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MiXaiLL76 committed Jun 24, 2024
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57 changes: 12 additions & 45 deletions faster_coco_eval/core/coco.py
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__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 # noqa: E501
# 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. # noqa: E501
# 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]
# Original work Copyright (c) Piotr Dollar and Tsung-Yi Lin, 2014.
# Modified work Copyright (c) 2024 MiXaiLL76

import json
import logging
Expand All @@ -54,6 +11,7 @@

import faster_coco_eval.faster_eval_api_cpp as _C
from faster_coco_eval.core import mask as maskUtils
from faster_coco_eval.version import __author__, __version__

logger = logging.getLogger(__name__)

Expand Down Expand Up @@ -576,3 +534,12 @@ def img_ann_idx_map(self):
@property
def img_cat_ann_idx_map(self):
return self.imgCatToAnnsIdx

def __repr__(self):
s = self.__class__.__name__
s += "("
s += "annotation_file"
s += ") # "
s += "__author__='{}'; ".format(__author__)
s += "__version__='{}';".format(__version__)
return s
114 changes: 64 additions & 50 deletions faster_coco_eval/core/cocoeval.py
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__author__ = "tsungyi"
# Original work Copyright (c) Piotr Dollar and Tsung-Yi Lin, 2014.
# Modified work Copyright (c) 2024 MiXaiLL76

import logging
from collections import defaultdict
Expand All @@ -8,60 +9,64 @@
import faster_coco_eval.faster_eval_api_cpp as _C
from faster_coco_eval.core import mask as maskUtils
from faster_coco_eval.core.coco import COCO
from faster_coco_eval.version import __author__, __version__

logger = logging.getLogger(__name__)


