def main(mode): path = "/local/attale00/extracted_pascal__4__Multi-PIE" path_ea = path + "/color128/" allLabelFiles = utils.getAllFiles("/local/attale00/a_labels") labeledImages = [i[0:16] + ".png" for i in allLabelFiles] # labs=utils.parseLabelFiles(path+'/Multi-PIE/labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels') labs = utils.parseLabelFiles( "/local/attale00/a_labels", "mouth", labeledImages, cutoffSeq=".png", suffix="_face0.labels" ) testSet = fg.dataContainer(labs) roi = (50, 74, 96, 160) X = fg.getAllImagesFlat(path_ea, testSet.fileNames, (128, 256), roi=roi) # perform ICA if mode not in ["s", "v"]: ica = FastICA(n_components=100, whiten=True) ica.fit(X) meanI = np.mean(X, axis=0) X1 = X - meanI data = ica.transform(X1) filters = ica.components_ elif mode in ["s", "v"]: W = np.load("/home/attale00/Desktop/classifiers/ica/filter1.npy") m = np.load("/home/attale00/Desktop/classifiers/ica/meanI1.npy") X1 = X - m data = np.dot(X1, W.T) for i in range(len(testSet.data)): testSet.data[i].extend(data[i, :]) strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) # fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 3, cells_per_block=(6,2),maskFromAlpha=False) # fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=10) testSet.targetNum = map(utils.mapMouthLabels2Two, testSet.target) rf = classifierUtils.standardRF(max_features=np.sqrt(len(testSet.data[0])), min_split=5, max_depth=40) if mode in ["s", "v"]: print "Classifying with loaded classifier" classifierUtils.classifyWithOld( path, testSet, mode, clfPath="/home/attale00/Desktop/classifiers/ica/rf128ICA_1" ) elif mode in ["c"]: print "cross validation of data" print "Scores" # print classifierUtils.standardCrossvalidation(rf,testSet,n_jobs=5) # _cvDissect(testSet,rf) classifierUtils.dissectedCV(rf, testSet) print "----" elif mode in ["save"]: print "saving new classifier" _saveRF(testSet) else: print "not doing anything"
def patches(): path = '/local/attale00/AFLW_ALL/' path_ea = '/local/attale00/AFLW_cropped/multiPIE_cropped3/' allLabelFiles = utils.getAllFiles('/local/attale00/a_labels') labeledImages = [i[0:16]+'.png' for i in allLabelFiles] labs=utils.parseLabelFiles('/local/attale00/a_labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels') fileNames = labeledImages; testSet = fg.dataContainer(labs) roi=(0,37,0,115) roi=None X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(40,120),roi=roi) W=np.load('/home/attale00/Desktop/classifiers/patches/filter2.npy') m=np.load('/home/attale00/Desktop/classifiers/patches/meanI2.npy') X1=X-m data=np.dot(X1,W.T) for i in range(len(fileNames)): testSet.data[i].extend(data[i,:]) fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 5, pixels_per_cell=(24,8),cells_per_block=(3,3),maskFromAlpha=False) fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=20) testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target) clfPath = '/home/attale00/Desktop/classifiers/patches/rfICAHogColor' f=file(clfPath,'r') print 'classifier used: '+ f.name clf = pickle.load(f) testSet.classifiedAs=clf.predict(testSet.data) testSet.probabilities=clf.predict_proba(testSet.data) return testSet
