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import numpy as np |
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from PIL import Image |
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import progressbar |
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from util.compute_boundary_acc import compute_boundary_acc |
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from util.file_buffer import FileBuffer |
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from argparse import ArgumentParser |
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import os |
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import re |
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parser = ArgumentParser() |
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parser.add_argument('--dir', help='Directory with image, gt, and mask') |
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parser.add_argument('--output', help='Output of temp results', |
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default=None) |
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args = parser.parse_args() |
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def get_iu(seg, gt): |
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intersection = np.count_nonzero(seg & gt) |
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union = np.count_nonzero(seg | gt) |
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return intersection, union |
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total_new_i = 0 |
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total_new_u = 0 |
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total_old_i = 0 |
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total_old_u = 0 |
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total_old_correct_pixels = 0 |
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total_new_correct_pixels = 0 |
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total_num_pixels = 0 |
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total_num_images = 0 |
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total_seg_acc = 0 |
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total_mask_acc = 0 |
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small_objects = 0 |
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all_h = 0 |
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all_w = 0 |
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all_max = 0 |
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all_gts = [gt for gt in os.listdir(args.dir) if '_gt.png' in gt] |
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file_buffer = FileBuffer(os.path.join(args.dir, 'results_post0.125.txt')) |
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if args.output is not None: |
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os.makedirs(args.output, exist_ok=True) |
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for gt_name in progressbar.progressbar(all_gts): |
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gt = np.array(Image.open(os.path.join(args.dir, gt_name) |
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).convert('L')) |
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seg = np.array(Image.open(os.path.join(args.dir, gt_name.replace('_gt', '_seg')) |
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).convert('L')) |
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mask_im = Image.open(os.path.join(args.dir, "0.125_"+(gt_name.replace('_gt', '_mask'))) |
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).convert('L') |
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mask = seg.copy() |
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this_class = int(re.search(r'\d+', gt_name[::-1]).group()[::-1]) - 1 |
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rmin = cmin = 0 |
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rmax, cmax = seg.shape |
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all_h += rmax |
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all_w += cmax |
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all_max += max(rmax, cmax) |
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mask_h, mask_w = mask.shape |
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if mask_h != cmax: |
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mask = np.array(mask_im.resize((cmax, rmax), Image.BILINEAR)) |
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if seg.sum() < 32*32: |
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small_objects += 1 |
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else: |
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if (cmax==cmin) or (rmax==rmin): |
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print(gt_name, this_class) |
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continue |
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class_mask_prob = np.array(mask_im.resize((cmax-cmin, rmax-rmin), Image.BILINEAR)) |
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mask[rmin:rmax, cmin:cmax] = class_mask_prob |
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""" |
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Compute IoU and boundary accuracy |
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""" |
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gt = gt > 128 |
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seg = seg > 128 |
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mask = mask > 128 |
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old_i, old_u = get_iu(gt, seg) |
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new_i, new_u = get_iu(gt, mask) |
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total_new_i += new_i |
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total_new_u += new_u |
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total_old_i += old_i |
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total_old_u += old_u |
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seg_acc, mask_acc = compute_boundary_acc(gt, seg, mask) |
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total_seg_acc += seg_acc |
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total_mask_acc += mask_acc |
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total_num_images += 1 |
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if args.output is not None: |
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gt = Image.fromarray(gt) |
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seg = Image.fromarray(seg) |
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mask = Image.fromarray(mask) |
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gt.save(os.path.join(args.output, gt_name)) |
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seg.save(os.path.join(args.output, gt_name.replace('_gt.png', '_seg.png'))) |
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mask.save(os.path.join(args.output, gt_name.replace('_gt.png', '_mask.png'))) |
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new_iou = total_new_i/total_new_u |
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old_iou = total_old_i/total_old_u |
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new_mba = total_mask_acc/total_num_images |
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old_mba = total_seg_acc/total_num_images |
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file_buffer.write('New IoU : ', new_iou) |
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file_buffer.write('Old IoU : ', old_iou) |
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file_buffer.write('IoU Delta: ', new_iou-old_iou) |
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file_buffer.write('New mBA : ', new_mba) |
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file_buffer.write('Old mBA : ', old_mba) |
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file_buffer.write('mBA Delta: ', new_mba-old_mba) |
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file_buffer.write('Avg. H+W : ', (all_h+all_w)/total_num_images) |
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file_buffer.write('Avg. Max(H,W) : ', all_max/total_num_images) |
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file_buffer.write('Number of small objects: ', small_objects) |
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