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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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94
FaceDetection/cascade.py
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@ -8,21 +8,20 @@ License : MIT License
""" """
from config import POSITIVE_SAMPLE
from config import POSITIVE_SAMPLE from config import NEGATIVE_SAMPLE
from config import NEGATIVE_SAMPLE from config import TRAINING_IMG_HEIGHT
from config import TRAINING_IMG_HEIGHT from config import TRAINING_IMG_WIDTH
from config import TRAINING_IMG_WIDTH from config import FEATURE_FILE_TRAINING
from config import FEATURE_FILE_TRAINING from config import FEATURE_NUM
from config import FEATURE_NUM from config import ADABOOST_LIMIT
from config import ADABOOST_LIMIT from config import ADABOOST_CACHE_FILE
from config import ADABOOST_CACHE_FILE from config import DEBUG_MODEL
from config import DEBUG_MODEL
from haarFeature import Feature from haarFeature import Feature
from image import ImageSet from image import ImageSet
from adaboost import AdaBoost from adaboost import AdaBoost
from adaboost import getCachedAdaBoost from adaboost import getCachedAdaBoost
import os import os
import numpy import numpy
@ -30,17 +29,17 @@ import numpy
class Cascade: class Cascade:
def __init__(self, face_dir = "", nonface_dir = "", train = True, limit = 30): def __init__(self, face_dir="", nonface_dir="", train=True, limit=30):
#tot_samples = 0 # tot_samples = 0
self.Face = ImageSet(face_dir, sampleNum = POSITIVE_SAMPLE) self.Face = ImageSet(face_dir, sampleNum=POSITIVE_SAMPLE)
self.nonFace = ImageSet(nonface_dir, sampleNum = NEGATIVE_SAMPLE) self.nonFace = ImageSet(nonface_dir, sampleNum=NEGATIVE_SAMPLE)
tot_samples = self.Face.sampleNum + self.nonFace.sampleNum tot_samples = self.Face.sampleNum + self.nonFace.sampleNum
self.classifier = AdaBoost self.classifier = AdaBoost
self.haar = Feature(TRAINING_IMG_WIDTH, TRAINING_IMG_HEIGHT) self.haar = Feature(TRAINING_IMG_WIDTH, TRAINING_IMG_HEIGHT)
if os.path.isfile(FEATURE_FILE_TRAINING + ".npy"): if os.path.isfile(FEATURE_FILE_TRAINING + ".npy"):
@ -50,14 +49,14 @@ class Cascade:
if DEBUG_MODEL is True: if DEBUG_MODEL is True:
self._mat = numpy.zeros((self.haar.featuresNum, tot_samples)) 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]) featureVec = self.haar.calFeatureForImg(self.Face.images[i])
for j in xrange(self.haar.featuresNum): for j in range(self.haar.featuresNum):
self._mat[j][i ] = featureVec[j] 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]) 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] self._mat[j][i + self.Face.sampleNum] = featureVec[j]
numpy.save(FEATURE_FILE_TRAINING, self._mat) numpy.save(FEATURE_FILE_TRAINING, self._mat)
@ -70,17 +69,16 @@ class Cascade:
featureNum, sampleNum = self._mat.shape featureNum, sampleNum = self._mat.shape
assert sampleNum == (POSITIVE_SAMPLE + NEGATIVE_SAMPLE) assert sampleNum == (POSITIVE_SAMPLE + NEGATIVE_SAMPLE)
assert featureNum == FEATURE_NUM assert featureNum == FEATURE_NUM
Label_Face = [+1 for i in xrange(POSITIVE_SAMPLE)] Label_Face = [+1 for i in range(POSITIVE_SAMPLE)]
Label_NonFace = [-1 for i in xrange(NEGATIVE_SAMPLE)] Label_NonFace = [-1 for i in range(NEGATIVE_SAMPLE)]
self._label = numpy.array(Label_Face + Label_NonFace) self._label = numpy.array(Label_Face + Label_NonFace)
self.limit = limit self.limit = limit
self.classifierNum = 0 self.classifierNum = 0
self.strong_classifier = [None for i in xrange(limit)] self.strong_classifier = [None for i in range(limit)]
def train(self): def train(self):
@ -92,10 +90,10 @@ class Cascade:
from config import LABEL_NEGATIVE from config import LABEL_NEGATIVE
cur_fpr = 1.0 cur_fpr = 1.0
mat = self._mat mat = self._mat
label = self._label label = self._label
for i in xrange(self.limit): for i in range(self.limit):
if cur_fpr < EXPECTED_FPR: if cur_fpr < EXPECTED_FPR:
break break
@ -103,12 +101,12 @@ class Cascade:
cache_filename = ADABOOST_CACHE_FILE + str(i) cache_filename = ADABOOST_CACHE_FILE + str(i)
if os.path.isfile(cache_filename): if os.path.isfile(cache_filename):
self.strong_classifier[i] = getCachedAdaBoost(mat = self._mat, self.strong_classifier[i] = getCachedAdaBoost(mat=self._mat,
label = self._label, label=self._label,
filename= cache_filename, filename=cache_filename,
limit = ADABOOST_LIMIT) limit=ADABOOST_LIMIT)
else: 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() output, fpr = self.strong_classifier[i].train()
cur_fpr *= fpr cur_fpr *= fpr
@ -120,7 +118,6 @@ class Cascade:
self.classifierNum += 1 self.classifierNum += 1
def updateTrainingDate(self, mat, output, fp_num): def updateTrainingDate(self, mat, output, fp_num):
fp_num = int(fp_num) fp_num = int(fp_num)
@ -131,35 +128,33 @@ class Cascade:
_mat[:, :POSITIVE_SAMPLE] = mat[:, :POSITIVE_SAMPLE] _mat[:, :POSITIVE_SAMPLE] = mat[:, :POSITIVE_SAMPLE]
""" """
for i in xrange(POSITIVE_SAMPLE): for i in range(POSITIVE_SAMPLE):
for j in xrange(FEATURE_NUM): for j in range(FEATURE_NUM):
mat[j][i] = self._mat[j][i] mat[j][i] = self._mat[j][i]
""" """
counter = 0 counter = 0
# only reserve negative samples which are classified wrong # 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]: 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] _mat[j][POSITIVE_SAMPLE + counter] = mat[j][i]
counter += 1 counter += 1
assert counter == fp_num assert counter == fp_num
Label_Face = [+1 for i in xrange(POSITIVE_SAMPLE)] Label_Face = [+1 for i in range(POSITIVE_SAMPLE)]
Label_NonFace = [-1 for i in xrange(fp_num)] Label_NonFace = [-1 for i in range(fp_num)]
_label = numpy.array(Label_Face + Label_NonFace) _label = numpy.array(Label_Face + Label_NonFace)
return _mat, _label return _mat, _label
def predict(self): def predict(self):
output = numpy.zeros(POSITIVE_SAMPLE + NEGATIVE_SAMPLE, dtype= numpy.float16) output = numpy.zeros(POSITIVE_SAMPLE + NEGATIVE_SAMPLE, dtype=numpy.float16)
for i in xrange(self.classifierNum): for i in range(self.classifierNum):
self.strong_classifier[i].prediction(output, th=0)
self.strong_classifier[i].prediction(mat, th = 0)
"""unfinished""" """unfinished"""
@ -168,4 +163,3 @@ class Cascade:
def is_goodenough(self): def is_goodenough(self):
pass pass

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

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

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