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imdb_data.py
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imdb_data.py
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# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
from __future__ import print_function
from builtins import range
import sys, os
from helpers import *
import scipy.sparse
import scipy.io as sio
import pickle as cp
import numpy as np
import fastRCNN
class imdb_data(fastRCNN.imdb):
def __init__(self, image_set, classes, maxNrRois, imgDir, roiDir, cacheDir, boAddGroundTruthRois):
fastRCNN.imdb.__init__(self, image_set + ".cache") #'data_' + image_set)
self._image_set = image_set
self._maxNrRois = maxNrRois
self._imgDir = imgDir
self._roiDir = roiDir
self._cacheDir = cacheDir #cache_path
self._imgSubdirs ={'train': ['positive', 'negative'], 'test': ['testImages']}
self._classes = classes
self._class_to_ind = dict(zip(self.classes, range(self.num_classes)))
self._image_ext = '.jpg'
self._image_index, self._image_subdirs = self._load_image_set_index()
self._roidb_handler = self.selective_search_roidb
self._boAddGroundTruthRois = boAddGroundTruthRois
#overwrite parent definition
@property
def cache_path(self):
return self._cacheDir
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(self._image_subdirs[i], self._image_index[i])
def image_path_from_index(self, subdir, fname):
"""
Construct an image path from the image's "index" identifier.
"""
image_path = os.path.join(self._imgDir, subdir, fname)
assert os.path.exists(image_path), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Compile list of image indices and the subdirectories they are in.
"""
image_index = []
image_subdirs = []
for subdir in self._imgSubdirs[self._image_set]:
imgFilenames = getFilesInDirectory(os.path.join(self._imgDir,subdir), self._image_ext)
image_index += imgFilenames
image_subdirs += [subdir] * len(imgFilenames)
return image_index, image_subdirs
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
roidb = cp.load(fid)
print ('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [self._load_annotation(i) for i in range(self.num_images)]
with open(cache_file, 'wb') as fid:
cp.dump(gt_roidb, fid, cp.HIGHEST_PROTOCOL)
print ('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def selective_search_roidb(self):
"""
Return the database of selective search regions of interest.
Ground-truth ROIs are also included.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path,
self.name + '_selective_search_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
if sys.version_info[0] < 3:
roidb = cp.load(fid)
else:
roidb = cp.load(fid, encoding='latin1')
print ('{} ss roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = self.gt_roidb()
ss_roidb = self._load_selective_search_roidb(gt_roidb)
#add ground truth ROIs
if self._boAddGroundTruthRois:
roidb = self.merge_roidbs(gt_roidb, ss_roidb)
else:
roidb = ss_roidb
#Keep max of e.g. 2000 rois
if self._maxNrRois and self._maxNrRois > 0:
print ("Only keeping the first %d ROIs.." % self._maxNrRois)
for i in range(self.num_images):
gt_overlaps = roidb[i]['gt_overlaps']
gt_overlaps = gt_overlaps.todense()[:self._maxNrRois]
gt_overlaps = scipy.sparse.csr_matrix(gt_overlaps)
roidb[i]['gt_overlaps'] = gt_overlaps
roidb[i]['boxes'] = roidb[i]['boxes'][:self._maxNrRois,:]
roidb[i]['gt_classes'] = roidb[i]['gt_classes'][:self._maxNrRois]
with open(cache_file, 'wb') as fid:
cp.dump(roidb, fid, cp.HIGHEST_PROTOCOL)
print ('wrote ss roidb to {}'.format(cache_file))
return roidb
def _load_selective_search_roidb(self, gt_roidb):
# box_list = nrImages x nrBoxes x 4
box_list = []
for imgFilename, subdir in zip(self._image_index, self._image_subdirs):
roiPath = "{}/{}/{}.roi.txt".format(self._roiDir, subdir, imgFilename[:-4])
assert os.path.exists(roiPath), "Error: rois file not found: " + roiPath
rois = np.loadtxt(roiPath, np.int32)
box_list.append(rois)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_annotation(self, imgIndex):
"""
Load image and bounding boxes info from human annotations.
"""
#negative images do not have any ground truth annotations
if self._image_subdirs[imgIndex].lower() == "negative":
return None
imgPath = self.image_path_at(imgIndex)
bboxesPaths = imgPath[:-4] + ".bboxes.tsv"
labelsPaths = imgPath[:-4] + ".bboxes.labels.tsv"
assert os.path.exists(bboxesPaths), "Error: ground truth bounding boxes file not found: " + bboxesPaths
assert os.path.exists(labelsPaths), "Error: ground truth labels file not found: " + bboxesPaths
bboxes = np.loadtxt(bboxesPaths, np.float32)
labels = readFile(labelsPaths)
# in case there's only one annotation and numpy read the array as single array,
# we need to make sure the input is treated as a multi dimensional array instead of a list/ 1D array
#if len(bboxes.shape) == 1:
if len(bboxes)>0 and type(bboxes[0]) == np.float32:
bboxes = np.array([bboxes])
#remove boxes marked as 'undecided' or 'exclude'
indicesToKeep = find(labels, lambda x: x!='EXCLUDE' and x!='UNDECIDED')
bboxes = [bboxes[i] for i in indicesToKeep]
labels = [labels[i] for i in indicesToKeep]