def main(mode): path = '/local/attale00/AFLW_ALL/' path_ea = '/local/attale00/AFLW_cropped/mouth_img_error/' # fileNames = utils.getAllFiles(path_ea); labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels') testSet = fg.dataContainer(labs) components = 150 roi=None X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(40,120),roi=roi) # X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(120,40),roi=roi,resizeFactor = .5) # # perform ICA if mode not in ['s','v']: ica = FastICA(n_components=components,whiten=True) ica.fit(X) meanI=np.mean(X,axis=0) X1=X-meanI data=ica.transform(X1) filters=ica.components_ elif mode in ['s','v']: W=np.load('/home/attale00/Desktop/classifiers/patches/filterMP1.npy') m=np.load('/home/attale00/Desktop/classifiers/patches/meanIMP1.npy') X1=X-m data=np.dot(X1,W.T) for i in range(len(fileNames)): testSet.data[i].extend(data[i,:]) print 'feature vector length: {}'.format(len(testSet.data[0])) testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target) rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])),min_split=13,max_depth=40) #rf = svm.NuSVC() #rf = linear_model.SGDClassifier(loss='perceptron', eta0=1, learning_rate='constant', penalty=None) if mode in ['s','v']: print 'Classifying with loaded classifier' _classifyWithOld(path,testSet,mode) elif mode in ['c']: print 'cross validation of data' rValues = classifierUtils.dissectedCV(rf,testSet) pickle.dump(rValues,open('errorpatch_ica','w')) elif mode in ['save']: print 'saving new classifier' _saveRF(testSet,rf,filters=filters,meanI=meanI) else: print 'not doing anything'
def texture(): path = '/local/attale00/extracted_pascal__4__Multi-PIE' path_ea = path+'/color128/' allLabelFiles = utils.getAllFiles('/local/attale00/a_labels') labeledImages = [i[0:16]+'.png' for i in allLabelFiles] labs=utils.parseLabelFiles('/local/attale00/a_labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels') testSet = fg.dataContainer(labs) roi=(50,74,96,160) X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(128,256),roi=roi) W=np.load('/home/attale00/Desktop/classifiers/ica/filter1.npy') m=np.load('/home/attale00/Desktop/classifiers/ica/meanI1.npy') X1=X-m data=np.dot(X1,W.T) for i in range(len(testSet.data)): testSet.data[i].extend(data[i,:]) fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 3, cells_per_block=(6,2),maskFromAlpha=False) fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=10) testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target) clfPath = '/home/attale00/Desktop/classifiers/ica/rf128ICAHOGCOLOR' f=file(clfPath,'r') print 'classifier used: '+ f.name clf = pickle.load(f) testSet.classifiedAs=clf.predict(testSet.data) testSet.probabilities=clf.predict_proba(testSet.data) return testSet
def main(mode): path = '/local/attale00/AFLW_ALL/' path_ea = '/local/attale00/AFLW_cropped/cropped3/' # fileNames = utils.getAllFiles(path_ea); # minr = 10000; # for f in fileNames: # im = cv2.imread(path_ea+f,-1) # if im.shape[0]!=40 or im.shape[1]!=120: # print f # print im.shape # minr = minr if im.shape[0]>= minr else im.shape[0] # # print minr # labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels') testSet = fg.dataContainer(labs) roi=(0,37,0,115) roi=None filters = None meanI = None #roi=(44,84,88,168) # eM=np.load('/home/attale00/Desktop/mouthMask.npy') # m=cv2.resize(np.uint8(eM),(256,256)); # strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)) # dil = cv2.dilate(m,strel) # # m=dil>0; components = 150 X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(40,120),roi=roi) # X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(120,40),roi=roi,resizeFactor = .5) # # perform ICA if mode not in ['s','v']: ica = FastICA(n_components=components,whiten=True) ica.fit(X) meanI=np.mean(X,axis=0) X1=X-meanI data=ica.transform(X1) filters=ica.components_ elif mode in ['s','v']: W=np.load('/home/attale00/Desktop/classifiers/patches/filterMP1.npy') m=np.load('/home/attale00/Desktop/classifiers/patches/meanIMP1.npy') X1=X-m