机器学习用在图像识别是非常有趣的话题。
我们可以利用OpenCV强大的功能结合机器学习算法实现图像识别系统。
首先，输入若干图像，加入分类标记。利用向量量化方法将特征点进行聚类，并得出中心点，这些中心点就是视觉码本的元素。
其次，利用图像分类器将图像分到已知的类别中，ERF（极端随机森林）算法非常流行，因为ERF具有较快的速度和比较精确的准确度。我们利用决策树进行正确决策。
最后，利用训练好的ERF模型后，创建目标识别器，可以识别未知图像的内容。
当然，这只是雏形，存在很多问题：
界面不友好。
准确率如何保证，如何调整超参数，只有认真研究算法机理，才能真正清除内部实现机制后给予改进。
下面，上代码：
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import osimport sysimport argparseimport jsonimport cv2import numpy as npfrom sklearn.cluster import KMeans# from star_detector import StarFeatureDetectorfrom sklearn.ensemble import ExtraTreesClassifierfrom sklearn import preprocessingtry: import cPickle as pickle #python 2except ImportError as e: import pickle #python 3def load_training_data(input_folder): training_data = [] if not os.path.isdir(input_folder): raise IOError("The folder " + input_folder + " doesn't exist") for root, dirs, files in os.walk(input_folder): for filename in (x for x in files if x.endswith('.jpg')): filepath = os.path.join(root, filename) print(filepath) object_class = filepath.split('\\')[-2] print("object_class",object_class) training_data.append({'object_class': object_class, 'image_path': filepath}) return training_dataclass StarFeatureDetector(object): def __init__(self): self.detector = cv2.xfeatures2d.StarDetector_create() def detect(self, img): return self.detector.detect(img)class FeatureBuilder(object): def extract_features(self, img): keypoints = StarFeatureDetector().detect(img) keypoints, feature_vectors = compute_sift_features(img, keypoints) return feature_vectors def get_codewords(self, input_map, scaling_size, max_samples=12): keypoints_all = [] count = 0 cur_class = '' for item in input_map: if count >= max_samples: if cur_class != item['object_class']: count = 0 else: continue count += 1 if count == max_samples: print ("Built centroids for", item['object_class']) cur_class = item['object_class'] img = cv2.imread(item['image_path']) img = resize_image(img, scaling_size) num_dims = 128 feature_vectors = self.extract_features(img) keypoints_all.extend(feature_vectors) kmeans, centroids = BagOfWords().cluster(keypoints_all) return kmeans, centroidsclass BagOfWords(object): def __init__(self, num_clusters=32): self.num_dims = 128 self.num_clusters = num_clusters self.num_retries = 10 def cluster(self, datapoints): kmeans = KMeans(self.num_clusters, n_init=max(self.num_retries, 1), max_iter=10, tol=1.0) res = kmeans.fit(datapoints) centroids = res.cluster_centers_ return kmeans, centroids def normalize(self, input_data): sum_input = np.sum(input_data) if sum_input > 0: return input_data / sum_input else: return input_data def construct_feature(self, img, kmeans, centroids): keypoints = StarFeatureDetector().detect(img) keypoints, feature_vectors = compute_sift_features(img, keypoints) labels = kmeans.predict(feature_vectors) feature_vector = np.zeros(self.num_clusters) for i, item in enumerate(feature_vectors): feature_vector[labels[i]] += 1 feature_vector_img = np.reshape(feature_vector, ((1, feature_vector.shape[0]))) return self.normalize(feature_vector_img)# Extract features from the input images and # map them to the corresponding object classesdef get_feature_map(input_map, kmeans, centroids, scaling_size): feature_map = [] for item in input_map: temp_dict = {} temp_dict['object_class'] = item['object_class'] print("Extracting features for", item['image_path']) img = cv2.imread(item['image_path']) img = resize_image(img, scaling_size) temp_dict['feature_vector'] = BagOfWords().construct_feature(img, kmeans, centroids) if temp_dict['feature_vector'] is not None: feature_map.append(temp_dict) return feature_map# Extract SIFT featuresdef compute_sift_features(img, keypoints): if img is None: raise TypeError('Invalid input