def loadAllGroundTruths(dir):
groundTruths = {}
for f in os.listdir(dir):
if os.path.isfile(dir + '/' + f):
category = f.split('_')[0]
groundTruths[category] = cu.loadBoxIndexFile(dir + '/' + f)
return groundTruths
def readPositivesData(positivesFeatures):
#pos,posIdx = cu.loadMatrixAndIndex(positivesFeatures)
pos = cu.loadMatrixNoCompression(positivesFeatures)
posIdx = cu.loadBoxIndexFile(positivesFeatures.replace('fc6_neuron_cudanet_out','idx'))
ari,osi = [],[]
print 'Positive Matrix loaded ('+str(pos.shape[0])+' instances)'
return (pos,posIdx,ari,osi)
def __init__(self, environment=None, groundTruthFile=None):
Task.__init__(self, environment)
if groundTruthFile is not None:
self.groundTruth = cu.loadBoxIndexFile(groundTruthFile)
self.image = ''
self.epochRecall = []
self.epochMaxIoU = []
self.epochLandmarks = []
def __init__(self, environment=None, groundTruthFile=None):
Task.__init__(self, environment)
if groundTruthFile is not None:
self.groundTruth = cu.loadBoxIndexFile(groundTruthFile)
self.image = ''
self.epochRecall = []
self.epochMaxIoU = []
self.epochLandmarks = []
def readPositivesData(positivesFeatures):
#pos,posIdx = cu.loadMatrixAndIndex(positivesFeatures)
pos = cu.loadMatrixNoCompression(positivesFeatures)
posIdx = cu.loadBoxIndexFile(
positivesFeatures.replace('fc6_neuron_cudanet_out', 'idx'))
ari, osi = [], []
print 'Positive Matrix loaded (' + str(pos.shape[0]) + ' instances)'
return (pos, posIdx, ari, osi)
def __init__(self, scoresDir, proposals):
self.dir = scoresDir
self.files = os.listdir(scoresDir)
self.files.sort()
self.categories = []
for f in self.files:
names = f.split('_')[0:2]
cat = '_'.join(names)
self.categories.append( cat )
self.categories.sort()
self.imgBoxes = {}
self.scores = {}
boxes = cu.loadBoxIndexFile(proposals)
for img in boxes.keys():
self.imgBoxes[img] = np.array(boxes[img])
self.scores[img] = -10*np.ones( (len(boxes[img]), len(self.categories)) )
def __init__(self, imageList, mode, groundTruthFile=None):
self.mode = mode
self.cnn = cn.ConvNet()
self.testRecord = None
self.idx = -1
self.imageList = [x.strip() for x in open(imageList)]
self.groundTruth = cu.loadBoxIndexFile(groundTruthFile)
#self.imageList = self.rankImages()
#self.imageList = self.imageList[0:10]
allImgs = set([x.strip() for x in open(config.get('allImagesList'))])
self.negativeSamples = list(allImgs.difference(set(self.groundTruth.keys())))
self.negativeEpisode = False
if self.mode == 'train':
self.negativeProbability = config.getf('negativeEpisodeProb')
random.shuffle(self.imageList)
self.loadNextEpisode()
def __init__(self, imageList, mode, groundTruthFile=None):
self.mode = mode
self.cnn = cn.ConvNet()
self.testRecord = None
self.idx = -1
self.imageList = [x.strip() for x in open(imageList)]
self.groundTruth = cu.loadBoxIndexFile(groundTruthFile)
#self.imageList = self.rankImages()
#self.imageList = self.imageList[0:10]
allImgs = set([x.strip() for x in open(config.get('allImagesList'))])
self.negativeSamples = list(
allImgs.difference(set(self.groundTruth.keys())))
self.negativeEpisode = False
if self.mode == 'train':
self.negativeProbability = config.getf('negativeEpisodeProb')
random.shuffle(self.imageList)
self.loadNextEpisode()
