Raspberrypi W 中的 PiCamera mmal 错误为零
PiCamera mmal Error in Raspberrypi W Zero
我在Raspberrypi W Zero上做检测掩码项目。但是我无法捕捉到错误。
这是我的 Tensorflow 和 openCV 代码
# import the necessary packages
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
from imutils.video import VideoStream
import picamera
#from picamera import sleep
import numpy as np
import imutils
import time
import cv2
import os
def detect_and_predict_mask(frame, faceNet, maskNet):
# grab the dimensions of the frame and then construct a blob
# from it
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, 1.0, (224, 224),
(104.0, 177.0, 123.0))
# pass the blob through the network and obtain the face detections
faceNet.setInput(blob)
detections = faceNet.forward()
print(detections.shape)
# initialize our list of faces, their corresponding locations,
# and the list of predictions from our face mask network
faces = []
locs = []
preds = []
# loop over the detections
for i in range(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with
# the detection
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the confidence is
# greater than the minimum confidence
if confidence > 0.5:
# compute the (x, y)-coordinates of the bounding box for
# the object
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# ensure the bounding boxes fall within the dimensions of
# the frame
(startX, startY) = (max(0, startX), max(0, startY))
(endX, endY) = (min(w - 1, endX), min(h - 1, endY))
# extract the face ROI, convert it from BGR to RGB channel
# ordering, resize it to 224x224, and preprocess it
face = frame[startY:endY, startX:endX]
face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
face = cv2.resize(face, (224, 224))
face = img_to_array(face)
face = preprocess_input(face)
# add the face and bounding boxes to their respective
# lists
faces.append(face)
locs.append((startX, startY, endX, endY))
# only make a predictions if at least one face was detected
if len(faces) > 0:
# for faster inference we'll make batch predictions on *all*
# faces at the same time rather than one-by-one predictions
# in the above `for` loop
faces = np.array(faces, dtype="float32")
preds = maskNet.predict(faces, batch_size=32)
# return a 2-tuple of the face locations and their corresponding
# locations
return (locs, preds)
# load our serialized face detector model from disk
prototxtPath = r"/home/pi/Desktop/pi/face_detector/deploy.prototxt"
weightsPath = r"/home/pi/Desktop/pi/face_detector/res10_300x300_ssd_iter_140000.caffemodel"
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
# load the face mask detector model from disk
maskNet = load_model("./mask_detector.model")
# initialize the video stream
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
cap = picamera.PiCamera()
# loop over the frames from the video stream
while(cap.isOpened()):
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
frame = vs.read()
frame = imutils.resize(frame, width=400)
# detect faces in the frame and determine if they are wearing a
# face mask or not
(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
# loop over the detected face locations and their corresponding
# locations
for (box, pred) in zip(locs, preds):
# unpack the bounding box and predictions
(startX, startY, endX, endY) = box
(mask, withoutMask) = pred
# determine the class label and color we'll use to draw
# the bounding box and text
label = "Mask" if mask > withoutMask else "No Mask"
color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
# include the probability in the label
label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
# display the label and bounding box rectangle on the output
# frame
cv2.putText(frame, label, (startX, startY - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
# show the output frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
cap.close()
break
# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()
我 运行 在 Raspberrypi W 零中的这个 Tensorflow 和 openCV 代码...但是这个错误困扰着我..
mmal: mmal_vc_port_enable: failed to enable port vc.null_sink:in:0(OPQV): ENOSPC
mmal: mmal_port_enable: failed to enable connected port (vc.null_sink:in:0(OPQV))0x34600e0 (ENOSPC)
mmal: mmal_connection_enable: output port couldn't be enabled
Traceback (most recent call last):
File "detect_mask_video.py", line 88, in <module>
cap = picamera.PiCamera()
File "/usr/lib/python3/dist-packages/picamera/camera.py", line 433, in __init__
self._init_preview()
File "/usr/lib/python3/dist-packages/picamera/camera.py", line 513, in _init_preview
self, self._camera.outputs[self.CAMERA_PREVIEW_PORT])
File "/usr/lib/python3/dist-packages/picamera/renderers.py", line 558, in __init__
self.renderer.inputs[0].connect(source).enable()
File "/usr/lib/python3/dist-packages/picamera/mmalobj.py", line 2212, in enable
prefix="Failed to enable connection")
File "/usr/lib/python3/dist-packages/picamera/exc.py", line 184, in mmal_check
raise PiCameraMMALError(status, prefix)
picamera.exc.PiCameraMMALError: Failed to enable connection: Out of resources
我在 google 中搜索了这个错误，但我无法解决这个问题...请帮助我...:D
到目前为止我做了什么：
raspberrypi update
Increasing memory
Reconnect PiCamera
在终端输入以下命令：
sudo modprobe bcm2835-v4l2
