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hcl_target/compute_iou.py

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+import numpy as np
+import argparse
+import json
+from PIL import Image
+from os.path import join
+
+
+def fast_hist(a, b, n):
+    k = (a >= 0) & (a < n)
+    return np.bincount(n * a[k].astype(int) + b[k], minlength=n ** 2).reshape(n, n)
+
+
+def per_class_iu(hist):
+    return np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
+
+
+def label_mapping(input, mapping):
+    output = np.copy(input)
+    for ind in range(len(mapping)):
+        output[input == mapping[ind][0]] = mapping[ind][1]
+    return np.array(output, dtype=np.int64)
+
+
+def compute_mIoU(gt_dir, pred_dir, devkit_dir=''):
+    """
+    Compute IoU given the predicted colorized images and 
+    """
+    with open(join(devkit_dir, 'info.json'), 'r') as fp:
+      info = json.load(fp)
+    num_classes = np.int(info['classes'])
+    print(('Num classes', num_classes))
+    name_classes = np.array(info['label'], dtype=np.str)
+    mapping = np.array(info['label2train'], dtype=np.int)
+    hist = np.zeros((num_classes, num_classes))
+
+    image_path_list = join(devkit_dir, 'val.txt')
+    label_path_list = join(devkit_dir, 'label.txt')
+    gt_imgs = open(label_path_list, 'r').read().splitlines()
+    gt_imgs = [join(gt_dir, x) for x in gt_imgs]
+    pred_imgs = open(image_path_list, 'r').read().splitlines()
+    pred_imgs = [join(pred_dir, x.split('/')[-1]) for x in pred_imgs]
+
+    for ind in range(len(gt_imgs)):
+        pred = np.array(Image.open(pred_imgs[ind]))
+        label = np.array(Image.open(gt_imgs[ind]))
+        label = label_mapping(label, mapping)
+        if len(label.shape) == 3 and label.shape[2]==4:
+            label = label[:,:,0]
+        if len(label.flatten()) != len(pred.flatten()):
+            print(('Skipping: len(gt) = {:d}, len(pred) = {:d}, {:s}, {:s}'.format(len(label.flatten()), len(pred.flatten()), gt_imgs[ind], pred_imgs[ind])))
+            continue
+        hist += fast_hist(label.flatten(), pred.flatten(), num_classes)
+        if ind > 0 and ind % 10 == 0:
+            print(('{:d} / {:d}: {:0.2f}'.format(ind, len(gt_imgs), 100*np.mean(per_class_iu(hist)))))
+
+    mIoUs = per_class_iu(hist)
+    for ind_class in range(num_classes):
+        print(('===>' + name_classes[ind_class] + ':\t' + str(round(mIoUs[ind_class] * 100, 2))))
+    print(('===> mIoU: ' + str(round(np.nanmean(mIoUs) * 100, 2))))
+    return mIoUs
+
+
+def main(args):
+   compute_mIoU(args.gt_dir, args.pred_dir, args.devkit_dir)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('gt_dir', type=str, help='directory which stores CityScapes val gt images')
+    parser.add_argument('pred_dir', type=str, help='directory which stores CityScapes val pred images')
+    parser.add_argument('--devkit_dir', default='dataset/cityscapes_list', help='base directory of cityscapes')
+    args = parser.parse_args()
+    main(args)

hcl_target/compute_iou_best.py

@@ -0,0 +1,81 @@
+import numpy as np
+import argparse
+import json
+from PIL import Image
+from os.path import join
+import os.path as osp
+
+
+def fast_hist(a, b, n):
+    k = (a >= 0) & (a < n)
+    return np.bincount(n * a[k].astype(int) + b[k], minlength=n ** 2).reshape(n, n)
+
+
+def per_class_iu(hist):
+    return np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
+
+
+def label_mapping(input, mapping):
+    output = np.copy(input)
+    for ind in range(len(mapping)):
+        output[input == mapping[ind][0]] = mapping[ind][1]
+    return np.array(output, dtype=np.int64)
+
+
+def compute_mIoU(gt_dir, pred_dir, devkit_dir=''):
+    """
+    Compute IoU given the predicted colorized images and 
+    """
+    with open(join(devkit_dir, 'info.json'), 'r') as fp:
+      info = json.load(fp)
+    num_classes = np.int(info['classes'])
+    print(('Num classes', num_classes))
+    name_classes = np.array(info['label'], dtype=np.str)
+    mapping = np.array(info['label2train'], dtype=np.int)
+    hist = np.zeros((num_classes, num_classes))
+
+    image_path_list = join(devkit_dir, 'val.txt')
+    label_path_list = join(devkit_dir, 'label.txt')
+    gt_imgs = open(label_path_list, 'r').read().splitlines()
+    gt_imgs = [join(gt_dir, x) for x in gt_imgs]
+    pred_imgs = open(image_path_list, 'r').read().splitlines()
+    pred_imgs = [join(pred_dir, x.split('/')[-1]) for x in pred_imgs]
+
+    for ind in range(len(gt_imgs)):
+        pred = np.array(Image.open(pred_imgs[ind]))
+        label = np.array(Image.open(gt_imgs[ind]))
+        label = label_mapping(label, mapping)
+        if len(label.shape) == 3 and label.shape[2]==4:
+            label = label[:,:,0]
+        if len(label.flatten()) != len(pred.flatten()):
+            print(('Skipping: len(gt) = {:d}, len(pred) = {:d}, {:s}, {:s}'.format(len(label.flatten()), len(pred.flatten()), gt_imgs[ind], pred_imgs[ind])))
+            continue
+        hist += fast_hist(label.flatten(), pred.flatten(), num_classes)
+        if ind > 0 and ind % 10 == 0:
+            print(('{:d} / {:d}: {:0.2f}'.format(ind, len(gt_imgs), 100*np.mean(per_class_iu(hist)))))
+
+    mIoUs = per_class_iu(hist)
+    scores = ''
+    for ind_class in range(num_classes):
+        print(('===>' + name_classes[ind_class] + ':\t' + str(round(mIoUs[ind_class] * 100, 2))))
+        scores = scores + str(round(mIoUs[ind_class] * 100, 2)) + ','
+    print(('===> mIoU: ' + str(round(np.nanmean(mIoUs) * 100, 2))))
+    scores = scores + str(round(np.nanmean(mIoUs) * 100, 2))
+
+    test_log = open(osp.join('./result', 'test_log.txt'), 'a')
+    test_log.write(scores + "\n")
+    test_log.close()
+    return mIoUs
+
+
+def main(args):
+   compute_mIoU(args.gt_dir, args.pred_dir, args.devkit_dir)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('gt_dir', type=str, help='directory which stores CityScapes val gt images')
+    parser.add_argument('pred_dir', type=str, help='directory which stores CityScapes val pred images')
+    parser.add_argument('--devkit_dir', default='dataset/cityscapes_list', help='base directory of cityscapes')
+    args = parser.parse_args()
+    main(args)