def __init__(self, scoresDir, proposals):
self.dir = scoresDir
self.files = os.listdir(scoresDir)
self.files.sort()
self.categories = []
for f in self.files:
names = f.split('_')[0:2]
cat = '_'.join(names)
self.categories.append(cat)
self.categories.sort()
self.imgBoxes = {}
self.scores = {}
boxes = cu.loadBoxIndexFile(proposals)
for img in boxes.keys():
self.imgBoxes[img] = np.array(boxes[img])
self.scores[img] = -10 * np.ones(
(len(boxes[img]), len(self.categories)))
return result
def reformatGroundTruth(gt, category):
result = []
for img in gt.keys():
for box in gt[img]:
result.append( [img, category + '_tight'] + box )
return result
def saveResults(outputFile, results):
outputFile = open(outputFile,'w')
for r in results:
outputFile.write(r[0] + ' ' + r[1] + ' ' + ' '.join(map(str,map(int,r[2:]))) + '\n')
outputFile.close()
if __name__ == "__main__":
params = cu.loadParams("proposalsFile groundTruthDir outputFile")
proposals = cu.loadBoxIndexFile(params['proposalsFile'])
records = []
files = os.listdir(params['groundTruthDir'])
files.sort()
for f in files:
category = f.split('_')[0]
print category
groundTruth = cu.loadBoxIndexFile(params['groundTruthDir'] + '/' + f)
records += selectRegions(proposals, groundTruth, category, big)
records += selectRegions(proposals, groundTruth, category, inside)
records += reformatGroundTruth(groundTruth, category)
saveResults(params['outputFile'], records)
for i in range(parts):
nb = [gt[0] + i * step, y1, gt[0] + i * step + adjustedSize, y2]
scaleBoxes.append(shiftBox(nb, w, h))
else:
x1, x2 = 0, adjustedSize
parts = int(round(bh / adjustedSize + 0.3))
step = (bh - adjustedSize) / max(parts - 1, 1)
for i in range(parts):
nb = [x1, gt[1] + i * step, x2, gt[1] + i * step + adjustedSize]
scaleBoxes.append(shiftBox(nb, w, h))
return scaleBoxes
if __name__ == "__main__":
params = cu.loadParams("groundTruthBoxes imageDir outputDir cropSize")
groundTruthBoxes = cu.loadBoxIndexFile(params["groundTruthBoxes"])
cropSize = int(params["cropSize"])
projections = {}
overlaps = []
ious = []
for img in groundTruthBoxes.keys():
print img
name = img.split("/")[1]
if not os.path.isfile(params["imageDir"] + "/" + name + ".JPEG"):
continue
im = Image.open(params["imageDir"] + "/" + name + ".JPEG")
w, h = im.size
try:
p = projections[img]
except:
r = [det.IoU(n[1:], t[1:]), det.overlap(t[1:], n[1:])]
if 0.8 >= r[0] and r[1] >= 0.8:
s = np.exp(-dist(idealMatch, r))
candidates.append([j, s, n[0], r])
if len(candidates) > 0:
candidates.sort(key=lambda x: x[1] + x[2], reverse=True)
for k in range(min(MAX_NUMBER_OF_PARTS, len(candidates))):
layouts[i].addPart(inside[candidates[k][0]], candidates[k][-1])
layouts.sort(key=lambda x: x.getScore(), reverse=True)
return layouts
params = cu.loadParams(
'bigDetections tightDetections insideDetections imageDir outputDir')
big = cu.loadBoxIndexFile(params['bigDetections'])
tight = cu.loadBoxIndexFile(params['tightDetections'])
inside = cu.loadBoxIndexFile(params['insideDetections'])
print 'Images:', len(big), len(tight), len(inside)
allLayouts = []
for k in big.keys():
layouts = findBigMatches(k, big[k], tight[k])
layouts = findInsideMatches(inside[k], layouts)
allLayouts += [layouts[0]]
allLayouts.sort(key=lambda x: x.getScore(), reverse=True)
