refactor __init__(2)
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lorenzo
parent
6fbc496702
commit
67bc780677
@ -19,6 +19,8 @@ class KittiEval:
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- 3DOP
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- MonoDepth
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"""
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dic_stds = defaultdict(lambda: defaultdict(list))
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dic_stats = defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(float))))
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def __init__(self, show=False, thresh_iou_our=0.3, thresh_iou_m3d=0.5, thresh_conf_m3d=0.5, thresh_conf_our=0.3):
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@ -54,15 +56,10 @@ class KittiEval:
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# Extract validation images for evaluation
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names_gt = tuple(os.listdir(self.dir_gt))
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_, self.set_val = split_training(names_gt, path_train, path_val)
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aa = 5
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def run(self):
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"""Evaluate Monoloco methods on ALP and ALE metrics"""
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self.dic_stds = defaultdict(lambda: defaultdict(list))
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dic_stats = defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(float))))
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cnt_gt = 0
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# Iterate over each ground truth file in the training set
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@ -73,7 +70,7 @@ class KittiEval:
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path_3dop = os.path.join(self.dir_3dop, name)
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path_md = os.path.join(self.dir_md, name)
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boxes_gt = []
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truncs_gt = [] # Float from 0 to 1
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truncs_gt = [] # Float from 0 to 1
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occs_gt = [] # Either 0,1,2,3 fully visible, partly occluded, largely occluded, unknown
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dds_gt = []
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dic_fin = defaultdict(list)
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@ -81,7 +78,7 @@ class KittiEval:
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# Iterate over each line of the gt file and save box location and distances
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with open(path_gt, "r") as f_gt:
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for line_gt in f_gt:
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if self.check_conditions(line_gt, mode='gt'):
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if check_conditions(line_gt, mode='gt'):
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truncs_gt.append(float(line_gt.split()[1]))
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occs_gt.append(int(line_gt.split()[2]))
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boxes_gt.append([float(x) for x in line_gt.split()[4:8]])
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@ -112,19 +109,19 @@ class KittiEval:
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# Save statistics
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for key in self.errors:
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for clst in self.clusters[:-2]: # M3d and pifpaf does not have annotations above 40 meters
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dic_stats['test'][key]['mean'][clst] = sum(self.errors[key][clst]) / float(len(self.errors[key][clst]))
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dic_stats['test'][key]['max'][clst] = max(self.errors[key][clst])
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dic_stats['test'][key]['cnt'][clst] = len(self.errors[key][clst])
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self.dic_stats['test'][key]['mean'][clst] = sum(self.errors[key][clst]) / float(len(self.errors[key][clst]))
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self.dic_stats['test'][key]['max'][clst] = max(self.errors[key][clst])
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self.dic_stats['test'][key]['cnt'][clst] = len(self.errors[key][clst])
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if key == 'our':
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for clst in self.clusters[:-2]:
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dic_stats['test'][key]['std_ale'][clst] = \
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self.dic_stats['test'][key]['std_ale'][clst] = \
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sum(self.dic_stds['ale'][clst]) / float(len(self.dic_stds['ale'][clst]))
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dic_stats['test'][key]['std_epi'][clst] = \
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self.dic_stats['test'][key]['std_epi'][clst] = \
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sum(self.dic_stds['epi'][clst]) / float(len(self.dic_stds['epi'][clst]))
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dic_stats['test'][key]['interval'][clst] = \
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self.dic_stats['test'][key]['interval'][clst] = \
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sum(self.dic_stds['interval'][clst]) / float(len(self.dic_stds['interval'][clst]))
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dic_stats['test'][key]['at_risk'][clst] = \
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self.dic_stats['test'][key]['at_risk'][clst] = \
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sum(self.dic_stds['at_risk'][clst]) / float(len(self.dic_stds['at_risk'][clst]))
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# Print statistics
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@ -145,14 +142,14 @@ class KittiEval:
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for clst in self.clusters[:-9]:
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print(" {} Average error in cluster {}: {:.2f} with a max error of {:.1f}, "
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"for {} annotations"
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.format(key, clst, dic_stats['test'][key]['mean'][clst], dic_stats['test'][key]['max'][clst],
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dic_stats['test'][key]['cnt'][clst]))
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.format(key, clst, self.dic_stats['test'][key]['mean'][clst], self.dic_stats['test'][key]['max'][clst],
