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Extended support for single channel images such as MNIST #40

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5 changes: 4 additions & 1 deletion caffevis/app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,8 @@ def __init__(self, settings, key_bindings):
settings.caffevis_deploy_prototxt,
settings.caffevis_network_weights,
mean = None, # Set to None for now, assign later # self._data_mean,
channel_swap = self._net_channel_swap,
channel_swap = None, #Set to None to extend support to single channel
#self._net_channel_swap,
raw_scale = self._range_scale,
)

Expand Down Expand Up @@ -85,6 +86,8 @@ def __init__(self, settings, key_bindings):
# self._data_mean = tuple(self._data_mean)
if self._data_mean is not None:
self.net.transformer.set_mean(self.net.inputs[0], self._data_mean)
if self.net.blobs[self.net.inputs[0]].data.shape[1] == 3: #RGB
self.net.transformer.set_channel_swap(self.net.inputs[0], self._net_channel_swap)

check_force_backward_true(settings.caffevis_deploy_prototxt)

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10 changes: 8 additions & 2 deletions caffevis/caffevis_helper.py
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Original file line number Diff line number Diff line change
@@ -1,13 +1,19 @@
import numpy as np
import cv2

from image_misc import get_tiles_height_width, caffe_load_image



def net_preproc_forward(net, img, data_hw):
appropriate_shape = data_hw + (3,)
assert img.shape == appropriate_shape, 'img is wrong size (got %s but expected %s)' % (img.shape, appropriate_shape)
#resized = caffe.io.resize_image(img, net.image_dims) # e.g. (227, 227, 3)
if net.blobs[net.inputs[0]].data.shape[1] == 1:
appropriate_shape = data_hw + (1,)
bw_img = 0.299*img[:,:,0] + 0.587*img[:,:,1] + 0.114*img[:,:,2]
img = bw_img.reshape(data_hw[0],data_hw[1],1)
elif net.blobs[net.inputs[0]].data.shape[1] == 3:
appropriate_shape = data_hw + (3,)
assert img.shape == appropriate_shape, 'img is wrong size (got %s but expected %s)' % (img.shape, appropriate_shape)
data_blob = net.transformer.preprocess('data', img) # e.g. (3, 227, 227), mean subtracted and scaled to [0,255]
data_blob = data_blob[np.newaxis,:,:,:] # e.g. (1, 3, 227, 227)
output = net.forward(data=data_blob)
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