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import numpy as np |
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import cv2 |
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def get_disk_kernel(radius): |
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return cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (radius*2+1, radius*2+1)) |
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def compute_boundary_acc(gt, seg, mask): |
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gt = gt.astype(np.uint8) |
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seg = seg.astype(np.uint8) |
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mask = mask.astype(np.uint8) |
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h, w = gt.shape |
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min_radius = 1 |
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max_radius = (w+h)/300 |
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num_steps = 5 |
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seg_acc = [None] * num_steps |
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mask_acc = [None] * num_steps |
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for i in range(num_steps): |
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curr_radius = min_radius + int((max_radius-min_radius)/num_steps*i) |
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kernel = get_disk_kernel(curr_radius) |
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boundary_region = cv2.morphologyEx(gt, cv2.MORPH_GRADIENT, kernel) > 0 |
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gt_in_bound = gt[boundary_region] |
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seg_in_bound = seg[boundary_region] |
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mask_in_bound = mask[boundary_region] |
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num_edge_pixels = (boundary_region).sum() |
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num_seg_gd_pix = ((gt_in_bound) * (seg_in_bound) + (1-gt_in_bound) * (1-seg_in_bound)).sum() |
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num_mask_gd_pix = ((gt_in_bound) * (mask_in_bound) + (1-gt_in_bound) * (1-mask_in_bound)).sum() |
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seg_acc[i] = num_seg_gd_pix / num_edge_pixels |
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mask_acc[i] = num_mask_gd_pix / num_edge_pixels |
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return sum(seg_acc)/num_steps, sum(mask_acc)/num_steps |
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def compute_boundary_acc_multi_class(gt, seg, mask): |
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h, w = gt.shape |
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min_radius = 1 |
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max_radius = (w+h)/300 |
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num_steps = 5 |
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seg_acc = [None] * num_steps |
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mask_acc = [None] * num_steps |
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classes = np.unique(gt) |
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for i in range(num_steps): |
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curr_radius = min_radius + int((max_radius-min_radius)/num_steps*i) |
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kernel = get_disk_kernel(curr_radius) |
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boundary_region = np.zeros_like(gt) |
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for c in classes: |
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if c == 0: |
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continue |
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gt_class = (gt == c).astype(np.uint8) |
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class_bound = cv2.morphologyEx(gt_class, cv2.MORPH_GRADIENT, kernel) |
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boundary_region += class_bound |
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boundary_region = boundary_region > 0 |
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gt_in_bound = gt[boundary_region] |
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seg_in_bound = seg[boundary_region] |
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mask_in_bound = mask[boundary_region] |
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void_count = (gt_in_bound == 0).sum() |
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num_edge_pixels = (boundary_region).sum() |
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num_seg_gd_pix = (gt_in_bound == seg_in_bound).sum() |
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num_mask_gd_pix = (gt_in_bound == mask_in_bound).sum() |
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seg_acc[i] = (num_seg_gd_pix-void_count) / (num_edge_pixels-void_count) |
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mask_acc[i] = (num_mask_gd_pix-void_count) / (num_edge_pixels-void_count) |
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return sum(seg_acc)/num_steps, sum(mask_acc)/num_steps |
