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eval.py
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eval.py
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# Modified from ScanNet evaluation script: https://github.com/ScanNet/ScanNet/blob/master/BenchmarkScripts/3d_evaluation/evaluate_semantic_instance.py
import os, sys, numpy as np
import util.utils_3d as util_3d
import util.utils as util
# ---------- Label info ---------- #
# CLASS_LABELS = ['Building', 'LowVegetation', 'MediumVegetation', 'HighVegetation', 'Vehicle', 'Truck', 'Aircraft', 'MilitaryVehicle', 'Bike', 'Motorcycle', 'LightPole', 'StreetSgin', 'Clutter', 'Fence', 'Road', 'railWay', 'Windows', 'Dirt', 'Grass']
# VALID_CLASS_IDS = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
CLASS_LABELS = ['Building', 'LowVegetation', 'MediumVegetation', 'HighVegetation', 'Vehicle', 'Truck', 'Aircraft', 'MilitaryVehicle', 'Bike', 'Motorcycle', 'LightPole', 'StreetSgin', 'Clutter', 'Fence']
VALID_CLASS_IDS = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
ID_TO_LABEL = {}
LABEL_TO_ID = {}
for i in range(len(VALID_CLASS_IDS)):
LABEL_TO_ID[CLASS_LABELS[i]] = VALID_CLASS_IDS[i]
ID_TO_LABEL[VALID_CLASS_IDS[i]] = CLASS_LABELS[i]
# ---------- Evaluation params ---------- #
# overlaps for evaluation
OVERLAPS = np.append(np.arange(0.5,0.95,0.05), 0.25)
# minimum region size for evaluation [verts]
MIN_REGION_SIZES = np.array( [ 10 ] )
# distance thresholds [m]
DISTANCE_THRESHES = np.array( [ float('inf') ] )
# distance confidences
DISTANCE_CONFS = np.array( [ -float('inf') ] )
def evaluate_matches(matches):
overlaps = OVERLAPS
min_region_sizes = [MIN_REGION_SIZES[0]]
dist_threshes = [DISTANCE_THRESHES[0]]
dist_confs = [DISTANCE_CONFS[0]]
# results: class x overlap
ap = np.zeros((len(dist_threshes), len(CLASS_LABELS), len(overlaps)), np.float)
for di, (min_region_size, distance_thresh, distance_conf) in enumerate(zip(min_region_sizes, dist_threshes, dist_confs)):
for oi, overlap_th in enumerate(overlaps):
pred_visited = {}
for m in matches:
for p in matches[m]['pred']:
for label_name in CLASS_LABELS:
for p in matches[m]['pred'][label_name]:
if 'filename' in p:
pred_visited[p['filename']] = False
for li, label_name in enumerate(CLASS_LABELS):
y_true = np.empty(0)
y_score = np.empty(0)
hard_false_negatives = 0
has_gt = False
has_pred = False
for m in matches:
pred_instances = matches[m]['pred'][label_name]
gt_instances = matches[m]['gt'][label_name]
# filter groups in ground truth
gt_instances = [gt for gt in gt_instances if
gt['instance_id'] >= 1 and gt['vert_count'] >= min_region_size and gt['med_dist'] <= distance_thresh and gt['dist_conf'] >= distance_conf]
if gt_instances:
has_gt = True
if pred_instances:
has_pred = True
cur_true = np.ones(len(gt_instances))
cur_score = np.ones(len(gt_instances)) * (-float("inf"))
cur_match = np.zeros(len(gt_instances), dtype=np.bool)
# collect matches
for (gti, gt) in enumerate(gt_instances):
found_match = False
num_pred = len(gt['matched_pred'])
for pred in gt['matched_pred']:
# greedy assignments
if pred_visited[pred['filename']]:
continue
overlap = float(pred['intersection']) / (
gt['vert_count'] + pred['vert_count'] - pred['intersection'])
if overlap > overlap_th:
confidence = pred['confidence']
# if already have a prediction for this gt,
# the prediction with the lower score is automatically a false positive
if cur_match[gti]:
max_score = max(cur_score[gti], confidence)
min_score = min(cur_score[gti], confidence)
cur_score[gti] = max_score
# append false positive
cur_true = np.append(cur_true, 0)
cur_score = np.append(cur_score, min_score)
cur_match = np.append(cur_match, True)
# otherwise set score
else:
found_match = True
cur_match[gti] = True
cur_score[gti] = confidence
pred_visited[pred['filename']] = True
if not found_match:
hard_false_negatives += 1
# remove non-matched ground truth instances
cur_true = cur_true[cur_match == True]
cur_score = cur_score[cur_match == True]
# collect non-matched predictions as false positive
for pred in pred_instances:
found_gt = False
for gt in pred['matched_gt']:
overlap = float(gt['intersection']) / (
gt['vert_count'] + pred['vert_count'] - gt['intersection'])
if overlap > overlap_th:
found_gt = True
break
if not found_gt:
num_ignore = pred['void_intersection']
for gt in pred['matched_gt']:
