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from __future__ import division, print_function | |
from pprint import pprint | |
import glob | |
import caffe | |
MODEL_FILE = '../models/lenet.prototxt' | |
PRETRAINED_FILE = '../models/lenet_pretrained.caffemodel' | |
IMAGE_DIR = 'test' | |
caffe.set_mode_cpu() | |
classifier = caffe.Classifier(MODEL_FILE, PRETRAINED_FILE, | |
image_dims=(28, 28), # this can be changed to enable autocropping | |
input_scale=255.0 # images are loaded as float arrays with values in 0.0..1.0 | |
) | |
print('\n' + '*' * 16 + ' end of Caffe log ' + '*' * 16 + '\n') | |
input_paths = glob.glob('%s/*.%s' % (IMAGE_DIR, 'png')) | |
input_paths.sort() | |
pprint(input_paths) | |
# Caffe loads images with 3 color channels by default | |
# actually, under the hood it calls a routine from skimage, | |
# so this can be safely replaced with another implementation | |
# ref: https://github.com/BVLC/caffe/blob/master/python/caffe/io.py#L274 | |
inputs = [caffe.io.load_image(im_f, color=False) for im_f in input_paths] | |
# oversampling means generating many crops and mirrored crops | |
# and running the net on them, which is useful for real-life photos | |
scores = classifier.predict(inputs, oversample=False) | |
print(scores) |
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