def decaf_features(ImagePath):
try:
sys.path.append("/share/code/caffe/python")
from caffe import imagenet
import numpy
# Set the right path to your model file, pretrained model,
# and the image you would like to classify.
MODEL_FILE = '/share/code/caffe/examples_old/imagenet_deploy.prototxt'
PRETRAINED = '/share/code/caffe/models/caffe_reference_imagenet_model'
IMAGE_FILE = ImagePath
net = imagenet.ImageNetClassifier(MODEL_FILE, PRETRAINED)
net.caffenet.set_phase_test()
net.caffenet.set_mode_cpu()
# Compute the features
net.predict(IMAGE_FILE)
blobs = net.caffenet.blobs()
features = blobs[-3].data[:]
print features.shape
print features[1].shape, features[1]
features = numpy.resize(features, (10, 4096))
print features.shape, features[1].shape
return features
except Exception as e:
raise e
def loadNetwork(self):
modelFile = config.get('networkDir') + 'deploy.prototxt'
meanImage = config.get('meanImagePickle')
self.net = imagenet.ImageNetClassifier(
modelFile,
self.networkFile,
IMAGE_DIM=config.geti('imageSize'),
CROPPED_DIM=config.geti('cropSize'),
MEAN_IMAGE=meanImage)
self.net.caffenet.set_phase_test()
self.net.caffenet.set_mode_gpu()
self.meanImage = self.net._IMAGENET_MEAN.swapaxes(1, 2).swapaxes(
0, 1).astype('float32')
def caffe_classify_image(ImagePath):
import sys
sys.path.append("/share/code/caffe/python")
import scipy.io as sio
from caffe import imagenet
results = {}
matWNID = sio.loadmat(
'/var/www/html/cloudcv/fileupload/executable/WNID.mat')
WNID_cells = matWNID['wordsortWNID']
# Set the right path to your model file, pretrained model,
# and the image you would like to classify.
MODEL_FILE = '/share/code/caffe/examples_old/imagenet_deploy.prototxt'
PRETRAINED = '/share/code/caffe/models/caffe_reference_imagenet_model'
numoutputs = 1000
net = imagenet.ImageNetClassifier(MODEL_FILE, PRETRAINED, False,
numoutputs)
# Predict the image classes, then return the top 5 indicies
net.caffenet.set_phase_test()
net.caffenet.set_mode_cpu()
print('Making predictions...')
if os.path.isfile(ImagePath):
prediction = net.predict(ImagePath)
print('Computed scores')
map = {}
for i, j in enumerate(prediction):
map[i] = j
print('Getting top five tags...')
predsorted = sorted(map.iteritems(),
key=operator.itemgetter(1),
reverse=True)
top5 = predsorted[0:5]
topresults = {}
m = 0
n = 1
for i in top5:
topresults[str(WNID_cells[i, 0][0][0])] = str(i[1])
results[ImagePath] = topresults
return results
def decaf_features_centre(ImagePath):
import sys
import numpy
import scipy.io as io
sys.path.append("/share/code/caffe/python")
from caffe import imagenet
# Set the right path to your model file, pretrained model,
# and the image you would like to classify.
MODEL_FILE = '/share/code/caffe/examples_old/imagenet_deploy.prototxt'
PRETRAINED = '/share/code/caffe/models/caffe_reference_imagenet_model'
IMAGE_FILE = ImagePath
net = imagenet.ImageNetClassifier(MODEL_FILE, PRETRAINED)
net.caffenet.set_phase_test()
net.caffenet.set_mode_cpu()
# Compute the features
net.predict(IMAGE_FILE)
blobs = net.caffenet.blobs()
features = blobs[-3].data[4]
features = numpy.resize(features, (1, 4096))
return features
##################################
# Parameter checking
#################################
if len(sys.argv) < 3:
print 'Use: extractCNNFeatures.py bboxes imgsDir outputDir'
sys.exit()
bboxes = [(x, x.split()) for x in open(sys.argv[1])]
imgsDir = sys.argv[2]
outDir = sys.argv[3]
from caffe import imagenet
MODEL_FILE = '/home/caicedo/software/caffe-master/examples/imagenet_deploy.prototxt'
PRETRAINED = '/home/caicedo/Downloads/caffe_reference_imagenet_model'
net = imagenet.ImageNetClassifier(MODEL_FILE, PRETRAINED)
net.caffenet.set_phase_test()
net.caffenet.set_mode_gpu()
# From: https://github.com/zachrahan/scikits-image/blob/master/skimage/io/tests/test_freeimage.py?source=cc
try:
import skimage.io._plugins.freeimage_plugin as fi
FI_available = True
io.use_plugin('freeimage')
except RuntimeError:
FI_available = False
##################################
# Functions
#################################
<title>Upload image file</title>
<h1>Upload image</h1>
<form action="" method=post enctype=multipart/form-data>
<p><input type=file name=image>
<input type=submit value=Upload>
</form>
'''
if __name__ == '__main__':
''' We provide access to two trained models:
1. Lenet trained with MNISTClassifier for handwritten digit recognition
2. Imagenet for object recognition
Both models are loaded before the web server. '''
# Set the path to your trained models
trained_models_path = '/home/allan/Git/caffe/examples'
net_lenet = lenet.MNISTClassifier(os.path.join(trained_models_path, 'lenet/lenet.prototxt'), \
os.path.join(trained_models_path, 'lenet/lenet_iter_10000'))
net_lenet.caffenet.set_phase_test()
net_lenet.caffenet.set_mode_gpu()
net_imagenet = imagenet.ImageNetClassifier(os.path.join(trained_models_path, 'imagenet/imagenet_deploy.prototxt'), \
os.path.join(trained_models_path, 'imagenet/caffe_reference_imagenet_model'))
net_imagenet.caffenet.set_phase_test()
net_imagenet.caffenet.set_mode_cpu()
app.debug = True
app.run(host='0.0.0.0')
from caffe import imagenet
#MODEL_FILE = '/u/sciteam/caicedor/models/imagenet_deploy.prototxt'
MODEL_FILE = '/home/caicedo/workspace/caffe/examples/imagenet/alexnet_deploy.prototxt.200'
#PRETRAINED = '/u/sciteam/caicedor/models/finetune_voc_2012_train_iter_70k'
#PRETRAINED = '/home/caicedo/software/rcnn-master/data/caffe_nets/finetune_voc_2012_train_iter_70k'
PRETRAINED = '/home/caicedo/software/rcnn-master/data/caffe_nets/ilsvrc_2012_train_iter_310k.original'
IMG_DIM = 256
CROP_SIZE = 227
CONTEXT_PAD = 0
#meanImage = '/u/sciteam/caicedor/scratch/caffe/python/caffe/imagenet/ilsvrc_2012_mean.npy'
meanImage = '/home/caicedo/workspace/caffe/python/caffe/imagenet/ilsvrc_2012_mean.npy'
net = imagenet.ImageNetClassifier(MODEL_FILE,
PRETRAINED,
IMAGE_DIM=IMG_DIM,
CROPPED_DIM=CROP_SIZE,
MEAN_IMAGE=meanImage)
net.caffenet.set_phase_test()
net.caffenet.set_mode_gpu()
ImageNetMean = net._IMAGENET_MEAN.swapaxes(1, 2).swapaxes(0,
1).astype('float32')
##################################
# Functions
#################################
def getWindow(img, box):
dx = int(float(box[2] - box[0]) * 0.10)