Comments (9)
Just find a solution:
- make sure the input image is 500 * 500
- add the following param to the crop layers in the deploy.prototxt
crop_param {
axis: 2
offset: 32
offset: 32
}
thanks to this post https://medium.com/@s1ddok/holistically-nested-edge-detection-on-ios-with-coreml-and-swift-e45df264cf66
from hed.
net.blobs['data'].reshape(1, 3,200,200)
net.reshape()
^reshape a net (changes the input)
also look up caffe net surgery for changing the output
from hed.
Thanks for your reply. The code I use is basically the tutorial. Both your suggestion and resizing the image in Python did the job of changing the size. But even if I use that, it does not remove the image boundary. In fact I have realized that the image is displaced by exactly 32 pixels to the bottom and right and then cropped, regardless of the image size.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import matplotlib.cm as cm
import scipy.misc
import Image
import scipy.io
import os
# Make sure that caffe is on the python path:
caffe_root = '../../' # this file is expected to be in {caffe_root}/examples/hed/
import sys
sys.path.insert(0, caffe_root + 'python')
import caffe
im_lst = []
im = Image.open('2008_000052.jpg')
im = im.resize((200, 200), Image.ANTIALIAS)
in_ = np.array(im, dtype=np.float32)
in_ = in_[:,:,::-1]
in_ -= np.array((104.00698793,116.66876762,122.67891434))
im_lst.append(in_)
idx = 0
gpu = 3
in_ = im_lst[idx]
in_ = in_.transpose((2,0,1))
#remove the following two lines if testing with cpu
caffe.set_mode_gpu()
caffe.set_device(gpu)
# load net
model_root = 'examples/hed/'
net = caffe.Net(model_root+'deploy.prototxt', model_root+'hed_pretrained_bsds.caffemodel', caffe.TEST)
# shape for input (data blob is N x C x H x W), set data
#net.blobs['data'].reshape(1, *in_.shape)
net.blobs['data'].reshape(1, 3,200,200)
net.reshape()
net.blobs['data'].data[...] = in_
# run net and take argmax for prediction
net.forward()
out1 = net.blobs['sigmoid-dsn1'].data[0][0,:,:]
out2 = net.blobs['sigmoid-dsn2'].data[0][0,:,:]
out3 = net.blobs['sigmoid-dsn3'].data[0][0,:,:]
out4 = net.blobs['sigmoid-dsn4'].data[0][0,:,:]
out5 = net.blobs['sigmoid-dsn5'].data[0][0,:,:]
fuse = net.blobs['sigmoid-fuse'].data[0][0,:,:]
import scipy.misc
scipy.misc.imsave('out1.jpg', out1)
scipy.misc.imsave('out2.jpg', out2)
scipy.misc.imsave('out3.jpg', out3)
scipy.misc.imsave('out4.jpg', out4)
scipy.misc.imsave('out5.jpg', out5)
scipy.misc.imsave('fuse.jpg', fuse)
from hed.
I had the same problem when running the code.
from hed.
I have the exactlly same problem.
from hed.
I also have the same problem. The output edge map is not aligned to the input image.
from hed.
I also meet the same problem
from hed.
Same problem.
from hed.
I meet the same problem, can u provide some solutions?
from hed.
Related Issues (20)
- ImportError: dynamic module does not define module export function (PyInit__caffe) HOT 1
- Batch Processing with HED without MATLAB
- Trouble with PostprocessHED.m parameters
- tenosrflow version
- link error undefined symbols for x86_64
- how to test it with RGBD data?
- Is this a memory leak? (python)
- How to get all the (X, Y) values of the edge for one object?
- train error
- Looking for BSDS500 test images computed by pretrained hed model
- NYUDv2 pretrainModel
- Can the parameters of the function edgeNmsMex be modified?
- 为什么,预测出来的边框比较宽? HOT 6
- requirement.txt file HOT 1
- Other datasets
- The preprocess of the dataset? HOT 1
- The augmented BSDS data (1.2GB), HOT 3
- The test output image and the input image are not aligned, and there are crops on the top and left HOT 1
- where is Yfuse implemented in code?
- questions about BN layer
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from hed.