Labellerr_Multilabel_classification_Keras_model/predict_function.py at master · saisubramani/Labellerr_Multilabel_classification_Keras_model · GitHub

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from keras.models import Sequential
from keras_preprocessing.image import ImageDataGenerator
from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization
from keras.layers import Conv2D, MaxPooling2D
from keras import regularizers, optimizers
from PIL import Image
import numpy as np
from skimage import transform
from itertools import compress

def get_model():
    model = Sequential()
    model.add(Conv2D(32, (3, 3), padding='same',input_shape=(100,100,3)))
    model.add(Activation('relu'))
    model.add(Conv2D(32, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Conv2D(64, (3, 3), padding='same'))
    model.add(Activation('relu'))
    model.add(Conv2D(64, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(40, activation='sigmoid'))
    model.compile(optimizers.rmsprop(lr=0.0001, decay=1e-6),loss="binary_crossentropy",metrics=["accuracy"])
    return model


def load(filename):
    np_image = Image.open(filename)
    np_image = np.array(np_image).astype('float32')/255
    np_image = transform.resize(np_image, (100, 100, 3))
    np_image = np.expand_dims(np_image, axis=0)
    return np_image

def predict_handler(event,context):
    url = event['url']
    img=requests.get(url, stream=True).raw
    image = load(img)
    pred = model.predict(image)
    pred_bool = (pred >0.5)
    print(pred_bool)
    Labels = ['5_o_Clock_Shadow','Arched_Eyebrows','Attractive','Bags_Under_Eyes','Bald','Bangs','Big_Lips','Big_Nose','Black_Hair','Blond_Hair','Blurry','Brown_Hair','Bushy_Eyebrows','Chubby','Double_Chin','Eyeglasses','Goatee','Gray_Hair','Heavy_Makeup','High_Cheekbones','Male','Mouth_Slightly_Open','Mustache','Narrow_Eyes','No_Beard','Oval_Face','Pale_Skin','Pointy_Nose','Receding_Hairline','Rosy_Cheeks','Sideburns','Smiling','Straight_Hair','Wavy_Hair','Wearing_Earrings','Wearing_Hat','Wearing_Lipstick','Wearing_Necklace','Wearing_Necktie','Young']
    output = list(compress(Labels, pred_bool[0]))
    return output

print("INITIALIZING......")
model = get_model()
model.load_weights("multilabel.h5")
path = str(input("Enter the Image Path"))
print("Path : ",path)
image = load(path)
pred = model.predict(image)
pred_bool = (pred >0.5)
print(pred_bool)
Labels = ['5_o_Clock_Shadow','Arched_Eyebrows','Attractive','Bags_Under_Eyes','Bald','Bangs','Big_Lips','Big_Nose','Black_Hair','Blond_Hair','Blurry','Brown_Hair','Bushy_Eyebrows','Chubby','Double_Chin','Eyeglasses','Goatee','Gray_Hair','Heavy_Makeup','High_Cheekbones','Male','Mouth_Slightly_Open','Mustache','Narrow_Eyes','No_Beard','Oval_Face','Pale_Skin','Pointy_Nose','Receding_Hairline','Rosy_Cheeks','Sideburns','Smiling','Straight_Hair','Wavy_Hair','Wearing_Earrings','Wearing_Hat','Wearing_Lipstick','Wearing_Necklace','Wearing_Necktie','Young']
output = list(compress(Labels, pred_bool[0]))
print("Output : ",output)