image = load_single_image(sys.argv[1])
else:
raise Exception("Image file " + sys.argv[1] + " does not exist")
from cnn import CNN
from misc.params import HyperParams
import skimage.io
import tensorflow as tf
hyper = HyperParams(verbose=False)
images_tf = tf.placeholder(tf.float32, [None, hyper.image_h, hyper.image_w, hyper.image_c], name="images")
class_tf = tf.placeholder(tf.int64, [None], name='class')
cnn = CNN()
conv_last, gap, class_prob = cnn.build(images_tf)
classmap = cnn.get_classmap(class_tf, conv_last)
with tf.Session() as sess:
tf.train.Saver().restore( sess, hyper.model_path )
conv_last_val, class_prob_val = sess.run([conv_last, class_prob], feed_dict={images_tf: image})
# use argsort instead of argmax to get all the classes
class_predictions_all = class_prob_val.argsort(axis=1)
roi_map = None
for i in xrange(-1 * hyper.top_k,0):
current_class = class_predictions_all[:,i]
classmap_vals = sess.run(classmap, feed_dict={class_tf: current_class, conv_last: conv_last_val})
normalized_classmap = normalize(classmap_vals[0])
if roi_map is None:
