def process_image_sw(image_fname, w_size, s):
image = sw.normalize_image(image_fname)
bin_map = sw.get_binary_map(image)
crops = sw.get_sliding_window_patches(image, bin_map, w_size, s)
coords = sw.get_sliding_window_patches_coord(image, bin_map, w_size, s)
return crops, coords
doc_probs = []
for fname in os.listdir(image_path):
print 'Process %s' % fname
image_name = os.path.join(image_path, fname)
patches, patches_coord = process_image_sw(image_name, window_size, stride)
output = net.predict(map(img_as_ubyte, patches))
bbox_list = [(patches_coord[i], output[i][1]) for i in range(len(output))]
doc_probs.append(np.mean([output[i][1] for i in range(len(output))]))
matplotlib.rcParams['figure.figsize'] = (10.0, 18.0)
viz.plot_image_estimated_al_maqrizi_probability2(sw.normalize_image(image_name), bbox_list)
print 'Al-Maqrizi document probability: %f' % np.mean(doc_probs)
elif patches_type == 'components':
patch_size = 28
model = '/home/andrew/Projects/al-maqrizi/nets/components_bin/deploy.prototxt'
pretrained = '/home/andrew/Projects/al-maqrizi/nets/components_bin/snapshot_iter_70100.caffemodel'
mean_file = '/home/andrew/Projects/al-maqrizi/nets/components_bin/mean.npy'
net = caffe.Classifier(model, pretrained, image_dims=(patch_size, patch_size),
mean=np.load(mean_file).mean(1).mean(1))
doc_probs = []
for fname in os.listdir(image_path):