data=np.dot(X1,W.T) for i in range(len(fileNames)): testSet.data[i].extend(data[i,:]) #orientations = 2 #strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)) #fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 5, cells_per_block=(3,3),pixels_per_cell=(24,8),maskFromAlpha=False) #fg.getColorHistogram(testSet,(0,40,40,80),path=path_ea,ending='.png',colorspace='lab',bins=bins) #fg.getImagePatchStat(testSet,path=path_ea,patchSize=(4,12)) #fg.getImagePatchStat(testSet,path='/local/attale00/AFLW_cropped/mouth_img_error/',patchSize=(4,12)) #pca # n_samples, n_features = X.shape # # mean_ = np.mean(X, axis=0) # X -= mean_ # U, S, V = linalg.svd(X) # explained_variance_ = (S ** 2) / n_samples # explained_variance_ratio_ = (explained_variance_ /explained_variance_.sum()) # K=V / S[:, np.newaxis] * np.sqrt(n_samples) # filters=K[:100] # data=np.dot(X,filters.T) testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target) rf=classifierUtils.standardRF(max_features = 23,min_split=15,max_depth=70,n_estimators=150) #rf=classifierUtils.standardRF(max_features = 5,min_split=12,max_depth=45) #rf = svm.NuSVC() #rf = linear_model.SGDClassifier(loss='perceptron', eta0=1, learning_rate='constant', penalty=None) if mode in ['s','v']: print 'Classifying with loaded classifier' _classifyWithOld(path,testSet,mode) elif mode in ['c']: print 'cross validation of data' rValues = classifierUtils.dissectedCV(rf,testSet) pickle.dump(rValues,open('patches_cv_ica_{}'.format(components),'w')) elif mode in ['save']: print 'saving new classifier' _saveRF(testSet,rf,filters=filters,meanI=meanI) else: print 'not doing anything'
path_ea = '/local/attale00/AFLW_cropped/cropped2/' # fileNames = utils.getAllFiles(path_ea); labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels') testSet = fg.dataContainer(labs) roi=(0,37,0,115) X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(37,115),roi=roi) # # perform ICA ica = FastICA(n_components=100,whiten=True) ica.fit(X) meanI=np.mean(X,axis=0) X1=X-meanI data=ica.transform(X1) filters=ica.components_ for i in range(len(fileNames)): testSet.data[i].extend(data[i,:]) testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
# fileNames = utils.getAllFiles(path_ea); labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels') testSet = fg.dataContainer(labs) roi=(0,37,0,115) roi=None #roi=(50,74,96,160) X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(40,120),roi=roi) # # perform ICA ica = FastICA(n_components=100,whiten=True) ica.fit(X) meanI=np.mean(X,axis=0) X1=X-meanI data=ica.transform(X1) filters=ica.components_ for i in range(len(fileNames)): testSet.data[i].extend(data[i,:]) #fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 5, cells_per_block=(3,3),pixels_per_cell=(24,8),maskFromAlpha=False) # #fg.getColorHistogram(testSet,(0,40,40,80),path=path_ea,ending='.png',colorspace='lab',bins=20)
def main(mode): path = '/local/attale00/AFLW_ALL' path_ea = path+'/color128/' fileNames = utils.getAllFiles(path_ea); labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels') testSet = fg.dataContainer(labs) testSetMirror = fg.dataContainer(labs) for f in range(len(testSetMirror.fileNames)): testSetMirror.fileNames[f]+='M' roi=(50,74,96,160) X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(128,256),roi=roi) Y=fg.getAllImagesFlat(path+'/mirror128/',testSet.fileNames,(128,256),roi=roi) Z=np.concatenate((X,Y),axis=0) # perform ICA ica = FastICA(n_components=100,whiten=True) ica.fit(Z) meanI=np.mean(Z,axis=0) X1=X-meanI Y1=Y-meanI