__author__ = 'tylin'
__version__ = '2.0'
# Interface for accessing the Microsoft COCO dataset.

# Microsoft COCO is a large image dataset designed for object detection,
# segmentation, and caption generation. pycocotools is a Python API that
# assists in loading, parsing and visualizing the annotations in COCO.
# Please visit http://mscoco.org/ for more information on COCO, including
# for the data, paper, and tutorials. The exact format of the annotations
# is also described on the COCO website. For example usage of the pycocotools
# please see pycocotools_demo.ipynb. In addition to this API, please download both
# the COCO images and annotations in order to run the demo.

# An alternative to using the API is to load the annotations directly
# into Python dictionary
# Using the API provides additional utility functions. Note that this API
# supports both *instance* and *caption* annotations. In the case of
# captions not all functions are defined (e.g. categories are undefined).

# The following API functions are defined:
# COCO - COCO api class that loads COCO annotation file and prepare data structures.
# decodeMask - Decode binary mask M encoded via run-length encoding.
# encodeMask - Encode binary mask M using run-length encoding.
# getAnnIds - Get ann ids that satisfy given filter conditions.
# getCatIds - Get cat ids that satisfy given filter conditions.
# getImgIds - Get img ids that satisfy given filter conditions.
# loadAnns - Load anns with the specified ids.
# loadCats - Load cats with the specified ids.
# loadImgs - Load imgs with the specified ids.
# annToMask - Convert segmentation in an annotation to binary mask.
# showAnns - Display the specified annotations.
# loadRes - Load algorithm results and create API for accessing them.
# download - Download COCO images from mscoco.org server.
# Throughout the API "ann"=annotation, "cat"=category, and "img"=image.
# Help on each functions can be accessed by: "help COCO>function".

# See also COCO>decodeMask,
# COCO>encodeMask, COCO>getAnnIds, COCO>getCatIds,
# COCO>getImgIds, COCO>loadAnns, COCO>loadCats,
# COCO>loadImgs, COCO>annToMask, COCO>showAnns

# Microsoft COCO Toolbox. version 2.0
# Data, paper, and tutorials available at: http://mscoco.org/
# Code written by Piotr Dollar and Tsung-Yi Lin, 2014.
# Licensed under the Simplified BSD License [see bsd.txt]

import json
import time
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from matplotlib.patches import Polygon
import numpy as np
import copy
import itertools
from . import mask as maskUtils
import os
from collections import defaultdict
import sys
PYTHON_VERSION = sys.version_info[0]
if PYTHON_VERSION == 2:
 from urllib import urlretrieve
elif PYTHON_VERSION == 3:
 from urllib.request import urlretrieve


def _isArrayLike(obj):
 return hasattr(obj, '__iter__') and hasattr(obj, '__len__')


class COCO:
 def __init__(self, annotation_file=None):
 """
 Constructor of Microsoft COCO helper class for reading and visualizing annotations.
 :param annotation_file (str): location of annotation file
 :param image_folder (str): location to the folder that hosts images.
 :return:
 """
 # load dataset
 self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict()
 self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)
 if not annotation_file == None:
 print('loading annotations into memory...')
 tic = time.time()
 dataset = json.load(open(annotation_file, 'r'))
 assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset))
 print('Done (t={:0.2f}s)'.format(time.time()- tic))
 self.dataset = dataset
 self.createIndex()

 def createIndex(self):
 # create index
 print('creating index...')
 anns, cats, imgs = {}, {}, {}
 imgToAnns,catToImgs = defaultdict(list),defaultdict(list)
 if 'annotations' in self.dataset:
 for ann in self.dataset['annotations']:
 imgToAnns[ann['image_id']].append(ann)
 anns[ann['id']] = ann

 if 'images' in self.dataset:
 for img in self.dataset['images']:
 imgs[img['id']] = img

 if 'categories' in self.dataset:
 for cat in self.dataset['categories']:
 cats[cat['id']] = cat

 if 'annotations' in self.dataset and 'categories' in self.dataset:
 for ann in self.dataset['annotations']:
 catToImgs[ann['category_id']].append(ann['image_id'])

 print('index created!')

 # create class members
 self.anns = anns
 self.imgToAnns = imgToAnns
 self.catToImgs = catToImgs
 self.imgs = imgs
 self.cats = cats

 def info(self):
 """
 Print information about the annotation file.
 :return:
 """
 for key, value in self.dataset['info'].items():
 print('{}: {}'.format(key, value))

 def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
 """
 Get ann ids that satisfy given filter conditions. default skips that filter
 :param imgIds (int array) : get anns for given imgs
 catIds (int array) : get anns for given cats
 areaRng (float array) : get anns for given area range (e.g. [0 inf])
 iscrowd (boolean) : get anns for given crowd label (False or True)
 :return: ids (int array) : integer array of ann ids
 """
 imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]
 catIds = catIds if _isArrayLike(catIds) else [catIds]

