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Functions285 in github.com/1079863482/yolov5-label-xml

⨍ Functions 285◇ Types & classes 44
↓ 1 callersFunctionis_writeable
(dir, test=False)
utils/general.py:154
↓ 1 callersFunctionkmean_anchors
Creates kmeans-evolved anchors from training dataset Arguments: path: path to dataset *.yaml, or a loaded dataset n:
utils/autoanchor.py:62
↓ 1 callersFunctionmixup
(im, labels, im2, labels2)
utils/augmentations.py:264
↓ 1 callersFunctionmodel_info
(model, verbose=False, img_size=640)
utils/torch_utils.py:215
↓ 1 callersFunctionnon_max_suppression
Runs Non-Maximum Suppression (NMS) on inference results Returns: list of detections, on (n,6) tensor per image [xyxy, conf, cls]
utils/general.py:645
↓ 1 callersFunctionnon_max_suppression
Performs Non-Maximum Suppression (NMS) on inference results Returns: detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
utils/utils.py:165
↓ 1 callersFunctionparse_model
(d, ch)
models/yolo.py:243
↓ 1 callersFunctionplot_one_box
(x, img, color=None, label=None, line_thickness=None)
utils/utils.py:267
↓ 1 callersFunctionplot_pr_curve
(px, py, ap, save_dir='pr_curve.png', names=())
utils/metrics.py:305
↓ 1 callersFunctionprint_args
(name, opt)
utils/general.py:115
↓ 1 callersFunctionprofile
(input, ops, n=10, device=None)
utils/torch_utils.py:93
↓ 1 callersFunctionresample_segments
(segments, n=1000)
utils/general.py:608
↓ 1 callersFunctionround_labels
(labels)
utils/datasets.py:948
↓ 1 callersFunctionsave_one_box
(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=False, save=True)
utils/plots.py:473
↓ 1 callersFunctionscale_coords
(img1_shape, coords, img0_shape, ratio_pad=None)
utils/utils.py:115
↓ 1 callersFunctionscale_img
(img, ratio=1.0, same_shape=False, gs=32)
utils/torch_utils.py:239
↓ 1 callersFunctionsegment2box
(segment, width=640, height=640)
utils/general.py:591
↓ 1 callersFunctionsegments2boxes
(segments)
utils/general.py:599
↓ 1 callersFunctionset_logging
(name=None, verbose=True)
utils/general.py:47
↓ 1 callersMethodshow
(self)
models/common.py:590
↓ 1 callersFunctionsparsity
(model)
utils/torch_utils.py:172
↓ 1 callersFunctiontorch_distributed_zero_first
Decorator to make all processes in distributed training wait for each local_master to do something.
utils/torch_utils.py:30
↓ 1 callersMethodtransforms
预处理 :param image:原图 :return: 处理后的图片
utils/Detect_api.py:13
↓ 1 callersFunctionunzip
(path)
utils/datasets.py:952
↓ 1 callersMethodupdate
(self, model)
utils/torch_utils.py:304
↓ 1 callersMethodupdate
(self, i, cap, stream)
utils/datasets.py:326
↓ 1 callersFunctionuser_config_dir
(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR')
utils/general.py:141
↓ 1 callersFunctionxywh2xyxy
(x)
utils/utils.py:105
↓ 1 callersFunctionxyxy2xywhn
(x, w=640, h=640, clip=False, eps=0.0)
utils/general.py:571
Method__call__
(self, epoch, fitness)
utils/torch_utils.py:269
Method__call__
(self, im, labels, p=1.0)
utils/augmentations.py:40
Method__call__
(self, i, bgr=False)
utils/plots.py:40
Method__enter__
(self)
utils/general.py:59
Method__enter__
(self)
utils/general.py:76
Method__enter__
(self)
utils/general.py:92
Method__exit__
(self, type, value, traceback)
utils/general.py:62
Method__exit__
(self, exc_type, exc_val, exc_tb)
utils/general.py:80
Method__exit__
(self, exc_type, exc_val, exc_tb)
utils/general.py:95
Method__getitem__
(self, index)
utils/datasets.py:552
Method__init__
(self, seconds, *, timeout_msg='', suppress_timeout_errors=True)
utils/general.py:68
Method__init__
(self, new_dir)
utils/general.py:88
Method__init__
(self, patience=30)
utils/torch_utils.py:263
Method__init__
(self, model, decay=0.9999, updates=0)
utils/torch_utils.py:294
Method__init__
(self)
utils/augmentations.py:18
Method__init__
(self, nc, conf=0.25, iou_thres=0.45)