class COCOeval:
# Interface for evaluating detection on the Microsoft COCO dataset.
#
# The usage for CocoEval is as follows:
# cocoGt=..., cocoDt=... # load dataset and results
# E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object
# E.params.recThrs = ...; # set parameters as desired
# E.evaluate(); # run per image evaluation
# E.accumulate(); # accumulate per image results
# E.summarize(); # display summary metrics of results
# For example usage see evalDemo.m and http://mscoco.org/.
#
# The evaluation parameters are as follows (defaults in brackets):
# imgIds - [all] N img ids to use for evaluation
# catIds - [all] K cat ids to use for evaluation
# iouThrs - [.5:.05:.95] T=10 IoU thresholds for evaluation
# recThrs - [0:.01:1] R=101 recall thresholds for evaluation
# areaRng - [...] A=4 object area ranges for evaluation
# maxDets - [1 10 100] M=3 thresholds on max detections per image
# iouType - ['segm'] set iouType to 'segm', 'bbox' or 'keypoints'
# iouType replaced the now DEPRECATED useSegm parameter.
# useCats - [1] if true use category labels for evaluation
# Note: if useCats=0 category labels are ignored as in proposal scoring.
# Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified.
#
# evaluate(): evaluates detections on every image and every category and
# concats the results into the "evalImgs" with fields:
# dtIds - [1xD] id for each of the D detections (dt)
# gtIds - [1xG] id for each of the G ground truths (gt)
# dtMatches - [TxD] matching gt id at each IoU or 0
# gtMatches - [TxG] matching dt id at each IoU or 0
# dtScores - [1xD] confidence of each dt
# gtIgnore - [1xG] ignore flag for each gt
# dtIgnore - [TxD] ignore flag for each dt at each IoU
#
# accumulate(): accumulates the per-image, per-category evaluation
# results in "evalImgs" into the dictionary "eval" with fields:
# params - parameters used for evaluation
# date - date evaluation was performed
# counts - [T,R,K,A,M] parameter dimensions (see above)
# precision - [TxRxKxAxM] precision for every evaluation setting
# recall - [TxKxAxM] max recall for every evaluation setting
# Note: precision and recall==-1 for settings with no gt objects.
#
# See also coco, mask, pycocoDemo, pycocoEvalDemo
#
# Microsoft COCO Toolbox. version 2.0
# Data, paper, and tutorials available at: http://mscoco.org/
# Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
# Licensed under the Simplified BSD License [see coco/license.txt]
"""Interface for evaluating detection on the Microsoft COCO dataset.
The usage for CocoEval is as follows:
cocoGt=..., cocoDt=... # load dataset and results
E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object
E.params.recThrs = ...; # set parameters as desired
E.evaluate(); # run per image evaluation
E.accumulate(); # accumulate per image results
E.summarize(); # display summary metrics of results
For example usage see evalDemo.m and http://mscoco.org/.
The evaluation parameters are as follows (defaults in brackets):
imgIds - [all] N img ids to use for evaluation
catIds - [all] K cat ids to use for evaluation
iouThrs - [.5:.05:.95] T=10 IoU thresholds for evaluation
recThrs - [0:.01:1] R=101 recall thresholds for evaluation
areaRng - [...] A=4 object area ranges for evaluation
maxDets - [1 10 100] M=3 thresholds on max detections per image
iouType - ['segm'] set iouType to 'segm', 'bbox' or 'keypoints'
iouType replaced the now DEPRECATED useSegm parameter.
useCats - [1] if true use category labels for evaluation
Note: if useCats=0 category labels are ignored as in proposal scoring.
Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified.
evaluate(): evaluates detections on every image and every category and
concats the results into the "evalImgs" with fields:
dtIds - [1xD] id for each of the D detections (dt)
gtIds - [1xG] id for each of the G ground truths (gt)
dtMatches - [TxD] matching gt id at each IoU or 0
gtMatches - [TxG] matching dt id at each IoU or 0
dtScores - [1xD] confidence of each dt
gtIgnore - [1xG] ignore flag for each gt
dtIgnore - [TxD] ignore flag for each dt at each IoU
accumulate(): accumulates the per-image, per-category evaluation
results in "evalImgs" into the dictionary "eval" with fields:
params - parameters used for evaluation
date - date evaluation was performed
counts - [T,R,K,A,M] parameter dimensions (see above)
precision - [TxRxKxAxM] precision for every evaluation setting
recall - [TxKxAxM] max recall for every evaluation setting
Note: precision and recall==-1 for settings with no gt objects.
See also coco, mask, pycocoDemo, pycocoEvalDemo
Microsoft COCO Toolbox. version 2.0
Data, paper, and tutorials available at: http://mscoco.org/
Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
Licensed under the Simplified BSD License [see coco/license.txt]
"""

def __init__(
self,
cocoGt=None,
Expand Down Expand Up @@ -525,6 +530,15 @@ def _summarizeKps():
def __str__(self):
self.summarize()

def __repr__(self):
s = self.__class__.__name__
s += "("
s += "annotation_file"
s += ") # "
s += "__author__='{}'; ".format(__author__)
s += "__version__='{}';".format(__version__)
return s


class Params:
"""Params for coco evaluation api."""
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2 changes: 1 addition & 1 deletion faster_coco_eval/core/faster_eval_api.py
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# Original work Copyright (c) Facebook, Inc. and its affiliates.
# Modified work Copyright (c) 2021 Sartorius AG
# Modified work Copyright (c) 2024 MiXaiLL76

import logging
import time
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77 changes: 2 additions & 75 deletions faster_coco_eval/core/mask.py
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__author__ = "tsungyi"

# import faster_coco_eval.mask_api_cpp as _mask
# import faster_coco_eval.mask_api_cpp as _mask_old
# Original work Copyright (c) Piotr Dollar and Tsung-Yi Lin, 2014.
# Modified work Copyright (c) 2024 MiXaiLL76
import faster_coco_eval.mask_api_new_cpp as _mask