 if len(imgIds) == len(catIds) == len(areaRng) == 0:
 anns = self.dataset['annotations']
 else:
 if not len(imgIds) == 0:
 lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns]
 anns = list(itertools.chain.from_iterable(lists))
 else:
 anns = self.dataset['annotations']
 anns = anns if len(catIds) == 0 else [ann for ann in anns if ann['category_id'] in catIds]
 anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]]
 if not iscrowd == None:
 ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd]
 else:
 ids = [ann['id'] for ann in anns]
 return ids

 def getCatIds(self, catNms=[], supNms=[], catIds=[]):
 """
 filtering parameters. default skips that filter.
 :param catNms (str array) : get cats for given cat names
 :param supNms (str array) : get cats for given supercategory names
 :param catIds (int array) : get cats for given cat ids
 :return: ids (int array) : integer array of cat ids
 """
 catNms = catNms if _isArrayLike(catNms) else [catNms]
 supNms = supNms if _isArrayLike(supNms) else [supNms]
 catIds = catIds if _isArrayLike(catIds) else [catIds]

 if len(catNms) == len(supNms) == len(catIds) == 0:
 cats = self.dataset['categories']
 else:
 cats = self.dataset['categories']
 cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name'] in catNms]
 cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms]
 cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id'] in catIds]
 ids = [cat['id'] for cat in cats]
 return ids

 def getImgIds(self, imgIds=[], catIds=[]):
 '''
 Get img ids that satisfy given filter conditions.
 :param imgIds (int array) : get imgs for given ids
 :param catIds (int array) : get imgs with all given cats
 :return: ids (int array) : integer array of img ids
 '''
 imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]
 catIds = catIds if _isArrayLike(catIds) else [catIds]

 if len(imgIds) == len(catIds) == 0:
 ids = self.imgs.keys()
 else:
 ids = set(imgIds)
 for i, catId in enumerate(catIds):
 if i == 0 and len(ids) == 0:
 ids = set(self.catToImgs[catId])
 else:
 ids &= set(self.catToImgs[catId])
 return list(ids)

 def loadAnns(self, ids=[]):
 """
 Load anns with the specified ids.
 :param ids (int array) : integer ids specifying anns
 :return: anns (object array) : loaded ann objects
 """
 if _isArrayLike(ids):
 return [self.anns[id] for id in ids]
 elif type(ids) == int:
 return [self.anns[ids]]

 def loadCats(self, ids=[]):
 """
 Load cats with the specified ids.
 :param ids (int array) : integer ids specifying cats
 :return: cats (object array) : loaded cat objects
 """
 if _isArrayLike(ids):
 return [self.cats[id] for id in ids]
 elif type(ids) == int:
 return [self.cats[ids]]

 def loadImgs(self, ids=[]):
 """
 Load anns with the specified ids.
 :param ids (int array) : integer ids specifying img
 :return: imgs (object array) : loaded img objects
 """
 if _isArrayLike(ids):
 return [self.imgs[id] for id in ids]
 elif type(ids) == int:
 return [self.imgs[ids]]

 def showAnns(self, anns, draw_bbox=False):
 """
 Display the specified annotations.
 :param anns (array of object): annotations to display
 :return: None
 """
 if len(anns) == 0:
 return 0
 if 'segmentation' in anns[0] or 'keypoints' in anns[0]:
 datasetType = 'instances'
 elif 'caption' in anns[0]:
 datasetType = 'captions'
 else:
 raise Exception('datasetType not supported')
 if datasetType == 'instances':
 ax = plt.gca()
 ax.set_autoscale_on(False)
 polygons = []
 color = []
 for ann in anns:
 c = (np.random.random((1, 3))*0.6+0.4).tolist()[0]
 if 'segmentation' in ann:
 if type(ann['segmentation']) == list:
 # polygon
 for seg in ann['segmentation']:
 poly = np.array(seg).reshape((int(len(seg)/2), 2))
 polygons.append(Polygon(poly))
 color.append(c)
 else:
 # mask
 t = self.imgs[ann['image_id']]
 if type(ann['segmentation']['counts']) == list:
 rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width'])
 else:
 rle = [ann['segmentation']]
 m = maskUtils.decode(rle)
 img = np.ones( (m.shape[0], m.shape[1], 3) )
 if ann['iscrowd'] == 1:
 color_mask = np.array([2.0,166.0,101.0])/255
 if ann['iscrowd'] == 0:
 color_mask = np.random.random((1, 3)).tolist()[0]
 for i in range(3):
 img[:,:,i] = color_mask[i]
 ax.imshow(np.dstack( (img, m*0.5) ))
 if 'keypoints' in ann and type(ann['keypoints']) == list:
 # turn skeleton into zero-based index
 sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1
 kp = np.array(ann['keypoints'])
 x = kp[0::3]
 y = kp[1::3]
 v = kp[2::3]
 for sk in sks:
 if np.all(v[sk]>0):
 plt.plot(x[sk],y[sk], linewidth=3, color=c)
 plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2)
 plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2)

