def main():
# Make output dir if not exists
if not os.path.isdir(args.output_dir):
os.makedirs(args.output_dir)
# Make image output dirs
if args.save_output_images > 0:
output_image_dir = args.output_dir + 'image_outputs/'
if not os.path.isdir(output_image_dir):
os.makedirs(output_image_dir)
# Create cityscapes manager class
CS = cityscapes(args.cityscapes_dir)
n_cl = len(CS.classes)
# Get labeled frames for evaluation
label_frames = CS.list_label_frames(args.split)
# GPU runs out of memory, so keep using CPU
# caffe.set_device(args.gpu_id)
# caffe.set_mode_gpu()
# Load FCN-8s network
net = caffe.Net(args.caffemodel_dir + '/deploy.prototxt',
args.caffemodel_dir + 'fcn-8s-cityscapes.caffemodel',
caffe.TEST)
hist_perframe = np.zeros((n_cl, n_cl))
# For all labeled frames
for i, idx in enumerate(label_frames):
if i % 10 == 0:
print('Evaluating: %d/%d' % (i, len(label_frames)))
city = idx.split('_')[0]
# idx is city_shot_frame
label = CS.load_label(args.split, city, idx)
im_file = args.result_dir + '/' + idx + '_leftImg8bit.png'
im = np.array(
Image.open(im_file).resize((label.shape[1], label.shape[2])))
out = segrun(net, CS.preprocess(im))
hist_perframe += fast_hist(label.flatten(), out.flatten(), n_cl)
if args.save_output_images > 0:
label_im = CS.palette(label)
pred_im = CS.palette(out)
scipy.misc.imsave(output_image_dir + '/' + str(i) + '_pred.jpg',
pred_im)
scipy.misc.imsave(output_image_dir + '/' + str(i) + '_gt.jpg',
label_im)
scipy.misc.imsave(output_image_dir + '/' + str(i) + '_input.jpg',
im)
mean_pixel_acc, mean_class_acc, mean_class_iou, per_class_acc, per_class_iou = get_scores(
hist_perframe)
with open(args.output_dir + '/evaluation_results.txt', 'w') as f:
f.write('Mean pixel accuracy: %f\n' % mean_pixel_acc)
f.write('Mean class accuracy: %f\n' % mean_class_acc)
f.write('Mean class IoU: %f\n' % mean_class_iou)
f.write('************ Per class numbers below ************\n')
for i, cl in enumerate(CS.classes):
while len(cl) < 15:
cl = cl + ' '
f.write('%s: acc = %f, iou = %f\n' %
(cl, per_class_acc[i], per_class_iou[i]))
def main():
if not os.path.isdir(args.output_dir):
os.makedirs(args.output_dir)
if args.save_output_images > 0:
output_image_dir = args.output_dir + 'image_outputs/'
if not os.path.isdir(output_image_dir):
os.makedirs(output_image_dir)
CS = cityscapes(args.cityscapes_dir)
n_cl = len(CS.classes)
label_frames = CS.list_label_frames(args.split)
print(label_frames)
caffe.set_mode_cpu()
net = caffe.Net(args.caffemodel_dir + '/deploy.prototxt',
args.caffemodel_dir + 'fcn-8s-cityscapes.caffemodel',
caffe.TEST)
hist_perframe = np.zeros((n_cl, n_cl))
for i, idx in enumerate(label_frames):
if i % 10 == 0:
print('Evaluating: %d/%d' % (i, len(label_frames)))
city = idx.split('_')[0]
# idx is city_shot_frame
label = CS.load_label(args.split, city, idx)
im_file = args.result_dir + '/' + idx + '_leftImg8bit.png'
##print(im_file)
im = np.array(Image.open(im_file))
#im = scipy.misc.imresize(im, (label.shape[1], label.shape[2]))
im = np.array(Image.fromarray(im).resize((label.shape[2], label.shape[1])))
out = segrun(net, CS.preprocess(im))
hist_perframe += fast_hist(label.flatten(), out.flatten(), n_cl)
if args.save_output_images > 0:
label_im = CS.palette(label)
pred_im = CS.palette(out)
#scipy.misc.imsave(output_image_dir + '/' + str(i) + '_pred.jpg', pred_im)
#scipy.misc.imsave(output_image_dir + '/' + str(i) + '_gt.jpg', label_im)
#scipy.misc.imsave(output_image_dir + '/' + str(i) + '_input.jpg', im)
imageio.imwrite(output_image_dir + '/' + str(i) + '_pred.jpg', pred_im)
imageio.imwrite(output_image_dir + '/' + str(i) + '_gt.jpg', label_im)
imageio.imwrite(output_image_dir + '/' + str(i) + '_input.jpg', im)
#if i > 3:
# break
mean_pixel_acc, mean_class_acc, mean_class_iou, per_class_acc, per_class_iou = get_scores(hist_perframe)
with open(args.output_dir + '/evaluation_results.txt', 'w') as f:
f.write('Mean pixel accuracy: %f\n' % mean_pixel_acc)
f.write('Mean class accuracy: %f\n' % mean_class_acc)
f.write('Mean class IoU: %f\n' % mean_class_iou)
f.write('************ Per class numbers below ************\n')
for i, cl in enumerate(CS.classes):
while len(cl) < 15:
cl = cl + ' '
f.write('%s: acc = %f, iou = %f\n' % (cl, per_class_acc[i], per_class_iou[i]))
