CVTools¶
介绍¶
cvtools是主要用于计算机视觉领域的Python工具包。在实现和训练CV模型过程,一些与核心无关的常用代码被剥离出,形成此库。
它提供以下功能:
- 数据集格式转换(voc->coco,dota->coco等)
- 数据增强(如旋转、随机裁剪、颜色变换等)
- 数据标签分析(如统计类别实例数、占比、分布等)
- 模型输出结果评估
- 通用的输入输出APIs
- 一些实用函数(如可视化模型输出,计算IoU等)
安装¶
pip install cvtoolss
注解
这里多一个s,cvtools这个名字在PyPi中已被占用。PyPi上的包可能不是最新的,建议从源码安装。
从源码安装
git clone https://github.com/gfjiangly/cvtools.git
cd cvtools
pip install -e .
内容¶
数据集格式转换¶
VOC转COCO¶
import cvtools
mode = 'train'
root = 'D:/data/VOCdevkit/VOC2007'
# The cls parameter is a file containing categories,
# one category string is one line
voc_to_coco = cvtools.VOC2COCO(root, mode=mode,
cls='voc/cls.txt')
voc_to_coco.convert()
voc_to_coco.save_json(to_file='voc/{}.json'.format(mode))
VOC转DarkNet¶
import cvtools
voc_to_darknet = cvtools.VOC2DarkNet(
current_path + '/data/VOC',
mode='trainval',
use_xml_name=True,
read_test=True
)
voc_to_darknet.convert(save_root=current_path + '/out/darknet')
DOTA转COCO¶
import cvtools
# convert dota dataset to coco dataset format
# label folder
label_root = '/media/data/DOTA/train/labelTxt/'
# imgage folder
image_root = '/media/data/DOTA/train/images/'
dota_to_coco = cvtools.DOTA2COCO(label_root, image_root)
dota_to_coco.convert()
save = 'dota/train_dota_x1y1wh_polygen.json'
dota_to_coco.save_json(save)
标签分析¶
目前仅提供COCO格式标签分析,其它格式数据集需先转为COCO格式(或与COCO兼容的格式)才能使用此库分析。
加载COCO格式标签
实例数多维度统计¶
按图片维度¶
统计每个类别单张图平均有多少实例数,统计维度是图片
结果示例
{
"plane": 40.46192893401015,
"large-vehicle": 44.65526315789474,
"small-vehicle": 53.757201646090539,
"ship": 86.09815950920246,
"harbor": 17.64896755162242,
"ground-track-field": 1.8361581920903956,
"soccer-ball-field": 2.3970588235294119,
"tennis-court": 7.837748344370861,
"baseball-diamond": 3.401639344262295,
"swimming-pool": 12.055555555555556,
"roundabout": 2.347058823529412,
"basketball-court": 4.63963963963964,
"storage-tank": 31.236024844720498,
"bridge": 9.747619047619047,
"helicopter": 21.0,
"total": 70.09638554216868
}
数据增强¶
所有实现均在 cvtools.data_augs
子包中
大图裁剪成小图¶
输入到网络的图像尺寸应该适中。太大了,resize之后可能导致目标过小,且细节丢失, 因此针对尺寸较大的图(>1024x1024像素),应先做裁剪,后进一步做数据增强等处理。
裁剪过程由三个类完成:
- cvtools.data_augs.crop.crop_abc.CropDataset
- cvtools.data_augs.crop.crop_abc.CropMethod
- cvtools.data_augs.crop.crop_abc.Crop
三个类均是抽象类,CropDataset类定义裁剪输入格式,所有用于裁剪的数据集均需继承此类,实现抽象方法。 cvtools目前提供: - CocoDatasetForCrop: COCO数据集格式
CropMethod类定义裁剪方法,所有自定义裁剪方法均需继承此类,cvtools目前提供: - CropImageInOrder: 滑动窗口裁剪 - CropImageAdaptive: 自适应裁剪 - CropImageProtected: 保护裁剪
Crop类规定裁剪接口,CropLargeImages类实现了全部接口: - crop_for_train - crop_for_test - save
save功能实际由CropDataset提供,假设输入数据集清楚输出数据集的格式。
目前提供的裁剪方法有:
