def main(args=None):
if args is None:
args = sys.argv[1:]
args = parse_args(args)
args_dic = vars(args)
if args.dataset_type == 'voc':
gt_dir = os.path.join(args.dataset_dir, '_annotations')
elif args.dataset_type == 'coco':
gt_loader = COCO(
os.path.join(args.dataset_dir, 'instances_val2017.json'))
if args.test_model == 'yolov3':
test_model = YOLOv3(sess=K.get_session())
img_size = (416, 416)
elif args.test_model == 'retina_resnet50':
test_model = KerasResNet50RetinaNetModel()
img_size = (416, 416)
elif args.test_model == 'ssd_mobile':
test_model = SSD_detector()
img_size = (500, 500)
test_folders = []
for temp_folder in os.listdir(args.dataset_dir):
if not os.path.isdir(os.path.join(args.dataset_dir, temp_folder)):
continue
if temp_folder == 'imagenet_val_5000' or temp_folder == '.git' or temp_folder == '_annotations' or temp_folder == '_segmentations':
continue
if len(PICK_LIST) != 0 and temp_folder not in PICK_LIST:
continue
if len(BAN_LIST) != 0 and temp_folder in BAN_LIST:
continue
test_folders.append(temp_folder)
result_dict = {}
for curt_folder in tqdm(test_folders):
print('Folder : {0}'.format(curt_folder))
currentDT = datetime.datetime.now()
result_dir = 'temp_dect_results_{0}_{1}'.format(
currentDT.strftime("%Y_%m_%d_%H_%M_%S"), currentDT.microsecond)
if os.path.exists(result_dir):
raise
os.mkdir(result_dir)
os.mkdir(os.path.join(result_dir, 'gt'))
os.mkdir(os.path.join(result_dir, 'pd'))
for adv_name in tqdm(
os.listdir(os.path.join(args.dataset_dir, curt_folder))):
temp_image_name_noext = os.path.splitext(adv_name)[0]
if args.dataset_type == 'voc':
gt_path = os.path.join(gt_dir, temp_image_name_noext + '.xml')
if curt_folder == 'ori':
adv_img_path = os.path.join(args.dataset_dir, curt_folder,
temp_image_name_noext + '.jpg')
else:
adv_img_path = os.path.join(args.dataset_dir, curt_folder,
temp_image_name_noext + '.png')
if not os.path.exists(adv_img_path):
print('File {0} not found.'.format(adv_img_path))
continue
if args.dataset_type == 'voc':
gt_out = load_voc_annotations(gt_path, img_size)
gt_out['classes'] = gt_out['classes'].astype(np.int)
elif args.dataset_type == 'coco':
gt_out = load_coco_annotations(temp_image_name_noext, img_size,
gt_loader)
image_adv_np = load_image(data_format='channels_last',
shape=img_size,
bounds=(0, 255),
abs_path=True,
fpath=adv_img_path)
Image.fromarray((image_adv_np).astype(np.uint8)).save(
os.path.join(result_dir, 'temp_adv.jpg'))
if args.test_model == 'retina_resnet50':
'''
labels_to_names = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
image = read_image_bgr(adv_img_path)
draw = image.copy()
draw = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)
image = preprocess_image(image)
image, scale = resize_image(image)
boxes, scores, labels = test_model._model.predict_on_batch(np.expand_dims(image, axis=0))
boxes /= scale
for box, score, label in zip(boxes[0], scores[0], labels[0]):
# scores are sorted so we can break
if score < 0.5:
break
color = label_color(label)
b = box.astype(int)
draw_box(draw, b, color=color)
caption = "{} {:.3f}".format(labels_to_names[label], score)
draw_caption(draw, b, caption)
Image.fromarray(draw).save('temp_img_out/' + adv_name)
'''
image = read_image_bgr(adv_img_path)
image = preprocess_image(image)
image = resize_image_2(image, img_size)
image, scale = resize_image(image)
pd_out = test_model.batch_predictions(