data=ica.transform(X1) datam=ica.transform(Y1) filters=ica.components_ for i in range(len(fileNames)): testSet.data[i].extend(data[i,:]) testSetMirror.data[i].extend(datam[i,:]) strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)) #fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 3, cells_per_block=(6,2),maskFromAlpha=False) #fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=10) #pca # n_samples, n_features = X.shape # # mean_ = np.mean(X, axis=0) # X -= mean_ # U, S, V = linalg.svd(X) # explained_variance_ = (S ** 2) / n_samples # explained_variance_ratio_ = (explained_variance_ /explained_variance_.sum()) # K=V / S[:, np.newaxis] * np.sqrt(n_samples) # filters=K[:100] # data=np.dot(X,filters.T) testSet.addContainer(testSetMirror) testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target) rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])),min_split=5,max_depth=40) if mode in ['s','v']: print 'Classifying with loaded classifier' _classifyWithOld(path,testSet,mode) elif mode in ['c']: print 'cross validation of data' classifierUtils.dissectedCV(rf,testSet) elif mode in ['save']: print 'saving new classifier' _saveRF(testSet,rf,filters=filters,meanI=meanI) else: print 'not doing anything'
def main(mode): path = '/local/attale00/AFLW_ALL/' path_ea = '/local/attale00/AFLW_cropped/multiPIE_cropped3/' allLabelFiles = utils.getAllFiles('/local/attale00/a_labels') labeledImages = [i[0:16]+'.png' for i in allLabelFiles] #labs=utils.parseLabelFiles(path+'/Multi-PIE/labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels') labs=utils.parseLabelFiles('/local/attale00/a_labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels') labs=dict((k,v) for (k,v) in labs.iteritems() if not v.startswith('narr')) # testSet = fg.dataContainer(labs) roi=(0,37,0,115) roi=None X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(40,120),roi=roi) # perform ICA # if mode not in ['s','v']: # ica = FastICA(n_components=100,whiten=True) # ica.fit(X) # meanI=np.mean(X,axis=0) # X1=X-meanI # data=ica.transform(X1) # filters=ica.components_ # # elif mode in ['s','v']: # W=np.load('/home/attale00/Desktop/classifiers/thesis/filtercombined.npy') # m=np.load('/home/attale00/Desktop/classifiers/thesis/meancombined.npy') # X1=X-m # data=np.dot(X1,W.T) # # for i in range(len(testSet.fileNames)): # testSet.data[i].extend(data[i,:]) # # strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)) #fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 9, pixels_per_cell=(24,8),cells_per_block=(3,3),maskFromAlpha=False) fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=40) #fg.getImagePatchStat(testSet,path='/local/attale00/AFLW_cropped/mouth_img_error_multiPie/',patchSize =(4,12)) #pca # n_samples, n_features = X.shape # # mean_ = np.mean(X, axis=0) # X -= mean_ # U, S, V = linalg.svd(X) # explained_variance_ = (S ** 2) / n_samples # explained_variance_ratio_ = (explained_variance_ /explained_variance_.sum()) # K=V / S[:, np.newaxis] * np.sqrt(n_samples) # filters=K[:100] # data=np.dot(X,filters.T) testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target) rf=classifierUtils.standardRF(max_features = 27,min_split=13,max_depth=40) #rf = svm.NuSVC() #rf = linear_model.SGDClassifier(loss='perceptron', eta0=1, learning_rate='constant', penalty=None) if mode in ['s','v']: print 'Classifying with loaded classifier' obj=classifierUtils.classifyWithOld(path,testSet,mode,clfPath = '/home/attale00/Desktop/classifiers/thesis/color_only') pickle.dump(obj,open('color_only','w')) elif mode in ['c']: print 'cross validation of data' rValues = classifierUtils.dissectedCV(rf,testSet) pickle.dump(rValues,open('patches_mp_','w')) elif mode in ['save']: print 'saving new classifier' _saveRF(testSet,rf,filters=filters,meanI=meanI) else: print 'not doing anything'
