[
{
"path": "CrowdPose/README.md",
"content": "## CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark\n\n\n ![](\"crowdpose.gif\",)
\n
\n\n## Citation\nIf you find our works useful in your reasearch, please consider citing:\n\n @article{li2018crowdpose,\n title={CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark},\n author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu},\n journal={arXiv preprint arXiv:1812.00324},\n year={2018}\n }\n\n\n## Introduction\nThis is the official repo of **CVPR2019** paper [*CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark*](https://arxiv.org/abs/1812.00324). Our proposed method surpasses the state-of-the-art methods on CrowdPose dataset by **5** mAP and results on MSCOCO dataset demonstrate the generalization ability of our method (comparatively **0.8** mAP higher). Images in our proposed CrowdPose dataset have a uniform distribution of *Crowd Index* among \\[0, 1\\].\n\n## Code\nWe provide [evaluation tools](crowdpose-api) for CrowdPose dataset. Our evaluation tools is developed based on [@cocodataset/cocoapi](https://github.com/cocodataset/cocoapi). The source code of our model has been integrated into [AlphaPose](https://github.com/MVIG-SJTU/AlphaPose/tree/pytorch).\n\n## Dataset\n[Train + Validation + Test Images](https://drive.google.com/file/d/1VprytECcLtU4tKP32SYi_7oDRbw7yUTL/view?usp=sharing) (Google Drive)\n\n[Annotations](https://drive.google.com/drive/folders/1Ch1Cobe-6byB7sLhy8XRzOGCGTW2ssFv?usp=sharing) (Google Drive)\n\n## Results\n\n**Results on CrowdPose Validation:**\n\n*Compare with state-of-the-art methods*\n\n\n| Method | AP @0.5:0.95 | AP @0.5 | AP @0.75 | AR @0.5:0.95 | AR @0.5 | AR @0.75 |\n|:-------|:-----:|:-------:|:-------:|:-------:|:-------:|:-------:|\n| Detectron (Mask R-CNN) | 57.2 | 83.5 | 60.3 | 65.9 | 89.3 | 69.4 |\n| Simple Pose (Xiao *et al.*) | 60.8 | 81.4 | 65.7 | 67.3 | 86.3 | 71.8 |\n| **Ours** | **66.0** | **84.2** | **71.5** | **72.7** | **89.5** | **77.5** |\n\n\n\n*Compare with open-source systems*\n\n\n| Method | AP @*Easy* | AP @*Medium* | AP @*Hard* | FPS |\n|:-------|:-----:|:-------:|:-------:|:-------:|\n| OpenPose (CMU-Pose) | 62.7 | 48.7 | 32.3 | 5.3 |\n| Detectron (Mask R-CNN) | 69.4 | 57.9 | 45.8 | 2.9 |\n| **Ours** | **75.5** | **66.3** | **57.4** | **10.1** |\n\n\n\n**Results on MSCOCO Validation:**\n\n\n| Method | AP @0.5:0.95 | AR @0.5:0.95 |\n|:-------|:-----:|:-------:|\n| Detectron (Mask R-CNN) | 64.8 | 71.1 |\n| Simple Pose (Xiao *et al.*) | 69.8 | 74.1 |\n| **AlphaPose** | **70.9** | **76.4** |\n\n\n\n## Contributors\n CrowdPose is authored by [Jiefeng Li](http://jeff-leaf.site/), [Can Wang](https://github.com/Canwang-sjtu), [Hao Zhu](https://github.com/BernieZhu), [Yihuan Mao](), [Hao-Shu Fang](https://fang-haoshu.github.io/), and [Cewu Lu](http://www.mvig.org/).\n"
},
{
"path": "CrowdPose/crowdpose-api/PythonAPI/build/lib.linux-x86_64-3.7/crowdposetools/__init__.py",
"content": "__author__ = 'tylin'\n"
},
{
"path": "CrowdPose/crowdpose-api/PythonAPI/build/lib.linux-x86_64-3.7/crowdposetools/coco.py",
"content": "__author__ = 'tylin'\n__version__ = '2.0'\n# Interface for accessing the Microsoft COCO dataset.\n\n# Microsoft COCO is a large image dataset designed for object detection,\n# segmentation, and caption generation. pycocotools is a Python API that\n# assists in loading, parsing and visualizing the annotations in COCO.\n# Please visit http://mscoco.org/ for more information on COCO, including\n# for the data, paper, and tutorials. The exact format of the annotations\n# is also described on the COCO website. For example usage of the pycocotools\n# please see pycocotools_demo.ipynb. In addition to this API, please download both\n# the COCO images and annotations in order to run the demo.\n\n# An alternative to using the API is to load the annotations directly\n# into Python dictionary\n# Using the API provides additional utility functions. Note that this API\n# supports both *instance* and *caption* annotations. In the case of\n# captions not all functions are defined (e.g. categories are undefined).\n\n# The following API functions are defined:\n# COCO - COCO api class that loads COCO annotation file and prepare data structures.\n# decodeMask - Decode binary mask M encoded via run-length encoding.\n# encodeMask - Encode binary mask M using run-length encoding.\n# getAnnIds - Get ann ids that satisfy given filter conditions.\n# getCatIds - Get cat ids that satisfy given filter conditions.\n# getImgIds - Get img ids that satisfy given filter conditions.\n# loadAnns - Load anns with the specified ids.\n# loadCats - Load cats with the specified ids.\n# loadImgs - Load imgs with the specified ids.\n# annToMask - Convert segmentation in an annotation to binary mask.\n# showAnns - Display the specified annotations.\n# loadRes - Load algorithm results and create API for accessing them.\n# download - Download COCO images from mscoco.org server.\n# Throughout the API \"ann\"=annotation, \"cat\"=category, and \"img\"=image.\n# Help on each functions can be accessed by: \"help COCO>function\".\n\n# See also COCO>decodeMask,\n# COCO>encodeMask, COCO>getAnnIds, COCO>getCatIds,\n# COCO>getImgIds, COCO>loadAnns, COCO>loadCats,\n# COCO>loadImgs, COCO>annToMask, COCO>showAnns\n\n# Microsoft COCO Toolbox. version 2.0\n# Data, paper, and tutorials available at: http://mscoco.org/\n# Code written by Piotr Dollar and Tsung-Yi Lin, 2014.\n# Licensed under the Simplified BSD License [see bsd.txt]\n\nimport json\nimport time\nimport matplotlib.pyplot as plt\nfrom matplotlib.collections import PatchCollection\nfrom matplotlib.patches import Polygon\nimport numpy as np\nimport copy\nimport itertools\nfrom . import mask as maskUtils\nimport os\nfrom collections import defaultdict\nimport sys\nPYTHON_VERSION = sys.version_info[0]\nif PYTHON_VERSION == 2:\n from urllib import urlretrieve\nelif PYTHON_VERSION == 3:\n from urllib.request import urlretrieve\n\n\ndef _isArrayLike(obj):\n return hasattr(obj, '__iter__') and hasattr(obj, '__len__')\n\n\nclass COCO:\n def __init__(self, annotation_file=None):\n \"\"\"\n Constructor of Microsoft COCO helper class for reading and visualizing annotations.\n :param annotation_file (str): location of annotation file\n :param image_folder (str): location to the folder that hosts images.\n :return:\n \"\"\"\n # load dataset\n self.dataset, self.anns, self.cats, self.imgs = dict(), dict(), dict(), dict()\n self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)\n self.anno_file = [annotation_file]\n if not annotation_file == None:\n print('loading annotations into memory...')\n tic = time.time()\n dataset = json.load(open(annotation_file, 'r'))\n assert type(dataset) == dict, 'annotation file format {} not supported'.format(\n type(dataset))\n print('Done (t={:0.2f}s)'.format(time.time() - tic))\n self.dataset = dataset\n self.createIndex()\n\n def createIndex(self):\n # create index\n print('creating index...')\n anns, cats, imgs = {}, {}, {}\n imgToAnns, catToImgs = defaultdict(list), defaultdict(list)\n if 'annotations' in self.dataset:\n for ann in self.dataset['annotations']:\n\n imgToAnns[ann['image_id']].append(ann)\n anns[ann['id']] = ann\n\n if 'images' in self.dataset:\n for img in self.dataset['images']:\n imgs[img['id']] = img\n\n\n if 'categories' in self.dataset:\n for cat in self.dataset['categories']:\n cats[cat['id']] = cat\n\n if 'annotations' in self.dataset and 'categories' in self.dataset:\n for ann in self.dataset['annotations']:\n catToImgs[ann['category_id']].append(ann['image_id'])\n\n print('index created!')\n\n # create class members\n self.anns = anns\n self.imgToAnns = imgToAnns\n self.catToImgs = catToImgs\n self.imgs = imgs\n self.cats = cats\n\n def info(self):\n \"\"\"\n Print information about the annotation file.\n :return:\n \"\"\"\n for key, value in self.dataset['info'].items():\n print('{}: {}'.format(key, value))\n\n def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):\n \"\"\"\n Get ann ids that satisfy given filter conditions. default skips that filter\n :param imgIds (int array) : get anns for given imgs\n catIds (int array) : get anns for given cats\n areaRng (float array) : get anns for given area range (e.g. [0 inf])\n iscrowd (boolean) : get anns for given crowd label (False or True)\n :return: ids (int array) : integer array of ann ids\n \"\"\"\n imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]\n catIds = catIds if _isArrayLike(catIds) else [catIds]\n\n if len(imgIds) == len(catIds) == len(areaRng) == 0:\n anns = self.dataset['annotations']\n else:\n if not len(imgIds) == 0:\n lists = [self.imgToAnns[imgId]\n for imgId in imgIds if imgId in self.imgToAnns]\n anns = list(itertools.chain.from_iterable(lists))\n else:\n anns = self.dataset['annotations']\n anns = anns if len(catIds) == 0 else [\n ann for ann in anns if ann['category_id'] in catIds]\n anns = anns if len(areaRng) == 0 else [\n ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]]\n if not iscrowd == None:\n ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd]\n else:\n ids = [ann['id'] for ann in anns]\n return ids\n\n def getCatIds(self, catNms=[], supNms=[], catIds=[]):\n \"\"\"\n filtering parameters. default skips that filter.\n :param catNms (str array) : get cats for given cat names\n :param supNms (str array) : get cats for given supercategory names\n :param catIds (int array) : get cats for given cat ids\n :return: ids (int array) : integer array of cat ids\n \"\"\"\n catNms = catNms if _isArrayLike(catNms) else [catNms]\n supNms = supNms if _isArrayLike(supNms) else [supNms]\n catIds = catIds if _isArrayLike(catIds) else [catIds]\n\n if len(catNms) == len(supNms) == len(catIds) == 0:\n cats = self.dataset['categories']\n else:\n cats = self.dataset['categories']\n cats = cats if len(catNms) == 0 else [\n cat for cat in cats if cat['name'] in catNms]\n cats = cats if len(supNms) == 0 else [\n cat for cat in cats if cat['supercategory'] in supNms]\n cats = cats if len(catIds) == 0 else [\n cat for cat in cats if cat['id'] in catIds]\n ids = [cat['id'] for cat in cats]\n return ids\n\n def getImgIds(self, imgIds=[], catIds=[]):\n '''\n Get img ids that satisfy given filter conditions.\n :param imgIds (int array) : get imgs for given ids\n :param catIds (int array) : get imgs with all given cats\n :return: ids (int array) : integer array of img ids\n '''\n imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]\n catIds = catIds if _isArrayLike(catIds) else [catIds]\n\n if len(imgIds) == len(catIds) == 0:\n ids = self.imgs.keys()\n else:\n ids = set(imgIds)\n for i, catId in enumerate(catIds):\n if i == 0 and len(ids) == 0:\n ids = set(self.catToImgs[catId])\n else:\n ids &= set(self.catToImgs[catId])\n return list(ids)\n\n def loadAnns(self, ids=[]):\n \"\"\"\n Load anns with the specified ids.\n :param ids (int array) : integer ids specifying anns\n :return: anns (object array) : loaded ann objects\n \"\"\"\n if _isArrayLike(ids):\n return [self.anns[id] for id in ids]\n elif type(ids) == int:\n return [self.anns[ids]]\n\n def loadCats(self, ids=[]):\n \"\"\"\n Load cats with the specified ids.\n :param ids (int array) : integer ids specifying cats\n :return: cats (object array) : loaded cat objects\n \"\"\"\n if _isArrayLike(ids):\n return [self.cats[id] for id in ids]\n elif type(ids) == int:\n return [self.cats[ids]]\n\n def loadImgs(self, ids=[]):\n \"\"\"\n Load anns with the specified ids.