_cascade = cv2.CascadeClassifier('%s/haarcascade_mcs_mouth.xml' % hc)
filename = 'output/'+datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
def recognizer():
while True:
ret, frame = cap.read()
profile_count = recognize_profile(frame)
face_count, eye_count = recognize_face(frame, True)
cv2.imwrite('%s.png' % filename, frame)
image = cv2.imread('%s.png' % filename)
cv2.imshow('image', image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def recognize_profile(frame):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
profiles = profile_cascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30)
# flags = cv2.CV_HAAR_SCALE_IMAGE
)
for (x, y, w, h) in profiles:
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
return len(profiles)
def recognize_face(frame, recognize_eyes=None):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30)
# flags = cv2.CV_HAAR_SCALE_IMAGE
)
eyes = []
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
if recognize_eyes:
roi_gray = gray[y:y + h, x:x + w]
roi_color = frame[y:y + h, x:x + w]
eyes = eye_cascade.detectMultiScale(roi_gray)
# draw bounding boxes around detected features
for (ex, ey, ew, eh) in eyes:
eye_center = (ex + ew // 2, ey + eh // 2)
radius = int(round((ew + eh) * 0.25))
cv2.circle(roi_color, eye_center, radius, (0, 0, 255), 4)
return len(faces), len(eyes)
def snapshot():
while True:
# Capture frame-by-frame
ret, frame = cap.read()
recognize_profile(frame)
recognize_face(frame, True)
cv2.imwrite('%s.png' % filename, frame)
# if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def live_video_recognizer():
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))
out = cv2.VideoWriter('%s.avi' % filename, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 10, (frame_width, frame_height))
while True:
ret, frame = cap.read()
recognize_profile(frame)
recognize_face(frame, True)
if ret is True:
out.write(frame)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cap.release()
out.release()
cv2.destroyAllWindows()
recognizer()
# snapshot()
# live_video_recognizer()