def inference_onnx(src, cfg):
predictor = DetPredictorTRT(cfg)
if isinstance(src, (str, list)): # 如果是list，则认为是图片预测
for result in predictor(src):
vis_all_opencv(*result, class_names=get_classes('coco'), score_thr=0.5)
if isinstance(src, int):
vis_cam(src, predictor, class_names=get_classes('coco'), score_thr=0.2)
def print_map_summary(mean_ap, results, dataset=None):
"""Print mAP and results of each class.
Args:
mean_ap(float): calculated from `eval_map`
results(list): calculated from `eval_map`
dataset(None or str or list): dataset name.
"""
num_scales = len(results[0]['ap']) if isinstance(results[0]['ap'],
np.ndarray) else 1
num_classes = len(results)
recalls = np.zeros((num_scales, num_classes), dtype=np.float32)
precisions = np.zeros((num_scales, num_classes), dtype=np.float32)
aps = np.zeros((num_scales, num_classes), dtype=np.float32)
num_gts = np.zeros((num_scales, num_classes), dtype=int)
for i, cls_result in enumerate(results):
if cls_result['recall'].size > 0:
recalls[:, i] = np.array(cls_result['recall'], ndmin=2)[:, -1]
precisions[:, i] = np.array(
cls_result['precision'], ndmin=2)[:, -1]
aps[:, i] = cls_result['ap']
num_gts[:, i] = cls_result['num_gts']
if dataset is None:
label_names = [str(i) for i in range(1, num_classes + 1)]
else:
label_names = get_classes(dataset)
if not isinstance(mean_ap, list):
mean_ap = [mean_ap]
header = ['class', 'gts', 'dets', 'recall', 'precision', 'ap']
for i in range(num_scales):
table_data = [header]
for j in range(num_classes):
row_data = [
label_names[j], num_gts[i, j], results[j]['num_dets'],
'{:.3f}'.format(recalls[i, j]), '{:.3f}'.format(
precisions[i, j]), '{:.3f}'.format(aps[i, j])
]
table_data.append(row_data)
table_data.append(['mAP', '', '', '', '', '{:.3f}'.format(mean_ap[i])])
table = AsciiTable(table_data)
table.inner_footing_row_border = True
print(table.table)
load_device='cuda')
if task == 'load': # 已有数据集评估文件，重新载入进行评估
eval_dataset_det(
cfg_path=cfg_path,
load_from='/home/ubuntu/mytrain/ssd_vgg_voc/epoch_11.pth',
load_device='cuda',
result_file=
'/home/ubuntu/mytrain/ssd_vgg_voc/20190928_084133_eval_result.pkl')
if task == 'test': # 测试单张图或多张图的结果： cpu上0.649 sec， gpu上0.388 sec
img = cv2.imread('/home/ubuntu/MyDatasets/misc/test13.jpg')
predictor = DetPredictor(
cfg_path,
load_from=
'/home/ubuntu/mytrain/ssd_vgg_voc/old weights/epoch_61.pth',
load_device='cpu')
for results in predictor([img]):
vis_all_pyplot(*results,
class_names=get_classes('voc'),
score_thr=0.5)
if task == 'video': # 测试视频预测结果：注意方法稍有不同，vis_cam需要传入一个predictor
src = 0 # src可以等于int(cam_id), str(video path), list(img_list)
predictor = DetPredictor(
cfg_path,
load_from=
'/home/ubuntu/mytrain/ssd_vgg_voc/old weights/epoch_61.pth',
load_device='cpu')
vis_cam(src, predictor, class_names=get_classes('voc'), score_thr=0.2)
if task == 'train': # 模型训练
train_fcos(cfg=cfg,
resume_from = None)
if task == 'eval': # 数据集评估
parse_log('/home/ubuntu/mytrain/ssd_vgg_voc/20190926_181047.log')
eval_dataset_det(cfg_path=cfg_path,
load_from = '/home/ubuntu/mytrain/ssd_vgg_voc/epoch_11.pth',
load_device='cuda')
if task == 'load': # 已有数据集评估文件，重新载入进行评估
eval_dataset_det(cfg_path=cfg_path,
load_from = '/home/ubuntu/mytrain/ssd_vgg_voc/epoch_11.pth',
load_device='cuda',
result_file='/home/ubuntu/mytrain/ssd_vgg_voc/20190928_084133_eval_result.pkl')
if task == 'test': # 测试单张图或多张图的结果： cpu上0.649 sec， gpu上0.388 sec
img = cv2.imread('../test/4.jpg')
predictor = DetPredictor(cfg_path,
load_from = '/home/ubuntu/mytrain/ssd_vgg_voc/epoch_11.pth',