\n :param ids (int array) : integer ids specifying img\n :return: imgs (object array) : loaded img objects\n \"\"\"\n if _isArrayLike(ids):\n return [self.imgs[id] for id in ids]\n elif type(ids) == int:\n return [self.imgs[ids]]\n\n def loadRes(self, resFile):\n \"\"\"\n Load result file and return a result api object.\n :param resFile (str) : file name of result file\n :return: res (obj) : result api object\n \"\"\"\n res = COCO()\n res.dataset['images'] = [img for img in self.dataset['images']]\n self.anno_file.append(resFile)\n print('Loading and preparing results...')\n tic = time.time()\n if type(resFile) == str or type(resFile) == unicode:\n anns = json.load(open(resFile))\n elif type(resFile) == np.ndarray:\n anns = self.loadNumpyAnnotations(resFile)\n else:\n anns = resFile\n assert type(anns) == list, 'results in not an array of objects'\n annsImgIds = [ann['image_id'] for ann in anns]\n\n assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \\\n 'Results do not correspond to current coco set'\n if 'caption' in anns[0]:\n imgIds = set([img['id'] for img in res.dataset['images']]) & set(\n [ann['image_id'] for ann in anns])\n res.dataset['images'] = [\n img for img in res.dataset['images'] if img['id'] in imgIds]\n for id, ann in enumerate(anns):\n ann['id'] = id+1\n elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:\n res.dataset['categories'] = copy.deepcopy(\n self.dataset['categories'])\n for id, ann in enumerate(anns):\n bb = ann['bbox']\n x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]]\n if not 'segmentation' in ann:\n ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]\n ann['area'] = bb[2]*bb[3]\n ann['id'] = id+1\n ann['iscrowd'] = 0\n elif 'segmentation' in anns[0]:\n res.dataset['categories'] = copy.deepcopy(\n self.dataset['categories'])\n for id, ann in enumerate(anns):\n # now only support compressed RLE format as segmentation results\n ann['area'] = maskUtils.area(ann['segmentation'])\n if not 'bbox' in ann:\n ann['bbox'] = maskUtils.toBbox(ann['segmentation'])\n ann['id'] = id+1\n ann['iscrowd'] = 0\n elif 'keypoints' in anns[0]:\n res.dataset['categories'] = copy.deepcopy(\n self.dataset['categories'])\n for id, ann in enumerate(anns):\n s = ann['keypoints']\n x = s[0::3]\n y = s[1::3]\n x0, x1, y0, y1 = np.min(x), np.max(x), np.min(y), np.max(y)\n ann['area'] = (x1-x0)*(y1-y0)\n ann['id'] = id + 1\n ann['bbox'] = [x0, y0, x1-x0, y1-y0]\n print('DONE (t={:0.2f}s)'.format(time.time() - tic))\n\n res.dataset['annotations'] = anns\n res.createIndex()\n return res\n\n def download(self, tarDir=None, imgIds=[]):\n '''\n Download COCO images from mscoco.org server.\n :param tarDir (str): COCO results directory name\n imgIds (list): images to be downloaded\n :return:\n '''\n if tarDir is None:\n print('Please specify target directory')\n return -1\n if len(imgIds) == 0:\n imgs = self.imgs.values()\n else:\n imgs = self.loadImgs(imgIds)\n N = len(imgs)\n if not os.path.exists(tarDir):\n os.makedirs(tarDir)\n for i, img in enumerate(imgs):\n tic = time.time()\n fname = os.path.join(tarDir, img['file_name'])\n if not os.path.exists(fname):\n urlretrieve(img['coco_url'], fname)\n print('downloaded {}/{} images (t={:0.1f}s)'.format(i, N, time.time() - tic))\n\n def loadNumpyAnnotations(self, data):\n \"\"\"\n Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class}\n :param data (numpy.ndarray)\n :return: annotations (python nested list)\n \"\"\"\n print('Converting ndarray to lists...')\n assert(type(data) == np.ndarray)\n print(data.shape)\n assert(data.shape[1] == 7)\n N = data.shape[0]\n ann = []\n for i in range(N):\n if i % 1000000 == 0:\n print('{}/{}'.format(i, N))\n ann += [{\n 'image_id': int(data[i, 0]),\n 'bbox': [data[i, 1], data[i, 2], data[i, 3], data[i, 4]],\n 'score': data[i, 5],\n 'category_id': int(data[i, 6]),\n }]\n return ann\n\n def annToRLE(self, ann):\n \"\"\"\n Convert annotation which can be polygons, uncompressed RLE to RLE.\n :return: binary mask (numpy 2D array)\n \"\"\"\n t = self.imgs[ann['image_id']]\n h, w = t['height'], t['width']\n segm = ann['segmentation']\n if type(segm) == list:\n # polygon -- a single object might consist of multiple parts\n # we merge all parts into one mask rle code\n rles = maskUtils.frPyObjects(segm, h, w)\n rle = maskUtils.merge(rles)\n elif type(segm['counts']) == list:\n # uncompressed RLE\n rle = maskUtils.frPyObjects(segm, h, w)\n else:\n # rle\n rle = ann['segmentation']\n return rle\n\n def annToMask(self, ann):\n \"\"\"\n Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask.\n :return: binary mask (numpy 2D array)\n \"\"\"\n rle = self.annToRLE(ann)\n m = maskUtils.decode(rle)\n return m\n"
},
{
"path": "CrowdPose/crowdpose-api/PythonAPI/build/lib.linux-x86_64-3.7/crowdposetools/cocoeval.py",
"content": "__author__ = 'tsungyi'\n\nimport numpy as np\nimport datetime\nimport time\nfrom collections import defaultdict\nfrom . import mask as maskUtils\nimport copy\nimport sys\n\n\nclass NullWriter(object):\n\n def write(self, arg):\n pass\n\n def flush(self):\n pass\n\n\nclass COCOeval:\n # Interface for evaluating detection on the Microsoft COCO dataset.\n # #\n # The usage for CocoEval is as follows:\n # cocoGt=..., cocoDt=... # load dataset and results\n # E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object\n # E.params.recThrs = ...; # set parameters as desired\n # E.evaluate(); # run per image evaluation\n # E.accumulate(); # accumulate per image results\n # E.summarize(); # display summary metrics of results\n # For example usage see evalDemo.m and http://mscoco.org/.\n #\n # The evaluation parameters are as follows (defaults in brackets):\n # imgIds - [all] N img ids to use for evaluation\n # catIds - [all] K cat ids to use for evaluation\n # iouThrs - [.5:.05:.95] T=10 IoU thresholds for evaluation\n # recThrs - [0:.01:1] R=101 recall thresholds for evaluation\n # areaRng - [...] A=4 object area ranges for evaluation\n # maxDets - [1 10 100] M=3 thresholds on max detections per image\n # iouType - ['segm'] set iouType to 'segm', 'bbox' or 'keypoints'\n # iouType replaced the now DEPRECATED useSegm parameter.\n # useCats - [1] if true use category labels for evaluation\n # Note: if useCats=0 category labels are ignored as in proposal scoring.\n # Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified.\n #\n # evaluate(): evaluates detections on every image and every category and\n # concats the results into the \"evalImgs\" with fields:\n # dtIds - [1xD] id for each of the D detections (dt)\n # gtIds - [1xG] id for each of the G ground truths (gt)\n # dtMatches - [TxD] matching gt id at each IoU or 0\n # gtMatches - [TxG] matching dt id at each IoU or 0\n # dtScores - [1xD] confidence of each dt\n # gtIgnore - [1xG] ignore flag for each gt\n # dtIgnore - [TxD] ignore flag for each dt at each IoU\n #\n # accumulate(): accumulates the per-image, per-category evaluation\n # results in \"evalImgs\" into the dictionary \"eval\" with fields:\n # params - parameters used for evaluation\n # date - date evaluation was performed\n # counts - [T,R,K,A,M] parameter dimensions (see above)\n # precision - [TxRxKxAxM] precision for every evaluation setting\n # recall - [TxKxAxM] max recall for every evaluation setting\n # Note: precision and recall==-1 for settings with no gt objects.\n #\n # See also coco, mask, pycocoDemo, pycocoEvalDemo\n #\n # Microsoft COCO Toolbox. version 2.0\n # Data, paper, and tutorials available at: http://mscoco.org/\n # Code written by Piotr Dollar and Tsung-Yi Lin, 2015.\n # Licensed under the Simplified BSD License [see coco/license.txt]\n def __init__(self, cocoGt=None, cocoDt=None, iouType='segm'):\n '''\n Initialize CocoEval using coco APIs for gt and dt\n :param cocoGt: coco object with ground truth annotations\n :param cocoDt: coco object with detection results\n :return: None\n '''\n if not iouType:\n print('iouType not specified. use default iouType segm')\n self.cocoGt = cocoGt # ground truth COCO API\n self.cocoDt = cocoDt # detections COCO API\n self.params = {} # evaluation parameters\n # per-image per-category evaluation results [KxAxI] elements\n self.evalImgs = defaultdict(list)\n self.eval = {} # accumulated evaluation results\n self._gts = defaultdict(list) # gt for evaluation\n self._dts = defaultdict(list) # dt for evaluation\n self.params = Params(iouType=iouType) # parameters\n self._paramsEval = {} # parameters for evaluation\n self.stats = [] # result summarization\n self.ious = {} # ious between all gts and dts\n if not cocoGt is None:\n self.params.imgIds = sorted(cocoGt.getImgIds())\n self.params.catIds = sorted(cocoGt.getCatIds())\n self.anno_file = cocoGt.anno_file\n\n def _prepare(self):\n '''\n Prepare ._gts and ._dts for evaluation based on params\n :return: None\n '''\n def _toMask(anns, coco):\n # modify ann['segmentation'] by reference\n for ann in anns:\n rle = coco.annToRLE(ann)\n ann['segmentation'] = rle\n p = self.params\n if p.useCats:\n gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(\n imgIds=p.imgIds, catIds=p.catIds))\n dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(\n imgIds=p.imgIds, catIds=p.catIds))\n else:\n gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds))\n dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds))\n\n # convert ground truth to mask if iouType == 'segm'\n if p.iouType == 'segm':\n _toMask(gts, self.cocoGt)\n _toMask(dts, self.cocoDt)\n # set ignore flag\n for gt in gts:\n gt['ignore'] = gt['ignore'] if 'ignore' in gt else 0\n gt['ignore'] = 'iscrowd' in gt and gt['iscrowd']\n if p.iouType == 'keypoints':\n gt['ignore'] = (gt['num_keypoints'] == 0) or gt['ignore']\n self._gts = defaultdict(list) # gt for evaluation\n self._dts = defaultdict(list) # dt for evaluation\n for gt in gts:\n self._gts[gt['image_id'], gt['category_id']].append(gt)\n for dt in dts:\n self._dts[dt['image_id'], dt['category_id']].append(dt)\n # per-image per-category evaluation results\n self.evalImgs = defaultdict(list)\n self.eval = {} # accumulated evaluation results\n\n def evaluate(self):\n '''\n Run per image evaluation on given images and store results (a list of dict) in self.evalImgs\n :return: None\n '''\n tic = time.time()\n print('Running per image evaluation...')\n p = self.params\n # add backward compatibility if useSegm is specified in params\n if not p.useSegm is None:\n p.iouType = 'segm' if p.useSegm == 1 else 'bbox'\n print(\n 'useSegm (deprecated) is not None. Running {} evaluation'.format(p.iouType))\n print('Evaluate annotation type *{}*'.format(p.iouType))\n p.imgIds = list(np.unique(p.imgIds))\n if p.useCats:\n p.catIds = list(np.unique(p.catIds))\n p.maxDets = sorted(p.maxDets)\n self.params = p\n\n self._prepare()\n # loop through images, area range, max detection number\n catIds = p.catIds if p.useCats else [-1]\n\n if p.iouType == 'segm' or p.iouType == 'bbox':\n computeIoU = self.computeIoU\n elif p.iouType == 'keypoints':\n computeIoU = self.computeOks\n self.ious = {(imgId, catId): computeIoU(imgId, catId)\n for imgId in p.imgIds\n for catId in catIds}\n\n evaluateImg = self.evaluateImg\n maxDet = p.maxDets[-1]\n self.evalImgs = [evaluateImg(imgId, catId, areaRng, maxDet)\n for catId in catIds\n for areaRng in p.areaRng\n for imgId in p.imgIds\n ]\n self._paramsEval = copy.deepcopy(self.params)\n toc = time.time()\n print('DONE (t={:0.2f}s).'