360確實(shí)很個(gè)性，哈哈,你個(gè)貔貅，只吃不吐！ Rtrees介紹！參考鏈接：http://docs.opencv.org/modules/ml/doc/random_trees.html原文鏈接：Python opencv實(shí)現(xiàn)的手寫字符串識(shí)別--SVM 、神經(jīng)網(wǎng)絡(luò)、 K近鄰、 Boosting、Rtrees

代碼copy：

#http://www.360doc.com/content/14/0811/10/13997450_400977867.shtml #code from! #dataset:http://yann.lecun.com/exdb/mnist/ from cv2.cv import * import cv2 import os import struct import numpyclass_n = 10 number_of_training_set = 2000 #0 for all, 60,000 max number_of_test_set = 0 #0 for all, 10,000 max #trainimagepath = r'.\data\train-images.idx3-ubyte' trainimagepath = r'E:\Develope\EclipseWorks\MeachinLearning\Ch16_RandomTrees\data\train-images.idx3-ubyte' #trainlabelpath = r'.\data\train-labels.idx1-ubyte' #testimagepath = r'.\data\t10k-images.idx3-ubyte' #testlabelpath = r'.\data\t10k-labels.idx1-ubyte' trainlabelpath = r'E:\Develope\EclipseWorks\MeachinLearning\Ch16_RandomTrees\data\train-labels.idx1-ubyte' testimagepath = r'E:\Develope\EclipseWorks\MeachinLearning\Ch16_RandomTrees\data\t10k-images.idx3-ubyte' testlabelpath = r'E:\Develope\EclipseWorks\MeachinLearning\Ch16_RandomTrees\data\t10k-labels.idx1-ubyte'def evalfun(method, y_val, test_labels, test_number_of_images):count = 0for item in range(test_number_of_images):if y_val[item] == test_labels[item]:count += 1print method + ':' + str(float(count)/test_number_of_images)def unroll_samples(samples):sample_n, var_n = samples.shapenew_samples = numpy.zeros((sample_n * class_n, var_n+1), numpy.float32)new_samples[:,:-1] = numpy.repeat(samples, class_n, axis=0)new_samples[:,-1] = numpy.tile(numpy.arange(class_n), sample_n)return new_samplesdef unroll_responses(responses):sample_n = len(responses)new_responses = numpy.zeros(sample_n*class_n, numpy.int32)resp_idx = numpy.int32( responses + numpy.arange(sample_n)*class_n )new_responses[resp_idx] = 1return new_responsesdef readImage(filepath, re_size, number_of_images_set):f = open(filepath, 'rb')byte = f.read(4)magic_number = struct.unpack('>i',byte)[0]byte = f.read(4)number_of_images = struct.unpack('>i',byte)[0]if number_of_images_set != 0:number_of_images = number_of_images_set#number_of_images = 30000byte = f.read(4)number_of_rows = struct.unpack('>i',byte)[0]byte = f.read(4)number_of_cols = struct.unpack('>i',byte)[0]images_readed_count = 0images = numpy.array([], dtype = numpy.float32)while images_readed_count < number_of_images: ## print 'starting read image' + str(images_readed_count) + \ ## ' of ' + str(number_of_images)byte = f.read(number_of_rows * number_of_cols)pixels = [numpy.float32(struct.unpack('B', item)[0])/numpy.float32(255) for item in byte]pixels = numpy.resize(pixels, (number_of_rows, number_of_cols))left = number_of_colsright = 0top = number_of_rowsbottom = 0for i in range(number_of_rows):for j in range(number_of_cols):if pixels[i, j] > 0:if j < left:left = jif j > right:right = jif i < top:top = iif i > bottom:bottom = iif (bottom-top) > (right-left):length = bottom - topelse:length = right - leftmidx = float(right + left)/2; midy = float(bottom + top)/2left = int(midx - float(length)/2)right = int(midx + float(length)/2) + 1top = int(midy - float(length)/2)bottom = int(midy + float(length)/2) + 1left = left if left > 0 else 0right = right if right < number_of_cols else number_of_colstop = top if top > 0 else 0bottom = bottom if bottom < number_of_rows else number_of_rowspixels = pixels[left:right, top:bottom] ## print str(int(midx - float(length)/2)) + ':' + \ ## str(int(midx + float(length)/2)) + ',' + \ ## str(int(midy - float(length)/2)) + ':' + \ ## str(int(midy + float(length)/2))pixels = numpy.resize(cv2.resize(pixels, re_size), (1, re_size[0]*re_size[1]))if images.size == 0:images = numpy.array(pixels, dtype = numpy.float32)else:images = numpy.append(images, pixels, axis = 0)images_readed_count += 1f.close()return number_of_images, imagesdef readLabel(filepath, number_of_images_set):f = open(filepath, 'rb')byte = f.read(4)magic_number = struct.unpack('>i',byte)[0]byte = f.read(4)number_of_images = struct.unpack('>i',byte)[0]if number_of_images_set != 0:number_of_images = number_of_images_set#number_of_images = 10000images_readed_count = 0labels = numpy.array([], dtype = numpy.float32)while images_readed_count < number_of_images:byte = f.read(1)labels = numpy.append(labels, numpy.float32(struct.unpack('B', byte)[0]))images_readed_count += 1f.close()return number_of_images, labels##[self.model.predict(s) for s in samples] ## params = dict( kernel_type = cv2.SVM_LINEAR, ## svm_type = cv2.SVM_C_SVC, ## C = 1 ) ## ##samples = numpy.array(numpy.random.random((4,2)), dtype = numpy.float32) ##y_train = numpy.array([1.,0.,0.,1.], dtype = numpy.float32) ## ##clf = SVM() ##clf.train(samples, y_train) ##y_val = clf.predict(samples)def ANN_Pridict():to_size = (8,8)number_of_training_set = 2000train_number_of_images, train_images = readImage(trainimagepath, to_size, number_of_training_set)train_number_of_images, train_labels = readLabel(trainlabelpath, number_of_training_set)##train_images = train_images * 255##train_images = cv2.normalize(train_images)number_of_test_set = 0test_number_of_images, test_images = readImage(testimagepath, to_size, number_of_test_set)test_number_of_images, test_labels = readLabel(testlabelpath, number_of_test_set)print 'loaded images and labels.'########ANN#########modelnn = cv2.ANN_MLP()sample_n, var_n = train_images.shapenew_train_labels = unroll_responses(train_labels).reshape(-1, class_n)layer_sizes = numpy.int32([var_n, 100, class_n])modelnn.create(layer_sizes)params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 300, 0.01),train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,bp_dw_scale = 0.001,bp_moment_scale = 0.0 )modelnn.train(train_images, numpy.float32(new_train_labels), None, params = params)ret, resp = modelnn.predict(test_images)y_val_nn = resp.argmax(-1)evalfun('nn', y_val_nn, test_labels, test_number_of_images)return []def KNearestPridict():to_size = (8,8)number_of_training_set = 2000train_number_of_images, train_images = readImage(trainimagepath, to_size, number_of_training_set)train_number_of_images, train_labels = readLabel(trainlabelpath, number_of_training_set)##train_images = train_images * 255##train_images = cv2.normalize(train_images)number_of_test_set = 0test_number_of_images, test_images = readImage(testimagepath, to_size, number_of_test_set)test_number_of_images, test_labels = readLabel(testlabelpath, number_of_test_set)print 'loaded images and labels.'########KNearest#########modelknn = cv2.KNearest()modelknn.train(train_images,train_labels)y_val_knn= modelknn.find_nearest(test_images, k = 3)count_knn = 0for item in range(test_number_of_images):if y_val_knn[1][item][0] == test_labels[item]:count_knn += 1print 'knn:'+str(float(count_knn)/test_number_of_images)return []def SVM_Pridict():to_size = (8,8)number_of_training_set = 2000train_number_of_images, train_images = readImage(trainimagepath, to_size, number_of_training_set)train_number_of_images, train_labels = readLabel(trainlabelpath, number_of_training_set)##train_images = train_images * 255##train_images = cv2.normalize(train_images)number_of_test_set = 0test_number_of_images, test_images = readImage(testimagepath, to_size, number_of_test_set)test_number_of_images, test_labels = readLabel(testlabelpath, number_of_test_set)print 'loaded images and labels.'#######SVM##########modelsvm = cv2.SVM()modelsvm.train(train_images, train_labels)#, params = paramsy_val_svm = [modelsvm.predict(test_image) for test_image in test_images]evalfun('svm', y_val_svm, test_labels, test_number_of_images)return []def RTrees_Pridict():to_size = (8,8)number_of_training_set = 2000train_number_of_images, train_images = readImage(trainimagepath, to_size, number_of_training_set)train_number_of_images, train_labels = readLabel(trainlabelpath, number_of_training_set)##train_images = train_images * 255##train_images = cv2.normalize(train_images)number_of_test_set = 0test_number_of_images, test_images = readImage(testimagepath, to_size, number_of_test_set)test_number_of_images, test_labels = readLabel(testlabelpath, number_of_test_set)print 'loaded images and labels.'#######RTrees##########modelRTtree = cv2.RTrees()sample_n, var_n = train_images.shapevar_types = numpy.