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import os
import numpy as np
import pickle
import cv2
from metrics.new_utils import *
class Metric():
def calc(self, gt_data, conv_data, thresh=8.0, iou_thresh=0.7):
### compute corners precision/recall
gts = gt_data['corners']
dets = conv_data['corners']
per_sample_corner_tp = 0.0
per_sample_corner_fp = 0.0
per_sample_corner_length = gts.shape[0]
found = [False] * gts.shape[0]
c_det_annot = {}
# for each corner detection
for i, det in enumerate(dets):
# get closest gt
near_gt = [0, 999999.0, (0.0, 0.0)]
for k, gt in enumerate(gts):
dist = np.linalg.norm(gt - det)
if dist < near_gt[1]:
near_gt = [k, dist, gt]
if near_gt[1] <= thresh and not found[near_gt[0]]:
per_sample_corner_tp += 1.0
found[near_gt[0]] = True
c_det_annot[i] = near_gt[0]
else:
per_sample_corner_fp += 1.0
per_corner_score = {
'recall': per_sample_corner_tp / gts.shape[0],
'precision': per_sample_corner_tp / (per_sample_corner_tp + per_sample_corner_fp + 1e-8)
}
### compute edges precision/recall
per_sample_edge_tp = 0.0
per_sample_edge_fp = 0.0
edge_corner_annots = gt_data['edges']
per_sample_edge_length = edge_corner_annots.shape[0]
false_edge_ids = []
match_gt_ids = set()
for l, e_det in enumerate(conv_data['edges']):
c1, c2 = e_det
# check if corners are mapped
if (c1 not in c_det_annot.keys()) or (c2 not in c_det_annot.keys()):
per_sample_edge_fp += 1.0
false_edge_ids.append(l)
continue
# check hit
c1_prime = c_det_annot[c1]
c2_prime = c_det_annot[c2]
is_hit = False
for k, e_annot in enumerate(edge_corner_annots):
c3, c4 = e_annot
if ((c1_prime == c3) and (c2_prime == c4)) or ((c1_prime == c4) and (c2_prime == c3)):
is_hit = True
match_gt_ids.add(k)
break
# hit
if is_hit:
per_sample_edge_tp += 1.0
else:
per_sample_edge_fp += 1.0
false_edge_ids.append(l)
per_edge_score = {
'recall': per_sample_edge_tp / edge_corner_annots.shape[0],
'precision': per_sample_edge_tp / (per_sample_edge_tp + per_sample_edge_fp + 1e-8)
}
# computer regions precision/recall
conv_mask = render(corners=conv_data['corners'], edges=conv_data['edges'], render_pad=0, edge_linewidth=1)[0]
conv_mask = 1 - conv_mask
conv_mask = conv_mask.astype(np.uint8)
labels, region_mask = cv2.connectedComponents(conv_mask, connectivity=4)
#cv2.imwrite('mask-pred.png', region_mask.astype(np.uint8) * 20)
background_label = region_mask[0, 0]
all_conv_masks = []
for region_i in range(1, labels):
if region_i == background_label:
continue
the_region = region_mask == region_i
if the_region.sum() < 20:
continue
all_conv_masks.append(the_region)
gt_mask = render(corners=gt_data['corners'], edges=gt_data['edges'], render_pad=0, edge_linewidth=1)[0]
gt_mask = 1 - gt_mask
gt_mask = gt_mask.astype(np.uint8)
labels, region_mask = cv2.connectedComponents(gt_mask, connectivity=4)
#cv2.imwrite('mask-gt.png', region_mask.astype(np.uint8) * 20)
background_label = region_mask[0, 0]
all_gt_masks = []
for region_i in range(1, labels):
if region_i == background_label:
continue
the_region = region_mask == region_i
if the_region.sum() < 20:
continue
all_gt_masks.append(the_region)
per_sample_region_tp = 0.0
per_sample_region_fp = 0.0
per_sample_region_length = len(all_gt_masks)
found = [False] * len(all_gt_masks)
for i, r_det in enumerate(all_conv_masks):
# gt closest gt
near_gt = [0, 0, None]