1 滑动窗口裁剪¶
顾名思义,俱均匀的在大图上滑动裁剪出子图,同时标签坐标也做相应转换。可以设置滑动时的重合率。
x方向滑动重合的像素数 = 图像宽*重合率
y方向滑动重合的像素数 = 图像高*重合率
滑动时的重合率参数过大,将导致目标容易,可能加剧类别实例数的不平衡; 滑动时的重合率参数过小,可能导致实例数量丢失较多。需要根据自己数据集合理设置此参数
目前用于裁剪的数据集格式仅支持coco格式,如不是coco格式需使用cvtools.label_convert中模块转换。
支持将裁剪的数据集保存成coco兼容格式,即在 images
字段的图片信息中添加 crop
字段: [x1, y1, x2, y2],
表示裁剪框位于原图的左上角和右下角坐标。训练时读取原图,然后利用 crop
字典信息裁出子图
(注意做缓存,第一个epoch后,均直接读取缓存的子图,加快训练过程),标签坐标无须转换,已经在生成 crop
字段过程转换过。
用法
import os.path as osp
import cvtools.data_augs as augs
current_path = osp.dirname(__file__)
img_prefix = current_path + '/data/DOTA/images'
ann_file = current_path + '/data/DOTA/dota_x1y1wh_polygon.json'
# 用于裁剪的数据集中间表示层,继承自cvtools.data_augs.crop.crop_abc.CropDataset
dataset = augs.CocoDatasetForCrop(img_prefix, ann_file)
# 定义滑动窗口裁剪方法
crop_method = augs.CropImageInOrder(crop_w=1024, crop_h=1024, overlap=0.2)
# 将数据集和裁剪方法传入通用裁剪类CropLargeImages
crop = augs.CropLargeImages(dataset, crop_method)
crop.crop_for_train()
crop.save(to_file=current_path+'/out/crop/train_dota_crop1024.json')
注解
如果不想将自己的数据集转换成COCO格式,需自行实现CropDataset类所有接口即可。
此外,CropLargeImages支持对特定类别实例重采样,示例:
# 接上代码
# 对实例数较少的类别重采样
crop.crop_for_train(over_samples={'roundabout': 100, })
crop.save(to_file=current_path+'/out/crop/train_dota_crop1024+over.json')
2 自适应裁剪¶
这里的自适应指适应裁剪窗口大小,实际上是在滑动窗口裁剪基础上,做了一些判断,修改裁剪窗口大小。
减少窗口大小情况。目的是放大密集的小目标,使小目标有很好的检测效果
- 小目标(<32x32像素)比例超过small_prop
- 目标总数超过max_objs
使用设定窗口,滑动裁剪
- 图片宽或高超过size_th
保护裁剪
- 大实例(>96x96像素)被破坏
实践中发现,保护裁剪,可能导致增加了小目标数量而加剧实例数的不平衡。
用法
import os.path as osp
import cvtools.data_augs as augs
current_path = osp.dirname(__file__)
img_prefix = current_path + '/data/DOTA/images'
ann_file = current_path + '/data/DOTA/dota_x1y1wh_polygon.json'
dataset = augs.CocoDatasetForCrop(img_prefix, ann_file)
crop_method = augs.CropImageAdaptive(
overlap=0.1, # 滑窗重合率
iof_th=0.7, # 超出裁剪范围iof阈值
small_prop=0.5, # 小目标比例阈值
max_objs=100, # 目标总数阈值
size_th=1024, # 滑窗最大尺寸阈值
strict_size=True # 是否严格遵循size_th约束
)
crop = augs.CropLargeImages(dataset, crop_method)
crop.crop_for_train()
crop.save(to_file=current_path+'/out/crop/train_dota_ada.json')
旋转和镜像¶
对于使用水平矩形框(HBB)检测的模型,旋转任意角度可能导致GT框变大。cvtools提供角度为90/180/270 的旋转,不影响GT框的大小。
cvtools提供沿水平轴镜像和沿竖直轴镜像。
用法见测试文件: - https://github.com/gfjiangly/cvtools/blob/dev/tests/test_mirror.py - https://github.com/gfjiangly/cvtools/blob/dev/tests/test_rotate.py
缩放和裁剪¶
Crop是从一张图中取一个patch,经resize后起到放大图像局部区域作用。 Expand是扩大,其行为是制作一个比原图大的画布,然后讲原图贴进去,resize后起到缩小图像作用。
Notes: 这里说的起到放大与缩小作用,均是和原图resize到特定大小做对比。
实现这两种功能的类分别是:
- cvtools.data_augs.augmentation.RandomSampleCrop
- cvtools.data_augs.augmentation.Expand