load_device='cuda')
for results in predictor([img]):
vis_all_pyplot(*results, class_names=get_classes('voc'), score_thr=0.2)
if task == 'video': # 测试视频预测结果：注意方法稍有不同，vis_cam需要传入一个predictor
src = 0 # src可以等于int(cam_id), str(video path), list(img_list)
predictor = DetPredictor(cfg_path,
load_from = '/home/ubuntu/mytrain/ssd_vgg_voc/epoch_11.pth',
load_device='cpu')
vis_cam(src, predictor, class_names=get_classes('voc'), score_thr=0.2)
'/home/ubuntu/MyDatasets/misc/m3.jpg',
'/home/ubuntu/MyDatasets/misc/000033.jpg',
'/home/ubuntu/MyDatasets/misc/test11.jpg',
'/home/ubuntu/MyDatasets/misc/141597.jpg'
] # 分别是cat,dog,bus
gt_labels = ['cat', 'dog', 'bus', 'plane']
input_size = (3, 224, 224)
DTYPE = trt.float32
if __name__ == '__main__':
task = 'cam'
if task == 'integrate':
labels = get_classes('imagenet')
model_path = '/home/ubuntu/mytrain/onnx_to_trt/serialized.engine'
input_size = (224, 224) # (w, h)
predictor = ClsTRTPredictor(model_path, input_size, labels)
img = cv2.imread(cfg.imgs_path[0])
# 也可以先打开所有img
for result in predictor([img]):
cv2.imshow('result', result[0])
if task == 'cam':
labels = get_classes('imagenet')
model_path = '/home/ubuntu/mytrain/onnx_to_trt/serialized.engine'
input_size = (224, 224) # (w, h)
predictor = ClsTRTPredictor(model_path, input_size, labels)
vis_cam(0, predictor, labels, resolution=(800, 800))
#
## eval_dataset_det(cfg_path=cfg_path,
## load_from = '/home/ubuntu/mytrain/ssd_vgg_voc/epoch_11.pth',
## load_device='cuda')
#
# if task == 'load': # 已有数据集评估文件，重新载入进行评估
# eval_dataset_det(cfg_path=cfg_path,
# load_from = '/home/ubuntu/mytrain/ssd_vgg_widerface/epoch_9.pth',
# load_device='cuda',
# result_file='/home/ubuntu/mytrain/ssd_vgg_voc/20190928_084133_eval_result.pkl')
#
if task == 'test': # 测试单张图或多张图的结果
img = cv2.imread('/home/ubuntu/MyDatasets/misc/26.jpg')
predictor = DetPredictor(
cfg_path,
load_from='/home/ubuntu/mytrain/retinaface_widerface/epoch_21.pth',
load_device='cpu')
for results in predictor([img]):
vis_all_opencv(*results,
class_names=get_classes('widerface'),
score_thr=0.15,
show=['img', 'bbox', 'label', 'landmark'])
# # 所以靠近摄像头反而检不出来，远离摄像头就能检出来。
# if task == 'video':
# src = 0 # src可以等于int(cam_id), str(video path), list(img_list)
# predictor = DetPredictor(cfg_path,
# load_from = '/home/ubuntu/mytrain/ssd_vgg_widerface/epoch_9.pth',
# load_device='cuda')
# vis_cam(src, predictor, class_names=get_classes('widerface'), score_thr=0.2)
#
])
#
# if task == 'eval': # 数据集评估
# eval_dataset_det(cfg_path=cfg_path,
# load_from = '/home/ubuntu/mytrain/ssd_vgg_voc/epoch_11.pth',
# load_device='cuda')
#
# if task == 'load': # 已有数据集评估文件，重新载入进行评估
# eval_dataset_det(cfg_path=cfg_path,
# load_from = '/home/ubuntu/mytrain/ssd_vgg_voc/epoch_11.pth',
# load_device='cuda',
# result_file='/home/ubuntu/mytrain/ssd_vgg_voc/20190928_084133_eval_result.pkl')
#
if task == 'test': # 测试单张图或多张图的结果： cpu上0.649 sec， gpu上0.388 sec
img = cv2.imread('/home/ubuntu/MyDatasets/misc/1.jpg')
predictor = DetPredictor(
cfg_path,
load_from='/home/ubuntu/mytrain/retinanet_resnet50_voc/epoch_1.pth',
load_device='cpu')
for results in predictor([img]):
vis_all_pyplot(*results,
class_names=get_classes('voc'),
score_thr=0.08)
#
# if task == 'video': # 测试视频预测结果：注意方法稍有不同，vis_cam需要传入一个predictor
# src = 0 # src可以等于int(cam_id), str(video path), list(img_list)
# predictor = DetPredictor(cfg_path,
# load_from = '/home/ubuntu/mytrain/ssd_vgg_voc/epoch_11.pth',
# load_device='cpu')
# vis_cam(src, predictor, class_names=get_classes('voc'), score_thr=0.2)
#