.format(toc-tic))\n\n def computeIoU(self, imgId, catId):\n p = self.params\n if p.useCats:\n gt = self._gts[imgId, catId]\n dt = self._dts[imgId, catId]\n else:\n gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]\n dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]\n if len(gt) == 0 and len(dt) == 0:\n return []\n inds = np.argsort([-d['score'] for d in dt], kind='mergesort')\n dt = [dt[i] for i in inds]\n if len(dt) > p.maxDets[-1]:\n dt = dt[0:p.maxDets[-1]]\n\n if p.iouType == 'segm':\n g = [g['segmentation'] for g in gt]\n d = [d['segmentation'] for d in dt]\n elif p.iouType == 'bbox':\n g = [g['bbox'] for g in gt]\n d = [d['bbox'] for d in dt]\n else:\n raise Exception('unknown iouType for iou computation')\n\n # compute iou between each dt and gt region\n iscrowd = [int(o['iscrowd']) for o in gt]\n ious = maskUtils.iou(d, g, iscrowd)\n return ious\n\n def computeOks(self, imgId, catId):\n p = self.params\n # dimention here should be Nxm\n # print imgId, catId\n gts = self._gts[imgId, catId]\n dts = self._dts[imgId, catId]\n inds = np.argsort([-d['score'] for d in dts], kind='mergesort')\n dts = [dts[i] for i in inds]\n if len(dts) > p.maxDets[-1]:\n dts = dts[0:p.maxDets[-1]]\n if len(gts) == 0 or len(dts) == 0:\n return []\n ious = np.zeros((len(dts), len(gts)))\n\n sigmas = np.array(\n [.79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89, .79, .79])/10.0\n\n vars = (sigmas * 2)**2\n k = len(sigmas)\n # compute oks between each detection and ground truth object\n for j, gt in enumerate(gts):\n # create bounds for ignore regions(double the gt bbox)\n g = np.array(gt['keypoints'])\n xg = g[0::3]\n yg = g[1::3]\n vg = g[2::3]\n k1 = np.count_nonzero(vg > 0)\n bb = gt['bbox']\n x0 = bb[0] - bb[2]\n x1 = bb[0] + bb[2] * 2\n y0 = bb[1] - bb[3]\n y1 = bb[1] + bb[3] * 2\n for i, dt in enumerate(dts):\n d = np.array(dt['keypoints'])\n xd = d[0::3]\n yd = d[1::3]\n if k1 > 0:\n # measure the per-keypoint distance if keypoints visible\n dx = xd - xg\n dy = yd - yg\n else:\n # measure minimum distance to keypoints in (x0,y0) & (x1,y1)\n # print 'k', k\n z = np.zeros((k))\n\n dx = np.max((z, x0-xd), axis=0) + \\\n np.max((z, xd-x1), axis=0)\n dy = np.max((z, y0-yd), axis=0)+np.max((z, yd-y1), axis=0)\n\n tmparea = gt['bbox'][3] * gt['bbox'][2] * 0.53\n e = (dx**2 + dy**2) / vars / (tmparea+np.spacing(1)) / 2\n\n if k1 > 0:\n e = e[vg > 0]\n ious[i, j] = np.sum(np.exp(-e)) / e.shape[0]\n return ious\n\n def evaluateImg(self, imgId, catId, aRng, maxDet):\n '''\n perform evaluation for single category and image\n :return: dict (single image results)\n '''\n p = self.params\n if p.useCats:\n gt = self._gts[imgId, catId]\n dt = self._dts[imgId, catId]\n else:\n gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]\n dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]\n if len(gt) == 0 and len(dt) == 0:\n return None\n\n for g in gt:\n tmp_area = g['bbox'][2] * g['bbox'][3] * 0.53\n if g['ignore'] or (tmp_area < aRng[0] or tmp_area > aRng[1]):\n g['_ignore'] = 1\n else:\n g['_ignore'] = 0\n\n # sort dt highest score first, sort gt ignore last\n gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort')\n gt = [gt[i] for i in gtind]\n dtind = np.argsort([-d['score'] for d in dt], kind='mergesort')\n dt = [dt[i] for i in dtind[0:maxDet]]\n iscrowd = [int(o['iscrowd']) for o in gt]\n # load computed ious\n ious = self.ious[imgId, catId][:, gtind] if len(\n self.ious[imgId, catId]) > 0 else self.ious[imgId, catId]\n\n T = len(p.iouThrs)\n G = len(gt)\n D = len(dt)\n gtm = np.zeros((T, G))\n dtm = np.zeros((T, D))\n gtIg = np.array([g['_ignore'] for g in gt])\n dtIg = np.zeros((T, D))\n if not len(ious) == 0:\n for tind, t in enumerate(p.iouThrs):\n for dind, d in enumerate(dt):\n # information about best match so far (m=-1 -> unmatched)\n iou = min([t, 1-1e-10])\n m = -1\n for gind, g in enumerate(gt):\n # if this gt already matched, and not a crowd, continue\n if gtm[tind, gind] > 0 and not iscrowd[gind]:\n continue\n # if dt matched to reg gt, and on ignore gt, stop\n if m > -1 and gtIg[m] == 0 and gtIg[gind] == 1:\n break\n # continue to next gt unless better match made\n if ious[dind, gind] < iou:\n continue\n # if match successful and best so far, store appropriately\n iou = ious[dind, gind]\n m = gind\n # if match made store id of match for both dt and gt\n if m == -1:\n continue\n dtIg[tind, dind] = gtIg[m]\n dtm[tind, dind] = gt[m]['id']\n gtm[tind, m] = d['id']\n # set unmatched detections outside of area range to ignore\n a = np.array([d['area'] < aRng[0] or d['area'] > aRng[1]\n for d in dt]).reshape((1, len(dt)))\n dtIg = np.logical_or(dtIg, np.logical_and(\n dtm == 0, np.repeat(a, T, 0)))\n # store results for given image and category\n return {\n 'image_id': imgId,\n 'category_id': catId,\n 'aRng': aRng,\n 'maxDet': maxDet,\n 'dtIds': [d['id'] for d in dt],\n 'gtIds': [g['id'] for g in gt],\n 'dtMatches': dtm,\n 'gtMatches': gtm,\n 'dtScores': [d['score'] for d in dt],\n 'gtIgnore': gtIg,\n 'dtIgnore': dtIg,\n }\n\n def accumulate(self, p=None):\n '''\n Accumulate per image evaluation results and store the result in self.eval\n :param p: input params for evaluation\n :return: None\n '''\n print('Accumulating evaluation results...')\n tic = time.time()\n if not self.evalImgs:\n print('Please run evaluate() first')\n # allows input customized parameters\n if p is None:\n p = self.params\n p.catIds = p.catIds if p.useCats == 1 else [-1]\n T = len(p.iouThrs)\n R = len(p.recThrs)\n K = len(p.catIds) if p.useCats else 1\n A = len(p.areaRng)\n M = len(p.maxDets)\n # -1 for the precision of absent categories\n precision = -np.ones((T, R, K, A, M))\n recall = -np.ones((T, K, A, M))\n scores = -np.ones((T, R, K, A, M))\n\n # create dictionary for future indexing\n _pe = self._paramsEval\n catIds = _pe.catIds if _pe.useCats else [-1]\n setK = set(catIds)\n setA = set(map(tuple, _pe.areaRng))\n setM = set(_pe.maxDets)\n setI = set(_pe.imgIds)\n # get inds to evaluate\n k_list = [n for n, k in enumerate(p.catIds) if k in setK]\n m_list = [m for n, m in enumerate(p.maxDets) if m in setM]\n a_list = [n for n, a in enumerate(\n map(lambda x: tuple(x), p.areaRng)) if a in setA]\n i_list = [n for n, i in enumerate(p.imgIds) if i in setI]\n I0 = len(_pe.imgIds)\n A0 = len(_pe.areaRng)\n # retrieve E at each category, area range, and max number of detections\n for k, k0 in enumerate(k_list):\n Nk = k0*A0*I0\n for a, a0 in enumerate(a_list):\n Na = a0*I0\n for m, maxDet in enumerate(m_list):\n E = [self.evalImgs[Nk + Na + i] for i in i_list]\n E = [e for e in E if not e is None]\n if len(E) == 0:\n continue\n dtScores = np.concatenate(\n [e['dtScores'][0:maxDet] for e in E])\n\n # different sorting method generates slightly different results.\n # mergesort is used to be consistent as Matlab implementation.\n inds = np.argsort(-dtScores, kind='mergesort')\n dtScoresSorted = dtScores[inds]\n\n dtm = np.concatenate([e['dtMatches'][:, 0:maxDet]\n for e in E], axis=1)[:, inds]\n dtIg = np.concatenate(\n [e['dtIgnore'][:, 0:maxDet] for e in E], axis=1)[:, inds]\n gtIg = np.concatenate([e['gtIgnore'] for e in E])\n npig = np.count_nonzero(gtIg == 0)\n if npig == 0:\n continue\n tps = np.logical_and(dtm, np.logical_not(dtIg))\n fps = np.logical_and(\n np.logical_not(dtm), np.logical_not(dtIg))\n\n tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)\n fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)\n for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):\n tp = np.array(tp)\n fp = np.array(fp)\n nd = len(tp)\n rc = tp / npig\n pr = tp / (fp+tp+np.spacing(1))\n q = np.zeros((R,))\n ss = np.zeros((R,))\n\n if nd:\n recall[t, k, a, m] = rc[-1]\n else:\n recall[t, k, a, m] = 0\n\n # numpy is slow without cython optimization for accessing elements\n # use python array gets significant speed improvement\n pr = pr.tolist()\n q = q.tolist()\n\n for i in range(nd-1, 0, -1):\n if pr[i] > pr[i-1]:\n pr[i-1] = pr[i]\n\n inds = np.searchsorted(rc, p.recThrs, side='left')\n try:\n for ri, pi in enumerate(inds):\n q[ri] = pr[pi]\n ss[ri] = dtScoresSorted[pi]\n except:\n pass\n precision[t, :, k, a, m] = np.array(q)\n scores[t, :, k, a, m] = np.array(ss)\n self.eval = {\n 'params': p,\n 'counts': [T, R, K, A, M],\n 'date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),\n 'precision': precision,\n 'recall': recall,\n 'scores': scores,\n }\n toc = time.time()\n print('DONE (t={:0.2f}s).'.format(toc-tic))\n\n def summarize(self):\n '''\n Compute and display summary metrics for evaluation results.\n Note this functin can *only* be applied on the default parameter setting\n '''\n def _summarize(ap=1, iouThr=None, areaRng='all', maxDets=100, type_score=0):\n p = self.params\n if ap != -1:\n iStr = ' {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}'\n else:\n iStr = ' {:<18} {} @[ IoU={:<9} | type={:>6s} | maxDets={:>3d} ] = {:0.3f}'\n titleStr = 'Average Precision' if ap == 1 else 'Average Recall'\n typeStr = '(AR)' if ap != 1 else '(AP)'\n iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \\\n if iouThr is None else '{:0.2f}'.format(iouThr)\n # print 'print ioustr' , iouStr\n aind = [i for i, aRng in enumerate(\n p.areaRngLbl) if aRng == areaRng]\n mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]\n if ap == 1:\n # dimension of precision: [TxRxKxAxM]\n s = self.eval['precision']\n # IoU\n if iouThr is not None:\n t = np.where(abs(iouThr - p.iouThrs) < 0.01)[0]\n s = s[t]\n s = s[:, :, :, aind, mind]\n else:\n # dimension of recall: [TxKxAxM]\n s = self.eval['recall']\n if iouThr is not None:\n t = np.where(iouThr == p.iouThrs)[0]\n s = s[t]\n s = s[:, :, aind, mind]\n\n if len(s[s > -1]) == 0:\n mean_s = -1\n else:\n mean_s = np.mean(s[s > -1])\n if ap == -1:\n mean_s = type_score\n print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))\n return mean_s\n\n def _summarizeDets():\n stats = np.zeros((12,))\n stats[0] = _summarize(1)\n stats[1] = _summarize(1, iouThr=.5, maxDets=self.params.maxDets[2])\n stats[2] = _summarize(\n 1, iouThr=.75, maxDets=self.params.maxDets[2])\n stats[3] = _summarize(1, areaRng='small',\n maxDets=self.params.maxDets[2])\n stats[4] = _summarize(1, areaRng='medium',\n maxDets=self.params.maxDets[2])\n stats[5] = _summarize(1, areaRng='large',\n maxDets=self.params.maxDets[2])\n stats[6] = _summarize(0, maxDets=self.params.maxDets[0])\n stats[7] = _summarize(0, maxDets=self.params.maxDets[1])\n stats[8] = _summarize(0, maxDets=self.params.maxDets[2])\n stats[9] = _summarize(0, areaRng='small',\n maxDets=self.params.maxDets[2])\n stats[10] = _summarize(0, areaRng='medium',\n maxDets=self.params.maxDets[2])\n stats[11] = _summarize(\n 0, areaRng='large', maxDets=self.params.maxDets[2])\n return stats\n\n def _summarizeKps():\n \n stats = np.zeros((11,))\n # print '-----------------------------------------------'\n stats[0] = _summarize(1, maxDets=20)\n stats[1] = _summarize(1, maxDets=20, iouThr=.5)\n stats[2] = _summarize(1, maxDets=20, iouThr=.75)\n stats[5] = _summarize(0, maxDets=20)\n stats[6] = _summarize(0, maxDets=20, iouThr=.5)\n stats[7] = _summarize(0, maxDets=20, iouThr=.75)\n