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self.dic_stats['test'][key]['cnt'][clst]))
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if key == 'our':
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print("% of annotation inside the confidence interval: {:.1f} %, "
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"of which {:.1f} % at higher risk"
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.format(100 * dic_stats['test'][key]['interval'][clst],
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100 * dic_stats['test'][key]['at_risk'][clst]))
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.format(100 * self.dic_stats['test'][key]['interval'][clst],
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100 * self.dic_stats['test'][key]['at_risk'][clst]))
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for perc in ['<0.5m', '<1m', '<2m']:
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print("{} Instances with error {}: {:.2f} %"
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@ -162,7 +159,7 @@ class KittiEval:
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print("-"*100)
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# Print images
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self.print_results(dic_stats, self.show)
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print_results(self.dic_stats, self.show)
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def parse_txts(self, path, method):
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boxes = []
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@ -179,7 +176,7 @@ class KittiEval:
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try:
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with open(path, "r") as ff:
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for line in ff:
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if self.check_conditions(line, thresh=self.dic_thresh_conf[method], mode=method):
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if check_conditions(line, thresh=self.dic_thresh_conf[method], mode=method):
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boxes.append([float(x) for x in line.split()[4:8]])
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loc = ([float(x) for x in line.split()[11:14]])
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dds.append(math.sqrt(loc[0] ** 2 + loc[1] ** 2 + loc[2] ** 2))
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@ -235,7 +232,7 @@ class KittiEval:
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file_lines = ff.readlines()
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for line_our in file_lines[:-1]:
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line_list = [float(x) for x in line_our.split()]
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if self.check_conditions(line_list, thresh=self.dic_thresh_conf[method], mode=method):
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if check_conditions(line_list, thresh=self.dic_thresh_conf[method], mode=method):
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boxes.append(line_list[:4])
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xyzs.append(line_list[4:7])
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dds.append(line_list[7])
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@ -264,8 +261,8 @@ class KittiEval:
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for idx, box in enumerate(boxes):
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if len(boxes_gt) >= 1:
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dd = dds[idx]
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idx_max, iou_max = self.get_idx_max(box, boxes_gt)
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cat = self.get_category(boxes_gt[idx_max], truncs_gt[idx_max], occs_gt[idx_max])
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idx_max, iou_max = get_idx_max(box, boxes_gt)
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cat = get_category(boxes_gt[idx_max], truncs_gt[idx_max], occs_gt[idx_max])
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# Update error if match is found
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if iou_max > self.dic_thresh_iou[method]:
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dd_gt = dds_gt[idx_max]
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@ -295,8 +292,8 @@ class KittiEval:
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epi = stds_epi[idx]
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xyz = xyzs[idx]
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xy_kp = xy_kps[idx]
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idx_max, iou_max = self.get_idx_max(box, boxes_gt)
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cat = self.get_category(boxes_gt[idx_max], truncs_gt[idx_max], occs_gt[idx_max])
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idx_max, iou_max = get_idx_max(box, boxes_gt)
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cat = get_category(boxes_gt[idx_max], truncs_gt[idx_max], occs_gt[idx_max])
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# Update error if match is found
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if iou_max > self.dic_thresh_iou['our']:
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@ -340,12 +337,12 @@ class KittiEval:
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if len(boxes_gt) >= 1:
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dd_our = dds_our[idx]
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dd_geom = dds_geom[idx]
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idx_max, iou_max = self.get_idx_max(box, boxes_gt)
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cat = self.get_category(boxes_gt[idx_max], truncs_gt[idx_max], occs_gt[idx_max])
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idx_max, iou_max = get_idx_max(box, boxes_gt)
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cat = get_category(boxes_gt[idx_max], truncs_gt[idx_max], occs_gt[idx_max])
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idx_max_3dop, iou_max_3dop = self.get_idx_max(box, boxes_3dop)
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idx_max_m3d, iou_max_m3d = self.get_idx_max(box, boxes_m3d)
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idx_max_md, iou_max_md = self.get_idx_max(box, boxes_md)
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idx_max_3dop, iou_max_3dop = get_idx_max(box, boxes_3dop)
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idx_max_m3d, iou_max_m3d = get_idx_max(box, boxes_m3d)
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idx_max_md, iou_max_md = get_idx_max(box, boxes_md)
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iou_min = min(iou_max_3dop, iou_max_m3d, iou_max_md)
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