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FaceDection
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修正少量错误

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master
wufayuan 2 years ago
parent 6dd1be543c
commit 4ab20abf82
5 changed files with 63 additions and 69 deletions
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60
FaceDetection/cascade.py
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@ -8,7 +8,6 @@ License : MIT License
"""
from config import POSITIVE_SAMPLE
from config import NEGATIVE_SAMPLE
from config import TRAINING_IMG_HEIGHT
@ -30,11 +29,11 @@ import numpy
class Cascade:
def __init__(self, face_dir = "", nonface_dir = "", train = True, limit = 30):
#tot_samples = 0
def __init__(self, face_dir="", nonface_dir="", train=True, limit=30):
# tot_samples = 0
self.Face = ImageSet(face_dir, sampleNum = POSITIVE_SAMPLE)
self.nonFace = ImageSet(nonface_dir, sampleNum = NEGATIVE_SAMPLE)
self.Face = ImageSet(face_dir, sampleNum=POSITIVE_SAMPLE)
self.nonFace = ImageSet(nonface_dir, sampleNum=NEGATIVE_SAMPLE)
tot_samples = self.Face.sampleNum + self.nonFace.sampleNum
@ -50,14 +49,14 @@ class Cascade:
if DEBUG_MODEL is True:
self._mat = numpy.zeros((self.haar.featuresNum, tot_samples))
for i in xrange(self.Face.sampleNum):
for i in range(self.Face.sampleNum):
featureVec = self.haar.calFeatureForImg(self.Face.images[i])
for j in xrange(self.haar.featuresNum):
self._mat[j][i ] = featureVec[j]
for j in range(self.haar.featuresNum):
self._mat[j][i] = featureVec[j]
for i in xrange(self.nonFace.sampleNum):
for i in range(self.nonFace.sampleNum):
featureVec = self.haar.calFeatureForImg(self.nonFace.images[i])
for j in xrange(self.haar.featuresNum):
for j in range(self.haar.featuresNum):
self._mat[j][i + self.Face.sampleNum] = featureVec[j]
numpy.save(FEATURE_FILE_TRAINING, self._mat)
@ -73,14 +72,13 @@ class Cascade:
assert sampleNum == (POSITIVE_SAMPLE + NEGATIVE_SAMPLE)
assert featureNum == FEATURE_NUM
Label_Face = [+1 for i in xrange(POSITIVE_SAMPLE)]
Label_NonFace = [-1 for i in xrange(NEGATIVE_SAMPLE)]
Label_Face = [+1 for i in range(POSITIVE_SAMPLE)]
Label_NonFace = [-1 for i in range(NEGATIVE_SAMPLE)]
self._label = numpy.array(Label_Face + Label_NonFace)
self.limit = limit
self.classifierNum = 0
self.strong_classifier = [None for i in xrange(limit)]
self.strong_classifier = [None for i in range(limit)]
def train(self):
@ -95,7 +93,7 @@ class Cascade:
mat = self._mat
label = self._label
for i in xrange(self.limit):
for i in range(self.limit):
if cur_fpr < EXPECTED_FPR:
break
@ -103,12 +101,12 @@ class Cascade:
cache_filename = ADABOOST_CACHE_FILE + str(i)
if os.path.isfile(cache_filename):
self.strong_classifier[i] = getCachedAdaBoost(mat = self._mat,
label = self._label,
filename= cache_filename,
limit = ADABOOST_LIMIT)
self.strong_classifier[i] = getCachedAdaBoost(mat=self._mat,
label=self._label,
filename=cache_filename,
limit=ADABOOST_LIMIT)
else:
self.strong_classifier[i] = AdaBoost(mat, label, limit = ADABOOST_LIMIT)
self.strong_classifier[i] = AdaBoost(mat, label, limit=ADABOOST_LIMIT)
output, fpr = self.strong_classifier[i].train()
cur_fpr *= fpr
@ -120,7 +118,6 @@ class Cascade:
self.classifierNum += 1
def updateTrainingDate(self, mat, output, fp_num):
fp_num = int(fp_num)
@ -131,35 +128,33 @@ class Cascade:
_mat[:, :POSITIVE_SAMPLE] = mat[:, :POSITIVE_SAMPLE]
"""
for i in xrange(POSITIVE_SAMPLE):
for j in xrange(FEATURE_NUM):
for i in range(POSITIVE_SAMPLE):
for j in range(FEATURE_NUM):
mat[j][i] = self._mat[j][i]
"""
counter = 0
# only reserve negative samples which are classified wrong
for i in xrange(POSITIVE_SAMPLE, self._label.size):
for i in range(POSITIVE_SAMPLE, self._label.size):
if output[i] != self._label[i]:
for j in xrange(FEATURE_NUM):
for j in range(FEATURE_NUM):
_mat[j][POSITIVE_SAMPLE + counter] = mat[j][i]
counter += 1
assert counter == fp_num
Label_Face = [+1 for i in xrange(POSITIVE_SAMPLE)]
Label_NonFace = [-1 for i in xrange(fp_num)]
Label_Face = [+1 for i in range(POSITIVE_SAMPLE)]
Label_NonFace = [-1 for i in range(fp_num)]
_label = numpy.array(Label_Face + Label_NonFace)
return _mat, _label
def predict(self):
output = numpy.zeros(POSITIVE_SAMPLE + NEGATIVE_SAMPLE, dtype= numpy.float16)
for i in xrange(self.classifierNum):
self.strong_classifier[i].prediction(mat, th = 0)
output = numpy.zeros(POSITIVE_SAMPLE + NEGATIVE_SAMPLE, dtype=numpy.float16)
for i in range(self.classifierNum):
self.strong_classifier[i].prediction(output, th=0)
"""unfinished"""
@ -168,4 +163,3 @@ class Cascade:
def is_goodenough(self):
pass