np.expand_dims(image, axis=0))[0]
boxes_list = pd_out['boxes']
for idx, temp_box in enumerate(boxes_list):
pd_out['boxes'][idx] = np.array(temp_box) / scale
else:
image_adv_pil = Image.fromarray(image_adv_np.astype(np.uint8))
pd_out = test_model.predict(image_adv_pil)
if args.dataset_type == 'voc':
pd_out = _transfer_label_to_voc(pd_out, args)
elif args.dataset_type == 'coco':
if args.test_model == 'yolov3' or args.test_model == 'retina_resnet50':
pd_out = _transfer_label_to_coco91(pd_out, args)
save_detection_to_file(
gt_out,
os.path.join(result_dir, 'gt', temp_image_name_noext + '.txt'),
'ground_truth')
save_detection_to_file(
pd_out,
os.path.join(result_dir, 'pd', temp_image_name_noext + '.txt'),
'detection')
if pd_out:
save_bbox_img(os.path.join(result_dir, 'temp_adv.jpg'),
pd_out['boxes'],
out_file='temp_adv_box.jpg')
else:
save_bbox_img(os.path.join(result_dir, 'temp_adv.jpg'), [],
out_file='temp_adv_box.jpg')
mAP_score = calculate_mAP_from_files(os.path.join(result_dir, 'gt'),
os.path.join(result_dir, 'pd'))
shutil.rmtree(result_dir)
print(curt_folder, ' : ', mAP_score)
result_dict[curt_folder] = 'mAP: {0:.04f}'.format(mAP_score)
with open(
'temp_det_results_gt_{0}_{1}.json'.format(
args.test_model, args.dataset_type), 'w') as fout:
json.dump(result_dict, fout, indent=2)
def main(args=None):
if args is None:
args = sys.argv[1:]
args = parse_args(args)
args_dic = vars(args)
with open('utils/labels.txt', 'r') as inf:
args_dic['imagenet_dict'] = eval(inf.read())
input_dir = os.path.join(args.dataset_dir, 'ori')
if args.test_model == 'fasterrcnn':
test_model = torchvision.models.detection.fasterrcnn_resnet50_fpn(
pretrained=True).cuda().eval()
img_size = (416, 416)
elif args.test_model == 'maskrcnn':
test_model = torchvision.models.detection.maskrcnn_resnet50_fpn(
pretrained=True).cuda().eval()
img_size = (416, 416)
elif args.test_model == 'keypointrcnn':
test_model = torchvision.models.detection.keypointrcnn_resnet50_fpn(
pretrained=True).cuda().eval()
img_size = (416, 416)
else:
raise ValueError('Invalid test_model {0}'.format(args.test_model))
test_folders = []
for temp_folder in os.listdir(args.dataset_dir):
if not os.path.isdir(os.path.join(args.dataset_dir, temp_folder)):
continue
if temp_folder == 'imagenet_val_5000' or temp_folder == 'ori' or temp_folder == '.git' or temp_folder == '_annotations' or temp_folder == '_segmentations':
continue
if len(PICK_LIST) != 0 and temp_folder not in PICK_LIST:
continue
if len(BAN_LIST) != 0 and temp_folder in BAN_LIST:
continue
test_folders.append(temp_folder)
result_dict = {}
for curt_folder in tqdm(test_folders):
print('Folder : {0}'.format(curt_folder))
currentDT = datetime.datetime.now()
result_dir = 'temp_dect_results_{0}_{1}'.format(
currentDT.strftime("%Y_%m_%d_%H_%M_%S"), currentDT.microsecond)
if os.path.exists(result_dir):
raise
os.mkdir(result_dir)
os.mkdir(os.path.join(result_dir, 'gt'))
os.mkdir(os.path.join(result_dir, 'pd'))
is_missing = False
for image_name in tqdm(os.listdir(input_dir)):
temp_image_name_noext = os.path.splitext(image_name)[0]
ori_img_path = os.path.join(input_dir, image_name)
adv_img_path = os.path.join(args.dataset_dir, curt_folder,
image_name)
adv_img_path = os.path.splitext(adv_img_path)[0] + '.png'
if not os.path.exists(adv_img_path):
if not is_missing:
is_missing = True
