I use Raspbery Pi B+ 2. I have a Python program that uses the ultrasonic sensor to measure the distance to an object. What I would like to is change the volume depending on the distance to a human. Having a Python code to obtain the distance, I have no idea, how can I change the Raspbery Pi volume by a code in Python.
Is there any way to do that?
You can use the package python-alsaaudio.
The installation and usage is very simple.
To install run:
sudo apt-get install python-alsaaudio
In your Python script, import the module:
import alsaaudio
Now, you need to get the main mixer and get/set the volume:
m = alsaaudio.Mixer()
current_volume = m.getvolume() # Get the current Volume
m.setvolume(70) # Set the volume to 70%.
If the line m = alsaaudio.Mixer() throws an error, then try:
m = alsaaudio.Mixer('PCM')
this might happen because the Pi uses PCM rather than a Master channel.
You can see more information about your Pi's audio channels, volume (etc..), by running the command amixer.
Collecting actual mixers available (will provide a list of available cards):
import alsaaudio as audio
scanCards = audio.cards()
print("cards:", scanCards)
In my case I've got following list:
[u'PCH', u'headset']
Scanning for the mixers per each card:
for card in scanCards:
scanMixers = audio.mixers(scanCards.index(card))
print("mixers:", scanMixers)
In my case I've got the following two lists:
[u'Master', u'Headphone', u'Speaker', u'PCM', u'Mic', u'Mic Boost', u'IEC958', u'IEC958', u'IEC958', u'IEC958', u'IEC958', u'Beep', u'Capture', u'Auto-Mute Mode', u'Internal Mic Boost', u'Loopback Mixing']
[u'Headphone', u'Mic', u'Auto Gain Control']
As you may see, "Master" is not always the mixer available, but traditionally expected an equivalent of the Master mixer to be at index 0. (Doesn't mean always!)
To control volume in this case for the USB Headset would be following procedure.
Volume Up
def volumeMasterUP():
mixer = audio.Mixer('Headphone', cardindex=1)
volume = mixer.getvolume()
newVolume = int(volume[0])+10
if newVolume <= 100:
mixer.setvolume(newVolume)
Volume Down
def volumeMasterDOWN():
mixer = audio.Mixer('Headphone', cardindex=1)
volume = mixer.getvolume()
newVolume = int(volume[0])-10
if newVolume >= 0:
mixer.setvolume(newVolume)
I made a simple python service for a two button volume control. Based on what #ant0nisk put.
https://gist.github.com/peteristhegreat/3c94963d5b3a876b27accf86d0a7f7c0
It shows getting and setting the volume, and muting.
Related
Background
I want to predict pathology images using keras with Inception-Resnet_v2. I have trained the model already and got a .hdf5 file. Because the pathology image is very large (for example: 20,000 x 20,000 pixels), so I have to scan the image to get small patches for prediction.
I want to speed up the prediction procedure using multiprocessing lib with python2.7. The main idea is using different subprocesses to scan different lines and then sending patches to model.
I saw somebody suggests importing keras and loading model in subprocesses. But I don't think it is suitable for my task. Loading model usingkeras.models.load_model() one time will take about 47s, which is very time-consuming. So I can't reload the model every time when I start a new subprocess.
Question
My question is can I load the model in my main process and pass it as a parameter to subprocesses?
I have tried two methods but both of them didn't work.
Method 1. Using multiprocessing.Pool
The code is :
import keras
from keras.models import load_model
import multiprocessing
def predict(num,model):
print dir(model)
print num
model.predict("image data, type:list")
if __name__ == '__main__':
model = load_model("path of hdf5 file")
list = [(1,model),(2,model),(3,model),(4,model),(5,model),(6,model)]
pool = multiprocessing.Pool(4)
pool.map(predict,list)
pool.close()
pool.join()
The output is
cPickle.PicklingError: Can't pickle <type 'module'>: attribute lookup __builtin__.module failed
I searched the error and found Pool can't map unpickelable parameters, so I try method 2.
Method 2. Using multiprocessing.Process
The code is
import keras
from keras.models import load_model
import multiprocessing
def predict(num,model):
print num
print dir(model)
model.predict("image data, type:list")
if __name__ == '__main__':
model = load_model("path of hdf5 file")
list = [(1,model),(2,model),(3,model),(4,model),(5,model),(6,model)]
proc = []
for i in range(4):
proc.append(multiprocessing.Process(predict, list[i]))
proc[i].start()
for i in range(4):
proc[i].join()
In Method 2, I can print dir(model). I think it means the model is passed to subprocesses successfully. But I got this error
E tensorflow/stream_executor/cuda/cuda_driver.cc:1296] failed to enqueue async memcpy from host to device: CUDA_ERROR_NOT_INITIALIZED; GPU dst: 0x13350b2200; host src: 0x2049e2400; size: 4=0x4
The environment which I use:
Ubuntu 16.04, python 2.7
keras 2.0.8 (tensorflow backend)
one Titan X, Driver version 384.98, CUDA 8.0
Looking forward to reply! Thanks!