image') img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) keypoints, descriptors = cv2.xfeatures2d.SIFT_create().compute(img_gray, keypoints) return keypoints, descriptors# Resize the shorter dimension to 'new_size' # while maintaining the aspect ratiodef resize_image(input_img, new_size): h, w = input_img.shape[:2] scaling_factor = new_size / float(h) if w < h: scaling_factor = new_size / float(w) new_shape = (int(w * scaling_factor), int(h * scaling_factor)) return cv2.resize(input_img, new_shape)def build_features_main(): data_folder = 'training_images\\' scaling_size = 200 codebook_file='codebook.pkl' feature_map_file='feature_map.pkl' # Load the training data training_data = load_training_data(data_folder) # Build the visual codebook print("====== Building visual codebook ======") kmeans, centroids = FeatureBuilder().get_codewords(training_data, scaling_size) if codebook_file: with open(codebook_file, 'wb') as f: pickle.dump((kmeans, centroids), f) # Extract features from input images print("\n====== Building the feature map ======") feature_map = get_feature_map(training_data, kmeans, centroids, scaling_size) if feature_map_file: with open(feature_map_file, 'wb') as f: pickle.dump(feature_map, f)# --feature-map-file feature_map.pkl --model- file erf.pkl#----------------------------------------------------------------------------------------------------------class ERFTrainer(object): def __init__(self, X, label_words): self.le = preprocessing.LabelEncoder() self.clf = ExtraTreesClassifier(n_estimators=100, max_depth=16, random_state=0) y = self.encode_labels(label_words) self.clf.fit(np.asarray(X), y) def encode_labels(self, label_words): self.le.fit(label_words) return np.array(self.le.transform(label_words), dtype=np.float32) def classify(self, X): label_nums = self.clf.predict(np.asarray(X)) label_words = self.le.inverse_transform([int(x) for x in label_nums]) return label_words#------------------------------------------------------------------------------------------class ImageTagExtractor(object): def __init__(self, model_file, codebook_file): with open(model_file, 'rb') as f: self.erf = pickle.load(f) with open(codebook_file, 'rb') as f: self.kmeans, self.centroids = pickle.load(f) def predict(self, img, scaling_size): img = resize_image(img, scaling_size) feature_vector = BagOfWords().construct_feature( img, self.kmeans, self.centroids) image_tag = self.erf.classify(feature_vector)[0] return image_tagdef train_Recognizer_main(): feature_map_file = 'feature_map.pkl' model_file = 'erf.pkl' # Load the feature map with open(feature_map_file, 'rb') as f: feature_map = pickle.load(f) # Extract feature vectors and the labels label_words = [x['object_class'] for x in feature_map] dim_size = feature_map[0]['feature_vector'].shape[1] X = [np.reshape(x['feature_vector'], (dim_size,)) for x in feature_map] # Train the Extremely Random Forests classifier erf = ERFTrainer(X, label_words) if model_file: with open(model_file, 'wb') as f: pickle.dump(erf, f) #-------------------------------------------------------------------- # args = build_arg_parser().parse_args() model_file = 'erf.pkl' codebook_file ='codebook.pkl' import os rootdir=r"F:\airplanes" list=os.listdir(rootdir) for i in range(0,len(list)): path=os.path.join(rootdir,list[i]) if os.path.isfile(path): try: print(path) input_image = cv2.imread(path) scaling_size = 200 print("\nOutput:", ImageTagExtractor(model_file,codebook_file)\ .predict(input_image, scaling_size)) except: continue #-----------------------------------------------------------------------build_features_main()train_Recognizer_main() |
以上这篇Python构建图像分类识别器的方法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/weixin_42039090/article/details/80673711