# group?
if gt['instance_id'] < 1:
num_ignore += gt['intersection']
# small ground truth instances
if gt['vert_count'] < min_region_size or gt['med_dist'] > distance_thresh or gt['dist_conf'] < distance_conf:
num_ignore += gt['intersection']
proportion_ignore = float(num_ignore) / pred['vert_count']
# if not ignored append false positive
if proportion_ignore <= overlap_th:
cur_true = np.append(cur_true, 0)
confidence = pred["confidence"]
cur_score = np.append(cur_score, confidence)
# append to overall results
y_true = np.append(y_true, cur_true)
y_score = np.append(y_score, cur_score)
# compute average precision
if has_gt and has_pred:
# compute precision recall curve first
# sorting and cumsum
score_arg_sort = np.argsort(y_score)
y_score_sorted = y_score[score_arg_sort]
y_true_sorted = y_true[score_arg_sort]
y_true_sorted_cumsum = np.cumsum(y_true_sorted)
# unique thresholds
(thresholds, unique_indices) = np.unique(y_score_sorted, return_index=True)
num_prec_recall = len(unique_indices) + 1
# prepare precision recall
num_examples = len(y_score_sorted)
if(len(y_true_sorted_cumsum) == 0):
num_true_examples = 0
else:
num_true_examples = y_true_sorted_cumsum[-1]
precision = np.zeros(num_prec_recall)
recall = np.zeros(num_prec_recall)
# deal with the first point
y_true_sorted_cumsum = np.append(y_true_sorted_cumsum, 0)
# deal with remaining
for idx_res, idx_scores in enumerate(unique_indices):
cumsum = y_true_sorted_cumsum[idx_scores - 1]
tp = num_true_examples - cumsum
fp = num_examples - idx_scores - tp
fn = cumsum + hard_false_negatives
p = float(tp) / (tp + fp)
r = float(tp) / (tp + fn)
precision[idx_res] = p
recall[idx_res] = r
# first point in curve is artificial
precision[-1] = 1.
recall[-1] = 0.
# compute average of precision-recall curve
recall_for_conv = np.copy(recall)
recall_for_conv = np.append(recall_for_conv[0], recall_for_conv)
recall_for_conv = np.append(recall_for_conv, 0.)
stepWidths = np.convolve(recall_for_conv, [-0.5, 0, 0.5], 'valid')
# integrate is now simply a dot product
ap_current = np.dot(precision, stepWidths)
elif has_gt:
ap_current = 0.0
else:
ap_current = float('nan')
ap[di, li, oi] = ap_current
return ap
def compute_averages(aps):
d_inf = 0
o50 = np.where(np.isclose(OVERLAPS,0.5))
o25 = np.where(np.isclose(OVERLAPS,0.25))
oAllBut25 = np.where(np.logical_not(np.isclose(OVERLAPS,0.25)))
avg_dict = {}
#avg_dict['all_ap'] = np.nanmean(aps[ d_inf,:,: ])
avg_dict['all_ap'] = np.nanmean(aps[ d_inf,:,oAllBut25])
avg_dict['all_ap_50%'] = np.nanmean(aps[ d_inf,:,o50])
avg_dict['all_ap_25%'] = np.nanmean(aps[ d_inf,:,o25])
avg_dict["classes"] = {}
for (li,label_name) in enumerate(CLASS_LABELS):
avg_dict["classes"][label_name] = {}
#avg_dict["classes"][label_name]["ap"] = np.average(aps[ d_inf,li, :])
avg_dict["classes"][label_name]["ap"] = np.average(aps[ d_inf,li,oAllBut25])
avg_dict["classes"][label_name]["ap50%"] = np.average(aps[ d_inf,li,o50])
avg_dict["classes"][label_name]["ap25%"] = np.average(aps[ d_inf,li,o25])
return avg_dict
def assign_instances_for_scan(scene_name, pred_info, gt_file):
try:
gt_ids = util_3d.load_ids(gt_file)
