def run_SIO_evaluation():
# set model to be tested
from WIDERFACE_LFD_XS_work_dir_20210120_125717.WIDERFACE_LFD_XS \
import config_dict, prepare_model
# prepare model --------------------------------------------------------
prepare_model()
param_file_path = './_WIDERFACE_LFD_XS_work_dir_20210120_125717/epoch_1000.pth'
load_checkpoint(config_dict['model'], load_path=param_file_path, strict=True)
val_image_root = '/home/yonghaohe/datasets/WIDER_FACE/WIDER_val/images'
results_save_root = './WIDERFACE_evaluation/LFD_XS_20210120_125717'
classification_threshold = 0.01
nms_threshold = 0.4
SIO_evaluation(
model=config_dict['model'],
val_image_root=val_image_root,
results_save_root=results_save_root,
classification_threshold=classification_threshold,
nms_threshold=nms_threshold
)
def evaluate():
# set model to be evaluated ----------------------------------------------------------
from TT100K_LFD_S_work_dir_20210127_170801.TT100K_LFD_S import config_dict, prepare_model
weight_file_path = './TT100K_LFD_S_work_dir_20210127_170801/epoch_500.pth'
classification_threshold = 0.1
nms_threshold = 0.1
prepare_model()
load_checkpoint(config_dict['model'],
load_path=weight_file_path,
strict=True)
# predict results and save to json
results_json = dict()
results_json['imgs'] = dict()
test_image_root = '/home/yonghaohe/datasets/TT100K/data/test'
test_image_paths_list = [
os.path.join(test_image_root, file_name)
for file_name in os.listdir(test_image_root)
if file_name.endswith('.jpg')
]
dataset_path = './TT100K_pack/train.pkl'
dataset = Dataset(load_path=dataset_path)
label_indexes_to_category_names = dataset.meta_info[
'label_indexes_to_category_names']
results_json_save_path = os.path.join(
'./TT100K_evaluation/',
os.path.dirname(weight_file_path).split('/')[-1] + '_results.json')
if not os.path.exists(results_json_save_path):
for i, image_path in enumerate(test_image_paths_list):
image_id = os.path.basename(image_path).split('.')[0]
image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
results = config_dict['model'].predict_for_single_image(
image,
aug_pipeline=simple_widerface_val_pipeline,
classification_threshold=classification_threshold,
nms_threshold=nms_threshold,
class_agnostic=True)
temp = dict(id=image_id, objects=list())
for result in results:
cat = label_indexes_to_category_names[result[0]]
score = result[1] * 100 # make score in [0, 100]
xmin = result[2]
ymin = result[3]
xmax = result[4] + result[2]
ymax = result[5] + result[3]
temp_bbox = dict(bbox={
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax
},
category=cat,
score=score)
temp['objects'].append(temp_bbox)
results_json['imgs'][image_id] = temp
print('[%5d] image is predicted.' % i)
if not os.path.exists(os.path.dirname(results_json_save_path)):
os.makedirs(os.path.dirname(results_json_save_path))
json.dump(results_json,
open(results_json_save_path, 'w'),
indent=4,
ensure_ascii=False)
# evaluate
gt_annotation_json_path = '/home/yonghaohe/datasets/TT100K/data/annotations.json'
gt_json = json.load(open(gt_annotation_json_path, 'r'))
results_json = json.load(open(results_json_save_path, 'r'))
eval_result = official_eval.eval_annos(annos_gd=gt_json,
annos_rt=results_json,
iou=0.5,
imgids=None,
check_type=True,
types=official_eval.type45,
minscore=90,
minboxsize=0,
maxboxsize=400,
match_same=True)
print(eval_result['report'])
# -*- coding: utf-8 -*-
import sys
sys.path.append('../..')
from lfd.execution.utils import load_checkpoint
from lfd.data_pipeline.augmentation import *
import cv2
from WIDERFACE_train.WIDERFACE_LFD_XS_FPN_work_dir_20210123_233858.WIDERFACE_LFD_XS_FPN import config_dict, prepare_model
prepare_model()
param_file_path = './WIDERFACE_LFD_XS_FPN_work_dir_20210123_233858/epoch_1000.pth'
load_checkpoint(config_dict['model'], load_path=param_file_path, strict=True)
image_path = '/home/yonghaohe/projects/LFD-A-Light-And-Fast-Detector/code_test/debug_images/WIDERFACE/worlds-largest-selfie1.jpg'
image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
results = config_dict['model'].predict_for_single_image(
image,
aug_pipeline=simple_widerface_val_pipeline,
classification_threshold=0.5,
nms_threshold=0.3)
for bbox in results:
print(bbox)
cv2.rectangle(image, (int(bbox[2]), int(bbox[3])),
(int(bbox[2] + bbox[4]), int(bbox[3] + bbox[5])),
(0, 255, 0), 1)
print('%d faces are detected!' % len(results))