機器學習用在圖像識別是非常有趣的話題。

我們可以利用OpenCV強大的功能結合機器學習算法實現圖像識別系統。

首先，輸入若干圖像，加入分類標記。利用向量量化方法將特征點進行聚類，并得出中心點，這些中心點就是視覺碼本的元素。

其次，利用圖像分類器將圖像分到已知的類別中，ERF（極端隨機森林）算法非常流行，因為ERF具有較快的速度和比較精確的準確度。我們利用決策樹進行正確決策。

最后，利用訓練好的ERF模型后，創建目標識別器，可以識別未知圖像的內容。

當然，這只是雛形，存在很多問題：

界面不友好。

準確率如何保證，如何調整超參數，只有認真研究算法機理，才能真正清除內部實現機制后給予改進。

下面，上代碼：

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()