def main(mode): path = '/local/attale00/AFLW_ALL/' path_ea = '/local/attale00/AFLW_cropped/cropped3/' # fileNames = utils.getAllFiles(path_ea); # minr = 10000; # for f in fileNames: # im = cv2.imread(path_ea+f,-1) # if im.shape[0]!=40 or im.shape[1]!=120: # print f # print im.shape # minr = minr if im.shape[0]>= minr else im.shape[0] # # print minr # labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels') testSet = fg.dataContainer(labs) testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target) roi=(0,37,0,115) roi=None #roi=(44,84,88,168) # eM=np.load('/home/attale00/Desktop/mouthMask.npy') # m=cv2.resize(np.uint8(eM),(256,256)); # strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)) # dil = cv2.dilate(m,strel) # # m=dil>0; #X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(40,120),roi=roi) # perform ICA names_open = [] names_closed = [] for i,f in enumerate(testSet.fileNames): if testSet.targetNum[i] == 0: names_closed.append(f) elif testSet.targetNum[i] == 1: names_open.append(f) Xopen = fg.getAllImagesFlat(path_ea,names_open,(40,120)) XClosed = fg.getAllImagesFlat(path_ea,names_closed,(40,120)) if mode not in ['s','v']: icaopen = FastICA(n_components=100,whiten=True) icaopen.fit(Xopen) meanIopen=np.mean(Xopen,axis=0) X1open=Xopen-meanIopen dataopen=icaopen.transform(X1open) filtersopen=icaopen.components_ plottingUtils.showICAComponents(filtersopen,(40,120),4,4) icaclosed = FastICA(n_components=100,whiten=True) icaclosed.fit(XClosed) meanIclosed=np.mean(XClosed,axis=0) X1closed=XClosed-meanIclosed dataclosed=icaclosed.transform(X1closed) filtersclosed=icaclosed.components_ plottingUtils.showICAComponents(filtersclosed,(40,120),4,4) plt.show() elif mode in ['s','v']: W=np.load('/home/attale00/Desktop/classifiers/patches/filterMP1.npy') m=np.load('/home/attale00/Desktop/classifiers/patches/meanIMP1.npy') X1=X-m data=np.dot(X1,W.T) for i in range(len(fileNames)): testSet.data[i].extend(data[i,:]) strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)) #fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 5, cells_per_block=(3,3),pixels_per_cell=(24,8),maskFromAlpha=False) #fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=20) #pca # n_samples, n_features = X.shape # # mean_ = np.mean(X, axis=0) # X -= mean_ # U, S, V = linalg.svd(X) # explained_variance_ = (S ** 2) / n_samples # explained_variance_ratio_ = (explained_variance_ /explained_variance_.sum()) # K=V / S[:, np.newaxis] * np.sqrt(n_samples) # filters=K[:100] # data=np.dot(X,filters.T) rf=classifierUtils.standardRF(max_features = 27,min_split=13,max_depth=40) #rf = svm.NuSVC() #rf = linear_model.SGDClassifier(loss='perceptron', eta0=1, learning_rate='constant', penalty=None) if mode in ['s','v']: print 'Classifying with loaded classifier' _classifyWithOld(path,testSet,mode) elif mode in ['c']: print 'cross validation of data' classifierUtils.dissectedCV(rf,testSet) elif mode in ['save']: print 'saving new classifier' _saveRF(testSet,rf,filters=filters,meanI=meanI) else: print 'not doing anything'