Maybe you can use apply_async() instead of Pool()
and you can find more details here:
Python multiprocessing pickling error
Multi-processing works on CPU, while model prediction happened in GPU, which there is only one. I cannot see how multi-processing can help you on prediction.
Instead, I think you can use multi-processing to scan different patches, which you seems to have already managed to achieve. Then stack these patches into a batch or batches to predict in parallel in GPU.
As noted by Statham multiprocess requires all args to be compatible with pickle. This blog post describes how to save a keras model as a pickle: [http://zachmoshe.com/2017/04/03/pickling-keras-models.html][1]
It may be a sufficient workaround to get your keras model passed as an arg to multiprocess, but I have not tested the idea myself.
I will also add that I had better luck running two keras processes on a single gpu using windows rather than linux. On linux I was getting out of memory errors on the 2nd process, but the same memory allocation (45% of total GPU ram for each) worked on windows. In my case they were fits - for running predictions only, maybe the memory requirements are less.
I am running an lstm net on ec2 p2.8xlarge. Of course I'd like to take advantage of all the gpus available(8). I an run it on one gpu easily, but not more. I get the following error when calling "multi_gpu_model":
"To call multi_gpu_model with gpus=8, we expect the following devices to be available: ['/cpu:0', '/gpu:0', '/gpu:1', '/gpu:2', '/gpu:3', '/gpu:4', '/gpu:5', '/gpu:6', '/gpu:7']. However this machine only has: ['/cpu:0']. Try reducing gpus."
When I type nvidia-smi, all 8 gpus show up in terminal. How can I add these to my tf (keras) environment?
when I run device_lib.list_local_devices() in jupyter notebook it returns only CPU0 when it should return 8 GPUs too. Here is the relevant bit of code:
model = Sequential()
model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2])))
model.add(Dropout(0.2))
model.add(Dense(y.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
# define the checkpoint
filepath="weights-improvement-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
# fit the model
model=multi_gpu_model(model, gpus=8)
model.fit(X, y, epochs=20, batch_size=128, callbacks=callbacks_list)
I am programming in python 2.7 with NLTK library for both text prepossessing and classification in sentiment analysis. I am using nltk wrapper of scikit-learn algorithms. bellow code is after prepossessing and separation to train and test sets.
from nltk.classify.scikitlearn import SklearnClassifier
from sklearn.svm import SVC, LinearSVC, NuSVC
training_set = nltk.classify.util.apply_features(extractFeatures, trainTweets)
testing_set = nltk.classify.util.apply_features(extractFeatures, testTweets)
#LinearSVC
LinearSVC_classifier = SklearnClassifier(LinearSVC())
LinearSVC_classifier.train(training_set)
LinearSVCAccuracy = nltk.classify.accuracy(LinearSVC_classifier, testing_set)*100
print "LinearSVC accuracy percentage:" + str(LinearSVCAccuracy)
it works fine when the number of rows are like 4000 tweets for training, but when it increases to for example 10000 tweets, possess is getting killed with following error.
Memory cgroup out of memory: Kill process 24293 (python) score 848 or
sacrifice child
Killed process 24293, UID 29091, (python) total-vm:14569168kB,
anon-rss:14206656kB, file-rss:3412kB
Clocksource tsc unstable (delta = -17179861691 ns). Enable clocksource
failover by adding clocksource_failover kernel parameter.
RAM of my pc is 8 Gig but I even tried with 16 Gig RAM and still has problem. How can I Classify this amount for tweets without any problem?
Which OS are you running? Which python distribution? Try to install cython and/or using scikit-learn directly. Have a look at scikit-learn optimization techniques
I'm using the Python Speech Recognition library to recognize speech input from the microphone.
This works fine with my default microphone.
This is the code I'm using. According to what I understood of the documentation
Creates a new Microphone instance, which represents a physical
microphone on the computer. Subclass of AudioSource.
If device_index is unspecified or None, the default microphone is used
as the audio source. Otherwise, device_index should be the index of
the device to use for audio input. https://pypi.python.org/pypi/SpeechRecognition/
The problem is that when I want to get the node with pyaudio.get_device_count() - 1. I'm getting this error.
