Practice using a trained Caffe model with Chainer on Google Colaboratory
A Caffe model that predicts the name of a flower from an image of the flower
#Import a library that provides access to resources by URL.
import urllib.request
#Specify resources on the web
url = 'https://s3.amazonaws.com/jgoode/oxford102.caffemodel'
#Download the resource from the specified URL and give it a name.
urllib.request.urlretrieve(url, 'oxford102.caffemodel')
('oxford102.caffemodel', <http.client.HTTPMessage at 0x7ff854b81b00>)
from chainer.links import caffe
func = caffe.CaffeFunction('oxford102.caffemodel')
/usr/local/lib/python3.6/dist-packages/chainer/links/caffe/caffe_function.py:174: UserWarning: Skip the layer "data", since CaffeFunction does not support it
'support it' % layer.name)
import urllib.request
from PIL import Image
import numpy as np
import chainer
import chainer.functions as F
filename = "whatisyourname.jpg "
def guess_flower_name(url, filename=filename):
labels = ['pink primrose', 'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea',
'english marigold', 'tiger lily', 'moon orchid', 'bird of paradise', 'monkshood',
'globe thistle', 'snapdragon', "colt's foot", 'king protea', 'spear thistle',
'yellow iris', 'globe-flower', 'purple coneflower', 'peruvian lily', 'balloon flower',
'giant white arum lily', 'fire lily', 'pincushion flower', 'fritillary', 'red ginger',
'grape hyacinth', 'corn poppy', 'prince of wales feathers', 'stemless gentian',
'artichoke', 'sweet william', 'carnation', 'garden phlox', 'love in the mist',
'mexican aster', 'alpine sea holly', 'ruby-lipped cattleya', 'cape flower',
'great masterwort', 'siam tulip', 'lenten rose', 'barbeton daisy', 'daffodil',
'sword lily', 'poinsettia', 'bolero deep blue', 'wallflower', 'marigold',
'buttercup', 'oxeye daisy', 'common dandelion', 'petunia', 'wild pansy',
'primula', 'sunflower', 'pelargonium', 'bishop of llandaff', 'gaura', 'geranium',
'orange dahlia', 'pink-yellow dahlia?', 'cautleya spicata', 'japanese anemone',
'black-eyed susan', 'silverbush', 'californian poppy', 'osteospermum',
'spring crocus', 'bearded iris', 'windflower', 'tree poppy', 'gazania', 'azalea',
'water lily', 'rose', 'thorn apple', 'morning glory', 'passion flower', 'lotus',
'toad lily', 'anthurium', 'frangipani', 'clematis', 'hibiscus', 'columbine',
'desert-rose', 'tree mallow', 'magnolia', 'cyclamen ', 'watercress', 'canna lily',
'hippeastrum ', 'bee balm', 'ball moss', 'foxglove', 'bougainvillea', 'camellia',
'mallow', 'mexican petunia', 'bromelia', 'blanket flower', 'trumpet creeper',
'blackberry lily']
urllib.request.urlretrieve(url, filename)
image = Image.open(filename).convert('RGB')
w, h = image.size
fixed_w, fixed_h = 224, 224
if w > h:
shape = (int(fixed_w * w / h), fixed_h)
else:
shape = (fixed_w, int(fixed_h * h / w))
left = (shape[0] - fixed_w) / 2
right = left + fixed_w
top = (shape[1] - fixed_h) / 2
bottom = top + fixed_h
image = image.resize(shape)
image = image.crop((left, top, right, bottom))
x_data = np.asarray(image).astype(np.float32)
x_data = x_data.transpose(2, 0, 1)
x_data = x_data[::-1, :, :]
mean_image = np.zeros(3*fixed_w*fixed_h).reshape(3, fixed_w, fixed_h).astype(np.float32)
mean_image[0] = 103
mean_image[1] = 117
mean_image[2] = 123
x_data -= mean_image
x_data = np.array([x_data])
x = chainer.Variable(x_data)
func = caffe.CaffeFunction('oxford102.caffemodel')
y, = func(inputs={'data': x}, outputs=['fc8_oxford_102'])
prob = F.softmax(y)
for i in range(5):
idx = np.argsort(prob.data[0])[::-1][i]
print(i + 1, "\t", labels[idx], "\t", prob.data[0][idx])
return image
guess_flower_name('https://d1f5hsy4d47upe.cloudfront.net/93/936a7824f1041bfef2cbe7d96c1fc7cc_t.jpeg')
/usr/local/lib/python3.6/dist-packages/chainer/links/caffe/caffe_function.py:174: UserWarning: Skip the layer "data", since CaffeFunction does not support it
'support it' % layer.name)
1 carnation 0.6862433
2 wallflower 0.075164974
3 corn poppy 0.057577945
4 hibiscus 0.037174866
5 great masterwort 0.033413775
url = "https://c8.alamy.com/comp/HMWBKR/crimson-cattleya-or-ruby-lipped-cattleya-cattleya-labiata-candida-HMWBKR.jpg "
guess_flower_name(url)
/usr/local/lib/python3.6/dist-packages/chainer/links/caffe/caffe_function.py:174: UserWarning: Skip the layer "data", since CaffeFunction does not support it
'support it' % layer.name)
1 carnation 0.56166863
2 sword lily 0.4131709
3 camellia 0.0039400724
4 bearded iris 0.003937593
5 ruby-lipped cattleya 0.003253354
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