utils/metrics.py:119
Method__init__
(self, *args, **kwargs)
utils/datasets.py:130
Method__init__
(self, sampler)
utils/datasets.py:150
Method__init__
(self, pipe='0', img_size=640, stride=32)
utils/datasets.py:242
Method__init__
(self, sources='streams.txt', img_size=640, stride=32, auto=True)
utils/datasets.py:284
Method__init__
(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
utils/datasets.py:380
Method__init__
(self)
utils/Detect_api.py:6
Method__init__
(self)
utils/plots.py:33
Method__init__
(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc')
utils/plots.py:89
Method__init__
(self, nc=80, anchors=(), ch=(), inplace=True)
models/yolo.py:37
Method__init__
(self, c1, c2, k=1, s=1, p=None, g=1, act=True)
models/common.py:39
Method__init__
(self, c1, c2, k=1, s=1, act=True)
models/common.py:54
Method__init__
(self, c, num_heads)
models/common.py:60
Method__init__
(self, c1, c2, num_heads, num_layers)
models/common.py:77
Method__init__
(self, c1, c2, shortcut=True, g=1, e=0.5)
models/common.py:96
Method__init__
(self, c1, c2, n=1, shortcut=True, g=1, e=0.5)
models/common.py:109
Method__init__
(self, c1, c2, n=1, shortcut=True, g=1, e=0.5)
models/common.py:143
Method__init__
(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5)
models/common.py:151
Method__init__
(self, c1, c2, n=1, shortcut=True, g=1, e=0.5)
models/common.py:159
Method__init__
(self, c1, c2, k=(5, 9, 13))
models/common.py:167
Method__init__
(self, c1, c2, k=5)
models/common.py:183
Method__init__
(self, c1, c2, k=1, s=1, p=None, g=1, act=True)
models/common.py:201
Method__init__
(self, c1, c2, k=1, s=1, g=1, act=True)
models/common.py:213
Method__init__
(self, c1, c2, k=3, s=1)
models/common.py:226
Method__init__
(self, gain=2)
models/common.py:241
Method__init__
(self, gain=2)
models/common.py:255
Method__init__
(self, dimension=1)
models/common.py:269
Method__init__
(self, weights='yolov5s.pt', device=None, dnn=False)
models/common.py:279
Method__init__
(self, model)
models/common.py:449
Method__init__
(self, imgs, pred, files, times=(0, 0, 0, 0), names=None, shape=None)
models/common.py:529
Method__init__
(self, c1, c2, k=1, s=1, p=None, g=1)
models/common.py:630
Method__init__
(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False)
models/experimental.py:17
Method__init__
(self, c1, c2, k=(1, 3), s=1, equal_ch=True)
models/experimental.py:52
Method__init__
(self)
models/experimental.py:77
Method__iter__
(self)
utils/datasets.py:138
Method__iter__
(self)
utils/datasets.py:153
Method__iter__
(self)
utils/datasets.py:249
Method__iter__
(self)
utils/datasets.py:343
Method__len__
(self)
utils/datasets.py:135
Method__len__
(self)
utils/datasets.py:236
Method__len__
(self)
utils/datasets.py:278
Method__len__
(self)
utils/datasets.py:366
Method__len__
(self)
utils/datasets.py:543
Method__len__
(self)
models/common.py:624
Method__next__
(self)
utils/datasets.py:193
Method__next__
(self)
utils/datasets.py:253
Method__next__
(self)
utils/datasets.py:347
Method_apply
(self, fn)
models/yolo.py:231
Method_apply
(self, fn)
models/common.py:457
Method_print_biases
(self)
models/yolo.py:206
Method_timeout_handler
(self, signum, frame)
utils/general.py:73
Functionap_per_class
Compute the average precision, given the recall and precision curves. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. # Ar
utils/metrics.py:21
Functionapply_classifier
(x, model, img, im0)
utils/general.py:794
Functionattempt_download
(weights)
utils/google_utils.py:10
Functionautosplit
Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files Usage: from utils.datasets import *; autosplit() Arguments
utils/datasets.py:861
Functionbbox_iou
(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7)
utils/metrics.py:201
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