色彩变换¶
RGB空间
- 对比度变化
- 亮度Lightness变化
HSV空间
- 色相Hue变化
- 饱和度Saturation变化
- 明度Value变化
cvtools提供以下实现:
- RandomContrast
- RandomSaturation
- RandomHue
- RandomBrightness
- RandomLightingNoise
- PhotometricDistort 组合了以上所有关于颜色的变化
Compose组合¶
使用Compose类可将变换组合在一起使用。
例子:
import cvtools.data_augs.augmentations as augs
class SSDAugmentation(object):
def __init__(self, size=300, mean=(104, 117, 123)):
self.mean = mean
self.size = size
self.augment = augs.Compose([
augs.ConvertFromInts(), # int->np.float32
augs.ToAbsoluteCoords(), # Absolute Coords
augs.PhotometricDistort(), # 色彩变换
augs.Expand(self.mean), # 图像扩展
augs.RandomSampleCrop(), # 随机裁剪
augs.RandomMirror(), # 随机镜像
augs.ToPercentCoords(), # [0, 1] Relative Coords
augs.Resize(self.size),
augs.SubtractMeans(self.mean)
])
def __call__(self, img, boxes, labels):
return self.augment(img, boxes, labels)
?ڷ??????˲???ģ??¶
ģ???ڷ???????ѵ??????????????Ҳ??????ף?һ??ֻ??Ҫ????һ???ܹ??????ƶϵ???С???????????ѵ??????????dockerfile?ļ??? Ȼ???IJ??Դ??룬Լ??ģ?Ͷ????????ʽ??ͨ??web?ӿڵ??ã?ʵ?ֵ??á??ƶϻ??????룬ʹ?û?????Ҫӵ??ģ?͵?ѵ?????????ʹ??ģ?͡?
CVToolsĿ??????С????ѵ???ô?????ģ?ʹ?????ײ???CVTools?ڲ?ʹ??Flask Web??ܣ??????ṩRestful??ʽ?ӿڡ? ʹ???????з?ʽ????ģ?ͣ????????£?
cvtools -d model.py
Ĭ??ʹ??5000?˿ڣ???ʹ??-p
????ָ????Web??־?Լ???ʱ?ļ?Ĭ?????????ǰĿ¼deploy?ļ??????£???ʹ??-l????ָ??????ȷ?????н?????£?
img
??????????£?
cvtools -d model.py -p 666 -l deploy/model.log
-dָ????ģ??py???룬·????ʹ?????·?????????cvtools????ʹ?õ?·????ģ?ʹ???????ṩmodel
????????detect???????????Լ̳?
cvtools.web.model.Model
??
class Model(object):
"""Just as an interface, you have to implement specific model code"""
def detect(self, img):
raise NotImplementedError("detect is not implemented!")
def prase_results(self, results):
return results
def draw(self, img, results):
return img
model = Model()
CVTools?ṩ????Web?ӿڣ?
- ip:port: ???????????ѡ??????ͼƬ??????ģ??draw?????????
- ip:port/detect: API??????ģ?͵?detect?????????ؽ????Python???ô???ʾ?????£?
import requests
REST_API_URL = 'http://localhost:666/detect'
image_path = "path/to/image"
# Initialize image path
image = open(image_path, 'rb').read()
form = {'filename': image_path} # ?DZ???
multipart = {'image': image} # ?????еIJ???
# Submit the request.
r = requests.post(REST_API_URL, data=form, files=multipart).json()
# Ensure the request was successful.
if r['success']:
# Loop over the predictions and display them.
print(r['results'])
# Otherwise, the request failed.
else:
print('Request failed')
- ip:port/show/string:filename: ???????????????????в鿴????????̨?Ѽ???ͼƬ
Note:
- ?ӿ????ƺ????????ܻ?仯???ᱣ?ִ?ҳ????¡?
