程序员深夜用Python跑神经网络，只为用中二动作关掉台灯！

# create lists for dab, tpose, other examplesdabs = []tposes = []other = []fps_time = 0# loop forever, reading webcam each timewhile True:    ret_val, frame = vs.read()    datum.cvInputData = frame    opWrapper.emplaceAndPop([datum])    # need to be able to see what's going on    image = datum.cvOutputData    cv2.putText(image,                "FPS: %f" % (1.0 / (time.time() - fps_time)),                (10, 10),  cv2.FONT_HERSHEY_SIMPLEX, 0.5,                (0, 255, 0), 2)        cv2.imshow("Openpose", image)    fps_time = time.time()        # quit with a q keypress, b or m to save data    key = cv2.waitKey(1) & 0xFF    if key == ord("q"):        break    elif key == ord("b"):        print("Dab: " + str(datum.poseKeypoints))        dabs.append(datum.poseKeypoints[0])    elif key == ord("m"):        print("TPose: " + str(datum.poseKeypoints))        tposes.append(datum.poseKeypoints[0])    elif key == ord("/"):        print("Other: " + str(datum.poseKeypoints))        other.append(datum.poseKeypoints[0])# write our data as numpy binary files# for analysis laterdabs = np.asarray(dabs)tposes = np.asarray(tposes)other = np.asarray(other)np.save('dabs.npy', dabs)np.save('tposes.npy', tposes)np.save('other.npy', other)

labels = np.zeros(len(otherDataset))
labels = np.append(labels, np.full((len(dabDataset)), 1))
labels = np.append(labels, np.full((len(tposeDataset)), 2))
print(labels)
print("%i total examples for training." % len(labels))

# now, let's shuffle labels and the array, the same way
from sklearn.utils import shuffle
X1, y1 = shuffle(dataset, labels)
# now let's label them for 'one hot'
from keras.utils.np_utils import to_categorical
y1 = to_categorical(y1, 3)  # we have 3 categories, dab, tpose, other
print(y1.shape[1)]

X1[:,:,0] = X1[:,:,0] / 720 # I think the dimensions are 1280 x 720 ?X1[:,:,1] = X1[:,:,1] / 1280  # let's see?X1 = X1[:,:,1:]print(X1.shape)X1 = X1.reshape(len(X1), 50)      # we got rid of confidence percentage

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.optimizers import SGD

model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(50,)))
model.add(Dropout(0.5))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(y1.shape[1], activation='softmax'))
model.compile(optimizer='Adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit(X1, y1, epochs=2000,batch_size=25)

model.save('data/dab-tpose-other.h5') # save our model as h5


# in our other code, or inline, load the model and test against sample dab dataset
import keras
modello = keras.models.load_model('data/dab-tpose-other.h5')
dabDataset = np.load('data/test-dabs.npy')
dabDataset[:,:,0] = dabDataset[:,:,0] / 720 # I think the dimensions are 1280 x 720 ?
dabDataset[:,:,1] = dabDataset[:,:,1] / 1280  # let's see?
dabDataset = dabDataset[:,:,1:]
dabDataset = dabDataset.reshape(len(dabDataset), 50)
modello.predict_classes(dabDataset) # returns array([1, 1, 1, 1, 1, 1])

import cv2import pyopenpose as opfrom imutils import translate, rotate, resizeimport openzwavefrom openzwave.option import ZWaveOptionfrom openzwave.network import ZWaveNetwork# make sure these commands get flushed by doing them first, then loading tensorflow...# tensorflow should take enough time to start for these commands to flushoptions = ZWaveOption('/dev/ttyACM0')options.lock()network = ZWaveNetwork(options)import timeimport numpy as npnp.random.seed(1337)import tensorflow as tf# make sure tensorflow doesn't take up all the gpu memoryconf = tf.ConfigProto()conf.gpu_options.allow_growth=Truesession = tf.Session(config=conf)import keras# Custom Params (refer to include/openpose/flags.hpp for more parameters)params = dict()params["model_folder"] = "../../models/"# built in TX2 video capture sourcevs = cv2.VideoCapture("nvarguscamerasrc ! video/x-raw(memory:NVMM), width=(int)1280, height=(int)720,format=(string)NV12, framerate=(fraction)24/1 ! nvvidconv flip-method=0 ! video/x-raw, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink")tposer = keras.models.load_model('dab-tpose-other.h5')# Starting OpenPoseopWrapper = op.WrapperPython()opWrapper.configure(params)opWrapper.start()datum = op.Datum()np.set_printoptions(precision=4)fps_time = 0DAB = 1TPOSE = 2OTHER = 0LIGHTS = 0bounced = time.time()debounce = 3 # wait 3 seconds before allowing another commandwhile True:    ret_val, frame = vs.read()    datum.cvInputData = frame    opWrapper.emplaceAndPop([datum])    # need to be able to see what's going on    image = datum.cvOutputData    cv2.putText(image,                "FPS: %f" % (1.0 / (time.time() - fps_time)),                (10, 20),  cv2.FONT_HERSHEY_SIMPLEX, 0.5,                (0, 255, 0), 2)        cv2.imshow("Openpose", image)    if datum.poseKeypoints.any():        first_input = datum.poseKeypoints        try:            first_input[:,:,0] = first_input[:,:,0] / 720            first_input[:,:,1] = first_input[:,:,1] / 1280            first_input = first_input[:,:,1:]            first_input = first_input.reshape(len(datum.poseKeypoints), 50)        except:            continue        output = tposer.predict_classes(first_input)        for j in output:            if j == 1:                print("dab detected")                if LIGHTS == 0 or (time.time() - bounced) < debounce:                    continue                for node in network.nodes:                    for val in network.nodes[node].get_switches():                        network.nodes[node].set_switch(val, False)                LIGHTS = 0                bounced = time.time()            elif j == 2:                print("tpose detected")                if LIGHTS == 1 or (time.time() - bounced) < debounce:                    continue                for node in network.nodes:                    for val in network.nodes[node].get_switches():                        network.nodes[node].set_switch(val, True)                LIGHTS = 1                bounced = time.time()    fps_time = time.time()        # quit with a q keypress, b or m to save data    key = cv2.waitKey(1) & 0xFF    if key == ord("q"):        break# clean up after yourselfvs.release()cv2.destroyAllWindows()