print('File {0} not found.'.format(image_name))
continue
image_ori_np = load_image(data_format='channels_first',
shape=img_size,
bounds=(0, 1),
abs_path=True,
fpath=ori_img_path)
Image.fromarray(
np.transpose(image_ori_np * 255.,
(1, 2, 0)).astype(np.uint8)).save(
os.path.join(result_dir, 'temp_ori.png'))
image_ori_var = numpy_to_variable(image_ori_np)
with torch.no_grad():
gt_out = test_model(image_ori_var)
gt_out = convert_torch_det_output(gt_out, cs_th=0.3)[0]
image_adv_np = load_image(data_format='channels_first',
shape=img_size,
bounds=(0, 1),
abs_path=True,
fpath=adv_img_path)
Image.fromarray(
np.transpose(image_adv_np * 255.,
(1, 2, 0)).astype(np.uint8)).save(
os.path.join(result_dir, 'temp_adv.png'))
image_adv_var = numpy_to_variable(image_adv_np)
with torch.no_grad():
pd_out = test_model(image_adv_var)
pd_out = convert_torch_det_output(pd_out, cs_th=0.3)[0]
save_detection_to_file(
gt_out,
os.path.join(result_dir, 'gt', temp_image_name_noext + '.txt'),
'ground_truth')
save_detection_to_file(
pd_out,
os.path.join(result_dir, 'pd', temp_image_name_noext + '.txt'),
'detection')
if gt_out:
save_bbox_img(os.path.join(result_dir, 'temp_ori.png'),
gt_out['boxes'],
out_file=os.path.join(result_dir,
'temp_ori_box.png'))
else:
save_bbox_img(os.path.join(result_dir, 'temp_ori.png'), [],
out_file=os.path.join(result_dir,
'temp_ori_box.png'))
if pd_out:
save_bbox_img(os.path.join(result_dir, 'temp_adv.png'),
pd_out['boxes'],
out_file=os.path.join(result_dir,
'temp_adv_box.png'))
else:
save_bbox_img(os.path.join(result_dir, 'temp_adv.png'), [],
out_file=os.path.join(result_dir,
'temp_adv_box.png'))
mAP_score = calculate_mAP_from_files(os.path.join(result_dir, 'gt'),
os.path.join(result_dir, 'pd'))
shutil.rmtree(result_dir)
print(curt_folder, ' : ', mAP_score)
result_dict[curt_folder] = 'mAP: {0:.04f}'.format(mAP_score)
with open('temp_det_results_{0}.json'.format(args.test_model),
'w') as fout:
json.dump(result_dict, fout, indent=2)
def main(args=None):
img_size = (500, 500)
if args is None:
args = sys.argv[1:]
args = parse_args(args)
args_dic = vars(args)
dataset_folder_name = args.dataset_dir.split('/')[-1]
pd_dic_dir = os.path.join(args.pd_folder, dataset_folder_name)
if args.dataset_type == 'voc':
gt_dir = os.path.join(args.dataset_dir, '_annotations')
elif args.dataset_type == 'coco':
gt_loader = COCO(
os.path.join(args.dataset_dir, 'instances_val2017.json'))
test_folders = []
for temp_folder in os.listdir(args.dataset_dir):
if not os.path.isdir(os.path.join(args.dataset_dir, temp_folder)):
continue
if temp_folder == 'imagenet_val_5000' or temp_folder == '.git' or temp_folder == '_annotations' or temp_folder == '_segmentations':
continue
if len(PICK_LIST) != 0 and temp_folder not in PICK_LIST:
continue
if len(BAN_LIST) != 0 and temp_folder in BAN_LIST:
continue
test_folders.append(temp_folder)
result_dict = {}
for curt_folder in tqdm(test_folders):
print('Folder : {0}'.format(curt_folder))
currentDT = datetime.datetime.now()
result_dir = 'temp_dect_results_{0}_{1}'.format(
currentDT.strftime("%Y_%m_%d_%H_%M_%S"), currentDT.microsecond)
if os.path.exists(result_dir):
raise
os.mkdir(result_dir)
os.mkdir(os.path.join(result_dir, 'gt'))
os.mkdir(os.path.join(result_dir, 'pd'))
for adv_name in tqdm(
os.listdir(os.path.join(args.dataset_dir, curt_folder))):
temp_image_name_noext = os.path.splitext(adv_name)[0]