type_result = self.get_type_result(first=0.2, second=0.8)\n stats[8] = _summarize(-1, maxDets=20,\n areaRng='easy', type_score=type_result[0])\n stats[9] = _summarize(-1, maxDets=20,\n areaRng='medium', type_score=type_result[1])\n stats[10]=_summarize(-1, maxDets=20,\n areaRng='hard', type_score=type_result[2]) \n return stats\n if not self.eval:\n raise Exception('Please run accumulate() first')\n iouType = self.params.iouType\n if iouType == 'segm' or iouType == 'bbox':\n summarize = _summarizeDets\n elif iouType == 'keypoints':\n summarize = _summarizeKps\n self.stats = summarize()\n\n def __str__(self):\n self.summarize()\n\n def get_type_result(self, first=0.01, second=0.85):\n gt_file = self.anno_file[0]\n easy, mid, hard = self.split(gt_file, first, second)\n # print (len(easy), len(mid), len(hard))\n res = []\n nullwrite = NullWriter()\n oldstdout = sys.stdout\n sys.stdout = nullwrite\n for curr_type in [easy, mid, hard]:\n curr_list = curr_type\n self.params.imgIds = curr_list\n self.evaluate()\n self.accumulate()\n score = self.eval['precision'][:, :, :, 0, :]\n res.append(round(np.mean(score), 4))\n sys.stdout = oldstdout\n return res\n\n\n def split(serlf, gt_file, first=0.01, second=0.85):\n import json\n data = json.load(\n open(gt_file, 'r'))\n easy = []\n mid = []\n hard = []\n for item in data['images']:\n if item['crowdIndex'] < first:\n easy.append(item['id'])\n elif item['crowdIndex'] < second:\n mid.append(item['id'])\n else:\n hard.append(item['id'])\n return easy, mid, hard\n\n\nclass Params:\n '''\n Params for coco evaluation api\n '''\n\n def setDetParams(self):\n self.imgIds = []\n self.catIds = []\n # np.arange causes trouble. the data point on arange is slightly larger than the true value\n self.iouThrs = np.linspace(.5, 0.95, int(np.round(\n (0.95 - .5) / .05) + 1), endpoint=True)\n self.recThrs = np.linspace(.0, 1.00, int(np.round(\n (1.00 - .0) / .01) + 1), endpoint=True)\n self.maxDets = [1, 10, 100]\n self.areaRng = [[0 ** 2, 1e5 ** 2], [0 ** 2, 32 ** 2],\n [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]]\n self.areaRngLbl = ['all', 'small', 'medium', 'large']\n self.useCats = 1\n\n def setKpParams(self):\n self.imgIds = []\n self.catIds = []\n # np.arange causes trouble. the data point on arange is slightly larger than the true value\n self.iouThrs = np.linspace(.5, 0.95, int(np.round(\n (0.95 - .5) / .05) + 1), endpoint=True)\n self.recThrs = np.linspace(.0, 1.00, int(np.round(\n (1.00 - .0) / .01) + 1), endpoint=True)\n self.maxDets = [20]\n self.areaRng = [[0 ** 2, 1e5 ** 2],\n [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]]\n self.areaRngLbl = ['all', 'medium', 'large']\n self.useCats = 1\n\n def __init__(self, iouType='segm'):\n if iouType == 'segm' or iouType == 'bbox':\n self.setDetParams()\n elif iouType == 'keypoints':\n self.setKpParams()\n else:\n raise Exception('iouType not supported')\n self.iouType = iouType\n # useSegm is deprecated\n self.useSegm = None\n"
},
{
"path": "CrowdPose/crowdpose-api/PythonAPI/build/lib.linux-x86_64-3.7/crowdposetools/mask.py",
"content": "__author__ = 'tsungyi'\n\nimport crowdposetools._mask as _mask\n\n# Interface for manipulating masks stored in RLE format.\n#\n# RLE is a simple yet efficient format for storing binary masks. RLE\n# first divides a vector (or vectorized image) into a series of piecewise\n# constant regions and then for each piece simply stores the length of\n# that piece. For example, given M=[0 0 1 1 1 0 1] the RLE counts would\n# be [2 3 1 1], or for M=[1 1 1 1 1 1 0] the counts would be [0 6 1]\n# (note that the odd counts are always the numbers of zeros). Instead of\n# storing the counts directly, additional compression is achieved with a\n# variable bitrate representation based on a common scheme called LEB128.\n#\n# Compression is greatest given large piecewise constant regions.\n# Specifically, the size of the RLE is proportional to the number of\n# *boundaries* in M (or for an image the number of boundaries in the y\n# direction). Assuming fairly simple shapes, the RLE representation is\n# O(sqrt(n)) where n is number of pixels in the object. Hence space usage\n# is substantially lower, especially for large simple objects (large n).\n#\n# Many common operations on masks can be computed directly using the RLE\n# (without need for decoding). This includes computations such as area,\n# union, intersection, etc. All of these operations are linear in the\n# size of the RLE, in other words they are O(sqrt(n)) where n is the area\n# of the object. Computing these operations on the original mask is O(n).\n# Thus, using the RLE can result in substantial computational savings.\n#\n# The following API functions are defined:\n# encode - Encode binary masks using RLE.\n# decode - Decode binary masks encoded via RLE.\n# merge - Compute union or intersection of encoded masks.\n# iou - Compute intersection over union between masks.\n# area - Compute area of encoded masks.\n# toBbox - Get bounding boxes surrounding encoded masks.\n# frPyObjects - Convert polygon, bbox, and uncompressed RLE to encoded RLE mask.\n#\n# Usage:\n# Rs = encode( masks )\n# masks = decode( Rs )\n# R = merge( Rs, intersect=false )\n# o = iou( dt, gt, iscrowd )\n# a = area( Rs )\n# bbs = toBbox( Rs )\n# Rs = frPyObjects( [pyObjects], h, w )\n#\n# In the API the following formats are used:\n# Rs - [dict] Run-length encoding of binary masks\n# R - dict Run-length encoding of binary mask\n# masks - [hxwxn] Binary mask(s) (must have type np.ndarray(dtype=uint8) in column-major order)\n# iscrowd - [nx1] list of np.ndarray. 1 indicates corresponding gt image has crowd region to ignore\n# bbs - [nx4] Bounding box(es) stored as [x y w h]\n# poly - Polygon stored as [[x1 y1 x2 y2...],[x1 y1 ...],...] (2D list)\n# dt,gt - May be either bounding boxes or encoded masks\n# Both poly and bbs are 0-indexed (bbox=[0 0 1 1] encloses first pixel).\n#\n# Finally, a note about the intersection over union (iou) computation.\n# The standard iou of a ground truth (gt) and detected (dt) object is\n# iou(gt,dt) = area(intersect(gt,dt)) / area(union(gt,dt))\n# For \"crowd\" regions, we use a modified criteria. If a gt object is\n# marked as \"iscrowd\", we allow a dt to match any subregion of the gt.\n# Choosing gt' in the crowd gt that best matches the dt can be done using\n# gt'=intersect(dt,gt). Since by definition union(gt',dt)=dt, computing\n# iou(gt,dt,iscrowd) = iou(gt',dt) = area(intersect(gt,dt)) / area(dt)\n# For crowd gt regions we use this modified criteria above for the iou.\n#\n# To compile run \"python setup.py build_ext --inplace\"\n# Please do not contact us for help with compiling.\n#\n# Microsoft COCO Toolbox. version 2.0\n# Data, paper, and tutorials available at: http://mscoco.org/\n# Code written by Piotr Dollar and Tsung-Yi Lin, 2015.\n# Licensed under the Simplified BSD License [see coco/license.txt]\n\niou = _mask.iou\nmerge = _mask.merge\nfrPyObjects = _mask.frPyObjects\n\ndef encode(bimask):\n if len(bimask.shape) == 3:\n return _mask.encode(bimask)\n elif len(bimask.shape) == 2:\n h, w = bimask.shape\n return _mask.encode(bimask.reshape((h, w, 1), order='F'))[0]\n\ndef decode(rleObjs):\n if type(rleObjs) == list:\n return _mask.decode(rleObjs)\n else:\n return _mask.decode([rleObjs])[:,:,0]\n\ndef area(rleObjs):\n if type(rleObjs) == list:\n return _mask.area(rleObjs)\n else:\n return _mask.area([rleObjs])[0]\n\ndef toBbox(rleObjs):\n if type(rleObjs) == list:\n return _mask.toBbox(rleObjs)\n else:\n return _mask.toBbox([rleObjs])[0]"
},
{
"path": "CrowdPose/crowdpose-api/PythonAPI/crowdposetools/__init__.py",
"content": "__author__ = 'tylin'\n"
},
{
"path": "CrowdPose/crowdpose-api/PythonAPI/crowdposetools/_mask.c",
"content": "/* Generated by Cython 0.28.5 */\n\n/* BEGIN: Cython Metadata\n{\n \"distutils\": {\n \"depends\": [\n \"../common/maskApi.h\",\n \"/home/wangcan/anaconda3/envs/py0/lib/python3.6/site-packages/numpy/core/include/numpy/arrayobject.h\",\n \"/home/wangcan/anaconda3/envs/py0/lib/python3.6/site-packages/numpy/core/include/numpy/ufuncobject.h\"\n ],\n \"extra_compile_args\": [\n \"-Wno-cpp\",\n \"-Wno-unused-function\",\n \"-std=c99\"\n ],\n \"include_dirs\": [\n \"/home/wangcan/anaconda3/envs/py0/lib/python3.6/site-packages/numpy/core/include\",\n \"../common\"\n ],\n \"language\": \"c\",\n \"name\": \"crowdposetools._mask\",\n \"sources\": [\n \"crowdposetools/_mask.pyx\",\n \"../common/maskApi.c\"\n ]\n },\n \"module_name\": \"crowdposetools._mask\"\n}\nEND: Cython Metadata */\n\n#define PY_SSIZE_T_CLEAN\n#include \"Python.h\"\n#ifndef Py_PYTHON_H\n #error Python headers needed to compile C extensions, please install development version of Python.\n#elif PY_VERSION_HEX < 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PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))\n#else\n #define CYTHON_PEP393_ENABLED 0\n #define PyUnicode_1BYTE_KIND 1\n #define PyUnicode_2BYTE_KIND 2\n #define PyUnicode_4BYTE_KIND 4\n #define __Pyx_PyUnicode_READY(op) (0)\n #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u)\n #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))\n #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)\n #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE))\n #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u))\n #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))\n #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch)\n #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u))\n#endif\n#if CYTHON_COMPILING_IN_PYPY\n #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b)\n #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b)\n#else\n #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b)\n #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\\\n PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)\n #define PyUnicode_Contains(u, s) PySequence_Contains(u, s)\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)\n #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type)\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)\n #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, \"__format__\", \"O\", fmt)\n#endif\n#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))\n#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))\n#if PY_MAJOR_VERSION >= 3\n #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b)\n#else\n #define __Pyx_PyString_Format(a, b) PyString_Format(a, b)\n#endif\n#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)\n #define PyObject_ASCII(o) PyObject_Repr(o)\n#endif\n#if PY_MAJOR_VERSION >= 3\n #define PyBaseString_Type PyUnicode_Type\n #define PyStringObject PyUnicodeObject\n #define PyString_Type PyUnicode_Type\n #define PyString_Check PyUnicode_Check\n #define PyString_CheckExact PyUnicode_CheckExact\n #define PyObject_Unicode PyObject_Str\n#endif\n#if PY_MAJOR_VERSION >= 3\n #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)\n #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)\n#else\n #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))\n #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))\n#endif\n#ifndef PySet_CheckExact\n #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type)\n#endif\n#if CYTHON_ASSUME_SAFE_MACROS\n #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq)\n#else\n #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq)\n#endif\n#if PY_MAJOR_VERSION >= 3\n #define PyIntObject PyLongObject\n #define PyInt_Type PyLong_Type\n #define PyInt_Check(op) PyLong_Check(op)\n #define PyInt_CheckExact(op) PyLong_CheckExact(op)\n #define PyInt_FromString PyLong_FromString\n #define PyInt_FromUnicode PyLong_FromUnicode\n #define PyInt_FromLong PyLong_FromLong\n #define PyInt_FromSize_t PyLong_FromSize_t\n #define PyInt_FromSsize_t PyLong_FromSsize_t\n #define PyInt_AsLong PyLong_AsLong\n #define PyInt_AS_LONG PyLong_AS_LONG\n #define PyInt_AsSsize_t PyLong_AsSsize_t\n #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask\n #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask\n #define PyNumber_Int PyNumber_Long\n#endif\n#if PY_MAJOR_VERSION >= 3\n #define PyBoolObject PyLongObject\n#endif\n#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY\n #ifndef PyUnicode_InternFromString\n #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)\n #endif\n#endif\n#if PY_VERSION_HEX < 0x030200A4\n typedef long Py_hash_t;\n #define __Pyx_PyInt_FromHash_t PyInt_FromLong\n #define __Pyx_PyInt_AsHash_t PyInt_AsLong\n#else\n #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t\n #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t\n#endif\n#if PY_MAJOR_VERSION >= 3\n #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : (Py_INCREF(func), func))\n#else\n #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)\n#endif\n#if CYTHON_USE_ASYNC_SLOTS\n #if PY_VERSION_HEX >= 0x030500B1\n #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods\n #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)\n #else\n #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))\n #endif\n#else\n #define __Pyx_PyType_AsAsync(obj) NULL\n#endif\n#ifndef __Pyx_PyAsyncMethodsStruct\n typedef struct {\n unaryfunc am_await;\n unaryfunc am_aiter;\n unaryfunc am_anext;\n } __Pyx_PyAsyncMethodsStruct;\n#endif\n\n#if defined(WIN32) || defined(MS_WINDOWS)\n #define _USE_MATH_DEFINES\n#endif\n#include \n#ifdef NAN\n#define __PYX_NAN() ((float) NAN)\n#else\nstatic CYTHON_INLINE float __PYX_NAN() {\n float value;\n memset(&value, 0xFF, sizeof(value));\n return value;\n}\n#endif\n#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)\n#define __Pyx_truncl trunc\n#else\n#define __Pyx_truncl truncl\n#endif\n\n\n#define __PYX_ERR(f_index, lineno, Ln_error) \\\n{ \\\n __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \\\n}\n\n#ifndef __PYX_EXTERN_C\n #ifdef __cplusplus\n #define __PYX_EXTERN_C extern \"C\"\n #else\n #define __PYX_EXTERN_C extern\n #endif\n#endif\n\n#define __PYX_HAVE__crowdposetools___mask\n#define __PYX_HAVE_API__crowdposetools___mask\n/* Early includes */\n#include \n#include \n#include \"numpy/arrayobject.h\"\n#include \"numpy/ufuncobject.h\"\n#include \n#include \"maskApi.h\"\n#ifdef _OPENMP\n#include \n#endif /* _OPENMP */\n\n#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)\n#define CYTHON_WITHOUT_ASSERTIONS\n#endif\n\ntypedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;\n const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;\n\n#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0\n#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0\n#define __PYX_DEFAULT_STRING_ENCODING \"\"\n#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString\n#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize\n#define __Pyx_uchar_cast(c) ((unsigned char)c)\n#define __Pyx_long_cast(x) ((long)x)\n#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\\\n (sizeof(type) < sizeof(Py_ssize_t)) ||\\\n (sizeof(type) > sizeof(Py_ssize_t) &&\\\n likely(v < (type)PY_SSIZE_T_MAX ||\\\n v == (type)PY_SSIZE_T_MAX) &&\\\n (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\\\n v == (type)PY_SSIZE_T_MIN))) ||\\\n (sizeof(type) == sizeof(Py_ssize_t) &&\\\n (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\\\n v == (type)PY_SSIZE_T_MAX))) )\n#if defined (__cplusplus) && __cplusplus >= 201103L\n #include \n #define __Pyx_sst_abs(value) std::abs(value)\n#elif SIZEOF_INT >= SIZEOF_SIZE_T\n #define __Pyx_sst_abs(value) abs(value)\n#elif SIZEOF_LONG >= SIZEOF_SIZE_T\n #define __Pyx_sst_abs(value) labs(value)\n#elif defined (_MSC_VER)\n #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))\n#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L\n #define __Pyx_sst_abs(value) llabs(value)\n#elif defined (__GNUC__)\n #define __Pyx_sst_abs(value) __builtin_llabs(value)\n#else\n #define __Pyx_sst_abs(value) ((value<0) ? -value : value)\n#endif\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);\n#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))\n#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)\n#define __Pyx_PyBytes_FromString PyBytes_FromString\n#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize\nstatic CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);\n#if PY_MAJOR_VERSION < 3\n #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString\n #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize\n#else\n #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString\n #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize\n#endif\n#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s)\n#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s)\n#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s)\n#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s)\n#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)\nstatic CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {\n const Py_UNICODE *u_end = u;\n while (*u_end++) ;\n return (size_t)(u_end - u - 1);\n}\n#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))\n#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode\n#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode\n#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)\n#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)\nstatic CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);\nstatic CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);\nstatic CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);\n#define __Pyx_PySequence_Tuple(obj)\\\n (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))\nstatic CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);\nstatic CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);\n#if CYTHON_ASSUME_SAFE_MACROS\n#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))\n#else\n#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)\n#endif\n#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))\n#if PY_MAJOR_VERSION >= 3\n#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))\n#else\n#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))\n#endif\n#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))\n#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\nstatic int __Pyx_sys_getdefaultencoding_not_ascii;\nstatic int __Pyx_init_sys_getdefaultencoding_params(void) {\n PyObject* sys;\n PyObject* default_encoding = NULL;\n PyObject* ascii_chars_u = NULL;\n PyObject* ascii_chars_b = NULL;\n const char* default_encoding_c;\n sys = PyImport_ImportModule(\"sys\");\n if (!sys) goto bad;\n default_encoding = PyObject_CallMethod(sys, (char*) \"getdefaultencoding\", NULL);\n Py_DECREF(sys);\n if (!default_encoding) goto bad;\n default_encoding_c = PyBytes_AsString(default_encoding);\n if (!default_encoding_c) goto bad;\n if (strcmp(default_encoding_c, \"ascii\") == 0) {\n __Pyx_sys_getdefaultencoding_not_ascii = 0;\n } else {\n char ascii_chars[128];\n int c;\n for (c = 0; c < 128; c++) {\n ascii_chars[c] = c;\n }\n __Pyx_sys_getdefaultencoding_not_ascii = 1;\n ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);\n if (!ascii_chars_u) goto bad;\n ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);\n if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {\n PyErr_Format(\n PyExc_ValueError,\n \"This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.\",\n default_encoding_c);\n goto bad;\n }\n Py_DECREF(ascii_chars_u);\n Py_DECREF(ascii_chars_b);\n }\n Py_DECREF(default_encoding);\n return 0;\nbad:\n Py_XDECREF(default_encoding);\n Py_XDECREF(ascii_chars_u);\n Py_XDECREF(ascii_chars_b);\n return -1;\n}\n#endif\n#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3\n#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)\n#else\n#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)\n#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT\nstatic char* __PYX_DEFAULT_STRING_ENCODING;\nstatic int __Pyx_init_sys_getdefaultencoding_params(void) {\n PyObject* sys;\n PyObject* default_encoding = NULL;\n char* default_encoding_c;\n sys = PyImport_ImportModule(\"sys\");\n if (!sys) goto bad;\n default_encoding = PyObject_CallMethod(sys, (char*) (const char*) \"getdefaultencoding\", NULL);\n Py_DECREF(sys);\n if (!default_encoding) goto bad;\n default_encoding_c = PyBytes_AsString(default_encoding);\n if (!default_encoding_c) goto bad;\n __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c));\n if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;\n strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);\n Py_DECREF(default_encoding);\n return 0;\nbad:\n Py_XDECREF(default_encoding);\n return -1;\n}\n#endif\n#endif\n\n\n/* Test for GCC > 2.95 */\n#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))\n #define likely(x) __builtin_expect(!!(x), 1)\n #define unlikely(x) __builtin_expect(!!(x), 0)\n#else /* !__GNUC__ or GCC < 2.95 */\n #define likely(x) (x)\n #define unlikely(x) (x)\n#endif /* __GNUC__ */\nstatic CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }\n\nstatic PyObject *__pyx_m = NULL;\nstatic PyObject *__pyx_d;\nstatic PyObject *__pyx_b;\nstatic PyObject *__pyx_cython_runtime = NULL;\nstatic PyObject *__pyx_empty_tuple;\nstatic PyObject *__pyx_empty_bytes;\nstatic PyObject *__pyx_empty_unicode;\nstatic int __pyx_lineno;\nstatic int __pyx_clineno = 0;\nstatic const char * __pyx_cfilenm= __FILE__;\nstatic const char *__pyx_filename;\n\n/* Header.proto */\n#if !defined(CYTHON_CCOMPLEX)\n #if defined(__cplusplus)\n #define CYTHON_CCOMPLEX 1\n #elif defined(_Complex_I)\n #define CYTHON_CCOMPLEX 1\n #else\n #define CYTHON_CCOMPLEX 0\n #endif\n#endif\n#if CYTHON_CCOMPLEX\n #ifdef __cplusplus\n #include \n #else\n #include \n #endif\n#endif\n#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)\n #undef _Complex_I\n #define _Complex_I 1.0fj\n#endif\n\n\nstatic const char *__pyx_f[] = {\n \"crowdposetools/_mask.pyx\",\n \"stringsource\",\n \"__init__.pxd\",\n \"type.pxd\",\n};\n/* BufferFormatStructs.proto */\n#define IS_UNSIGNED(type) (((type) -1) > 0)\nstruct __Pyx_StructField_;\n#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)\ntypedef struct {\n const char* name;\n struct __Pyx_StructField_* fields;\n size_t size;\n size_t arraysize[8];\n int ndim;\n char typegroup;\n char is_unsigned;\n int flags;\n} __Pyx_TypeInfo;\ntypedef struct __Pyx_StructField_ {\n __Pyx_TypeInfo* type;\n const char* name;\n size_t offset;\n} __Pyx_StructField;\ntypedef struct {\n __Pyx_StructField* field;\n size_t parent_offset;\n} __Pyx_BufFmt_StackElem;\ntypedef struct {\n __Pyx_StructField root;\n __Pyx_BufFmt_StackElem* head;\n size_t fmt_offset;\n size_t new_count, enc_count;\n size_t struct_alignment;\n int is_complex;\n char enc_type;\n char new_packmode;\n char enc_packmode;\n char is_valid_array;\n} __Pyx_BufFmt_Context;\n\n\n/* \"../../../anaconda3/envs/py0/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":730\n * # in Cython to enable them only on the right systems.