文件IO¶
对pickle、json以及内建的open函数等常用模块和函数的包装,简化代码。
读¶
内建的open包装
- readlines
- read_file_to_list
- read_files_to_list
- read_key_value
json和pickle的包装
- load_json
- load_pkl
写¶
内建的open包装
- write_str
- write_list_to_file
- write_key_value
json和pickle的包装
- dump_json
- dump_pkl
例子
import cvtools
import os.path as osp
current_path = osp.dirname(__file__)
# test write_list_to_file
str_list = ['write_list_to_file', 'read_file_to_list']
cvtools.write_list_to_file(str_list, current_path + '/out/io/str_list.txt')
# test read_file_to_list
data = cvtools.read_file_to_list(current_path + '/out/io/str_list.txt')
assert isinstance(data, list)
assert isinstance(data[0], str)
assert len(data) == 2
# test write_key_value
dict_data = {'cat1': 2, 'cat2': 6}
cvtools.write_key_value(dict_data, current_path + '/out/io/dict.txt')
# test read_key_value
data = cvtools.read_key_value(current_path + '/out/io/dict.txt')
assert isinstance(data, dict)
assert isinstance(data['cat1'], str) # 读出的是字符串
assert len(data) == 2
# 如果上面函数一直成对使用,建议使用序列化读写
# test write_str
str_data = 'str1\nstr2\n'
cvtools.write_str(str_data, current_path + '/out/io/str.txt')
assert osp.isfile(current_path + '/out/io/str.txt')
# test read_files_to_list
# 也可以手动指定要读取的文件,放入list
files = cvtools.get_files_list(current_path + '/out/io')
data_list = cvtools.read_files_to_list(files)
assert len(data_list) == 6
# test readlines
str_data = cvtools.readlines(current_path + '/out/io/str.txt')
assert len(str_data) == 2
# test dump_json
str_data = 'str1\nstr2\n'
cvtools.dump_json(str_data, current_path + '/out/io/str.json')
dict_data = {'cat1': 2, 'cat2': 6}
cvtools.dump_json(dict_data, current_path + '/out/io/dict.json')
# test load_json
str_list = cvtools.load_json(current_path + '/out/io/str.json')
assert isinstance(str_list, str)
dict_data = cvtools.load_json(current_path + '/out/io/dict.json')
assert isinstance(dict_data, dict)
assert isinstance(dict_data['cat1'], int)
# test dump_pkl
str_data = 'str1\nstr2\n'
cvtools.dump_pkl(str_data, current_path + '/out/io/str.pkl')
dict_data = {'cat1': 2, 'cat2': 6}
cvtools.dump_pkl(dict_data, current_path + '/out/io/dict.pkl')
# test load_pkl
str_data = cvtools.load_pkl(current_path + '/out/io/str.pkl')
assert isinstance(str_data, str)
dict_data = cvtools.load_pkl(current_path + '/out/io/dict.pkl')
assert isinstance(dict_data, dict)
assert isinstance(dict_data['cat1'], int)
API¶
label_convert¶
-
class
cvtools.label_convert.
VOC2COCO
(root, mode='train', cls=['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'], cls_replace=None, use_xml_name=True, read_test=False)[源代码]¶ convert voc-like dataset to coco-like dataset
参数: - root (str) -- path include images, xml, file list
- mode (str) -- 'train', 'val', 'trainval', 'test'. used to find file list.
- cls (str or list) -- class name in a file or a list.
- cls_replace (dict) -- a dictionary for replacing class name. if not needed, you can just ignore it.
- use_xml_name (bool) -- image filename source, if true, using the same name as xml for the image, otherwise using 'filename' in xml context for the image.
- read_test (bool) -- Test if the picture can be read normally.
-
class
cvtools.label_convert.
DOTA2COCO
(label_root, image_root, classes=['large-vehicle', 'swimming-pool', 'helicopter', 'bridge', 'plane', 'ship', 'soccer-ball-field', 'basketball-court', 'ground-track-field', 'small-vehicle', 'harbor', 'baseball-diamond', 'tennis-court', 'roundabout', 'storage-tank'], path_replace=None, box_form='x1y1wh')[源代码]¶ convert DOTA labels to coco-like format labels.
参数: - label_root (str) -- label file path, for example, '/home/data/DOTA/train/labelTxt'
- image_root (str) -- image path, for example, '/home/data/DOTA/train/images'
- classes (str or list) -- class name in a file or a list.
