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) fg_mode = 0 size=(4,12) overlap=2 #size=(40,120) fg.getImagePatchStat(testSet,path=path_ea,patchSize=size,overlap = overlap,mode=fg_mode) 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_size_{}'.format(size[0]),'w')) elif mode in ['save']: print 'saving new classifier' _saveRF(testSet,rf) else: print 'not doing anything'
def errorPatch(): 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('/local/attale00/a_labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels') # fileNames = labeledImages; testSet = fg.dataContainer(labs) fg.getImagePatchStat(testSet,path=path_ea,patchSize=(4,12),overlap = 2) testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target) clfPath = '/home/attale00/Desktop/classifiers/errorpatches/rferror' 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); # 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 #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) # X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(120,40),roi=roi,resizeFactor = .5) # data=X # ## 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/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,:]) #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) fg.getImagePatchStat(testSet,path=path_ea,patchSize=(4,12),overlap = 3) #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' _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) else: print 'not doing anything'
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') # fileNames = labeledImages; # 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 # # testSet = fg.dataContainer(labs) roi=(0,37,0,115) roi=None fg.getImagePatchStat(testSet,path=path_ea,patchSize=(4,12),overlap = 2) #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' classifierUtils.classifyWithOld(path,testSet,mode,clfPath = '/home/attale00/Desktop/classifiers/errorpatches/rferror') 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'