if args.dataset_type == 'voc':
gt_path = os.path.join(gt_dir, temp_image_name_noext + '.xml')
if curt_folder == 'ori':
adv_img_path = os.path.join(args.dataset_dir, curt_folder,
temp_image_name_noext + '.jpg')
else:
adv_img_path = os.path.join(args.dataset_dir, curt_folder,
temp_image_name_noext + '.png')
if not os.path.exists(adv_img_path):
print('File {0} not found.'.format(adv_img_path))
continue
if args.dataset_type == 'voc':
gt_out = load_voc_annotations(gt_path, img_size)
gt_out['classes'] = gt_out['classes'].astype(np.int)
elif args.dataset_type == 'coco':
gt_out = load_coco_annotations(temp_image_name_noext, img_size,
gt_loader)
with open(
os.path.join(pd_dic_dir, curt_folder,
temp_image_name_noext + '.pkl'), 'rb') as f:
pd_out_ori = pickle.load(f)
pd_out = {
'scores': [],
'boxes': [],
'classes': [],
}
for temp_score, temp_class, temp_box in zip(
pd_out_ori['detection_scores'],
pd_out_ori['detection_classes'],
pd_out_ori['detection_boxes']):
pd_out['scores'].append(temp_score)
pd_out['classes'].append(temp_class)
pd_out['boxes'].append([
temp_box[0] * img_size[0], temp_box[1] * img_size[1],
temp_box[2] * img_size[0], temp_box[3] * img_size[1]
])
if args.dataset_type == 'voc':
pd_out = _transfer_label_to_voc(pd_out, args)
save_detection_to_file(
gt_out,
os.path.join(result_dir, 'gt', temp_image_name_noext + '.txt'),
'ground_truth')
save_detection_to_file(
pd_out,
os.path.join(result_dir, 'pd', temp_image_name_noext + '.txt'),
'detection')
mAP_score = calculate_mAP_from_files(os.path.join(result_dir, 'gt'),
os.path.join(result_dir, 'pd'))
shutil.rmtree(result_dir)
print(curt_folder, ' : ', mAP_score)
result_dict[curt_folder] = 'mAP: {0:.04f}'.format(mAP_score)
with open(
'temp_det_results_gt_{0}_{1}.json'.format(
'tfdic', args.dataset_type), 'w') as fout:
json.dump(result_dict, fout, indent=2)
def main(args=None):
if args is None:
args = sys.argv[1:]
args = parse_args(args)
args_dic = vars(args)
with open('utils/labels.txt', 'r') as inf:
args_dic['imagenet_dict'] = eval(inf.read())
input_dir = os.path.join(args.dataset_dir, 'ori')
if args.test_model == 'yolov3':
test_model = YOLOv3(sess=K.get_session())
img_size = (416, 416)
elif args.test_model == 'retina_resnet50':
test_model = KerasResNet50RetinaNetModel()
img_size = (416, 416)
elif args.test_model == 'ssd_mobile':
test_model = SSD_detector()
img_size = (500, 500)
test_folders = []
for temp_folder in os.listdir(args.dataset_dir):
if not os.path.isdir(os.path.join(args.dataset_dir, temp_folder)):
continue
if temp_folder == 'imagenet_val_5000' or temp_folder == 'ori' or temp_folder == '.git' or temp_folder == '_annotations' or temp_folder == '_segmentations':
continue
if len(PICK_LIST) != 0 and temp_folder not in PICK_LIST:
continue
if len(BAN_LIST) != 0 and temp_folder in BAN_LIST:
continue
test_folders.append(temp_folder)
result_dict = {}
for curt_folder in tqdm(test_folders):
print('Folder : {0}'.format(curt_folder))
currentDT = datetime.datetime.now()
result_dir = 'temp_dect_results_{0}_{1}'.format(
currentDT.strftime("%Y_%m_%d_%H_%M_%S"), currentDT.microsecond)
if os.path.exists(result_dir):
raise
os.mkdir(result_dir)
os.mkdir(os.path.join(result_dir, 'gt'))
os.mkdir(os.path.join(result_dir, 'pd'))
for image_name in tqdm(os.listdir(input_dir)):
temp_image_name_noext = os.path.splitext(image_name)[0]