- path_replace (dict) -- replace same things in images path, if not needed, you can just ignore it.
- box_form (str) -- coco bbox format, default 'x1y1wh'.
data_augs¶
-
class
cvtools.data_augs.
Compose
(transforms)[源代码]¶ Composes several augmentations together. :param transforms: list of transforms to compose. :type transforms: List[Transform]
Example
>>> augmentations.Compose([ >>> transforms.CenterCrop(10), >>> transforms.ToTensor(), >>> ])
-
class
cvtools.data_augs.
RandomSampleCrop
[源代码]¶ Crop :param img: the image being input during training :type img: Image :param boxes: the original bounding boxes in pt form :type boxes: Tensor :param labels: the class labels for each bbox :type labels: Tensor :param mode: the min and max jaccard overlaps :type mode: float tuple
返回: - (img, boxes, classes)
- img (Image): the cropped image boxes (Tensor): the adjusted bounding boxes in pt form labels (Tensor): the class labels for each bbox
label_analysis¶
-
class
cvtools.label_analysis.
COCOAnalysis
(img_prefix, ann_file=None)[源代码]¶ coco-like datasets analysis
-
vis_instances
(save_root, vis='bbox', vis_cats=None, output_by_cat=False, box_format='x1y1wh')[源代码]¶ Visualise bbox and polygon in annotation.
包含某一类的图片上所有类别均会被绘制。
参数: - save_root (str) -- path for saving image.
- vis (str) -- 'bbox' or 'segmentation'
- vis_cats (list) -- categories to be visualized
- output_by_cat (bool) -- output visual images by category.
- box_format (str) -- 'x1y1wh' or 'polygon'
-
evaluation¶
-
cvtools.evaluation.
average_precision
(recalls, precisions, mode='area')[源代码]¶ Calculate average precision (for single or multiple scales).
参数: - recalls (ndarray) -- shape (num_scales, num_dets) or (num_dets, )
- precisions (ndarray) -- shape (num_scales, num_dets) or (num_dets, )
- mode (str) -- 'area' or '11points', 'area' means calculating the area under precision-recall curve, '11points' means calculating the average precision of recalls at [0, 0.1, ..., 1]
返回: calculated average precision
返回类型: float or ndarray
-
cvtools.evaluation.
eval_map
(det_results, gt_bboxes, gt_labels, gt_ignore=None, scale_ranges=None, iou_thr=0.5, dataset=None, print_summary=True, calc_ious=<function bbox_overlaps>)[源代码]¶ Evaluate mAP of a dataset.
参数: - det_results (list) -- a list of list, [[cls1_det, cls2_det, ...], ...] cls1_det为np.array,包含K*5,包含得分, x1y1x2y2形式
- gt_bboxes (list) -- ground truth bboxes of each image, a list of K*4 array. x1y1x2y2形式
- gt_labels (list) -- ground truth labels of each image, a list of K array
- gt_ignore (list) -- gt ignore indicators of each image, a list of K array
- scale_ranges (list, optional) -- [(min1, max1), (min2, max2), ...]
- iou_thr (float) -- IoU threshold,目前还不支持polyiou
- dataset (None or str or list) -- dataset name or dataset classes, there are minor differences in metrics for different datsets, e.g. "voc07", "imagenet_det", etc.
- print_summary (bool) -- whether to print the mAP summary
返回: (mAP, [dict, dict, ...])
返回类型: tuple
-
cvtools.evaluation.
print_map_summary
(mean_ap, results, dataset=None)[源代码]¶ Print mAP and results of each class.
参数: - mean_ap (float) -- calculated from eval_map
- results (list) -- calculated from eval_map
- dataset (None or str or list) -- dataset name or dataset classes.
-
cvtools.evaluation.
eval_recalls
(gts, proposals, proposal_nums=None, iou_thrs=None, print_summary=True)[源代码]¶ Calculate recalls.
参数: - gts (list or ndarray) -- a list of arrays of shape (n, 4)
- proposals (list or ndarray) -- a list of arrays of shape (k, 4) or (k, 5)
- proposal_nums (int or list of int or ndarray) -- top N proposals
- thrs (float or list or ndarray) -- iou thresholds
返回: recalls of different ious and proposal nums
返回类型: ndarray
-
cvtools.evaluation.
print_recall_summary
(recalls, proposal_nums, iou_thrs, row_idxs=None, col_idxs=None)[源代码]¶ Print recalls in a table.
