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test.py
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import os
import cv2
import json
import argparse
import numpy as np
from db import Transform
from model.rebuilder import Rebuilder
from model.segmentation import ssim_seg, seg_mask
from tools import Timer
from factory import *
from db.eval_func import cal_good_index
def parse_args():
parser = argparse.ArgumentParser(description='Object detection base on anchor.')
parser.add_argument('--cfg', help="Path of config file", type=str, required=True)
parser.add_argument('--model_path', help="Path of model", type=str,required=True)
parser.add_argument('--gpu_id', help="ID of GPU", type=int, default=0)
parser.add_argument('--res_dir', help="Directory path of result", type=str, default='./eval_result')
parser.add_argument('--retest', default=False, type=bool)
return parser.parse_args()
def val_mvtec(val_set, rebuilder, transform):
threshold_seg_dict = dict()
for item in val_set.val_dict:
item_list = list()
item_list = val_set.val_dict[item]
good_count = 0
for threshold_temp in range(0, 256):
for path in item_list:
image = cv2.imread(path, cv2.IMREAD_COLOR)
ori_h, ori_w, _ = image.shape
ori_img, input_tensor = transform(image)
out = rebuilder.inference(input_tensor)
re_img = out.transpose((1, 2, 0))
s_map = ssim_seg(ori_img, re_img)
s_map = cv2.resize(s_map, (ori_w, ori_h))
mask = seg_mask(s_map, threshold_temp)
good_count += cal_good_index(mask, 400)
if good_count >= int(0.99*len(item_list)):
threshold_seg_dict[item] = threshold_temp
break
print('validation: Item:{} finishes'.format(item))
return threshold_seg_dict
def test_mvtec(test_set, rebuilder, transform, save_dir, threshold_seg_dict, val_index):
_t = Timer()
cost_time = list()
threshold_dict = dict()
if not os.path.exists(os.path.join(save_dir, 'ROC_curve')):
os.mkdir(os.path.join(save_dir, 'ROC_curve'))
for item in test_set.test_dict:
threshold_list = list()
item_dict = test_set.test_dict[item]
if not os.path.exists(os.path.join(save_dir, item)):
os.mkdir(os.path.join(save_dir, item))
os.mkdir(os.path.join(save_dir, item, 'ori'))
os.mkdir(os.path.join(save_dir, item, 'gen'))
os.mkdir(os.path.join(save_dir, item, 'mask'))
for type in item_dict:
if not os.path.exists(os.path.join(save_dir, item, 'ori', type)):
os.mkdir(os.path.join(save_dir, item, 'ori', type))
if not os.path.exists(os.path.join(save_dir, item, 'gen', type)):
os.mkdir(os.path.join(save_dir, item, 'gen', type))
if not os.path.exists(os.path.join(save_dir, item, 'mask', type)):
os.mkdir(os.path.join(save_dir, item, 'mask', type))
_time = list()
img_list = item_dict[type]
for path in img_list:
image = cv2.imread(path, cv2.IMREAD_COLOR)
ori_h, ori_w, _ = image.shape
_t.tic()
ori_img, input_tensor = transform(image)
out = rebuilder.inference(input_tensor)
re_img = out.transpose((1, 2, 0))
s_map = ssim_seg(ori_img, re_img, win_size=11, gaussian_weights=True)
s_map = cv2.resize(s_map, (ori_w, ori_h))
if val_index == 1:
mask = seg_mask(s_map, threshold=threshold_seg_dict[item])
elif val_index == 0:
mask = seg_mask(s_map, threshold=threshold_seg_dict)
else:
raise Exception("Invalid val_index")
inference_time = _t.toc()
img_id = path.split('.')[0][-3:]
cv2.imwrite(os.path.join(save_dir, item, 'ori', type, '{}.png'.format(img_id)), ori_img)
cv2.imwrite(os.path.join(save_dir, item, 'gen', type, '{}.png'.format(img_id)), re_img)
cv2.imwrite(os.path.join(save_dir, item, 'mask', type, '{}.png'.format(img_id)), mask)
_time.append(inference_time)
if type != 'good':
threshold_list.append(s_map)
else:
pass
cost_time += _time
mean_time = np.array(_time).mean()
print('Evaluate: Item:{}; Type:{}; Mean time:{:.1f}ms'.format(item, type, mean_time*1000))
_t.clear()
threshold_dict[item] = threshold_list
# calculate mean time
cost_time = np.array(cost_time)
cost_time = np.sort(cost_time)
num = cost_time.shape[0]
num90 = int(num*0.9)
cost_time = cost_time[0:num90]
mean_time = np.mean(cost_time)
print('Mean_time: {:.1f}ms'.format(mean_time*1000))
# evaluate results
print('Evaluating...')