matchCounter = 0
for ol in allLayouts:
img, boxes, scores = ol.getLayoutData()
def __init__(self, environment, groundTruthFile):
Task.__init__(self, environment)
self.groundTruth = cu.loadBoxIndexFile(groundTruthFile)
self.image = ''
self.prevAction = 0
self.prevPos = (0, 0, 0)
def __init__(self, environment, groundTruthFile): Task.__init__(self, environment) self.groundTruth = cu.loadBoxIndexFile(groundTruthFile)
scaleBoxes.append(shiftBox(nb, w, h))
else:
x1, x2 = 0, adjustedSize
parts = int(round(bh / adjustedSize + 0.3))
step = (bh - adjustedSize) / max(parts - 1, 1)
for i in range(parts):
nb = [
x1, gt[1] + i * step, x2, gt[1] + i * step + adjustedSize
]
scaleBoxes.append(shiftBox(nb, w, h))
return scaleBoxes
if __name__ == "__main__":
params = cu.loadParams("groundTruthBoxes imageDir outputDir cropSize")
groundTruthBoxes = cu.loadBoxIndexFile(params['groundTruthBoxes'])
cropSize = int(params['cropSize'])
projections = {}
overlaps = []
ious = []
for img in groundTruthBoxes.keys():
print img
name = img.split('/')[1]
if not os.path.isfile(params['imageDir'] + '/' + name + '.JPEG'):
continue
im = Image.open(params['imageDir'] + '/' + name + '.JPEG')
w, h = im.size
try:
p = projections[img]
except:
def __init__(self, environment, groundTruthFile):
Task.__init__(self, environment)
self.groundTruth = cu.loadBoxIndexFile(groundTruthFile)
def __init__(self, environment, groundTruthFile): Task.__init__(self, environment) self.groundTruth = cu.loadBoxIndexFile(groundTruthFile) self.image = '' self.prevAction = 0 self.prevPos = (0,0,0)
nFeat = r[0].shape[1]
featureMatrix = np.zeros( (nBoxes,nFeat) )
i = 0
outputFile = open(outputDir + '/' + category + '.idx','w')
for r in result:
featureMatrix[i:i+r[0].shape[0]] = r[0]
for box in r[1]:
outputFile.write(box[0] + ' ' + ' '.join(map(str,map(int,box[1:]))) + '\n')
i += r[0].shape[0]
outputFile.close()
cu.saveMatrix(featureMatrix,outputDir + '/' + category + '.' + featExt)
print 'Total of',nBoxes,'positive examples collected for',category
if __name__ == "__main__":
params = cu.loadParams("imageList featuresDir groundTruthFile outputDir featuresExt category operation")
groundTruths = cu.loadBoxIndexFile(params['groundTruthFile'])
imageList = [x.replace('\n','') for x in open(params['imageList'])]
operator = None
if params['operation'] == 'big':
operator = big
elif params['operation'] == 'tight':
operator = tight
elif params['operation'] == 'inside':
operator = inside
elif params['operation'] == 'background':
operator = background
else:
print 'Select a valid operation: [big | tight | inside | background]'
sys.exit()
selectRegions(imageList, params['featuresDir'], groundTruths, params['outputDir'], params['featuresExt'], params['category'], operator)
for j in range(len(inside)):
n = inside[j]
r = [ det.IoU(n[1:], t[1:]), det.overlap(t[1:], n[1:]) ]
if 0.8 >= r[0] and r[1] >= 0.8:
s = np.exp( -dist(idealMatch, r) )
candidates.append( [j,s,n[0],r] )
if len(candidates) > 0:
candidates.sort(key=lambda x:x[1]+x[2],reverse=True)
for k in range( min(MAX_NUMBER_OF_PARTS,len(candidates)) ):
layouts[i].addPart( inside[ candidates[k][0] ], candidates[k][-1] )
layouts.sort(key=lambda x: x.getScore(), reverse=True)
return layouts
