| import os |
| import caffe |
| import argparse |
| import numpy as np |
| import scipy.misc |
| from PIL import Image |
| from util import segrun, fast_hist, get_scores |
| from cityscapes import cityscapes |
|
|
| parser = argparse.ArgumentParser() |
| parser.add_argument("--cityscapes_dir", type=str, required=True, help="Path to the original cityscapes dataset") |
| parser.add_argument("--result_dir", type=str, required=True, help="Path to the generated images to be evaluated") |
| parser.add_argument("--output_dir", type=str, required=True, help="Where to save the evaluation results") |
| parser.add_argument("--caffemodel_dir", type=str, default='./scripts/eval_cityscapes/caffemodel/', help="Where the FCN-8s caffemodel stored") |
| parser.add_argument("--gpu_id", type=int, default=0, help="Which gpu id to use") |
| parser.add_argument("--split", type=str, default='val', help="Data split to be evaluated") |
| parser.add_argument("--save_output_images", type=int, default=0, help="Whether to save the FCN output images") |
| args = parser.parse_args() |
|
|
|
|
| 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) |
| caffe.set_device(args.gpu_id) |
| caffe.set_mode_gpu() |
| 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] |
| |
| label = CS.load_label(args.split, city, idx) |
| im_file = args.result_dir + '/' + idx + '_leftImg8bit.png' |
| im = np.array(Image.open(im_file)) |
| im = scipy.misc.imresize(im, (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])) |
|
|
|
|
| main() |
|
|