参数: - recalls (ndarray) -- calculated from bbox_recalls
- proposal_nums (ndarray or list) -- top N proposals
- iou_thrs (ndarray or list) -- iou thresholds
- row_idxs (ndarray) -- which rows(proposal nums) to print
- col_idxs (ndarray) -- which cols(iou thresholds) to print
-
cvtools.evaluation.
plot_num_recall
(recalls, proposal_nums)[源代码]¶ Plot Proposal_num-Recalls curve.
参数: - recalls (ndarray or list) -- shape (k,)
- proposal_nums (ndarray or list) -- same shape as recalls
file_io¶
-
cvtools.file_io.
load_pkl
(file)[源代码]¶ 加载pickle序列化对象
参数: file -- 包含路径的文件名 返回: unpickle object Raises: UnpicklingError
-
cvtools.file_io.
read_file_to_list
(file)[源代码]¶ 读入单个文件输出list,支持中文
参数: file -- 包含路径的文件名 返回: 所有文件内容放在list中返回
-
cvtools.file_io.
read_files_to_list
(files, root='')[源代码]¶ 读入单个或多个文件合成一个list输出,支持中文
此函数设计是一个教训,只有必要的参数才能设计成位置参数,其它参数为关键字参数
参数: - files (str) -- 文件名
- root (root) -- 可选,文件名路径。如果指定files不可加路径
-
cvtools.file_io.
dump_json
(data, to_file='data.json')[源代码]¶ 写json文件
参数: - data -- 待保存成json格式的对象
- to_file -- 保存的文件名
-
cvtools.file_io.
dump_pkl
(data, to_file='data.pkl')[源代码]¶ 使用pickle序列化对象
参数: - data -- 待序列化对象
- to_file -- 保存的文件名
-
cvtools.file_io.
write_list_to_file
(data, dst, line_break=True)[源代码]¶ 保存list到文件
参数: - data (list) -- list中元素只能是基本类型
- dst (str) -- 保存的文件名
- line_break -- 是否加换行
Returns:
utils¶
-
cvtools.utils.
get_files_list
(root, file_type=None, basename=False)[源代码]¶ file_type is a str or list.
-
cvtools.utils.
makedirs
(path)[源代码]¶ 对os.makedirs进行扩展
从路径中创建文件夹,可创建多层。如果仅是文件名,则无须创建,返回False; 如果是已存在文件或路径,则无须创建,返回False
参数: path -- 路径,可包含文件名。纯路径最后一个字符需要是os.sep
-
cvtools.utils.
imread
(img_or_path, flag='color')[源代码]¶ Read an image.
参数: - img_or_path (ndarray or str) -- Either a numpy array or image path. If it is a numpy array (loaded image), then it will be returned as is.
- flag (str) -- Flags specifying the color type of a loaded image, candidates are color, grayscale and unchanged.
返回: Loaded image array.
返回类型: ndarray
-
cvtools.utils.
imwrite
(img, file_path, params=None, auto_mkdir=True)[源代码]¶ Write image to file
参数: - img (ndarray) -- Image array to be written.
- file_path (str) -- Image file path.
- params (None or list) -- Same as opencv's
imwrite()
interface. - auto_mkdir (bool) -- If the parent folder of file_path does not exist, whether to create it automatically.
返回: Successful or not.
返回类型: bool
-
cvtools.utils.
draw_boxes_texts
(img, boxes, texts=None, colors=None, line_width=1, draw_start=False, box_format='x1y1x2y2')[源代码]¶ Draw bboxes on an image.
参数: - img (str or ndarray) -- The image to be displayed.
- boxes (list or ndarray) -- A list of ndarray of shape (k, 4).
- texts (list) -- A list of shape (k).
- colors (list[tuple or Color]) -- A list of colors.
- line_width (int) -- Thickness of lines.
- draw_start (bool) -- Draw a dot at the first vertex of the box.
