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- import sys
- import os
- import random
- import numpy as np
- import json
- sys.path.append("/home/shanwei-luo/userdata/mmdetection")
- from mmdet.apis import (async_inference_detector, inference_detector,
- init_detector, show_result_pyplot)
-
- data_coco = json.load(open('/home/shanwei-luo/userdata/pd_datasets/20220606_coco/annotations/val_cat_mode.json'))
- data_name = data_coco["images"]
- data_ann = data_coco['annotations']
- boxes = {}
- for res in data_ann:
- print(res)
- img_id_1 = res["image_id"]
- img_name = data_name[int(img_id_1)]["file_name"]
- #print(img_name)
- #print(res)
- bbox = res["bbox"]
- label = res["category_id"]
- bbox.append(int(label))
- if img_name in boxes.keys():
- boxes[img_name].append(bbox)
- else:
- boxes[img_name]=[]
- boxes[img_name].append(bbox)
- #print(boxes)
-
- 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'
-
- img_path = '/home/shanwei-luo/userdata/pd_datasets/20220606_coco/images/'
-
- model_1 = init_detector(config_file_1, checkpoint_file_1, device='cuda:0')
-
- imgs = os.listdir(img_path)
- #img_id = random.randint(0, len(label_path))
- imgs_labels = []
- imgs_name = []
- num_ng = 0
- for i in range(len(data_name)):
- res_label = 0
- if data_name[i]["file_name"] in boxes.keys():
- res_label = 1
- num_ng += 1
- imgs_labels.append(res_label)
- imgs_name.append(img_path+data_name[i]["file_name"])
- num_ok = len(data_name)-num_ng
- print(len(imgs_labels), num_ok, num_ng)
- imgs_name = imgs_name[:10]
- 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(0.01, 0.2, 0.01):
- imgs_results_1 = []
- for result in results_1:
- res_predict = 0
- print(len(result))
- for i in result:
- print(i.shape)
- for j in range(i.shape[0]):
- if i[j, 4]>score_thr:
- res_predict = 1
- imgs_results_1.append(res_predict)
-
- 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/num_ok, " recall(ng):", count_ng/num_ng)
-
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