import os
import warnings
import numpy as np
from keras import layers
from keras.preprocessing import image
from keras.layers import Input
from keras.layers import Dense,Activation,Flatten
from keras.layers import Conv2D,MaxPooling2D,AveragePooling2D,GlobalAveragePooling2D,GlobalMaxPooling2D
from keras.layers import BatchNormalization
from keras.models import Model
from keras import backend as K
from keras.engine.topology import get_source_inputs
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import decode_predictions
from keras.applications.imagenet_utils import preprocess_input
from keras.applications.imagenet_utils import _obtain_input_shape


WEIGHTS_PATH='https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5'
WEIGHTS_PATH_NO_TOP='https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'

def identity_block(input_tensor,kernel_size,filters,stage,block):
    #The identity block is the block that has no conv layer at shortcut.
    filters1,filters2,filters3=filters
    if K.image_data_format()=='channels_last':
        bn_axis=3
    else:
        bn_axis=1
    conv_name_base='res'+str(stage)+block+'_branch'
    bn_name_base='bn'+str(stage)+block+'_branch'
    
    x=Conv2D(filters1,(1,1),name=conv_name_base+'2a')(input_tensor)
    x=BatchNormalization(axis=bn_axis,name=bn_name_base+'2a')(x)
    x=Activation('relu')(x)
    
    x=Conv2D(filters2,kernel_size,padding='same',name=conv_name_base+'2b')(x)
    x=BatchNormalization(axis=bn_axis,name=bn_name_base+'2b')(x)
    x=Activation('relu')(x)
    
    x=Conv2D(filters3,(1,1),name=conv_name_base+'2c')(x)
    x=BatchNormalization(axis=bn_axis,name=bn_name_base+'2c')(x)
    
    x=layers.add([x,input_tensor])
    x=Activation('relu')(x)
    return x

def conv_block(input_tensor,kernel_size,filters,stage,block,strides=(2,2)):
    #A block that has a conv layer at shortcut.
    filters1, filters2, filters3 = filters
    if K.image_data_format() == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'
    x = Conv2D(filters1, (1, 1), strides=strides,
               name=conv_name_base + '2a')(input_tensor)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = Conv2D(filters2, kernel_size, padding='same',
               name=conv_name_base + '2b')(x)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
    
    shortcut=Conv2D(filters3,(1,1),strides=strides,name=conv_name_base+'1')(input_tensor)
    shortcut=BatchNormalization(axis=bn_axis,name=bn_name_base+'1')(shortcut)
    x=layers.add([x,shortcut])
    x=Activation('relu')(x)
    return x


def ResNet50(include_top=True, weights='imagenet',
             input_tensor=None, input_shape=None,
             pooling=None,
             classes=1000):
    
    if not (weights in {'imagenet', None} or os.path.exists(weights)):
        raise ValueError('The `weights` argument should be either '
                         '`None` (random initialization), `imagenet` '
                         '(pre-training on ImageNet), '
                         'or the path to the weights file to be loaded.')

    if weights == 'imagenet' and include_top and classes != 1000:
        raise ValueError('If using `weights` as imagenet with `include_top`'
                         ' as true, `classes` should be 1000')

    # Determine proper input shape
    input_shape = _obtain_input_shape(input_shape,
                                      default_size=224,
                                      min_size=197,
                                      data_format=K.image_data_format(),
                                      require_flatten=include_top,
                                      weights=weights)
    if input_tensor is None:
        img_input = Input(shape=input_shape)
    else:
        if not K.is_keras_tensor(input_tensor):
            img_input = Input(tensor=input_tensor, shape=input_shape)
        else:
            img_input = input_tensor
    if K.image_data_format() == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1

    x = Conv2D(
        64, (7, 7), strides=(2, 2), padding='same', name='conv1')(img_input)
    x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
    x = Activation('relu')(x)
    x = MaxPooling2D((3, 3), strides=(2, 2))(x)

    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')

    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')

    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')

    x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')

    x = AveragePooling2D((7, 7), name='avg_pool')(x)

    if include_top:
        x = Flatten()(x)
        x = Dense(classes, activation='softmax', name='fc1000')(x)
    else:
        if pooling == 'avg':
            x = GlobalAveragePooling2D()(x)
        elif pooling == 'max':
            x = GlobalMaxPooling2D()(x)

    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
        inputs = get_source_inputs(input_tensor)
    else:
        inputs = img_input
    # Create model.
    model = Model(inputs, x, name='resnet50')

    # load weights
    if weights == 'imagenet':
        if include_top:
            weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels.h5',
                                    WEIGHTS_PATH,
                                    cache_subdir='models',
                                    md5_hash='a7b3fe01876f51b976af0dea6bc144eb')
        else:
            weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
                                    WEIGHTS_PATH_NO_TOP,
                                    cache_subdir='models',
                                    md5_hash='a268eb855778b3df3c7506639542a6af')
        model.load_weights(weights_path)
        if K.backend() == 'theano':
            layer_utils.convert_all_kernels_in_model(model)
            if include_top:
                maxpool = model.get_layer(name='avg_pool')
                shape = maxpool.output_shape[1:]
                dense = model.get_layer(name='fc1000')
                layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')

        if K.image_data_format() == 'channels_first' and K.backend() == 'tensorflow':
            warnings.warn('You are using the TensorFlow backend, yet you '
                          'are using the Theano '
                          'image data format convention '
                          '(`image_data_format="channels_first"`). '
                          'For best performance, set '
                          '`image_data_format="channels_last"` in '
                          'your Keras config '
                          'at ~/.keras/keras.json.')
    elif weights is not None:
        model.load_weights(weights)

    return model

model=ResNet50(weights='imagenet')
img_path = 'F:/elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

preds = model.predict(x)
print('Predicted:', decode_predictions(preds, top=3)[0])