ori_img_path = os.path.join(input_dir, image_name)
adv_img_path = os.path.join(args.dataset_dir, curt_folder,
image_name)
adv_img_path = os.path.splitext(adv_img_path)[0] + '.png'
if not os.path.exists(adv_img_path):
print('File {0} not found.'.format(image_name))
continue
image_ori_np = load_image(data_format='channels_last',
shape=img_size,
bounds=(0, 255),
abs_path=True,
fpath=ori_img_path)
Image.fromarray((image_ori_np).astype(np.uint8)).save(
os.path.join(result_dir, 'ori.jpg'))
if args.test_model == 'retina_resnet50':
image = read_image_bgr(ori_img_path)
image = preprocess_image(image)
image = resize_image_2(image, img_size)
image, scale = resize_image(image)
gt_out = test_model.batch_predictions(
np.expand_dims(image, axis=0))[0]
boxes_list = gt_out['boxes']
for idx, temp_box in enumerate(boxes_list):
gt_out['boxes'][idx] = np.array(temp_box) / scale
else:
image_ori_pil = Image.fromarray(image_ori_np.astype(np.uint8))
gt_out = test_model.predict(image_ori_pil)
image_adv_np = load_image(data_format='channels_last',
shape=img_size,
bounds=(0, 255),
abs_path=True,
fpath=adv_img_path)
Image.fromarray((image_adv_np).astype(np.uint8)).save(
os.path.join(result_dir, 'temp_adv.jpg'))
if args.test_model == 'retina_resnet50':
image = read_image_bgr(adv_img_path)
image = preprocess_image(image)
image = resize_image_2(image, img_size)
image, scale = resize_image(image)
pd_out = test_model.batch_predictions(
np.expand_dims(image, axis=0))[0]
boxes_list = pd_out['boxes']
for idx, temp_box in enumerate(boxes_list):
pd_out['boxes'][idx] = np.array(temp_box) / scale
else:
image_adv_pil = Image.fromarray(image_adv_np.astype(np.uint8))
pd_out = test_model.predict(image_adv_pil)
save_detection_to_file(
gt_out,
os.path.join(result_dir, 'gt', temp_image_name_noext + '.txt'),
'ground_truth')
save_detection_to_file(
pd_out,
os.path.join(result_dir, 'pd', temp_image_name_noext + '.txt'),
'detection')
if gt_out:
save_bbox_img(os.path.join(result_dir, 'ori.jpg'),
gt_out['boxes'],
out_file='temp_ori_box.jpg')
else:
save_bbox_img(os.path.join(result_dir, 'ori.jpg'), [],
out_file='temp_ori_box.jpg')
if pd_out:
save_bbox_img(os.path.join(result_dir, 'temp_adv.jpg'),
pd_out['boxes'],
out_file='temp_adv_box.jpg')
else:
save_bbox_img(os.path.join(result_dir, 'temp_adv.jpg'), [],
out_file='temp_adv_box.jpg')
mAP_score = calculate_mAP_from_files(os.path.join(result_dir, 'gt'),
os.path.join(result_dir, 'pd'))
shutil.rmtree(result_dir)
print(curt_folder, ' : ', mAP_score)
result_dict[curt_folder] = 'mAP: {0:.04f}'.format(mAP_score)
with open('temp_det_results_{0}.json'.format(args.test_model),
'w') as fout:
json.dump(result_dict, fout, indent=2)
def main(args=None):
if args is None:
args = sys.argv[1:]
args = parse_args(args)
args_dic = vars(args)
if args.dataset_type == 'voc':
gt_dir = os.path.join(args.dataset_dir, '_annotations')
elif args.dataset_type == 'coco':
gt_loader = COCO(
os.path.join(args.dataset_dir, 'instances_val2017.json'))
if args.test_model == 'fasterrcnn':
test_model = torchvision.models.detection.fasterrcnn_resnet50_fpn(
pretrained=True).cuda().eval()
img_size = (416, 416)
elif args.test_model == 'maskrcnn':
test_model = torchvision.models.detection.maskrcnn_resnet50_fpn(
pretrained=True).cuda().eval()
img_size = (416, 416)
elif args.test_model == 'keypointrcnn':