12
FaceDetection/image.py
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@ -53,15 +53,15 @@ class Image:
iImg = numpy.zeros((row, col))
"""
for i in xrange(0, row):
for j in xrange(0, col):
for i in range(0, row):
for j in range(0, col):
if j == 0:
iImg[i][j] = image[i][j]
else:
iImg[i][j] = iImg[i][j - 1] + image[i][j]
for j in xrange(0, col):
for i in xrange(1, row):
for j in range(0, col):
for i in range(1, row):
iImg[i][j] += iImg[i - 1][j]
"""
@ -81,8 +81,8 @@ class Image:
What image.sum() do is the same as the following code
but more faster than this.
for i in xrange(self.Row):
for j in xrange(self.Col):
for i in range(self.Row):
for j in range(self.Col):
sigma += image[i][j]
"""
# sigma = image.sum()

14
FaceDetection/mapReduce.py
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@ -36,7 +36,7 @@ def map(Face, NonFace):
images_num= len(images)
processes = []
for i in xrange(PROCESS_NUM):
for i in range(PROCESS_NUM):
start = int((i *1./PROCESS_NUM) * images_num)
end = int(((i+1)*1./PROCESS_NUM) * images_num )
sub_imgs = images[start:end]
@ -46,10 +46,10 @@ def map(Face, NonFace):
FEATURE_FILE_SUBSET + str(i) + ".cache"))
processes.append(process)
for i in xrange(PROCESS_NUM):
for i in range(PROCESS_NUM):
processes[i].start()
for i in xrange(PROCESS_NUM):
for i in range(PROCESS_NUM):
processes[i].join()
@ -60,7 +60,7 @@ def reduce():
mats = []
tot_samples = 0
for i in xrange(PROCESS_NUM):
for i in range(PROCESS_NUM):
sub_mat = numpy.load(FEATURE_FILE_SUBSET + str(i) + ".cache" + ".npy")
mats.append(sub_mat)
tot_samples += sub_mat.shape[1]
@ -69,9 +69,9 @@ def reduce():
mat = numpy.zeros((haar.featuresNum, tot_samples), numpy.float32)
sample_readed = 0
for i in xrange(PROCESS_NUM):
for m in xrange(mats[i].shape[0]): # feature number
for n in xrange(mats[i].shape[1]): # sample number
for i in range(PROCESS_NUM):
for m in range(mats[i].shape[0]): # feature number
for n in range(mats[i].shape[1]): # sample number
mat[m][n + sample_readed] = mats[i][m][n]

4
FaceDetection/mr_routine.py
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@ -33,9 +33,9 @@ def routine(images, filename):
mat = numpy.zeros((haar.featuresNum, tot_samples), dtype = numpy.float32)
for i in xrange(tot_samples):
for i in range(tot_samples):
featureVec = haar.calFeatureForImg(images[i])
for j in xrange(haar.featuresNum):
for j in range(haar.featuresNum):
mat[j][i] = featureVec[j]
numpy.save(filename, mat)

8
FaceDetection/testing.py
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@ -23,14 +23,14 @@ haar = Feature(TRAINING_IMG_WIDTH, TRAINING_IMG_HEIGHT)
mat = numpy.zeros((haar.featuresNum, tot_samples))
for i in xrange(face.sampleNum):
for i in range(face.sampleNum):
featureVec = haar.calFeatureForImg(face.images[i])
for j in xrange(haar.featuresNum):
for j in range(haar.featuresNum):
mat[j][i ] = featureVec[j]
for i in xrange(nonFace.sampleNum):
for i in range(nonFace.sampleNum):
featureVec = haar.calFeatureForImg(nonFace.images[i])
for j in xrange(haar.featuresNum):
for j in range(haar.featuresNum):
mat[j][i + face.sampleNum] = featureVec[j]

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