将 mmal 设备作为标准 v4l（Linux 的视频）设备进行访问。
我在Raspberrypi W Zero上做检测掩码项目。但是我无法捕捉到错误。
这是我的 Tensorflow 和 openCV 代码
# import the necessary packages
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
from imutils.video import VideoStream
import picamera
#from picamera import sleep
import numpy as np
import imutils
import time
import cv2
import os
def detect_and_predict_mask(frame, faceNet, maskNet):
# grab the dimensions of the frame and then construct a blob
# from it
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, 1.0, (224, 224),
(104.0, 177.0, 123.0))
# pass the blob through the network and obtain the face detections
faceNet.setInput(blob)
detections = faceNet.forward()
print(detections.shape)
# initialize our list of faces, their corresponding locations,
# and the list of predictions from our face mask network
faces = []
locs = []
preds = []
# loop over the detections
for i in range(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with
# the detection
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the confidence is
# greater than the minimum confidence
if confidence > 0.5:
# compute the (x, y)-coordinates of the bounding box for
# the object
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# ensure the bounding boxes fall within the dimensions of
# the frame
(startX, startY) = (max(0, startX), max(0, startY))
(endX, endY) = (min(w - 1, endX), min(h - 1, endY))
# extract the face ROI, convert it from BGR to RGB channel
# ordering, resize it to 224x224, and preprocess it
face = frame[startY:endY, startX:endX]
face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
face = cv2.resize(face, (224, 224))
face = img_to_array(face)
face = preprocess_input(face)
# add the face and bounding boxes to their respective
# lists
faces.append(face)
locs.append((startX, startY, endX, endY))
# only make a predictions if at least one face was detected
if len(faces) > 0:
# for faster inference we'll make batch predictions on *all*
# faces at the same time rather than one-by-one predictions
# in the above `for` loop
faces = np.array(faces, dtype="float32")
preds = maskNet.predict(faces, batch_size=32)
# return a 2-tuple of the face locations and their corresponding
# locations
return (locs, preds)
# load our serialized face detector model from disk
prototxtPath = r"/home/pi/Desktop/pi/face_detector/deploy.prototxt"
weightsPath = r"/home/pi/Desktop/pi/face_detector/res10_300x300_ssd_iter_140000.caffemodel"
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
# load the face mask detector model from disk
maskNet = load_model("./mask_detector.model")
# initialize the video stream
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
cap = picamera.PiCamera()
# loop over the frames from the video stream
while(cap.isOpened()):
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
frame = vs.read()
frame = imutils.resize(frame, width=400)
# detect faces in the frame and determine if they are wearing a
# face mask or not
(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
# loop over the detected face locations and their corresponding
# locations
for (box, pred) in zip(locs, preds):
# unpack the bounding box and predictions
(startX, startY, endX, endY) = box
(mask, withoutMask) = pred
# determine the class label and color we'll use to draw
# the bounding box and text
label = "Mask" if mask > withoutMask else "No Mask"
color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
# include the probability in the label
label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
# display the label and bounding box rectangle on the output
# frame
cv2.putText(frame, label, (startX, startY - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
# show the output frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
cap.close()
break
# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()
我 运行 在 Raspberrypi W 零中的这个 Tensorflow 和 openCV 代码...但是这个错误困扰着我..
mmal: mmal_vc_port_enable: failed to enable port vc.null_sink:in:0(OPQV): ENOSPC
mmal: mmal_port_enable: failed to enable connected port (vc.null_sink:in:0(OPQV))0x34600e0 (ENOSPC)
mmal: mmal_connection_enable: output port couldn't be enabled
Traceback (most recent call last):
File "detect_mask_video.py", line 88, in <module>
cap = picamera.PiCamera()
File "/usr/lib/python3/dist-packages/picamera/camera.py", line 433, in __init__
self._init_preview()
File "/usr/lib/python3/dist-packages/picamera/camera.py", line 513, in _init_preview
self, self._camera.outputs[self.CAMERA_PREVIEW_PORT])
File "/usr/lib/python3/dist-packages/picamera/renderers.py", line 558, in __init__
self.renderer.inputs[0].connect(source).enable()
File "/usr/lib/python3/dist-packages/picamera/mmalobj.py", line 2212, in enable
prefix="Failed to enable connection")
File "/usr/lib/python3/dist-packages/picamera/exc.py", line 184, in mmal_check
raise PiCameraMMALError(status, prefix)
picamera.exc.PiCameraMMALError: Failed to enable connection: Out of resources
我在 google 中搜索了这个错误，但我无法解决这个问题...请帮助我...:D
到目前为止我做了什么：
raspberrypi update
Increasing memory
Reconnect PiCamera
在终端输入以下命令：
sudo modprobe bcm2835-v4l2
将 mmal 设备作为标准 v4l（Linux 的视频）设备进行访问。