\n * \n * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<<\n * ctypedef npy_int16 int16_t\n * ctypedef npy_int32 int32_t\n */\ntypedef npy_int8 __pyx_t_5numpy_int8_t;\n\n/* \"../../../anaconda3/envs/py0/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":731\n * \n * ctypedef npy_int8 int8_t\n * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<<\n * ctypedef npy_int32 int32_t\n * ctypedef npy_int64 int64_t\n */\ntypedef npy_int16 __pyx_t_5numpy_int16_t;\n\n/* \"../../../anaconda3/envs/py0/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":732\n * ctypedef npy_int8 int8_t\n * ctypedef npy_int16 int16_t\n * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<<\n * ctypedef npy_int64 int64_t\n * #ctypedef npy_int96 int96_t\n */\ntypedef npy_int32 __pyx_t_5numpy_int32_t;\n\n/* \"../../../anaconda3/envs/py0/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":733\n * ctypedef npy_int16 int16_t\n * ctypedef npy_int32 int32_t\n * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<<\n * #ctypedef npy_int96 int96_t\n * #ctypedef npy_int128 int128_t\n */\ntypedef npy_int64 __pyx_t_5numpy_int64_t;\n\n/* \"../../../anaconda3/envs/py0/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":737\n * #ctypedef npy_int128 int128_t\n * \n * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<<\n * ctypedef npy_uint16 uint16_t\n * ctypedef 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PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2))\n#endif\n\n/* PyIntBinop.proto */\n#if !CYTHON_COMPILING_IN_PYPY\nstatic PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, long intval, int inplace);\n#else\n#define __Pyx_PyInt_EqObjC(op1, op2, intval, inplace)\\\n PyObject_RichCompare(op1, op2, Py_EQ)\n #endif\n\n/* GetModuleGlobalName.proto */\nstatic CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name);\n\n/* DictGetItem.proto */\n#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY\nstatic PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key);\n#define __Pyx_PyObject_Dict_GetItem(obj, name)\\\n (likely(PyDict_CheckExact(obj)) ?\\\n __Pyx_PyDict_GetItem(obj, name) : PyObject_GetItem(obj, name))\n#else\n#define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key)\n#define __Pyx_PyObject_Dict_GetItem(obj, name) PyObject_GetItem(obj, name)\n#endif\n\n/* GetItemInt.proto */\n#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\\\n (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\\\n __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\\\n (is_list ? (PyErr_SetString(PyExc_IndexError, \"list index out of range\"), (PyObject*)NULL) :\\\n __Pyx_GetItemInt_Generic(o, to_py_func(i))))\n#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\\\n (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\\\n __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\\\n (PyErr_SetString(PyExc_IndexError, \"list index out of range\"), (PyObject*)NULL))\nstatic CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,\n int wraparound, int boundscheck);\n#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\\\n (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\\\n __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\\\n (PyErr_SetString(PyExc_IndexError, \"tuple index out of range\"), (PyObject*)NULL))\nstatic CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,\n int wraparound, int boundscheck);\nstatic PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);\nstatic CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,\n int is_list, int wraparound, int boundscheck);\n\n/* IsLittleEndian.proto */\nstatic CYTHON_INLINE int __Pyx_Is_Little_Endian(void);\n\n/* BufferFormatCheck.proto */\nstatic const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);\nstatic void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,\n __Pyx_BufFmt_StackElem* stack,\n __Pyx_TypeInfo* type);\n\n/* BufferGetAndValidate.proto */\n#define __Pyx_GetBufferAndValidate(buf, obj, dtype, flags, nd, cast, stack)\\\n ((obj == Py_None || obj == NULL) ?\\\n (__Pyx_ZeroBuffer(buf), 0) :\\\n __Pyx__GetBufferAndValidate(buf, obj, dtype, flags, nd, cast, stack))\nstatic int __Pyx__GetBufferAndValidate(Py_buffer* buf, PyObject* obj,\n __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack);\nstatic void __Pyx_ZeroBuffer(Py_buffer* buf);\nstatic CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info);\nstatic Py_ssize_t __Pyx_minusones[] = { -1, -1, -1, -1, -1, -1, -1, -1 };\nstatic Py_ssize_t __Pyx_zeros[] = { 0, 0, 0, 0, 0, 0, 0, 0 };\n\n/* ListCompAppend.proto */\n#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS\nstatic CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {\n PyListObject* L = (PyListObject*) list;\n Py_ssize_t len = Py_SIZE(list);\n if (likely(L->allocated > len)) {\n Py_INCREF(x);\n PyList_SET_ITEM(list, len, x);\n Py_SIZE(list) = len+1;\n return 0;\n }\n return PyList_Append(list, x);\n}\n#else\n#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)\n#endif\n\n/* FetchCommonType.proto */\nstatic PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type);\n\n/* CythonFunction.proto */\n#define __Pyx_CyFunction_USED 1\n#define __Pyx_CYFUNCTION_STATICMETHOD 0x01\n#define __Pyx_CYFUNCTION_CLASSMETHOD 0x02\n#define __Pyx_CYFUNCTION_CCLASS 0x04\n#define __Pyx_CyFunction_GetClosure(f)\\\n (((__pyx_CyFunctionObject *) (f))->func_closure)\n#define __Pyx_CyFunction_GetClassObj(f)\\\n (((__pyx_CyFunctionObject *) (f))->func_classobj)\n#define __Pyx_CyFunction_Defaults(type, f)\\\n ((type *)(((__pyx_CyFunctionObject *) (f))->defaults))\n#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\\\n ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g)\ntypedef struct {\n PyCFunctionObject func;\n#if PY_VERSION_HEX < 0x030500A0\n PyObject *func_weakreflist;\n#endif\n PyObject *func_dict;\n PyObject *func_name;\n PyObject *func_qualname;\n PyObject *func_doc;\n PyObject *func_globals;\n PyObject *func_code;\n PyObject *func_closure;\n PyObject *func_classobj;\n void *defaults;\n int defaults_pyobjects;\n int flags;\n PyObject *defaults_tuple;\n PyObject *defaults_kwdict;\n PyObject *(*defaults_getter)(PyObject *);\n PyObject *func_annotations;\n} __pyx_CyFunctionObject;\nstatic PyTypeObject *__pyx_CyFunctionType = 0;\n#define __Pyx_CyFunction_NewEx(ml, flags, qualname, self, module, globals, code)\\\n __Pyx_CyFunction_New(__pyx_CyFunctionType, ml, flags, qualname, self, module, globals, code)\nstatic PyObject *__Pyx_CyFunction_New(PyTypeObject *, PyMethodDef *ml,\n int flags, PyObject* qualname,\n PyObject *self,\n PyObject *module, PyObject *globals,\n PyObject* code);\nstatic CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m,\n size_t size,\n int pyobjects);\nstatic CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m,\n PyObject *tuple);\nstatic CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m,\n PyObject *dict);\nstatic CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m,\n PyObject *dict);\nstatic int __pyx_CyFunction_init(void);\n\n/* BufferFallbackError.proto */\nstatic void __Pyx_RaiseBufferFallbackError(void);\n\n/* None.proto */\nstatic CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t);\n\n/* BufferIndexError.proto */\nstatic void __Pyx_RaiseBufferIndexError(int axis);\n\n#define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0)\n/* PySequenceContains.proto */\nstatic CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) {\n int result = PySequence_Contains(seq, item);\n return unlikely(result < 0) ? result : (result == (eq == Py_EQ));\n}\n\n/* RaiseTooManyValuesToUnpack.proto */\nstatic CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);\n\n/* RaiseNeedMoreValuesToUnpack.proto */\nstatic CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);\n\n/* RaiseNoneIterError.proto */\nstatic CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);\n\n/* SaveResetException.proto */\n#if CYTHON_FAST_THREAD_STATE\n#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)\nstatic CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);\n#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)\nstatic CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);\n#else\n#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb)\n#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb)\n#endif\n\n/* PyErrExceptionMatches.proto */\n#if CYTHON_FAST_THREAD_STATE\n#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err)\nstatic CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err);\n#else\n#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err)\n#endif\n\n/* GetException.proto */\n#if CYTHON_FAST_THREAD_STATE\n#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb)\nstatic int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);\n#else\nstatic int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);\n#endif\n\n/* PyObject_GenericGetAttrNoDict.proto */\n#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);\n#else\n#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr\n#endif\n\n/* PyObject_GenericGetAttr.proto */\n#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000\nstatic PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name);\n#else\n#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr\n#endif\n\n/* SetupReduce.proto */\nstatic int __Pyx_setup_reduce(PyObject* type_obj);\n\n/* Import.proto */\nstatic PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);\n\n/* CLineInTraceback.proto */\n#ifdef CYTHON_CLINE_IN_TRACEBACK\n#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)\n#else\nstatic int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);\n#endif\n\n/* CodeObjectCache.proto */\ntypedef struct {\n PyCodeObject* code_object;\n int code_line;\n} __Pyx_CodeObjectCacheEntry;\nstruct __Pyx_CodeObjectCache {\n int count;\n int max_count;\n __Pyx_CodeObjectCacheEntry* entries;\n};\nstatic struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};\nstatic int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);\nstatic PyCodeObject *__pyx_find_code_object(int code_line);\nstatic void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);\n\n/* AddTraceback.proto */\nstatic void __Pyx_AddTraceback(const char *funcname, int c_line,\n int py_line, const char *filename);\n\n/* BufferStructDeclare.proto */\ntypedef struct {\n Py_ssize_t shape, strides, suboffsets;\n} __Pyx_Buf_DimInfo;\ntypedef struct {\n size_t refcount;\n Py_buffer pybuffer;\n} __Pyx_Buffer;\ntypedef struct {\n __Pyx_Buffer *rcbuffer;\n char *data;\n __Pyx_Buf_DimInfo diminfo[8];\n} __Pyx_LocalBuf_ND;\n\n#if PY_MAJOR_VERSION < 3\n static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);\n static void __Pyx_ReleaseBuffer(Py_buffer *view);\n#else\n #define __Pyx_GetBuffer PyObject_GetBuffer\n #define __Pyx_ReleaseBuffer PyBuffer_Release\n#endif\n\n\n/* CIntToPy.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);\n\n/* CIntToPy.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyInt_From_siz(siz value);\n\n/* CIntToPy.