File size: 7,419 Bytes
6d35aff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 | import os
import sys
import numpy as np
import json
import pandas as pd
from tqdm import tqdm
import argparse
import inspect
# Use original COCO API
# from pycocotools.coco import COCO
# from pycocotools.cocoeval import COCOeval
# from pycocotools.cocoeval import Params
# Ues modified ovod COCO API
from pycocotools_ovod.coco import COCO
from pycocotools_ovod.cocoeval import COCOeval
from pycocotools_ovod.cocoeval import Params
from pycocotools_ovod.semantic_matching import is_semantic_match, get_semantic_match_anns, gt_cat_match_path, reset_gt_cat_match_cache
def _process_temp_path(env_name: str, basename: str) -> str:
override = os.environ.get(env_name)
if override:
return override
tmp_dir = os.environ.get("ORIENTER_EVALUATION_TMPDIR", os.path.join(os.getcwd(), ".orienter_eval_tmp"))
return os.path.join(tmp_dir, f"{basename}.{os.getpid()}.json")
tmp_ann_path = _process_temp_path("ORIENTER_TMP_ANN_PATH", "tmp_ann")
def _ensure_parent_dir(path: str) -> None:
parent = os.path.dirname(path)
if parent:
os.makedirs(parent, exist_ok=True)
def _remove_temp_file(path: str) -> None:
if os.path.exists(path):
os.remove(path)
def cleanup_temp_outputs() -> None:
_remove_temp_file(gt_cat_match_path)
_remove_temp_file(tmp_ann_path)
reset_gt_cat_match_cache()
def evaluate_results(cocoEval, params = None, display_summary = False):
if params:
cocoEval.params = params
print("IoU Thresholds: ",cocoEval.params.iouThrs)
iou_index = {float(format(val, '.2f')): index for index, val in enumerate(cocoEval.params.iouThrs)}
cocoEval.evaluate()
cocoEval.accumulate(p = params)
if display_summary:
cocoEval.summarize()
precision = cocoEval.eval["precision"]
recall = cocoEval.eval["recall"]
scores = cocoEval.eval["scores"]
return precision, recall, scores, iou_index
def my_format(x : float) -> str:
formatted_number = '%.3e' % x
parts = formatted_number.split('e')
result = f"{parts[0]}e{int(parts[1]):01d}"
return result
# Print final results
def cal_metrics(precision_array, recall_array, scores_array, iou_index, class_name=None):
df = pd.DataFrame(columns=['class', 'IoU', 'mAP', 'F1-Score', 'Precision', 'Recall'])
if not class_name:
class_name = 'all'
mask = precision_array == -1
precision_array = np.ma.array(precision_array, mask=mask)
for iou in iou_index.keys():
map = precision_array[iou_index[iou], :, :, 0, -1].mean(1).mean()
mprecision = precision_array[iou_index[iou], :, :, 0, -1].mean(1)
max_f1 = -1
max_f1_index = 0
for i in range(mprecision.shape[0]):
recall = i * 0.01
precision = mprecision[i]
f1 = 2 * precision * recall / (precision + recall + 1e-8)
if f1 > max_f1:
max_f1 = f1
max_f1_index = i
# map = my_format(map)
# prec = my_format(mprecision[max_f1_index])
# rec = my_format(max_f1_index * 0.01)
# f1 = my_format(max_f1)
prec = mprecision[max_f1_index]
rec = max_f1_index * 0.01
f1 = max_f1
df.loc[len(df), df.columns] = [class_name, iou, map, f1, prec, rec]
return df
def match_cats(coco_gt, coco_pred, eval_dimension):
if os.path.exists(gt_cat_match_path):
os.remove(gt_cat_match_path)
reset_gt_cat_match_cache()
dt_cats = set()
for ann in coco_pred.dataset['annotations']:
dt_cats.add(ann['category_id'])
gt_cats = set()
for cat in coco_gt.dataset['categories']:
