gesture.py

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.optimizers import SGD,RMSprop,adam
from keras.utils import np_utils
from keras import backend as K
K.set_image_dim_ordering('th')
import numpy as np
import os
import theano
from PIL import Image
import json
import cv2
img_rows, img_cols = 200, 200
img_channels = 1
batch_size = 32
nb_classes = 5
nb_epoch = 15  #25
nb_filters = 32
nb_pool = 2
nb_conv = 3

path = "./"
path1 = "./gestures"   
path2 = './imgfolder_b'

WeightFileName = ["ori_4015imgs_weights.hdf5","bw_4015imgs_weights.hdf5","bw_2510imgs_weights.hdf5","./bw_weight.hdf5","./final_c_weights.hdf5","./semiVgg_1_weights.hdf5","/new_wt_dropout20.hdf5","./weights-CNN-gesture_skinmask.hdf5"]


output = ["OK", "NOTHING","PEACE", "PUNCH", "STOP"]

def convertToGrayImg(path1, path2):
    listing = os.listdir(path1)
    for file in listing:
        if file.startswith('.'):
            continue
        img = Image.open(path1 +'/' + file)
 
        grayimg = img.convert('L')
        grayimg.save(path2 + '/' +  file, "PNG")

def modlistdir(path):
    listing = os.listdir(path)
    retlist = []
    for name in listing:

        if name.startswith('.'):
            continue
        retlist.append(name)
    return retlist

def loadCNN(wf_index):
    global get_output
    model = Sequential()
    
    
    model.add(Conv2D(nb_filters, (nb_conv, nb_conv),
                        padding='valid',
                        input_shape=(img_channels, img_rows, img_cols)))
    convout1 = Activation('relu')
    model.add(convout1)
    model.add(Conv2D(nb_filters, (nb_conv, nb_conv)))
    convout2 = Activation('relu')
    model.add(convout2)
    model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(128))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])
    model.summary()
    model.get_config()

    

    if wf_index >= 0:
      
        fname = WeightFileName[int(wf_index)]
        print "loading ", fname
        model.load_weights(fname)
    
    layer = model.layers[11]
    get_output = K.function([model.layers[0].input, K.learning_phase()], [layer.output,])
    
    
    return model

# This function does the guessing work based on input images
def guessGesture(model, img):
    global output, get_output
    image = np.array(img).flatten()
    image = image.reshape(img_channels, img_rows,img_cols)
    image = image.astype('float32') 
    image = image / 255
    rimage = image.reshape(1, img_channels, img_rows, img_cols)   
    prob_array = get_output([rimage, 0])[0]
    
    d = {}
    i = 0
    for items in output:
        d[items] = prob_array[0][i] * 100
        i += 1
    import operator
    
    guess = max(d.iteritems(), key=operator.itemgetter(1))[0]
    prob  = d[guess]

    if prob > 70.0:       
        with open('gesturejson.txt', 'w') as outfile:
            json.dump(d, outfile)
        return output.index(guess)

    else:
        return 1