#!/usr/bin/env python
# coding: utf-8

# # Predict with pre-trained models
# 
# This is a demo for predicting with a pre-trained model on the full imagenet dataset, which contains over 10 million images and 10 thousands classes. For a more detailed explanation, please refer to [predict.ipynb](https://github.com/dmlc/mxnet-notebooks/blob/master/python/how_to/predict.ipynb).
# 
# We first load the pre-trained model.

# In[1]:


import os, urllib
import mxnet as mx
def download(url,prefix=''):
    filename = prefix+url.split("/")[-1]
    if not os.path.exists(filename):
        urllib.urlretrieve(url, filename)

path='http://data.mxnet.io/models/imagenet-11k/'
download(path+'resnet-152/resnet-152-symbol.json', 'full-')
download(path+'resnet-152/resnet-152-0000.params', 'full-')
download(path+'synset.txt', 'full-')

with open('full-synset.txt', 'r') as f:
    synsets = [l.rstrip() for l in f]

sym, arg_params, aux_params = mx.model.load_checkpoint('full-resnet-152', 0)


# Create a model for this model on GPU 0.

# In[2]:


mod = mx.mod.Module(symbol=sym, label_names=None, context=mx.gpu())
mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))], label_shapes=mod._label_shapes)
mod.set_params(arg_params, aux_params, allow_missing=True)


# Next we define the function to obtain an image by a given URL and the function for predicting.

# In[3]:


get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib
matplotlib.rc("savefig", dpi=100)
import matplotlib.pyplot as plt
import cv2
import numpy as np
from collections import namedtuple
Batch = namedtuple('Batch', ['data'])

def get_image(url, show=True):
    filename = url.split("/")[-1]
    urllib.urlretrieve(url, filename)
    img = cv2.imread(filename)
    if img is None:
        print('failed to download ' + url)
    if show:
        plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
        plt.axis('off')
    return filename

def predict(filename, mod, synsets):
    img = cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2RGB)
    if img is None:
        return None
    img = cv2.resize(img, (224, 224))
    img = np.swapaxes(img, 0, 2)
    img = np.swapaxes(img, 1, 2) 
    img = img[np.newaxis, :] 
    
    mod.forward(Batch(data=[mx.nd.array(img)]))
    prob = mod.get_outputs()[0].asnumpy()
    prob = np.squeeze(prob)

    a = np.argsort(prob)[::-1]    
    for i in a[0:5]:
        print('probability=%f, class=%s' %(prob[i], synsets[i]))
    


# We are able to classify an image and output the top predicted classes. 

# In[4]:


url = 'http://writm.com/wp-content/uploads/2016/08/Cat-hd-wallpapers.jpg'
predict(get_image(url), mod, synsets)


# In[5]:


url = 'https://images-na.ssl-images-amazon.com/images/G/01/img15/pet-products/small-tiles/23695_pets_vertical_store_dogs_small_tile_8._CB312176604_.jpg'
predict(get_image(url), mod, synsets)