gt_cats.add(cat['name'])
dt_cats = list(dt_cats)
gt_cats = list(gt_cats)
dt_cats.sort()
gt_cats.sort()
# dt_cats = gt_cats
gt_cat_match = {gt_cat: [] for gt_cat in gt_cats}
print('matching dt cats to gt cats...', file=sys.stderr)
for gt_cat in tqdm(gt_cats):
for dt_cat in dt_cats:
if is_semantic_match(gt_cat, dt_cat, eval_dimension=eval_dimension):
gt_cat_match[gt_cat].append(dt_cat)
# print(gt_cat, gt_cat_match[gt_cat])
_ensure_parent_dir(gt_cat_match_path)
with open(gt_cat_match_path, 'w') as f:
json.dump(gt_cat_match, f)
def do_evalutate(args):
coco_gt = COCO(args.gt,)
coco_pred = coco_gt.loadRes(args.dt)
if 'ovod' in inspect.getfile(COCOeval):
cocoEval = COCOeval(coco_gt, coco_pred, args.iouType, args.dimension) # modified cocoEval
else:
cocoEval = COCOeval(coco_gt, coco_pred, args.iouType) # original cocoEval
# Load the default parameters for COCOEvaluation
params = cocoEval.params
### Modify required parameters. Available params are:
# imgIds - [all],
# catIds - [all],
# iouThrs - [.5:.05:.95],
# areaRng,maxDets - [1 10 100],
# iouType - ['bbox'],useCats
# eg. param.iouType = 'bbox'
# params.iouThrs = np.linspace(.5, .9, int(np.round((.9 - .5) / .1)) + 1, endpoint=True)
if 'ovod' in inspect.getfile(COCOeval):
match_cats(coco_gt, coco_pred, args.dimension)
# Evaluate the results
precision, recall, scores, iou_index = evaluate_results(cocoEval, params, args.summary)
df = cal_metrics(precision, recall, scores, iou_index)
# Calculate metrics for each category
# for cat in coco_gt.loadCats(coco_gt.getCatIds()):
# # Calculate the metrics
# params.catIds = [cat["id"]]
# precision, recall, scores, iou_index = evaluate_results(cocoEval, params, args.summary)
# class_df = cal_metrics(precision, recall, scores, iou_index, class_name=cat["name"])
# df = pd.concat([df, class_df], ignore_index=True)
df.to_csv(args.log, index=False)
def id2name(args):
with open(args.gt, 'r') as f:
gt = json.load(f)
with open(args.dt, 'r') as f:
dt = json.load(f)
cat_id_name = {cat['id']: cat['name'] for cat in gt['categories']}
for res in dt:
res['category_id'] = cat_id_name[res['category_id']]
_ensure_parent_dir(tmp_ann_path)
with open(tmp_ann_path, 'w') as f:
json.dump(dt, f)
args.dt = tmp_ann_path
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Evaluate Metrics from the predictions and Ground Truths")
parser.add_argument('-d', '--dimension', type=str, help='i / s for interactable / semantics', required=True)
parser.add_argument('-gt', '--gt', type=str, help='path to ground truth json', required=True)
parser.add_argument('-dt', '--dt', type=str, help='path to detection json', required=True)
parser.add_argument('-i', '--iouType', type=str, default='bbox', help='iou type')
parser.add_argument('-l', '--log', type=str, default="evaluation.log")
parser.add_argument('-s', '--summary', action="store_true", help="Print summary of metrics")
parser.add_argument('-n', '--name_id', action="store_true", help="Change category id to the corresponding name")
args = parser.parse_args()
# if os.path.exists(args.log):
# print(f'Output file already exists, skipping evaluation for {args.log}', file=sys.stderr)
# sys.exit(0)
try:
if args.name_id and 'ovod' in inspect.getfile(COCOeval):
id2name(args)
do_evalutate(args)
finally:
cleanup_temp_outputs()
|