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value);\n\n/* RealImag.proto */\n#if CYTHON_CCOMPLEX\n #ifdef __cplusplus\n #define __Pyx_CREAL(z) ((z).real())\n #define __Pyx_CIMAG(z) ((z).imag())\n #else\n #define __Pyx_CREAL(z) (__real__(z))\n #define __Pyx_CIMAG(z) (__imag__(z))\n #endif\n#else\n #define __Pyx_CREAL(z) ((z).real)\n #define __Pyx_CIMAG(z) ((z).imag)\n#endif\n#if defined(__cplusplus) && CYTHON_CCOMPLEX\\\n && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103)\n #define __Pyx_SET_CREAL(z,x) ((z).real(x))\n #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))\n#else\n #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)\n #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)\n#endif\n\n/* Arithmetic.proto */\n#if CYTHON_CCOMPLEX\n #define __Pyx_c_eq_float(a, b) ((a)==(b))\n #define __Pyx_c_sum_float(a, b) ((a)+(b))\n #define __Pyx_c_diff_float(a, b) ((a)-(b))\n #define __Pyx_c_prod_float(a, b) ((a)*(b))\n #define __Pyx_c_quot_float(a, b) ((a)/(b))\n #define __Pyx_c_neg_float(a) (-(a))\n #ifdef __cplusplus\n #define __Pyx_c_is_zero_float(z) ((z)==(float)0)\n #define __Pyx_c_conj_float(z) (::std::conj(z))\n #if 1\n #define __Pyx_c_abs_float(z) (::std::abs(z))\n #define __Pyx_c_pow_float(a, b) (::std::pow(a, b))\n #endif\n #else\n #define __Pyx_c_is_zero_float(z) ((z)==0)\n #define __Pyx_c_conj_float(z) (conjf(z))\n #if 1\n #define __Pyx_c_abs_float(z) (cabsf(z))\n #define __Pyx_c_pow_float(a, b) (cpowf(a, b))\n #endif\n #endif\n#else\n static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex);\n static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex);\n static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex);\n static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex);\n static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex);\n static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex);\n static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex);\n static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex);\n #if 1\n static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex);\n static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex);\n #endif\n#endif\n\n/* Arithmetic.proto */\n#if CYTHON_CCOMPLEX\n #define __Pyx_c_eq_double(a, b) ((a)==(b))\n #define __Pyx_c_sum_double(a, b) ((a)+(b))\n #define __Pyx_c_diff_double(a, b) ((a)-(b))\n #define __Pyx_c_prod_double(a, b) ((a)*(b))\n #define __Pyx_c_quot_double(a, b) ((a)/(b))\n #define __Pyx_c_neg_double(a) (-(a))\n #ifdef __cplusplus\n #define __Pyx_c_is_zero_double(z) ((z)==(double)0)\n #define __Pyx_c_conj_double(z) (::std::conj(z))\n #if 1\n #define __Pyx_c_abs_double(z) (::std::abs(z))\n #define __Pyx_c_pow_double(a, b) (::std::pow(a, b))\n #endif\n #else\n #define __Pyx_c_is_zero_double(z) ((z)==0)\n #define __Pyx_c_conj_double(z) (conj(z))\n #if 1\n #define __Pyx_c_abs_double(z) (cabs(z))\n #define __Pyx_c_pow_double(a, b) (cpow(a, b))\n #endif\n #endif\n#else\n static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex);\n static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex);\n static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex);\n static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex);\n static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex);\n static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex);\n static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex);\n static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex);\n #if 1\n static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex);\n static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex);\n #endif\n#endif\n\n/* CIntToPy.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);\n\n/* CIntToPy.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value);\n\n/* CIntFromPy.proto */\nstatic CYTHON_INLINE siz __Pyx_PyInt_As_siz(PyObject *);\n\n/* CIntFromPy.proto */\nstatic CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *);\n\n/* CIntFromPy.proto */\nstatic CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);\n\n/* CIntFromPy.proto */\nstatic CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);\n\n/* FastTypeChecks.proto */\n#if CYTHON_COMPILING_IN_CPYTHON\n#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)\nstatic CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);\n#else\n#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)\n#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)\n#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))\n#endif\n#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)\n\n/* CheckBinaryVersion.proto */\nstatic int __Pyx_check_binary_version(void);\n\n/* PyIdentifierFromString.proto */\n#if !defined(__Pyx_PyIdentifier_FromString)\n#if PY_MAJOR_VERSION < 3\n #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s)\n#else\n #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s)\n#endif\n#endif\n\n/* ModuleImport.proto */\nstatic PyObject *__Pyx_ImportModule(const char *name);\n\n/* TypeImport.proto */\nstatic PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict);\n\n/* InitStrings.proto */\nstatic int __Pyx_InitStrings(__Pyx_StringTabEntry *t);\n\n\n/* Module declarations from 'cpython.buffer' */\n\n/* Module declarations from 'libc.string' */\n\n/* Module declarations from 'libc.stdio' */\n\n/* Module declarations from '__builtin__' */\n\n/* Module declarations from 'cpython.type' */\nstatic PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;\n\n/* Module declarations from 'cpython' */\n\n/* Module declarations from 'cpython.object' */\n\n/* Module declarations from 'cpython.ref' */\n\n/* Module declarations from 'cpython.mem' */\n\n/* Module declarations from 'numpy' */\n\n/* Module declarations from 'numpy' */\nstatic PyTypeObject *__pyx_ptype_5numpy_dtype = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;\nstatic CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/\nstatic CYTHON_INLINE int __pyx_f_5numpy_import_array(void); /*proto*/\n\n/* Module declarations from 'libc.stdlib' */\n\n/* Module declarations from 'crowdposetools._mask' */\nstatic PyTypeObject *__pyx_ptype_14crowdposetools_5_mask_RLEs = 0;\nstatic PyTypeObject *__pyx_ptype_14crowdposetools_5_mask_Masks = 0;\nstatic __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_uint8_t = { \"uint8_t\", NULL, sizeof(__pyx_t_5numpy_uint8_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_uint8_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_uint8_t), 0 };\nstatic __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_double_t = { \"double_t\", NULL, sizeof(__pyx_t_5numpy_double_t), { 0 }, 0, 'R', 0, 0 };\nstatic __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_uint32_t = { \"uint32_t\", NULL, sizeof(__pyx_t_5numpy_uint32_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_uint32_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_uint32_t), 0 };\n#define __Pyx_MODULE_NAME \"crowdposetools._mask\"\nextern int __pyx_module_is_main_crowdposetools___mask;\nint __pyx_module_is_main_crowdposetools___mask = 0;\n\n/* Implementation of 'crowdposetools._mask' */\nstatic PyObject *__pyx_builtin_range;\nstatic PyObject *__pyx_builtin_AttributeError;\nstatic PyObject *__pyx_builtin_TypeError;\nstatic PyObject *__pyx_builtin_enumerate;\nstatic PyObject *__pyx_builtin_ValueError;\nstatic PyObject *__pyx_builtin_RuntimeError;\nstatic PyObject *__pyx_builtin_ImportError;\nstatic const char __pyx_k_F[] = \"F\";\nstatic const char __pyx_k_N[] = \"N\";\nstatic const char __pyx_k_R[] = \"R\";\nstatic const char __pyx_k_a[] = \"_a\";\nstatic const char __pyx_k_h[] = \"h\";\nstatic const char __pyx_k_i[] = \"i\";\nstatic const char __pyx_k_j[] = \"j\";\nstatic const char __pyx_k_m[] = \"m\";\nstatic const char __pyx_k_n[] = \"n\";\nstatic const char __pyx_k_p[] = \"p\";\nstatic const char __pyx_k_w[] = \"w\";\nstatic const char __pyx_k_Rs[] = \"Rs\";\nstatic const char __pyx_k_bb[] = \"bb\";\nstatic const char __pyx_k_dt[] = \"dt\";\nstatic const char __pyx_k_gt[] = \"gt\";\nstatic const char __pyx_k_np[] = \"np\";\nstatic const char __pyx_k_a_2[] = \"a\";\nstatic const char __pyx_k_all[] = \"all\";\nstatic const char __pyx_k_iou[] = \"_iou\";\nstatic const char __pyx_k_len[] = \"_len\";\nstatic const char __pyx_k_obj[] = \"obj\";\nstatic const char __pyx_k_sys[] = \"sys\";\nstatic const char __pyx_k_area[] = \"area\";\nstatic const char __pyx_k_bb_2[] = \"_bb\";\nstatic const char __pyx_k_cnts[] = \"cnts\";\nstatic const char __pyx_k_data[] = \"data\";\nstatic const char __pyx_k_main[] = \"__main__\";\nstatic const char __pyx_k_mask[] = \"mask\";\nstatic const char __pyx_k_name[] = \"__name__\";\nstatic const char __pyx_k_objs[] = \"objs\";\nstatic const char __pyx_k_poly[] = \"poly\";\nstatic const char __pyx_k_size[] = \"size\";\nstatic const char __pyx_k_test[] = \"__test__\";\nstatic const char __pyx_k_utf8[] = \"utf8\";\nstatic const char __pyx_k_array[] = \"array\";\nstatic const char __pyx_k_bbIou[] = \"_bbIou\";\nstatic const char __pyx_k_dtype[] = \"dtype\";\nstatic const char __pyx_k_iou_2[] = \"iou\";\nstatic const char __pyx_k_isbox[] = \"isbox\";\nstatic const char __pyx_k_isrle[] = \"isrle\";\nstatic const char __pyx_k_masks[] = \"masks\";\nstatic const char __pyx_k_merge[] = \"merge\";\nstatic const char __pyx_k_numpy[] = \"numpy\";\nstatic const char __pyx_k_order[] = \"order\";\nstatic const char __pyx_k_pyobj[] = \"pyobj\";\nstatic const char __pyx_k_range[] = \"range\";\nstatic const char __pyx_k_shape[] = \"shape\";\nstatic const char __pyx_k_uint8[] = \"uint8\";\nstatic const char __pyx_k_zeros[] = \"zeros\";\nstatic const char __pyx_k_astype[] = \"astype\";\nstatic const char __pyx_k_author[] = \"__author__\";\nstatic const char __pyx_k_counts[] = \"counts\";\nstatic const char __pyx_k_decode[] = \"decode\";\nstatic const char __pyx_k_double[] = \"double\";\nstatic const char __pyx_k_encode[] = \"encode\";\nstatic const char __pyx_k_frBbox[] = \"frBbox\";\nstatic const char __pyx_k_frPoly[] = \"frPoly\";\nstatic const char __pyx_k_import[] = \"__import__\";\nstatic const char __pyx_k_iouFun[] = \"_iouFun\";\nstatic const char __pyx_k_reduce[] = \"__reduce__\";\nstatic const char __pyx_k_rleIou[] = \"_rleIou\";\nstatic const char __pyx_k_toBbox[] = \"toBbox\";\nstatic const char __pyx_k_ucRles[] = \"ucRles\";\nstatic const char __pyx_k_uint32[] = \"uint32\";\nstatic const char __pyx_k_iscrowd[] = \"iscrowd\";\nstatic const char __pyx_k_np_poly[] = \"np_poly\";\nstatic const char __pyx_k_preproc[] = \"_preproc\";\nstatic const char __pyx_k_reshape[] = \"reshape\";\nstatic const char __pyx_k_rleObjs[] = \"rleObjs\";\nstatic const char __pyx_k_tsungyi[] = \"tsungyi\";\nstatic const char __pyx_k_c_string[] = \"c_string\";\nstatic const char __pyx_k_frString[] = \"_frString\";\nstatic const char __pyx_k_getstate[] = \"__getstate__\";\nstatic const char __pyx_k_setstate[] = \"__setstate__\";\nstatic const char __pyx_k_toString[] = \"_toString\";\nstatic const char __pyx_k_TypeError[] = \"TypeError\";\nstatic const char __pyx_k_enumerate[] = \"enumerate\";\nstatic const char __pyx_k_intersect[] = \"intersect\";\nstatic const char __pyx_k_py_string[] = \"py_string\";\nstatic const char __pyx_k_pyiscrowd[] = \"pyiscrowd\";\nstatic const char __pyx_k_reduce_ex[] = \"__reduce_ex__\";\nstatic const char __pyx_k_ValueError[] = \"ValueError\";\nstatic const char __pyx_k_ImportError[] = \"ImportError\";\nstatic const char __pyx_k_frPyObjects[] = \"frPyObjects\";\nstatic const char __pyx_k_RuntimeError[] = \"RuntimeError\";\nstatic const char __pyx_k_version_info[] = \"version_info\";\nstatic const char __pyx_k_reduce_cython[] = \"__reduce_cython__\";\nstatic const char __pyx_k_AttributeError[] = \"AttributeError\";\nstatic const char __pyx_k_PYTHON_VERSION[] = \"PYTHON_VERSION\";\nstatic const char __pyx_k_iou_locals__len[] = \"iou.._len\";\nstatic const char __pyx_k_setstate_cython[] = \"__setstate_cython__\";\nstatic const char __pyx_k_frUncompressedRLE[] = \"frUncompressedRLE\";\nstatic const char __pyx_k_iou_locals__bbIou[] = \"iou.._bbIou\";\nstatic const char __pyx_k_cline_in_traceback[] = \"cline_in_traceback\";\nstatic const char __pyx_k_iou_locals__rleIou[] = \"iou.._rleIou\";\nstatic const char __pyx_k_iou_locals__preproc[] = \"iou.._preproc\";\nstatic const char __pyx_k_crowdposetools__mask[] = \"crowdposetools._mask\";\nstatic const char __pyx_k_crowdposetools__mask_pyx[] = \"crowdposetools/_mask.pyx\";\nstatic const char __pyx_k_input_data_type_not_allowed[] = \"input data type not allowed.