 if draw_bbox:
 [bbox_x, bbox_y, bbox_w, bbox_h] = ann['bbox']
 poly = [[bbox_x, bbox_y], [bbox_x, bbox_y+bbox_h], [bbox_x+bbox_w, bbox_y+bbox_h], [bbox_x+bbox_w, bbox_y]]
 np_poly = np.array(poly).reshape((4,2))
 polygons.append(Polygon(np_poly))
 color.append(c)

 p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
 ax.add_collection(p)
 p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2)
 ax.add_collection(p)
 elif datasetType == 'captions':
 for ann in anns:
 print(ann['caption'])

 def loadRes(self, resFile):
 """
 Load result file and return a result api object.
 :param resFile (str) : file name of result file
 :return: res (obj) : result api object
 """
 res = COCO()
 res.dataset['images'] = [img for img in self.dataset['images']]

 print('Loading and preparing results...')
 tic = time.time()
 if type(resFile) == str or (PYTHON_VERSION == 2 and type(resFile) == unicode):
 anns = json.load(open(resFile))
 elif type(resFile) == np.ndarray:
 anns = self.loadNumpyAnnotations(resFile)
 else:
 anns = resFile
 assert type(anns) == list, 'results in not an array of objects'
 annsImgIds = [ann['image_id'] for ann in anns]
 assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \
 'Results do not correspond to current coco set'
 if 'caption' in anns[0]:
 imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns])
 res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds]
 for id, ann in enumerate(anns):
 ann['id'] = id+1
 elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
 res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
 for id, ann in enumerate(anns):
 bb = ann['bbox']
 x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]]
 if not 'segmentation' in ann:
 ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
 ann['area'] = bb[2]*bb[3]
 ann['id'] = id+1
 ann['iscrowd'] = 0
 elif 'segmentation' in anns[0]:
 res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
 for id, ann in enumerate(anns):
 # now only support compressed RLE format as segmentation results
 ann['area'] = maskUtils.area(ann['segmentation'])
 if not 'bbox' in ann:
 ann['bbox'] = maskUtils.toBbox(ann['segmentation'])
 ann['id'] = id+1
 ann['iscrowd'] = 0
 elif 'keypoints' in anns[0]:
 res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
 for id, ann in enumerate(anns):
 s = ann['keypoints']
 x = s[0::3]
 y = s[1::3]
 x0,x1,y0,y1 = np.min(x), np.max(x), np.min(y), np.max(y)
 ann['area'] = (x1-x0)*(y1-y0)
 ann['id'] = id + 1
 ann['bbox'] = [x0,y0,x1-x0,y1-y0]
 print('DONE (t={:0.2f}s)'.format(time.time()- tic))

 res.dataset['annotations'] = anns
 res.createIndex()
 return res

 def download(self, tarDir = None, imgIds = [] ):
 '''
 Download COCO images from mscoco.org server.
 :param tarDir (str): COCO results directory name
 imgIds (list): images to be downloaded
 :return:
 '''
 if tarDir is None:
 print('Please specify target directory')
 return -1
 if len(imgIds) == 0:
 imgs = self.imgs.values()
 else:
 imgs = self.loadImgs(imgIds)
 N = len(imgs)
 if not os.path.exists(tarDir):
 os.makedirs(tarDir)
 for i, img in enumerate(imgs):
 tic = time.time()
 fname = os.path.join(tarDir, img['file_name'])
 if not os.path.exists(fname):
 urlretrieve(img['coco_url'], fname)
 print('downloaded {}/{} images (t={:0.1f}s)'.format(i, N, time.time()- tic))

 def loadNumpyAnnotations(self, data):
 """
 Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class}
 :param data (numpy.ndarray)
 :return: annotations (python nested list)
 """
 print('Converting ndarray to lists...')
 assert(type(data) == np.ndarray)
 print(data.shape)
 assert(data.shape[1] == 7)
 N = data.shape[0]
 ann = []
 for i in range(N):
 if i % 1000000 == 0:
 print('{}/{}'.format(i,N))
 ann += [{
 'image_id' : int(data[i, 0]),
 'bbox' : [ data[i, 1], data[i, 2], data[i, 3], data[i, 4] ],
 'score' : data[i, 5],
 'category_id': int(data[i, 6]),
 }]
 return ann

 def annToRLE(self, ann):
 """
 Convert annotation which can be polygons, uncompressed RLE to RLE.
 :return: binary mask (numpy 2D array)
 """
 t = self.imgs[ann['image_id']]
 h, w = t['height'], t['width']
 segm = ann['segmentation']
 if type(segm) == list:
 # polygon -- a single object might consist of multiple parts
 # we merge all parts into one mask rle code
 rles = maskUtils.frPyObjects(segm, h, w)
 rle = maskUtils.merge(rles)
 elif type(segm['counts']) == list:
 # uncompressed RLE
 rle = maskUtils.frPyObjects(segm, h, w)
 else:
 # rle
 rle = ann['segmentation']
 return rle

 def annToMask(self, ann):
 """
 Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask.
 :return: binary mask (numpy 2D array)
 """
 rle = self.annToRLE(ann)
 m = maskUtils.decode(rle)
 return m