人脸表情/微表情识别，毕设作品 使用gabor滤波，pca+lda降维，svm分类 图形界面使用pyqt

import cv import svm import svmutil import glob import operator import os import os.path import numpy import pickle import math import thread import sys from sklearn.lda import LDA from sklearn.decomposition import PCA from config import * models = {} pca = {} lda = {} def get_features(img, height = 8, width = 6): features = [] step_x = img.height / height; step_y = img.width / width; for x in xrange(0 + step_x, img.height, step_x): for y in xrange(0 + step_y, img.width, step_y): features.append(img[x, y]) return features def get_image_features(img): features = [] for phase in gabor_phase: for pulsation in gabor_pulsation: (t_img_mag, t_img) = gabor(img, kernel_var, pulsation, phase, gabor_psi) features.extend(get_features(t_img_mag)) return features def gabor(image, pos_var, pos_w, pos_phase, pos_psi): global kernel_size if kernel_size % 2 == 0: kernel_size += 1 kernel = cv.CreateMat(kernel_size, kernel_size, cv.CV_32FC1) src = cv.CreateImage((image.width, image.height), cv.IPL_DEPTH_8U, 1) src_f = cv.CreateImage((image.width, image.height), cv.IPL_DEPTH_32F, 1) if cv.GetElemType(image) == cv.CV_8UC3: cv.CvtColor(image, src, cv.CV_BGR2GRAY) else: src = image cv.ConvertScale(src, src_f, 1.0 / 255, 0) dest = cv.CloneImage(src_f) dest_mag = cv.CloneImage(src_f) var = pos_var / 1.0 w = pos_w / 10.0 phase = pos_phase * cv.CV_PI / 180.0 psi = cv.CV_PI * pos_psi / 180.0 cv.Zero(kernel) for x in range(-kernel_size / 2 + 1, kernel_size / 2 + 1): for y in range(-kernel_size / 2 + 1, kernel_size / 2 + 1): kernel_val = math.exp(-((x * x) + (y * y)) / (2 * var)) * math.cos(w * x * math.cos(phase) + w * y * math.sin(phase) + psi) cv.Set2D(kernel, y + kernel_size / 2, x + kernel_size / 2, cv.Scalar(kernel_val)) cv.Filter2D(src_f, dest, kernel, (-1, -1)) cv.Pow(dest, dest_mag, 2) return (dest_mag, dest) def data_gen(img_kind, subdir = "data/train/"): classes = [] data = [] the_ones = glob.glob(subdir + "f_" + img_kind + "*.jpg") all_of_them = glob.glob(subdir + "f_*_*.jpg") the_others = [] for x in all_of_them: if the_ones.count(x) < 1: the_others.append(x) for x in the_ones: classes.append(1) data.append(get_image_features(cv.LoadImageM(x))) for x in the_others: classes.append(-1) data.append(get_image_features(cv.LoadImageM(x))) return (classes, data) def fit_pca_and_lda(img_kind, data, classes): print 'Fiting ' + img_kind c_pca = PCA(pca_components) c_lda = LDA(lda_components) c_pca.fit(data) c_lda.fit(data, classes) num = len(data) for i in xrange(num): pca_list = c_pca.transform(data[i]).tolist()[0] lda_list = c_lda.transform(data[i]).tolist()[0] data_list = [] data_list.extend(pca_list) data_list.extend(lda_list) data[i] = data_list pca[img_kind] = c_pca lda[img_kind] = c_lda def train(): data_dict = {} classes_dict = {} global pca global lda pca = {} lda = {} print 'Fiting PCA and LDA' for img_kind in img_kinds: (classes, data) = data_gen(img_kind, train_subdir) fit_pca_and_lda(img_kind, data, classes) data_dict[img_kind] = data classes_dict[img_kind] = classes print '================================' write_pca_to_file() write_lda_to_file() global models models = {} print 'BUILDING TRAIN MODELS' for img_kind in img_kinds: print "\t" + img_kind data = data_dict[img_kind] classes = classes_dict[img_kind] problem = svm.svm_problem(classes, data) param = svm.svm_parameter(svm_params) models[img_kind] = svmutil.svm_train(problem, param) save_model(models[img_kind], img_kind) print_model(img_kind) print '================================' def test(): total_count = 0 correct_count = 0 wrong_count = 0 print 'TESTING MODELS' for img_kind in img_kinds: images = glob.glob(test_subdir + "f_" + img_kind + "*.jpg") for image in images: print "\t" + image image_data = get_image_features(cv.LoadImage(image)) (sorted_results, result) = test_image(image_data) total_count += 1 if result == img_kind: print 'YES :' + result correct_count += 1 else: print 'NO :' + result print sorted_results wrong_count += 1 print '-----------------------' print '================================' print "Total Pictures: " + str(total_count) print "Correct: " + str(correct_count) print "Wrong: " + str(wrong_count) print "Accuracy: " + str(correct_count/float(total_count) * 100) def test_image(image_data): results = {} for kind in img_kinds: pca_data = pca[kind].transform([image_data]).tolist()[0] lda_data = lda[kind].transform([image_data]).tolist()[0] data = [] data.extend(pca_data) data.extend(lda_data) predict_input_data = [] predict_input_data.append(data) print 'match: ' + kind (val, val_2, label) = svmutil.svm_predict([1] ,predict_input_data, models[kind]) results[kind] = label[0][0] sorted_results = sorted(results.iteritems(), key=operator.itemgetter(1)) result = sorted_results[len(sorted_results)-1][0] return sorted_results, result def get_gray_image(image): #image_2 = cv.CreateImage((img_width, img_height), 8, 1) #cv.Resize(image, image_2) img_gray = cv.CreateImage(cv.GetSize(image), 8, 1) cv.CvtColor(image, img_gray, cv.CV_RGB2GRAY) cv.EqualizeHist(img_gray, img_gray) return img_gray def build_gray_image(src_dir, dst_dir): images = glob.glob(src_dir + "*.jpg") for image in images: img = cv.LoadImage(image) img_gray = None try: (img_f, img_r) = face.handle_camera_image(img) img_gray = img_r except TypeError: img_gray = get_gray_image(img) cv.SaveImage(dst_dir + os.path.basename(image), img_gray) def write_models_to_file(): for img_kind in img_kinds: print "Writing model: " + img_kind svmutil.svm_save_model(models_dir + img_kind + '.model', models[img_kind]) def read_models_from_file(): for img_kind in img_kinds: print "Loading model: " + img_kind models[img_kind] = svmutil.svm_load_model(models_dir + img_kind + '.model') def save_model(model, img_kind): svmutil.svm_save_model(models_dir + img_kind + '.model', model) def print_model(img_kind): f = open(models_dir + img_kind + '.model') for line in f: print line f.close() def write_pca_to_file(): f = open(models_dir + 'pca.pkl', "wb") pickle.dump(pca, f) f.close() def read_pca_from_file(): print 'Loading PCA model' f = open(models_dir + 'pca.pkl', "rb") global pca pca = pickle.load(f) f.close() def write_lda_to_file(): f = open(models_dir + 'lda.pkl', "wb") pickle.dump(lda, f) f.close() def read_lda_from_file(): print 'Loading LDA model' f = open(models_dir + 'lda.pkl', "rb") global lda lda = pickle.load(f) f.close() def pca_test(img_kind): import pylab as pl from mpl_toolkits.mplot3d import Axes3D (classes, data) = data_gen(img_kind, train_subdir) pca = PCA(pca_components, whiten=True) print 'fiting' pca.fit(data) print 'transforming' X_r = pca.transform(data) print '----' print X_r.shape x0 = [x[0] for x in X_r] x1 = [x[1] for x in X_r] pl.figure() for i in xrange(0,len(x0)): if classes[i] == 1: pl.scatter(x0[i], x1[i], c = 'r') else: pl.scatter(x0[i], x1[i], c = 'b') pl.legend() pl.title('PCA of dataset ' + img_kind) pl.show() def lda_test(img_kind): import pylab as pl (classes, data) = data_gen(img_kind, train_subdir) lda = LDA(lda_components) print 'fiting' lda.fit(data, classes) print 'transforming' X_r = lda.transform(data) print '----' print X_r.shape x0 = [x[0] for x in X_r] x1 = [x[1] for x in X_r] pl.figure() for i in xrange(0,len(x0)): if classes[i] == 1: pl.scatter(x0[i], x1[i], c = 'r') else: pl.scatter(x0[i], x1[i], c = 'b') pl.legend() pl.title('LDA of dataset ' + img_kind) pl.show() def gabor_test(file_path): import cv2 image = cv.LoadImage(file_path) hcatImage = None for phase in gabor_phase: vcatImage = None for pulsation in gabor_pulsation: (t_img_mag, t_img) = gabor(i