X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Fdata%2Flib_coco%2FPythonAPI%2Fpycocotools%2Fmask.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Fdata%2Flib_coco%2FPythonAPI%2Fpycocotools%2Fmask.py;h=40853ba0d395f444eb83c9be0148b2de12177ee9;hb=a785567fb9acfc68536767d20f60ba917ae85aa1;hp=0000000000000000000000000000000000000000;hpb=94a133e696b9b2a7f73544462c2714986fa7ab4a;p=ealt-edge.git diff --git a/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/mask.py b/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/mask.py new file mode 100755 index 0000000..40853ba --- /dev/null +++ b/example-apps/PDD/pcb-defect-detection/data/lib_coco/PythonAPI/pycocotools/mask.py @@ -0,0 +1,103 @@ +__author__ = 'tsungyi' + +import pycocotools._mask 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. +# +# 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) +# iscrowd - [nx1] list of np.ndarray. 1 indicates corresponding gt image has crowd region to ignore +# 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 + +def encode(bimask): + if len(bimask.shape) == 3: + return _mask.encode(bimask) + elif len(bimask.shape) == 2: + h, w = bimask.shape + return _mask.encode(bimask.reshape((h, w, 1), order='F'))[0] + +def decode(rleObjs): + if type(rleObjs) == list: + return _mask.decode(rleObjs) + else: + return _mask.decode([rleObjs])[:,:,0] + +def area(rleObjs): + if type(rleObjs) == list: + return _mask.area(rleObjs) + else: + return _mask.area([rleObjs])[0] + +def toBbox(rleObjs): + if type(rleObjs) == list: + return _mask.toBbox(rleObjs) + else: + return _mask.toBbox([rleObjs])[0] \ No newline at end of file