test_model = torchvision.models.detection.keypointrcnn_resnet50_fpn(
pretrained=True).cuda().eval()
img_size = (416, 416)
else:
raise ValueError('Invalid test_model {0}'.format(args.test_model))
test_folders = []
for temp_folder in os.listdir(args.dataset_dir):
if not os.path.isdir(os.path.join(args.dataset_dir, temp_folder)):
continue
if temp_folder == 'imagenet_val_5000' or temp_folder == '.git' or temp_folder == '_annotations' or temp_folder == '_segmentations':
continue
if len(PICK_LIST) != 0 and temp_folder not in PICK_LIST:
continue
if len(BAN_LIST) != 0 and temp_folder in BAN_LIST:
continue
test_folders.append(temp_folder)
result_dict = {}
for curt_folder in tqdm(test_folders):
print('Folder : {0}'.format(curt_folder))
currentDT = datetime.datetime.now()
result_dir = 'temp_dect_results_{0}_{1}'.format(
currentDT.strftime("%Y_%m_%d_%H_%M_%S"), currentDT.microsecond)
if os.path.exists(result_dir):
raise
os.mkdir(result_dir)
os.mkdir(os.path.join(result_dir, 'gt'))
os.mkdir(os.path.join(result_dir, 'pd'))
for adv_name in tqdm(
os.listdir(os.path.join(args.dataset_dir, curt_folder))):
temp_image_name_noext = os.path.splitext(adv_name)[0]
if args.dataset_type == 'voc':
gt_path = os.path.join(gt_dir, temp_image_name_noext + '.xml')
if curt_folder == 'ori':
adv_img_path = os.path.join(args.dataset_dir, curt_folder,
temp_image_name_noext + '.jpg')
else:
adv_img_path = os.path.join(args.dataset_dir, curt_folder,
temp_image_name_noext + '.png')
if not os.path.exists(adv_img_path):
print('File {0} not found.'.format(adv_name))
continue
if args.dataset_type == 'voc':
gt_out = load_voc_annotations(gt_path, img_size)
gt_out['classes'] = gt_out['classes'].astype(np.int)
elif args.dataset_type == 'coco':
gt_out = load_coco_annotations(temp_image_name_noext, img_size,
gt_loader)
if args.test_model == 'keypointrcnn':
gt_out = only_person(gt_out, args)
image_adv_np = load_image(data_format='channels_first',
shape=img_size,
bounds=(0, 1),
abs_path=True,
fpath=adv_img_path)
Image.fromarray(
np.transpose(image_adv_np * 255.,
(1, 2, 0)).astype(np.uint8)).save(
os.path.join(result_dir, 'temp_adv.png'))
image_adv_var = numpy_to_variable(image_adv_np)
with torch.no_grad():
pd_out = test_model(image_adv_var)
pd_out = convert_torch_det_output(pd_out, cs_th=0.5)[0]
if args.dataset_type == 'voc':
pd_out = _transfer_label_to_voc(pd_out, args)
bbox_list = pd_out['boxes']
for idx, temp_bbox in enumerate(bbox_list):
pd_out['boxes'][idx] = [
temp_bbox[1], temp_bbox[0], temp_bbox[3], temp_bbox[2]
]
save_detection_to_file(
gt_out,
os.path.join(result_dir, 'gt', temp_image_name_noext + '.txt'),
'ground_truth')
save_detection_to_file(
pd_out,
os.path.join(result_dir, 'pd', temp_image_name_noext + '.txt'),
'detection')
if pd_out:
save_bbox_img(os.path.join(result_dir, 'temp_adv.png'),
pd_out['boxes'],
out_file=os.path.join(result_dir,
'temp_adv_box.png'))
else:
save_bbox_img(os.path.join(result_dir, 'temp_adv.png'), [],
out_file=os.path.join(result_dir,
'temp_adv_box.png'))
mAP_score = calculate_mAP_from_files(os.path.join(result_dir, 'gt'),
os.path.join(result_dir, 'pd'))
shutil.rmtree(result_dir)
print(curt_folder, ' : ', mAP_score)
result_dict[curt_folder] = 'mAP: {0:.04f}'.format(mAP_score)
with open(
'temp_det_results_gt_{0}_{1}.json'.format(
args.test_model, args.dataset_type), 'w') as fout:
json.dump(result_dict, fout, indent=2)