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL], numpy.uint8)params = dict(max_depth=10)modelRTtree.train(train_images, cv2.CV_ROW_SAMPLE, train_labels, varType = var_types, params = params)y_val_RTtree = numpy.float32( [modelRTtree.predict(s) for s in test_images] )evalfun('RTtree', y_val_RTtree, test_labels, test_number_of_images)return []def Boost_Pridict():to_size = (8,8)number_of_training_set = 2000train_number_of_images, train_images = readImage(trainimagepath, to_size, number_of_training_set)train_number_of_images, train_labels = readLabel(trainlabelpath, number_of_training_set)##train_images = train_images * 255##train_images = cv2.normalize(train_images)number_of_test_set = 0test_number_of_images, test_images = readImage(testimagepath, to_size, number_of_test_set)test_number_of_images, test_labels = readLabel(testlabelpath, number_of_test_set)print 'loaded images and labels.'#######Boost#########modelBoost = cv2.Boost()sample_n, var_n = train_images.shapenew_train_images = unroll_samples(train_images)new_train_labels = unroll_responses(train_labels)var_types = numpy.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL, cv2.CV_VAR_CATEGORICAL], numpy.uint8)params = dict(max_depth=5) #, use_surrogates=False)modelBoost.train(new_train_images, cv2.CV_ROW_SAMPLE, new_train_labels, varType = var_types, params=params)new_test_images = unroll_samples(test_images)y_val_Boost = numpy.array( [modelBoost.predict(s, returnSum = True) for s in new_test_images] )y_val_Boost = y_val_Boost.reshape(-1, class_n).argmax(1)evalfun('Boost', y_val_Boost, test_labels, test_number_of_images)return []def ML_Pridict():to_size = (8,8)number_of_training_set = 2000train_number_of_images, train_images = readImage(trainimagepath, to_size, number_of_training_set)train_number_of_images, train_labels = readLabel(trainlabelpath, number_of_training_set)##train_images = train_images * 255##train_images = cv2.normalize(train_images)number_of_test_set = 0test_number_of_images, test_images = readImage(testimagepath, to_size, number_of_test_set)test_number_of_images, test_labels = readLabel(testlabelpath, number_of_test_set)print 'loaded images and labels.'########ANN#########modelnn = cv2.ANN_MLP()sample_n, var_n = train_images.shapenew_train_labels = unroll_responses(train_labels).reshape(-1, class_n)layer_sizes = numpy.int32([var_n, 100, class_n])modelnn.create(layer_sizes)params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 300, 0.01),train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,bp_dw_scale = 0.001,bp_moment_scale = 0.0 )modelnn.train(train_images, numpy.float32(new_train_labels), None, params = params)ret, resp = modelnn.predict(test_images)y_val_nn = resp.argmax(-1)evalfun('nn', y_val_nn, test_labels, test_number_of_images)########KNearest#########modelknn = cv2.KNearest()modelknn.train(train_images,train_labels)y_val_knn= modelknn.find_nearest(test_images, k = 3)count_knn = 0for item in range(test_number_of_images):if y_val_knn[1][item][0] == test_labels[item]:count_knn += 1print 'knn:'+str(float(count_knn)/test_number_of_images)#######SVM##########modelsvm = cv2.SVM()modelsvm.train(train_images, train_labels)#, params = paramsy_val_svm = [modelsvm.predict(test_image) for test_image in test_images]evalfun('svm', y_val_svm, test_labels, test_number_of_images)#######RTrees##########modelRTtree = cv2.RTrees()sample_n, var_n = train_images.shapevar_types = numpy.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL], numpy.uint8)params = dict(max_depth=10)modelRTtree.train(train_images, cv2.CV_ROW_SAMPLE, train_labels, varType = var_types, params = params)y_val_RTtree = numpy.float32( [modelRTtree.predict(s) for s in test_images] )evalfun('RTtree', y_val_RTtree, test_labels, test_number_of_images)#######Boost#########modelBoost = cv2.Boost()sample_n, var_n = train_images.shapenew_train_images = unroll_samples(train_images)new_train_labels = unroll_responses(train_labels)var_types = numpy.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL, cv2.CV_VAR_CATEGORICAL], numpy.uint8)params = dict(max_depth=5) #, use_surrogates=False)modelBoost.train(new_train_images, cv2.CV_ROW_SAMPLE, new_train_labels, varType = var_types, params=params)new_test_images = unroll_samples(test_images)y_val_Boost = numpy.array( [modelBoost.predict(s, returnSum = True) for s in new_test_images] )y_val_Boost = y_val_Boost.reshape(-1, class_n).argmax(1)evalfun('Boost', y_val_Boost, test_labels, test_number_of_images)return []運(yùn)行結(jié)果：

tree.SVM_Pridict() loaded images and labels. svm:0.6251

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