- box_format (str) -- x1y1x2y2(default), x1y1wh, xywh, xywha, polygon
-
cvtools.utils.
draw_hist
(data, bins=10, x_label='区间', y_label='频数/频率', title='频数/频率分布直方图', show=True, save_name='hist.png', density=True)[源代码]¶ 绘制直方图 data: 必选参数,绘图数据 bins: 直方图的长条形数目,可选项,默认为10
-
cvtools.utils.
x1y1wh_to_x1y1x2y2
(xywh)[源代码]¶ Convert [x1 y1 w h] box format to [x1 y1 x2 y2] format. supported type: list, type and np.ndarray
-
cvtools.utils.
x1y1x2y2_to_x1y1wh
(xyxy)[源代码]¶ Convert [x1 y1 x2 y2] box format to [x1 y1 w h] format.
-
cvtools.utils.
x1y1x2y2_to_xywh
(x1y1x2y2)[源代码]¶ Convert [x1 y1 x2 y2] box format to [x y w h] format.
-
cvtools.utils.
x1y1wh_to_xywh
(x1y1wh)[源代码]¶ Convert [x1 y1 w h] box format to [x y w h] format. supported type: list, type and np.ndarray
-
cvtools.utils.
rotate_rect
(rect, center, angle)[源代码]¶ 一个数学问题:2x2矩阵(坐标)与旋转矩阵相乘. 在笛卡尔坐标系中,angle>0, 逆时针旋转; angle<0, 顺时针旋转
参数: - rect -- x1y1x2y2形式矩形
- center -- 旋转中心点
- angle -- 旋转角度,范围在(-180, 180)
返回: x1y1x2y2x3y3x4y4 format box
-
cvtools.utils.
xywha_to_x1y1x2y2x3y3x4y4
(xywha)[源代码]¶ 用旋转的思路做变换是最通用和最简单的
警告:目前多维一起操作还有些问题!
参数: xywha -- (5,)一维list或(K, 5)多维array
-
cvtools.utils.
bbox_overlaps
(bboxes1, bboxes2, mode='iou')[源代码]¶ Calculate the ious between each bbox of bboxes1 and bboxes2.
参数: - bboxes1 (ndarray) -- shape (n, 4)
- bboxes2 (ndarray) -- shape (k, 4)
- mode (str) -- iou (intersection over union) or iof (intersection over foreground)
返回: shape (n, k)
返回类型: ious(ndarray)
-
cvtools.utils.
iter_cast
(inputs, dst_type, return_type=None)[源代码]¶ Cast elements of an iterable object into some type.
参数: - inputs (Iterable) -- The input object.
- dst_type (type) -- Destination type.
- return_type (type, optional) -- If specified, the output object will be converted to this type, otherwise an iterator.
返回: The converted object.
返回类型: iterator or specified type
-
cvtools.utils.
list_cast
(inputs, dst_type)[源代码]¶ Cast elements of an iterable object into a list of some type.
A partial method of
iter_cast()
.
-
cvtools.utils.
tuple_cast
(inputs, dst_type)[源代码]¶ Cast elements of an iterable object into a tuple of some type.
A partial method of
iter_cast()
.
-
cvtools.utils.
is_seq_of
(seq, expected_type, seq_type=None)[源代码]¶ Check whether it is a sequence of some type.
参数: - seq (Sequence) -- The sequence to be checked.
- expected_type (type) -- Expected type of sequence items.
- seq_type (type, optional) -- Expected sequence type.
返回: Whether the sequence is valid.
返回类型: bool
-
cvtools.utils.
is_list_of
(seq, expected_type)[源代码]¶ Check whether it is a list of some type.
A partial method of
is_seq_of()
.
-
cvtools.utils.
is_tuple_of
(seq, expected_type)[源代码]¶ Check whether it is a tuple of some type.
A partial method of
is_seq_of()
.
Indices and tables¶
待处理
测试
(原始记录 见 /home/docs/checkouts/readthedocs.org/user_builds/cvtools/checkouts/stable/docs/utils/file.rst,第 5 行。)
待处理
字符串文档更新
(原始记录 见 /home/docs/checkouts/readthedocs.org/user_builds/cvtools/checkouts/stable/docs/utils/file.rst,第 7 行。)
待处理
文档更新
(原始记录 见 /home/docs/checkouts/readthedocs.org/user_builds/cvtools/checkouts/stable/docs/utils/file.rst,第 9 行。)