Python构建图像分类识别器的方法

机器学习用在图像识别是非常有趣的话题图像识别 方法。

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。

再进一步：有许多不同的核可以产生多种feature map，比如锐化图像（强调细节），或者模糊图像（减少细节），并且每个feature map都可能帮助算法做出决策（一些细节，比如衣服上有3个纽扣而不是两个，可能可以区分一些服饰）。采用最高支持1080p60的238万像素cmos传感器，基于独特的人眼模型图像增强技术和基于运动估计的3d降噪算法，降低图像噪点，精确还原图像色彩，使画面层次感强、细节丰富，为视频会议提供、真实的全高清图像效果。 图像配准算法粗略分类有直接利用像素点数据，利用频域参数，利用图像 低层次特征和利用图像高层次特征的算法等。

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图像识别 方法。

下面，上代码：

import os
import sys
import argparse
import json
import cv2
import numpy as np
from sklearn.cluster import KMeans
# from star_detector import StarFeatureDetector
from sklearn.ensemble import ExtraTreesClassifier
from sklearn import preprocessing
try:
 import cPickle as pickle #python 2
except ImportError as e:
 import pickle #python 3
def 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_data
class 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, centroids
class 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 classes
def 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 features
def 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 ratio
def 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_tag
def 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()

最近25年是从图像视角通过提取丰富的图像特征描述物体的外观来做识别、分类: appearance-based and view-centered。6 ）图像分类（识别）：图像分类（识别）属于模式识别的范畴，其主要内容是图像经过某些预处理（增强、复原、压缩）后，进行图像分割和特征提取，从而进行判决分类。图1展示了通用图像分类效果，即模型可以正确识别图像上的主要物体。

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