![]() You can download the pretrained Caffe model files in here: Drive. Used net surgery to duplicate the Inception Network and copy its pretrained weights to its Siamese version. Network was trained using categories coming from the ILSVRC 2012 challange. ![]() This is an implementation of the Inception V1 GoogleNet Siamese Neural Network (inspired by the work of Koch et al.). Caffe implementation of the Siamese Neural Network for image data Requirements: ![]() loadToNCHW ( img, mean, input_size ) # submit the image to net and get a tensor of results results = p. ![]() SerializeToString ()) # use whatever image you want (urls work too) img = "images/flower.jpg" # average mean to subtract from the image mean = 128 # the size of images that the model was trained with input_size = 227 # use the image helper to load the image and convert it to NCHW img = helpers. predict_net # you must name it something predict_net. download - i squeezenet # load up the caffe2 workspace from caffe2.python import workspace # choose your model here (use the downloader first) from import squeezenet as mynet # helper image processing functions import helpers # load the pre-trained model init_net = mynet. shape ) + " in HWC" ) return imgScaled 加载均值 resize ( img, ( input_height, input_width )) print ( "New image shape:" + str ( imgScaled. resize ( img, ( res, input_width )) if ( aspect = 1 ): imgScaled = skimage. resize ( img, ( input_height, res )) if ( aspect < 1 ): # portrait orientation - tall image res = int ( input_width / aspect ) imgScaled = skimage. shape ) print ( "Orginal aspect ratio: " + str ( aspect )) if ( aspect > 1 ): # landscape orientation - wide image res = int ( aspect * input_height ) imgScaled = skimage. shape ) + " and remember it should be in H, W, C!" ) print ( "Model's input shape is %d x %d " ) % ( input_height, input_width ) aspect = img. shape startx = x // 2 - ( cropx // 2 ) starty = y // 2 - ( cropy // 2 ) return img def rescale ( img, input_height, input_width ): print ( "Original image shape:" + str ( img. # The list of output codes for the AlexNet models (squeezenet) codes = "" print "Config set!" crop_center和rescale函数ĭef crop_center ( img, cropx, cropy ): y, x, c = img. # format below is the model's: # folder, INIT_NET, predict_net, mean, input image size # you can switch squeezenet out with 'bvlc_alexnet', 'bvlc_googlenet' or others that you have downloaded # if you have a mean file, place it in the same dir as the model MODEL = 'squeezenet', 'init_net.pb', 'predict_net.pb', 'ilsvrc_2012_mean.npy', 227 # codes - these help decypher the output and source from a list from AlexNet's object codes to provide an result like "tabby cat" or "lemon" depending on what's in the picture you submit to the neural network. IMAGE_LOCATION = "images/flower.jpg" # What model are we using? You should have already converted or downloaded one. insert ( 0, '/usr/local' ) from caffe2.proto import caffe2_pb2 import numpy as np import skimage.io import ansform from matplotlib import pyplot import os from caffe2.python import core, workspace, models import urllib2 print ( "Required modules imported." ) # Configuration - Change to your setup and preferences! CAFFE_MODELS = "/usr/local/caffe2/python/models" # sample images you can try, or use any URL to a regular image.
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