# Load object bounding boxes into a data frame.
num_objs = len(bboxes)
boxes = np.zeros((num_objs,4), dtype=np.uint16)
gt_classes = np.zeros(num_objs, dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
for bboxIndex,(bbox,label) in enumerate(zip(bboxes,labels)):
cls = self._class_to_ind[label] #.decode('utf-8')]
boxes[bboxIndex, :] = bbox
gt_classes[bboxIndex] = cls
overlaps[bboxIndex, cls] = 1.0
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes' : boxes,
'gt_classes': gt_classes,
'gt_overlaps' : overlaps,
'flipped' : False}
# main call to compute per-calass average precision
# shape of all_boxes: e.g. 21 classes x 4952 images x 58 rois x 5 coords+score
# (see also test_net() in fastRCNN\test.py)
def evaluate_detections(self, all_boxes, output_dir, use_07_metric=False, overlapThreshold = 0.5):
aps = []
for classIndex, className in enumerate(self._classes):
if className != '__background__':
rec, prec, ap = self._evaluate_detections(classIndex, all_boxes, use_07_metric, overlapThreshold)
aps += [[className,ap]]
print('AP for {:>15} = {:.4f}'.format(className, ap))
print('Mean AP = {:.4f}'.format(np.nanmean(getColumn(aps,1))))
return aps
def _evaluate_detections(self, classIndex, all_boxes, use_07_metric = False, overlapThreshold = 0.5):
"""
Top level function that does the PASCAL VOC evaluation.
[overlapThreshold]: Overlap threshold (default = 0.5)
[use_07_metric]: Whether to use VOC07's 11 point AP computation (default False)
"""
assert (len(all_boxes) == self.num_classes)
assert (len(all_boxes[0]) == self.num_images)
# load ground truth annotations for this class
gtInfos = []
for imgIndex in range(self.num_images):
imgPath = self.image_path_at(imgIndex)
imgSubir = os.path.normpath(imgPath).split(os.path.sep)[-2]
if imgSubir != 'negative':
gtBoxes, gtLabels = readGtAnnotation(imgPath)
gtBoxes = [box for box, label in zip(gtBoxes, gtLabels) if label == self.classes[classIndex]] #.decode('utf-8')
else:
gtBoxes = []
gtInfos.append({'bbox': np.array(gtBoxes),
'difficult': [False] * len(gtBoxes),
'det': [False] * len(gtBoxes)})
# parse detections for this class
# shape of all_boxes: e.g. 21 classes x 4952 images x 58 rois x 5 coords+score
detBboxes = []
detImgIndices = []
detConfidences = []
for imgIndex in range(self.num_images):
dets = all_boxes[classIndex][imgIndex]
if dets != []:
for k in range(dets.shape[0]):
detImgIndices.append(imgIndex)
detConfidences.append(dets[k, -1])
# the VOCdevkit expects 1-based indices
detBboxes.append([dets[k, 0] + 1, dets[k, 1] + 1, dets[k, 2] + 1, dets[k, 3] + 1])
detBboxes = np.array(detBboxes)
detConfidences = np.array(detConfidences)
# debug: visualize GT and detections
# if classIndex == 15: # and imgPath.endswith("WIN_20160803_11_42_36_Pro.jpg"):
# imgIndex = 6
# imgPath = self.image_path_at(imgIndex)
# img = imread(imgPath)
# tmp_gtBoxes = gtInfos[imgIndex]['bbox']
# inds = np.where(np.array(detImgIndices) == 1)[0]
# tmp_detBoxes = detBboxes[inds]
# print(detConfidences[inds])
# drawRectangles(img, tmp_gtBoxes, color = (255, 0, 0)) #thickness=thickness)
# drawRectangles(img, tmp_detBoxes, color= (0, 255, 0)) # thickness=thickness)
# imshow(img, maxDim=800)
# compute precision / recall / ap
rec, prec, ap = self._voc_computePrecisionRecallAp(
class_recs=gtInfos,
confidence=detConfidences,
image_ids=detImgIndices,
BB=detBboxes,
ovthresh=overlapThreshold,
use_07_metric=use_07_metric)
return rec, prec, ap
#########################################################################
# Python evaluation functions (copied/refactored from faster-RCNN)
##########################################################################
def _voc_computePrecisionRecallAp(self, class_recs, confidence, image_ids, BB, ovthresh=0.5, use_07_metric=False):
# sort by confidence
sorted_ind = np.argsort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
# go down dets and mark TPs and FPs
nd = len(image_ids)
tp = np.zeros(nd)
fp = np.zeros(nd)
for d in range(nd):
R = class_recs[image_ids[d]]
bb = BB[d, :].astype(float)
ovmax = -np.inf
BBGT = R['bbox'].astype(float)
if BBGT.size > 0:
# compute overlaps
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih
# union
uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(BBGT[:, 2] - BBGT[:, 0] + 1.) *
(BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)
overlaps = inters / uni
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
if ovmax > ovthresh:
if not R['difficult'][jmax]:
if not R['det'][jmax]:
tp[d] = 1.
R['det'][jmax] = 1
else:
fp[d] = 1.
else:
fp[d] = 1.
# compute precision recall
npos = sum([len(cr['bbox']) for cr in class_recs])
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(npos)
# avoid divide by zero in case the first detection matches a difficult
# ground truth
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = computeAveragePrecision(rec, prec, use_07_metric)
return rec, prec, ap