#X = image_patches.data # ## 1000 patches a 32x32 ## not so much data, reshape to 16000 patches a 8x8 #X = X.reshape(1000, 4, 8, 4, 8) #X = np.rollaxis(X, 3, 2).reshape(-1, 8 * 8) path = '/local/attale00/AFLW_ALL' path_ea = path+'/color128/' fileNames = utils.getAllFiles(path_ea); path_mp = '/local/attale00/extracted_pascal__4__Multi-PIE/color128/' mpFiles = utils.getAllFiles(path_mp) roi=(50,74,96,160) X=fg.getAllImagesFlat(path_ea,fileNames,(128,256),roi=roi) #Y=fg.getAllImagesFlat(path_mp,mpFiles,(128,256),roi=roi) #Z=np.concatenate((X,Y),axis=0) Z=X ##perform ICA ica = FastICA(n_components=49,whiten=True) ica.fit(X) filters = ica.unmixing_matrix_ #pca ## Center data #n_samples, n_features = X.shape # #mean_ = np.mean(X, axis=0) #X -= mean_
def main(): logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") ############################################################################### # data path = "/local/attale00/AFLW_ALL/" path_ea = "/local/attale00/AFLW_cropped/cropped3/" fileNames = utils.getAllFiles(path_ea) labs = utils.parseLabelFiles( path + "/labels/labels", "mouth_opening", fileNames, cutoffSeq=".png", suffix="_face0.labels" ) testSet = fg.dataContainer(labs) X = fg.getAllImagesFlat(path_ea, testSet.fileNames, (40, 120), roi=None) # # perform ICA ica = FastICA(n_components=250, whiten=True) ica.fit(X) meanI = np.mean(X, axis=0) X1 = X - meanI data = ica.transform(X1) for i in range(len(fileNames)): testSet.data[i].extend(data[i, :]) DATA = np.zeros((len(testSet.data), len(testSet.data[0]))) for i in range(len(testSet.data)): DATA[i, :] = np.array(testSet.data[i]) testSet.targetNum = map(utils.mapMouthLabels2Two, testSet.target) classLabels = np.array(testSet.targetNum) scores = [] parameters = { "n_estimators": range(90, 260, 20), "max_depth": range(50, 110, 20), "min_samples_split": range(5, 35, 5), "max_features": range(25, 75, 10), "min_samples_leaf": range(1, 10, 5), } # parameters = {'n_estimators': range(90, 100,20), # 'max_depth': range(50, 60,20), # 'min_samples_split':range(5,15,5), # 'max_features':range(25,35,10), # 'min_samples_leaf':range(1,10,5)} print "Performing grid search..." print "parameters:" pprint(parameters) for i in range(80, 260, 15): mf = i if i < 75 else 75 parameters["max_features"] = range(10, mf, 10) s = GridSearchUtils.doGridSearch(data[:, 0:i], classLabels, parameters) temp = (s[0], s[1], i) scores.append(temp) scores_s = sorted(scores, key=lambda x: x[0]) print scores_s pickle.dump(scores_s, open("/local/attale00/gridsearches/ICA_3", "w"))
## fetch natural image patches # image_patches = fetch_mldata("natural scenes data") # X = image_patches.data # ## 1000 patches a 32x32 ## not so much data, reshape to 16000 patches a 8x8 # X = X.reshape(1000, 4, 8, 4, 8) # X = np.rollaxis(X, 3, 2).reshape(-1, 8 * 8) # path = '/local/attale00/AFLW_cropped/cropped3/' path = "/local/attale00/AFLW_cropped/mouth_img_error/" fileNames = utils.getAllFiles(path) roi = (0, 40, 0, 120) X = fg.getAllImagesFlat(path, fileNames, (40, 120), roi=roi) # perform ICA ica = FastICA(n_components=49, whiten=True, max_iter=500) ica.fit(X) filters = ica.unmixing_matrix_ # pca ## Center data # n_samples, n_features = X.shape # # mean_ = np.mean(X, axis=0) # X -= mean_ # U, S, V = linalg.svd(X) # explained_variance_ = (S ** 2) / n_samples
def main(mode): path = '/local/attale00/AFLW_ALL' path_ea = path+'/color128/' fileNames = utils.getAllFiles(path_ea); labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels') testSet = fg.dataContainer(labs) roi=(50,74,96,160) #roi=(44,84,88,168) # eM=np.load('/home/attale00/Desktop/mouthMask.npy') # m=cv2.resize(np.uint8(eM),(256,256)); # strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)) # dil = cv2.dilate(m,strel) # # m=dil>0; path_mp = '/local/attale00/extracted_pascal__4__Multi-PIE/color128/' mpFiles = utils.getAllFiles(path_mp) X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(128,256),roi=roi) #Y=fg.getAllImagesFlat(path_mp,mpFiles,(128,256),roi=roi) #Z=np.concatenate((X,Y),axis=0) Z=X # # perform ICA # ica = FastICA(n_components=50,whiten=True) # ica.fit(Z) # meanI=np.mean(X,axis=0) # # X1=X-meanI # data=ica.transform(X1) # filters=ica.components_ # for i in range(len(fileNames)): # testSet.data[i].extend(data[i,:]) bins = 40 strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)) #fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 3, cells_per_block=(6,2),maskFromAlpha=False) fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=bins) testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target) rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])),min_split=1,max_depth=40) if mode in ['s','v']: print 'Classifying with loaded classifier' _classifyWithOld(path,testSet,mode) elif mode in ['c']: print 'cross validation of data' rValues = classifierUtils.dissectedCV(rf,testSet) pickle.dump(rValues,open('texture_color_{}'.format(bins),'w')) elif mode in ['save']: print 'saving new classifier' _saveRF(testSet,rf,filters=None,meanI=None) else: print 'not doing anything' return
def main(mode): path = '/local/attale00/AFLW_ALL/' path_ea = '/local/attale00/AFLW_cropped/mouth_img_error_multiPie/' allLabelFiles = utils.getAllFiles('/local/attale00/a_labels') labeledImages = [i[0:16]+'.png' for i in allLabelFiles] #labs=utils.parseLabelFiles(path+'/Multi-PIE/labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels') labs=utils.parseLabelFiles('/local/attale00/a_labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels') labs=dict((k,v) for (k,v) in labs.iteritems() if not v.startswith('narr')) # fileNames = labeledImages; # roi=None testSet = fg.dataContainer(labs) X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(40,120),roi=roi) fgmode = 0 #fg.getImagePatchStat(testSet,path=path_ea,patchSize=(8,24),overlap = 2,mode=fgmode) roi=None orientations = 9 #fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = orientations, cells_per_block=(3,3),pixels_per_cell=(24,8),maskFromAlpha=False) # perform ICA if mode not in ['s','v']: ica = FastICA(n_components=100,whiten=True) ica.fit(X) meanI=np.mean(X,axis=0) X1=X-meanI data=ica.transform(X1) filters=ica.components_ elif mode in ['s','v']: W=np.load('/home/attale00/Desktop/classifiers/thesis/errorpatches/filter1.npy') m=np.load('/home/attale00/Desktop/classifiers/thesis/errorpatches/meanI1.npy') X1=X-m data=np.dot(X1,W.T) for i in range(len(testSet.fileNames)): testSet.data[i].extend(data[i,:]) testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target) rf=classifierUtils.standardRF(max_features = 27,min_split=13,max_depth=40) #rf = svm.NuSVC() #rf = linear_model.SGDClassifier(loss='perceptron', eta0=1, learning_rate='constant', penalty=None) if mode in ['s','v']: print 'Classifying with loaded classifier' #r=classifierUtils.classifyWithOld(path,testSet,mode,clfPath = '/home/attale00/Desktop/classifiers/thesis/errorpatches/mode{}'.format(fgmode)) r=classifierUtils.classifyWithOld(path,testSet,mode,clfPath='/home/attale00/Desktop/classifiers/thesis/errorpatches/errorpatch_ica') pickle.dump(r,open('errorpatch_test_ica'.format(fgmode),'w')) elif mode in ['c']: print 'cross validation of data' classifierUtils.dissectedCV(rf,testSet) print classifierUtils.standardCrossvalidation(rf,testSet) elif mode in ['save']: print 'saving new classifier' _saveRF(testSet,rf,filters=filters,meanI=meanI) else: print 'not doing anything'