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import os |
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import caffe |
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import argparse |
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import numpy as np |
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import scipy.misc |
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from PIL import Image |
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from util import segrun, fast_hist, get_scores |
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from cityscapes import cityscapes |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--cityscapes_dir", type=str, required=True, help="Path to the original cityscapes dataset") |
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parser.add_argument("--result_dir", type=str, required=True, help="Path to the generated images to be evaluated") |
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parser.add_argument("--output_dir", type=str, required=True, help="Where to save the evaluation results") |
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parser.add_argument("--caffemodel_dir", type=str, default='./scripts/eval_cityscapes/caffemodel/', help="Where the FCN-8s caffemodel stored") |
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parser.add_argument("--gpu_id", type=int, default=0, help="Which gpu id to use") |
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parser.add_argument("--split", type=str, default='val', help="Data split to be evaluated") |
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parser.add_argument("--save_output_images", type=int, default=0, help="Whether to save the FCN output images") |
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args = parser.parse_args() |
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def main(): |
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if not os.path.isdir(args.output_dir): |
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os.makedirs(args.output_dir) |
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if args.save_output_images > 0: |
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output_image_dir = args.output_dir + 'image_outputs/' |
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if not os.path.isdir(output_image_dir): |
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os.makedirs(output_image_dir) |
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CS = cityscapes(args.cityscapes_dir) |
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n_cl = len(CS.classes) |
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label_frames = CS.list_label_frames(args.split) |
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caffe.set_device(args.gpu_id) |
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caffe.set_mode_gpu() |
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net = caffe.Net(args.caffemodel_dir + '/deploy.prototxt', |
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args.caffemodel_dir + 'fcn-8s-cityscapes.caffemodel', |
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caffe.TEST) |
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hist_perframe = np.zeros((n_cl, n_cl)) |
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for i, idx in enumerate(label_frames): |
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if i % 10 == 0: |
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print('Evaluating: %d/%d' % (i, len(label_frames))) |
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city = idx.split('_')[0] |
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label = CS.load_label(args.split, city, idx) |
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im_file = args.result_dir + '/' + idx + '_leftImg8bit.png' |
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im = np.array(Image.open(im_file)) |
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im = scipy.misc.imresize(im, (label.shape[1], label.shape[2])) |
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out = segrun(net, CS.preprocess(im)) |
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hist_perframe += fast_hist(label.flatten(), out.flatten(), n_cl) |
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if args.save_output_images > 0: |
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label_im = CS.palette(label) |
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pred_im = CS.palette(out) |
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scipy.misc.imsave(output_image_dir + '/' + str(i) + '_pred.jpg', pred_im) |
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scipy.misc.imsave(output_image_dir + '/' + str(i) + '_gt.jpg', label_im) |
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scipy.misc.imsave(output_image_dir + '/' + str(i) + '_input.jpg', im) |
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mean_pixel_acc, mean_class_acc, mean_class_iou, per_class_acc, per_class_iou = get_scores(hist_perframe) |
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with open(args.output_dir + '/evaluation_results.txt', 'w') as f: |
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f.write('Mean pixel accuracy: %f\n' % mean_pixel_acc) |
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f.write('Mean class accuracy: %f\n' % mean_class_acc) |
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f.write('Mean class IoU: %f\n' % mean_class_iou) |
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f.write('************ Per class numbers below ************\n') |
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for i, cl in enumerate(CS.classes): |
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while len(cl) < 15: |
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cl = cl + ' ' |
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f.write('%s: acc = %f, iou = %f\n' % (cl, per_class_acc[i], per_class_iou[i])) |
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main() |
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