params = cu.loadParams('bigDetections tightDetections insideDetections imageDir outputDir')
big = cu.loadBoxIndexFile( params['bigDetections'] )
tight = cu.loadBoxIndexFile( params['tightDetections'] )
inside = cu.loadBoxIndexFile( params['insideDetections'] )
print 'Images:',len(big),len(tight),len(inside)
allLayouts = []
for k in big.keys():
layouts = findBigMatches(k, big[k], tight[k])
layouts = findInsideMatches(inside[k], layouts)
allLayouts += [ layouts[0] ]
allLayouts.sort(key=lambda x: x.getScore(), reverse=True)
matchCounter = 0
for ol in allLayouts:
img, boxes, scores = ol.getLayoutData()
import os,sys
import utils as cu
import libDetection as ldet
import numpy as np
params = cu.loadParams('scoresFile groundTruth relation output')
scores = [x.split() for x in open(params['scoresFile'])]
ground = cu.loadBoxIndexFile(params['groundTruth'])
scores.sort(key=lambda x:float(x[1]), reverse=True)
if params['relation'] == 'big':
operator = lambda x,y: np.exp( -( (1.0-ldet.overlap(x,y))**2 + (0.25-ldet.IoU(x,y))**2 ) ) >= 0.7
if params['relation'] == 'inside':
operator = lambda x,y: np.exp( -( (1.0-ldet.overlap(y,x))**2 + (0.25-ldet.IoU(x,y))**2 ) ) >= 0.7
if params['relation'] == 'tight':
operator = lambda x,y: ldet.IoU(x,y) >= 0.5
out = open(params['output'],'w')
for s in scores:
box = map(float,s[2:7])
img = s[0]
try: gtBoxes = ground[img]
except: gtBoxes = []
match = '0'
for gt in gtBoxes:
if operator(box,gt):
match = '1'
out.write(' '.join(s) + ' ' + match + '\n')
out.close()
try:
boxes = groundTruth[d[0]]
except:
continue
bestIoU = 0.0
for gt in boxes:
iou = det.IoU(d[2:6], gt)
if iou > bestIoU:
bestIoU = iou
print bestIoU
if bestIoU > minOverlap:
candidates.append(d)
return candidates
def saveCandidates(candidates, output):
out = open(output, 'w')
for k in candidates:
out.write(k[0] + ' ' + ' '.join(map(str, map(int, k[2:6]))) + '\n')
out.close()
if __name__ == "__main__":
params = cu.loadParams("detectionsFile groundTruths output")
detectionsData = [x.split() for x in open(params['detectionsFile'])]
detections = eval.loadDetections(detectionsData)
groundTruth = cu.loadBoxIndexFile(params['groundTruths'])
candidates = selectBestBoxes(detections, groundTruth, 0.5)
print 'Selected candidates:', len(candidates)
saveCandidates(candidates, params['output'])
def reformatGroundTruth(gt, category):
result = []
for img in gt.keys():
for box in gt[img]:
result.append([img, category + '_tight'] + box)
return result
def saveResults(outputFile, results):
outputFile = open(outputFile, 'w')
for r in results:
outputFile.write(r[0] + ' ' + r[1] + ' ' +
' '.join(map(str, map(int, r[2:]))) + '\n')
outputFile.close()
if __name__ == "__main__":
params = cu.loadParams("proposalsFile groundTruthDir outputFile")
proposals = cu.loadBoxIndexFile(params['proposalsFile'])
records = []
files = os.listdir(params['groundTruthDir'])
files.sort()
for f in files:
category = f.split('_')[0]
print category
groundTruth = cu.loadBoxIndexFile(params['groundTruthDir'] + '/' + f)
records += selectRegions(proposals, groundTruth, category, big)
records += selectRegions(proposals, groundTruth, category, inside)
records += reformatGroundTruth(groundTruth, category)
saveResults(params['outputFile'], records)