\";\nstatic const char __pyx_k_input_type_is_not_supported[] = \"input type is not supported.\";\nstatic const char __pyx_k_ndarray_is_not_C_contiguous[] = \"ndarray is not C contiguous\";\nstatic const char __pyx_k_Python_version_must_be_2_or_3[] = \"Python version must be 2 or 3\";\nstatic const char __pyx_k_numpy_core_multiarray_failed_to[] = \"numpy.core.multiarray failed to import\";\nstatic const char __pyx_k_numpy_ndarray_input_is_only_for[] = \"numpy ndarray input is only for *bounding boxes* and should have Nx4 dimension\";\nstatic const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = \"unknown dtype code in numpy.pxd (%d)\";\nstatic const char __pyx_k_unrecognized_type_The_following[] = \"unrecognized type. The following type: RLEs (rle), np.ndarray (box), and list (box) are supported.\";\nstatic const char __pyx_k_Format_string_allocated_too_shor[] = \"Format string allocated too short, see comment in numpy.pxd\";\nstatic const char __pyx_k_Non_native_byte_order_not_suppor[] = \"Non-native byte order not supported\";\nstatic const char __pyx_k_The_dt_and_gt_should_have_the_sa[] = \"The dt and gt should have the same data type, either RLEs, list or np.ndarray\";\nstatic const char __pyx_k_list_input_can_be_bounding_box_N[] = \"list input can be bounding box (Nx4) or RLEs ([RLE])\";\nstatic const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = \"ndarray is not Fortran contiguous\";\nstatic const char __pyx_k_no_default___reduce___due_to_non[] = \"no default __reduce__ due to non-trivial __cinit__\";\nstatic const char __pyx_k_numpy_core_umath_failed_to_impor[] = \"numpy.core.umath failed to import\";\nstatic const char __pyx_k_Format_string_allocated_too_shor_2[] = \"Format string allocated too short.\";\nstatic PyObject *__pyx_n_s_AttributeError;\nstatic PyObject *__pyx_n_s_F;\nstatic PyObject *__pyx_kp_u_Format_string_allocated_too_shor;\nstatic PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2;\nstatic PyObject *__pyx_n_s_ImportError;\nstatic PyObject *__pyx_n_s_N;\nstatic PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor;\nstatic PyObject *__pyx_n_s_PYTHON_VERSION;\nstatic PyObject *__pyx_kp_s_Python_version_must_be_2_or_3;\nstatic PyObject *__pyx_n_s_R;\nstatic PyObject *__pyx_n_s_Rs;\nstatic PyObject *__pyx_n_s_RuntimeError;\nstatic PyObject *__pyx_kp_s_The_dt_and_gt_should_have_the_sa;\nstatic PyObject *__pyx_n_s_TypeError;\nstatic PyObject *__pyx_n_s_ValueError;\nstatic PyObject *__pyx_n_s_a;\nstatic PyObject *__pyx_n_s_a_2;\nstatic PyObject *__pyx_n_s_all;\nstatic PyObject *__pyx_n_s_area;\nstatic PyObject *__pyx_n_s_array;\nstatic PyObject *__pyx_n_s_astype;\nstatic PyObject *__pyx_n_s_author;\nstatic PyObject *__pyx_n_s_bb;\nstatic PyObject *__pyx_n_s_bbIou;\nstatic PyObject *__pyx_n_s_bb_2;\nstatic PyObject *__pyx_n_s_c_string;\nstatic PyObject *__pyx_n_s_cline_in_traceback;\nstatic PyObject *__pyx_n_s_cnts;\nstatic PyObject *__pyx_n_s_counts;\nstatic PyObject *__pyx_n_s_crowdposetools__mask;\nstatic PyObject *__pyx_kp_s_crowdposetools__mask_pyx;\nstatic PyObject *__pyx_n_s_data;\nstatic PyObject *__pyx_n_s_decode;\nstatic PyObject *__pyx_n_s_double;\nstatic PyObject *__pyx_n_s_dt;\nstatic PyObject *__pyx_n_s_dtype;\nstatic PyObject *__pyx_n_s_encode;\nstatic PyObject *__pyx_n_s_enumerate;\nstatic PyObject *__pyx_n_s_frBbox;\nstatic PyObject *__pyx_n_s_frPoly;\nstatic PyObject *__pyx_n_s_frPyObjects;\nstatic PyObject *__pyx_n_s_frString;\nstatic PyObject *__pyx_n_s_frUncompressedRLE;\nstatic PyObject *__pyx_n_s_getstate;\nstatic PyObject *__pyx_n_s_gt;\nstatic PyObject *__pyx_n_s_h;\nstatic PyObject *__pyx_n_s_i;\nstatic PyObject *__pyx_n_s_import;\nstatic PyObject *__pyx_kp_s_input_data_type_not_allowed;\nstatic PyObject *__pyx_kp_s_input_type_is_not_supported;\nstatic PyObject *__pyx_n_s_intersect;\nstatic PyObject *__pyx_n_s_iou;\nstatic PyObject *__pyx_n_s_iouFun;\nstatic PyObject *__pyx_n_s_iou_2;\nstatic PyObject *__pyx_n_s_iou_locals__bbIou;\nstatic PyObject *__pyx_n_s_iou_locals__len;\nstatic PyObject *__pyx_n_s_iou_locals__preproc;\nstatic PyObject *__pyx_n_s_iou_locals__rleIou;\nstatic PyObject *__pyx_n_s_isbox;\nstatic PyObject *__pyx_n_s_iscrowd;\nstatic PyObject *__pyx_n_s_isrle;\nstatic PyObject *__pyx_n_s_j;\nstatic PyObject *__pyx_n_s_len;\nstatic PyObject *__pyx_kp_s_list_input_can_be_bounding_box_N;\nstatic PyObject *__pyx_n_s_m;\nstatic PyObject *__pyx_n_s_main;\nstatic PyObject *__pyx_n_s_mask;\nstatic PyObject *__pyx_n_s_masks;\nstatic PyObject *__pyx_n_s_merge;\nstatic PyObject *__pyx_n_s_n;\nstatic PyObject *__pyx_n_s_name;\nstatic PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous;\nstatic PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou;\nstatic PyObject *__pyx_kp_s_no_default___reduce___due_to_non;\nstatic PyObject *__pyx_n_s_np;\nstatic PyObject *__pyx_n_s_np_poly;\nstatic PyObject *__pyx_n_s_numpy;\nstatic PyObject *__pyx_kp_s_numpy_core_multiarray_failed_to;\nstatic PyObject *__pyx_kp_s_numpy_core_umath_failed_to_impor;\nstatic PyObject *__pyx_kp_s_numpy_ndarray_input_is_only_for;\nstatic PyObject *__pyx_n_s_obj;\nstatic PyObject *__pyx_n_s_objs;\nstatic PyObject *__pyx_n_s_order;\nstatic PyObject *__pyx_n_s_p;\nstatic PyObject *__pyx_n_s_poly;\nstatic PyObject *__pyx_n_s_preproc;\nstatic PyObject *__pyx_n_s_py_string;\nstatic PyObject *__pyx_n_s_pyiscrowd;\nstatic PyObject *__pyx_n_s_pyobj;\nstatic PyObject *__pyx_n_s_range;\nstatic PyObject *__pyx_n_s_reduce;\nstatic PyObject *__pyx_n_s_reduce_cython;\nstatic PyObject *__pyx_n_s_reduce_ex;\nstatic PyObject *__pyx_n_s_reshape;\nstatic PyObject *__pyx_n_s_rleIou;\nstatic PyObject *__pyx_n_s_rleObjs;\nstatic PyObject *__pyx_n_s_setstate;\nstatic PyObject *__pyx_n_s_setstate_cython;\nstatic PyObject *__pyx_n_s_shape;\nstatic PyObject *__pyx_n_s_size;\nstatic PyObject *__pyx_n_s_sys;\nstatic PyObject *__pyx_n_s_test;\nstatic PyObject *__pyx_n_s_toBbox;\nstatic PyObject *__pyx_n_s_toString;\nstatic PyObject *__pyx_n_s_tsungyi;\nstatic PyObject *__pyx_n_s_ucRles;\nstatic PyObject *__pyx_n_s_uint32;\nstatic PyObject *__pyx_n_s_uint8;\nstatic PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd;\nstatic PyObject *__pyx_kp_s_unrecognized_type_The_following;\nstatic PyObject *__pyx_n_s_utf8;\nstatic PyObject *__pyx_n_s_version_info;\nstatic PyObject *__pyx_n_s_w;\nstatic PyObject *__pyx_n_s_zeros;\nstatic int __pyx_pf_14crowdposetools_5_mask_4RLEs___cinit__(struct __pyx_obj_14crowdposetools_5_mask_RLEs *__pyx_v_self, siz __pyx_v_n); /* proto */\nstatic void __pyx_pf_14crowdposetools_5_mask_4RLEs_2__dealloc__(struct __pyx_obj_14crowdposetools_5_mask_RLEs *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_4RLEs_4__getattr__(struct __pyx_obj_14crowdposetools_5_mask_RLEs *__pyx_v_self, PyObject *__pyx_v_key); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_4RLEs_6__reduce_cython__(CYTHON_UNUSED struct __pyx_obj_14crowdposetools_5_mask_RLEs *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_4RLEs_8__setstate_cython__(CYTHON_UNUSED struct __pyx_obj_14crowdposetools_5_mask_RLEs *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */\nstatic int __pyx_pf_14crowdposetools_5_mask_5Masks___cinit__(struct __pyx_obj_14crowdposetools_5_mask_Masks *__pyx_v_self, PyObject *__pyx_v_h, PyObject *__pyx_v_w, PyObject *__pyx_v_n); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_5Masks_2__array__(struct __pyx_obj_14crowdposetools_5_mask_Masks *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_5Masks_4__reduce_cython__(CYTHON_UNUSED struct __pyx_obj_14crowdposetools_5_mask_Masks *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_5Masks_6__setstate_cython__(CYTHON_UNUSED struct __pyx_obj_14crowdposetools_5_mask_Masks *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask__toString(CYTHON_UNUSED PyObject *__pyx_self, struct __pyx_obj_14crowdposetools_5_mask_RLEs *__pyx_v_Rs); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_2_frString(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_rleObjs); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_4encode(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_mask); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_6decode(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_rleObjs); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_8merge(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_rleObjs, PyObject *__pyx_v_intersect); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_10area(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_rleObjs); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_3iou__preproc(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_objs); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_3iou_2_rleIou(CYTHON_UNUSED PyObject *__pyx_self, struct __pyx_obj_14crowdposetools_5_mask_RLEs *__pyx_v_dt, struct __pyx_obj_14crowdposetools_5_mask_RLEs *__pyx_v_gt, PyArrayObject *__pyx_v_iscrowd, siz __pyx_v_m, siz __pyx_v_n, PyArrayObject *__pyx_v__iou); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_3iou_4_bbIou(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_dt, PyArrayObject *__pyx_v_gt, PyArrayObject *__pyx_v_iscrowd, siz __pyx_v_m, siz __pyx_v_n, PyArrayObject *__pyx_v__iou); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_3iou_6_len(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_obj); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_12iou(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_dt, PyObject *__pyx_v_gt, PyObject *__pyx_v_pyiscrowd); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_14toBbox(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_rleObjs); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_16frBbox(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_bb, siz __pyx_v_h, siz __pyx_v_w); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_18frPoly(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_poly, siz __pyx_v_h, siz __pyx_v_w); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_20frUncompressedRLE(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_ucRles, CYTHON_UNUSED siz __pyx_v_h, CYTHON_UNUSED siz __pyx_v_w); /* proto */\nstatic PyObject *__pyx_pf_14crowdposetools_5_mask_22frPyObjects(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_pyobj, 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