for k, r_gt in enumerate(all_gt_masks):
iou = np.logical_and(r_gt, r_det).sum() / float(np.logical_or(r_gt, r_det).sum())
if iou > near_gt[1]:
near_gt = [k, iou, r_gt]
if near_gt[1] >= iou_thresh and not found[near_gt[0]]:
per_sample_region_tp += 1.0
found[near_gt[0]] = True
else:
per_sample_region_fp += 1.0
per_region_score = {
'recall': per_sample_region_tp / len(all_gt_masks),
'precision': per_sample_region_tp / (per_sample_region_tp + per_sample_region_fp + 1e-8)
}
return {
'corner_tp': per_sample_corner_tp,
'corner_fp': per_sample_corner_fp,
'corner_length': per_sample_corner_length,
'edge_tp': per_sample_edge_tp,
'edge_fp': per_sample_edge_fp,
'edge_length': per_sample_edge_length,
'region_tp': per_sample_region_tp,
'region_fp': per_sample_region_fp,
'region_length': per_sample_region_length,
'corner': per_corner_score,
'edge': per_edge_score,
'region': per_region_score
}
def compute_metrics(gt_data, pred_data):
metric = Metric()
score = metric.calc(gt_data, pred_data)
return score
def get_recall_and_precision(tp, fp, length):
recall = tp / (length + 1e-8)
precision = tp / (tp + fp + 1e-8)
return recall, precision
if __name__ == '__main__':
base_path = './'
gt_datapath = '../data/cities_dataset/annot'
metric = Metric()
corner_tp = 0.0
corner_fp = 0.0
corner_length = 0.0
edge_tp = 0.0
edge_fp = 0.0
edge_length = 0.0
region_tp = 0.0
region_fp = 0.0
region_length = 0.0
for file_name in os.listdir(base_path):
if len(file_name) < 10:
continue
f = open(os.path.join(base_path, file_name), 'rb')
gt_data = np.load(os.path.join(gt_datapath, file_name + '.npy'), allow_pickle=True).tolist()
candidate = pickle.load(f)
conv_corners = candidate.graph.getCornersArray()
conv_edges = candidate.graph.getEdgesArray()
conv_data = {'corners': conv_corners, 'edges': conv_edges}
score = metric.calc(gt_data, conv_data)
corner_tp += score['corner_tp']
corner_fp += score['corner_fp']
corner_length += score['corner_length']
edge_tp += score['edge_tp']
edge_fp += score['edge_fp']
edge_length += score['edge_length']
region_tp += score['region_tp']
region_fp += score['region_fp']
region_length += score['region_length']
f = open(os.path.join(base_path, 'score.txt'), 'w')
# corner
recall, precision = get_recall_and_precision(corner_tp, corner_fp, corner_length)
f_score = 2.0 * precision * recall / (recall + precision + 1e-8)
print('corners - precision: %.3f recall: %.3f f_score: %.3f' % (precision, recall, f_score))
f.write('corners - precision: %.3f recall: %.3f f_score: %.3f\n' % (precision, recall, f_score))
# edge
recall, precision = get_recall_and_precision(edge_tp, edge_fp, edge_length)
f_score = 2.0 * precision * recall / (recall + precision + 1e-8)
print('edges - precision: %.3f recall: %.3f f_score: %.3f' % (precision, recall, f_score))
f.write('edges - precision: %.3f recall: %.3f f_score: %.3f\n' % (precision, recall, f_score))
# region
recall, precision = get_recall_and_precision(region_tp, region_fp, region_length)
f_score = 2.0 * precision * recall / (recall + precision + 1e-8)
print('regions - precision: %.3f recall: %.3f f_score: %.3f' % (precision, recall, f_score))
f.write('regions - precision: %.3f recall: %.3f f_score: %.3f\n' % (precision, recall, f_score))
f.close()
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