code_test/test06.py

import sys
import os
import random
import numpy as np
sys.path.append("/home/shanwei-luo/userdata/mmdetection")
from mmdet.apis import (async_inference_detector, inference_detector,
                        init_detector, show_result_pyplot)

class_AOI_name = {"bu_pi_pei":"1","fang_xiang_fan":"2","err.txt_c_not_f":"3", "shang_xi_bu_lia":"4"}

config_file_1 = '/home/shanwei-luo/userdata/mmdetection/work_dirs/AD_pd_test01/AD_pd_test01.py'
checkpoint_file_1 = '/home/shanwei-luo/userdata/mmdetection/work_dirs/AD_pd_test01/epoch_18.pth'

imgs_ok_path = '/home/shanwei-luo/userdata/pd_datasets/20220605/OK/'
imgs_ng_path = '/home/shanwei-luo/userdata/pd_datasets/20220605/NG/'

model_1 = init_detector(config_file_1, checkpoint_file_1, device='cuda:0')

imgs_ok = os.listdir(imgs_ok_path)
imgs_ng = os.listdir(imgs_ng_path)

count_label_ok = len(imgs_ok)
count_label_ng = len(imgs_ng)
print(count_label_ok,count_label_ng)
imgs_labels = []
imgs_name = []
for img in imgs_ok:
    '''img_name = img.split("@")
    if img_name[2] in class_AOI_name.keys():
        count_label_ok -= 1
        continue'''
    imgs_labels.append(0)
    imgs_name.append(imgs_ok_path+img)

for img in imgs_ng:
    '''img_name = img.split("@")
    if img_name[2] in class_AOI_name.keys():
        count_label_ng -= 1
        continue'''
    imgs_labels.append(1)
    imgs_name.append(imgs_ng_path+img)

print(count_label_ok,count_label_ng, len(imgs_labels))
print("before infer")
index = 0
num = len(imgs_name)
results_1 = []
step = 64
while index<num: 
    index += step
    if index < num:
        results_1_tmp = inference_detector(model_1, imgs_name[index-step:index])
    else:
        results_1_tmp = inference_detector(model_1, imgs_name[index-step:num])
    results_1 += results_1_tmp
    print(len(results_1))
print("after infer")

#score_thrs = [0.01, 0.011, 0.012, 0.013, 0.014, 0.015, 0.016, 0.017, 0.018, 0.019, 0.02]
for score_thr in np.arange(0.05, 0.1, 0.001):
    imgs_results_1 = []
    for result in results_1:
        res_predict = 0
        for i in result:
            for j in range(i.shape[0]):
                if i[j, 4]>score_thr:
                    res_predict = 1
        imgs_results_1.append(res_predict)

    #print(len(imgs_results_1))

    count_ng = 0
    count_ok = 0
    for i in range(len(imgs_labels)):
        if imgs_labels[i]==0 and imgs_results_1[i]==0:
            count_ok += 1
        if imgs_labels[i]==1 and imgs_results_1[i]==1:
            count_ng += 1
        if imgs_labels[i]==1 and imgs_results_1[i]==0:
            print(imgs_name[i])

    print("score_thr:", score_thr, "  recall(ok):", count_ok/count_label_ok, "  recall(ng):", count_ng/count_label_ng)