AssertionError：クラスのフォーマットは '<label>ファイル'

Question

これは、SVMの画像のセット内の特徴を検出するためのPythonスクリプトです。

Traceback (most recent call last): 
    File "create_features.py", line 164, in <module> 
    assert len(cls) >= 2, ("Format for classes is `<label> file`") 
AssertionError: Format for classes is `<label> file` 

間違っている可能性が何の任意のアイデア：

import os 
import sys 
import argparse 
import _pickle as cPickle 
import json 

import cv2 
import numpy as np 
from sklearn.cluster import KMeans 

def build_arg_parser(): 
    parser = argparse.ArgumentParser(description='Creates features for given images') 
    parser.add_argument("--samples", dest="cls", nargs="+", action="append", 
      required=True, help="Folders containing the training images. \ 
      The first element needs to be the class label.") 
    parser.add_argument("--codebook-file", dest='codebook_file', required=True, 
      help="Base file name to store the codebook") 
    parser.add_argument("--feature-map-file", dest='feature_map_file', required=True, 
      help="Base file name to store the feature map") 
    parser.add_argument("--scale-image", dest="scale", type=int, default=150, 
      help="Scales the longer dimension of the image down to this size.") 

    return parser 

def load_input_map(label, input_folder): 
    combined_data = [] 

    if not os.path.isdir(input_folder): 
     print ("The folder " + input_folder + " doesn't exist") 
     raise IOError 

    for root, dirs, files in os.walk(input_folder): 
     for filename in (x for x in files if x.endswith('.jpg')): 
      combined_data.append({'label': label, 'image': os.path.join(root, filename)}) 

    return combined_data 

class FeatureExtractor(object): 
    def extract_image_features(self, img): 
     kps = DenseDetector().detect(img) 
     kps, fvs = SIFTExtractor().compute(img, kps) 
     return fvs 

    def get_centroids(self, input_map, num_samples_to_fit=10): 
     kps_all = [] 

     count = 0 
     cur_label = '' 
     for item in input_map: 
      if count >= num_samples_to_fit: 
       if cur_label != item['label']: 
        count = 0 
       else: 
        continue 

      count += 1 

      if count == num_samples_to_fit: 
       print ("Built centroids for", item['label']) 

      cur_label = item['label'] 
      img = cv2.imread(item['image']) 
      img = resize_to_size(img, 150) 

      num_dims = 128 
      fvs = self.extract_image_features(img) 
      kps_all.extend(fvs) 

     kmeans, centroids = Quantizer().quantize(kps_all) 
     return kmeans, centroids 

    def get_feature_vector(self, img, kmeans, centroids): 
     return Quantizer().get_feature_vector(img, kmeans, centroids) 

def extract_feature_map(input_map, kmeans, centroids): 
    feature_map = [] 

    for item in input_map: 
     temp_dict = {} 
     temp_dict['label'] = item['label'] 

     print ("Extracting features for", item['image']) 
     img = cv2.imread(item['image']) 
     img = resize_to_size(img, 150) 

     temp_dict['feature_vector'] = FeatureExtractor().get_feature_vector(
        img, kmeans, centroids) 

     if temp_dict['feature_vector'] is not None: 
      feature_map.append(temp_dict) 

    return feature_map 

class Quantizer(object): 
    def __init__(self, num_clusters=32): 
     self.num_dims = 128 
     self.extractor = SIFTExtractor() 
     self.num_clusters = num_clusters 
     self.num_retries = 10 

    def quantize(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 get_feature_vector(self, img, kmeans, centroids): 
     kps = DenseDetector().detect(img) 
     kps, fvs = self.extractor.compute(img, kps) 
     labels = kmeans.predict(fvs) 
     fv = np.zeros(self.num_clusters) 

     for i, item in enumerate(fvs): 
      fv[labels[i]] += 1 

     fv_image = np.reshape(fv, ((1, fv.shape[0]))) 
     return self.normalize(fv_image) 

class DenseDetector(object): 
    def __init__(self, step_size=20, feature_scale=40, img_bound=20): 
     self.detector = cv2.xfeatures2d.SIFT_create("Dense") 
     self.detector.setInt("initXyStep", step_size) 
     self.detector.setInt("initFeatureScale", feature_scale) 
     self.detector.setInt("initImgBound", img_bound) 

    def detect(self, img): 
     return self.detector.detect(img) 

class SIFTExtractor(object): 
    def compute(self, image, kps): 
     if image is None: 
      print ("Not a valid image") 
      raise TypeError 

     gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
     kps, des = cv2.SIFT().compute(gray_image, kps) 
     return kps, des 

# Resize the shorter dimension to 'new_size' 
# while maintaining the aspect ratio 
def resize_to_size(input_image, new_size=150): 
    h, w = input_image.shape[0], input_image.shape[1] 
    ds_factor = new_size/float(h) 
    if w < h: 
     ds_factor = new_size/float(w) 
    new_size = (int(w * ds_factor), int(h * ds_factor)) 
    return cv2.resize(input_image, new_size) 

if __name__=='__main__': 
    args = build_arg_parser().parse_args() 

    input_map = [] 
    for cls in args.cls: 
     assert len(cls) >= 2, "Format for classes is `<label> file`" 
     label = cls[0] 
     input_map += load_input_map(label, cls[1]) 

    downsample_length = args.scale 

    # Building the codebook 
    print ("===== Building codebook =====") 
    kmeans, centroids = FeatureExtractor().get_centroids(input_map) 
    if args.codebook_file: 
     with open(args.codebook_file, 'w') as f: 
      pickle.dump((kmeans, centroids), f) 

    # Input data and labels 
    print ("===== Building feature map =====") 
    feature_map = extract_feature_map(input_map, kmeans, centroids) 
    if args.feature_map_file: 
     with open(args.feature_map_file, 'w') as f: 
      pickle.dump(feature_map, f) 

は、私は次のエラーを受け取りますか？私はPrateek Joshiの「OpenCV with Example by Example」の指示​​にちょうど従っています。ページ494-526

Answer 1

条件を確認するためにアサーションが使用されます。条件が満たされない場合、AssertionErrorを嫌う。あなたの場合、len(cls) >= 2が満たされていません。これはlen（cls）が2より小さいことを意味します。clsは明らかにprogrammに渡される引数のリストです。そしてこのリストの最初の要素はラベルでなければなりません。引数（ファイル）を追加するときは、このファイルのラベルを指定する必要があります。

たとえば、ラベル名my_labelを選択する場合は、ファイルをmy_label my_fileで追加する必要があります。