test_set.eval(save_dir,threshold_dict)
def test_chip(test_set, rebuilder, transform, save_dir):
_t = Timer()
cost_time = list()
for type in test_set.test_dict:
img_list = test_set.test_dict[type]
if not os.path.exists(os.path.join(save_dir, type)):
os.mkdir(os.path.join(save_dir, type))
for k, path in enumerate(img_list):
image = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
_t.tic()
ori_img, input_tensor = transform(image)
out = rebuilder.inference(input_tensor)
re_img = out[0]
s_map = ssim_seg(ori_img, re_img, win_size=11, gaussian_weights=True)
mask = seg_mask(s_map, threshold=32)
inference_time = _t.toc()
cat_img = np.concatenate((ori_img, re_img, mask), axis=1)
cv2.imwrite(os.path.join(save_dir, type, '{:d}.png'.format(k)), cat_img)
cost_time.append(inference_time)
if (k+1) % 20 == 0:
print('{}th image, cost time: {:.1f}'.format(k+1, inference_time*1000))
_t.clear()
# calculate mean time
cost_time = np.array(cost_time)
cost_time = np.sort(cost_time)
num = cost_time.shape[0]
num90 = int(num*0.9)
cost_time = cost_time[0:num90]
mean_time = np.mean(cost_time)
print('Mean_time: {:.1f}ms'.format(mean_time*1000))
if __name__ == '__main__':
args = parse_args()
# load config file
cfg_file = os.path.join('./config', args.cfg + '.json')
with open(cfg_file, "r") as f:
configs = json.load(f)
if not os.path.exists(args.res_dir):
os.mkdir(args.res_dir)
# load data set
test_set = load_data_set_from_factory(configs, 'test')
print('Data set: {} has been loaded'.format(configs['db']['name']))
# retest
if args.retest is True:
print('Evaluating...')
test_set.eval(args.res_dir)
exit(0)
# init and load Rebuilder
# load model
transform = Transform(tuple(configs['db']['resize']))
net = load_test_model_from_factory(configs)
rebuilder = Rebuilder(net, gpu_id=args.gpu_id)
rebuilder.load_params(args.model_path)
print('Model: {} has been loaded'.format(configs['model']['name']))
threshold_seg_dict = {}
val_index = 0
if configs['db']['name'] == 'mvtec':
if configs['db']['use_validation_set'] is True:
# load validation set
val_index = 1
val_set = load_data_set_from_factory(configs, 'validation')
print('Data set: {} has been loaded'.format(configs['db']['name']))
# validation for threshold selection
print('Start Validation... ')
threshold_seg_dict = val_mvtec(val_set, rebuilder, transform)
elif configs['db']['use_validation_set'] is False:
val_index = 0
else:
raise Exception("Invalid input")
elif configs['db']['name'] == 'chip':
pass
else:
raise Exception("Invalid set name")
# test each image
print('Start Testing... ')
if configs['db']['name'] == 'mvtec':
if configs['db']['use_validation_set'] is True:
test_mvtec(test_set, rebuilder, transform, args.res_dir, threshold_seg_dict, val_index)
elif configs['db']['use_validation_set'] is False:
test_mvtec(test_set, rebuilder, transform, args.res_dir, 64, val_index)
else:
raise Exception("Invalid input")
elif configs['db']['name'] == 'chip':
test_chip(test_set, rebuilder, transform, args.res_dir)
else:
raise Exception("Invalid set name")