AttributeError: 'module' object has no attribute 'get_device_count'
So I'm not sure how to configure the microphone to use a usb microphone
import pyaudio
import speech_recognition as sr
index = pyaudio.get_device_count() - 1
print index
r = sr.Recognizer()
with sr.Microphone(index) as source:
audio = r.listen(source)
try:
print("You said " + r.recognize(audio))
except LookupError:
print("Could not understand audio")
myPyAudio=pyaudio.PyAudio()
print "Seeing pyaudio devices:",myPyAudio.get_device_count()
That's a bug in the library. I just pushed out a fix in 1.3.1, so this should now be fixed!
Version 1.3.1 retains full backwards compatibility with previous versions.
All,
I'm attempting to use Python and DAQmx Base to record analog input and generate analog output from my USB 6009 device. I've been using a wrapper I found and have been able to get AI but am struggling with AO.
There is a base class NITask which handles task generation etc. The class i'm calling is below. The function throws an error when I try to configure the clock. When I do not there is no error but nor is there voltage generated on the output. Any help would be appreciated.
Thanks!
class AOTask(NITask):
def __init__(self, min=0.0, max=5.0,
channels=["Dev1/ao0"],
timeout=10.0):
NITask.__init__(self)
self.min = min
self.max = max
self.channels = channels
self.timeout = timeout
self.clockSource ="OnboardClock"
sampleRate=100
self.sampleRate = 100
self.timeout = timeout
self.samplesPerChan = 1000
self.numChan = chanNumber(channels)
if self.numChan is None:
raise ValueError("Channel specification is invalid")
chan = ", ".join(self.channels)
self.CHK(self.nidaq.DAQmxBaseCreateTask("",ctypes.byref(self.taskHandle)))
self.CHK(self.nidaq.DAQmxBaseCreateAOVoltageChan(self.taskHandle, "Dev1/ao0", "", float64(self.min), float64(self.max), DAQmx_Val_Volts, None))
self.CHK(self.nidaq.DAQmxBaseCfgSampClkTiming(self.taskHandle, "", float64(self.sampleRate), DAQmx_Val_Rising, DAQmx_Val_FiniteSamps, uInt64(self.samplesPerChan)))
"""Data needs to be of type ndarray"""
def write(self, data):
nWritten = int32()
# data = numpy.float64(3.25)
data = data.astype(numpy.float64)
self.CHK(self.nidaq.DAQmxBaseWriteAnalogF64(self.taskHandle,
int32(1000), 0,float64(-1),DAQmx_Val_GroupByChannel,
data.ctypes.data,None,None))
# if nWritten.value != self.numChan:
# print "Expected to write %d samples!" % self.numChan
Your question covers two problems:
Why does DAQmxBaseCfgSampClkTiming return an error?
Without using that function, why isn't any output generated?
1. Hardware vs Software Timing
rjb3 wrote:
The function throws an error when I try to configure the clock. When I do not there is no error but nor is there voltage generated on the output.
Your program receives the error because the USB 600x devices do not support hardware-timed analog output [1]:
The NI USB-6008/6009 has two independent analog output channels that can generate outputs from 0 to 5 V. All updates of analog output channels are software-timed. GND is the ground-reference signal for the analog output channels.
"Software-timed" means a sample is written on demand by the program whenever DAQmxBaseWriteAnalogF64 is called. If an array of samples is written, then that array is written one at a time. You can learn more about how NI defines timing from the DAQmx help [2]. While that document is for DAQmx, the same concepts apply to DAQmx Base since the behavior is defined by the devices and not their drivers. The differences are in how much of the hardware's capabilities are implemented by the driver -- DAQmx implements everything, while DAQmx Base is a small select subset.
2. No Output When Software Timed
rjb3 wrote:
When I do not there is no error but nor is there voltage generated on the output.
I am not familiar with the Python bindings for the DAQmx Base API, but I can recommend two things:
Try using the installed genVoltage.c C example and confirm that you can see voltage on the ao channel.
Examples are installed here: /Applications/National Instruments/NI-DAQmx Base/examples
If you see output, you've confirmed that the device and driver are working correctly, and that the bug is likely in the python file.
If you don't see output, then the device or driver has a problem, and the best place to get help troubleshooting is the NI discussion forums at http://forums.ni.com.
Try porting genVoltage.c using the python bindings. At first glance, I would try:
Use DAQmxBaseStartTask before DAQmxBaseWriteAnalogF64
Or set the autostart parameter in your call to DAQmxBaseWriteAnalogF64 to true.
References
[1] NI USB-6008/6009 User Guide And Specifications :: Analog Output (page 16)
http://digital.ni.com/manuals.nsf/websearch/CE26701AA052E1F0862579AD0053BE19
[2] Timing, Hardware Versus Software
http://zone.ni.com/reference/en-XX/help/370466V-01/TOC11.htm