except Exception as e:
util.print_error('unable to load ' + gt_file + ': ' + str(e))
# get gt instances
gt_instances = util_3d.get_instances(gt_ids, VALID_CLASS_IDS, CLASS_LABELS, ID_TO_LABEL)
# associate
gt2pred = gt_instances.copy()
for label in gt2pred:
for gt in gt2pred[label]:
gt['matched_pred'] = []
pred2gt = {}
for label in CLASS_LABELS:
pred2gt[label] = []
num_pred_instances = 0
# mask of void labels in the groundtruth
bool_void = np.logical_not(np.in1d(gt_ids//1, VALID_CLASS_IDS))
# go thru all prediction masks
nMask = pred_info['label_id'].shape[0]
for i in range(nMask):
label_id = int(pred_info['label_id'][i])
conf = pred_info['conf'][i]
if not label_id in ID_TO_LABEL:
continue
label_name = ID_TO_LABEL[label_id]
# read the mask
pred_mask = pred_info['mask'][i] # (N), long
if len(pred_mask) != len(gt_ids):
util.print_error('wrong number of lines in mask#%d: ' % (i) + '(%d) vs #mesh vertices (%d)' % (len(pred_mask), len(gt_ids)))
# convert to binary
pred_mask = np.not_equal(pred_mask, 0)
num = np.count_nonzero(pred_mask)
if num < MIN_REGION_SIZES[0]:
continue # skip if empty
pred_instance = {}
pred_instance['filename'] = '{}_{:03d}'.format(scene_name, num_pred_instances)
pred_instance['pred_id'] = num_pred_instances
pred_instance['label_id'] = label_id
pred_instance['vert_count'] = num
pred_instance['confidence'] = conf
pred_instance['void_intersection'] = np.count_nonzero(np.logical_and(bool_void, pred_mask))
# matched gt instances
matched_gt = []
# go thru all gt instances with matching label
for (gt_num, gt_inst) in enumerate(gt2pred[label_name]):
intersection = np.count_nonzero(np.logical_and(gt_ids == gt_inst['instance_id'], pred_mask))
if intersection > 0:
gt_copy = gt_inst.copy()
pred_copy = pred_instance.copy()
gt_copy['intersection'] = intersection
pred_copy['intersection'] = intersection
matched_gt.append(gt_copy)
gt2pred[label_name][gt_num]['matched_pred'].append(pred_copy)
pred_instance['matched_gt'] = matched_gt
num_pred_instances += 1
pred2gt[label_name].append(pred_instance)
return gt2pred, pred2gt
def print_results(avgs):
from util.log import logger
sep = ""
col1 = ":"
lineLen = 64
logger.info("")
logger.info("#" * lineLen)
line = ""
line += "{:<15}".format("what" ) + sep + col1
line += "{:>15}".format("AP" ) + sep
line += "{:>15}".format("AP_50%" ) + sep
line += "{:>15}".format("AP_25%" ) + sep
logger.info(line)
logger.info("#" * lineLen)
for (li,label_name) in enumerate(CLASS_LABELS):
ap_avg = avgs["classes"][label_name]["ap"]
ap_50o = avgs["classes"][label_name]["ap50%"]
ap_25o = avgs["classes"][label_name]["ap25%"]
line = "{:<15}".format(label_name) + sep + col1
line += sep + "{:>15.3f}".format(ap_avg ) + sep
line += sep + "{:>15.3f}".format(ap_50o ) + sep
line += sep + "{:>15.3f}".format(ap_25o ) + sep
logger.info(line)
all_ap_avg = avgs["all_ap"]
all_ap_50o = avgs["all_ap_50%"]
all_ap_25o = avgs["all_ap_25%"]
logger.info("-"*lineLen)
line = "{:<15}".format("average") + sep + col1
line += "{:>15.3f}".format(all_ap_avg) + sep
line += "{:>15.3f}".format(all_ap_50o) + sep
line += "{:>15.3f}".format(all_ap_25o) + sep
logger.info(line)
logger.info("")