# Interface for manipulating masks stored in RLE format.
#
# RLE is a simple yet efficient format for storing binary masks. RLE
# first divides a vector (or vectorized image) into a series of piecewise
# constant regions and then for each piece simply stores the length of
# that piece. For example, given M=[0 0 1 1 1 0 1] the RLE counts would
# be [2 3 1 1], or for M=[1 1 1 1 1 1 0] the counts would be [0 6 1]
# (note that the odd counts are always the numbers of zeros). Instead of
# storing the counts directly, additional compression is achieved with a
# variable bitrate representation based on a common scheme called LEB128.
#
# Compression is greatest given large piecewise constant regions.
# Specifically, the size of the RLE is proportional to the number of
# *boundaries* in M (or for an image the number of boundaries in the y
# direction). Assuming fairly simple shapes, the RLE representation is
# O(sqrt(n)) where n is number of pixels in the object. Hence space usage
# is substantially lower, especially for large simple objects (large n).
#
# Many common operations on masks can be computed directly using the RLE
# (without need for decoding). This includes computations such as area,
# union, intersection, etc. All of these operations are linear in the
# size of the RLE, in other words they are O(sqrt(n)) where n is the area
# of the object. Computing these operations on the original mask is O(n).
# Thus, using the RLE can result in substantial computational savings.
#
# The following API functions are defined:
# encode - Encode binary masks using RLE.
# decode - Decode binary masks encoded via RLE.
# merge - Compute union or intersection of encoded masks.
# iou - Compute intersection over union between masks.
# area - Compute area of encoded masks.
# toBbox - Get bounding boxes surrounding encoded masks.
# frPyObjects - Convert polygon, bbox, and uncompressed RLE to encoded RLE mask. # noqa: E501
#
# Usage:
# Rs = encode( masks )
# masks = decode( Rs )
# R = merge( Rs, intersect=false )
# o = iou( dt, gt, iscrowd )
# a = area( Rs )
# bbs = toBbox( Rs )
# Rs = frPyObjects( [pyObjects], h, w )
#
# In the API the following formats are used:
# Rs - [dict] Run-length encoding of binary masks
# R - dict Run-length encoding of binary mask
# masks - [hxwxn] Binary mask(s) (must have type np.ndarray(dtype=uint8) in column-major order) # noqa: E501
# iscrowd - [nx1] list of np.ndarray. 1 indicates corresponding gt image has crowd region to ignore # noqa: E501
# bbs - [nx4] Bounding box(es) stored as [x y w h]
# poly - Polygon stored as [[x1 y1 x2 y2...],[x1 y1 ...],...] (2D list)
# dt,gt - May be either bounding boxes or encoded masks
# Both poly and bbs are 0-indexed (bbox=[0 0 1 1] encloses first pixel).
#
# Finally, a note about the intersection over union (iou) computation.
# The standard iou of a ground truth (gt) and detected (dt) object is
# iou(gt,dt) = area(intersect(gt,dt)) / area(union(gt,dt))
# For "crowd" regions, we use a modified criteria. If a gt object is
# marked as "iscrowd", we allow a dt to match any subregion of the gt.
# Choosing gt' in the crowd gt that best matches the dt can be done using
# gt'=intersect(dt,gt). Since by definition union(gt',dt)=dt, computing
# iou(gt,dt,iscrowd) = iou(gt',dt) = area(intersect(gt,dt)) / area(dt)
# For crowd gt regions we use this modified criteria above for the iou.
#
# To compile run "python setup.py build_ext --inplace"
# Please do not contact us for help with compiling.
#
# Microsoft COCO Toolbox. version 2.0
# Data, paper, and tutorials available at: http://mscoco.org/
# Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
# Licensed under the Simplified BSD License [see coco/license.txt]

iou = _mask.iou
merge = _mask.merge
frPyObjects = _mask.frPyObjects
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2 changes: 1 addition & 1 deletion faster_coco_eval/version.py
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__version__ = "1.5.7"
__version__ = "1.6.0"
__author__ = "MiXaiLL76"

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