質問編集履歴
3
情報の詳細化
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print("expected: ", className[classId], ", actual: ", actual, result)
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print("suceess percentage:", success/(success+fail))
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```
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```
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取り込んだ画像をpatchにしたものから特徴点(keypoint)を抽出する段階で、特徴点をpatchの中心に指定したいのですが，そこがうまくいってません。
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2
情報の詳細化
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# 特徴点とその特徴を計算
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while x<9604:
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#keypoints, descriptors= detector.detectAndCompute(patches, None)
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keypoints = cv2.KeyPoint(patches[x][1][1]
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keypoints = cv2.KeyPoint(patches[x][1][0],patches[x][0][1],size=9, angele=-1, response=0, octave=0, class_id=-1)
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#keypoints = [cv2.KeyPoint(patches[x][1][1], 1) for x in range(9604)]
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descriptors = detector.compute(patches[x], keypoints)
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#print(patches[x].dtype, keypoints)
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x=x+1
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1
情報の詳細化
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keypoints, descriptors= detector.detectAndCompute(patches[x], None)
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```
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patch[x].dtypeはuint8なのですが、descriptorsがNonetypeになってしまいます。
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どのように修正すればよいのか教えていただければ幸いです。
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どのように修正すればよいのか教えていただければ幸いです。
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```ここに言語を入力
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# -*- coding: utf-8 -*-
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import os
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import sys
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import cv2
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import numpy as np
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from sklearn.feature_extraction import image
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## 画像データのクラスIDとパスを取得
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#
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# @param dir_path 検索ディレクトリ
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# @return data_sets [クラスID, 画像データのパス]のリスト
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def getDataSet(dir_path):
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data_sets = []
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sub_dirs = os.listdir(dir_path)
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for classId in sub_dirs:
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sub_dir_path = dir_path + '/' + classId
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img_files = os.listdir(sub_dir_path)
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for f in img_files:
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data_sets.append([classId, sub_dir_path + '/' + f])
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return data_sets
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"""
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main
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"""
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# 定数定義
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GRAYSCALE = 0
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# KAZE特徴量抽出器
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detector = cv2.xfeatures2d.SIFT_create()
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"""
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train
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"""
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print("train start")
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# 訓練データのパスを取得
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train_set = getDataSet('train_img')
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# 辞書サイズ
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dictionarySize = 9
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# Bag Of Visual Words分類器
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bowTrainer = cv2.BOWKMeansTrainer(dictionarySize)
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x=0
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# 各画像を分析
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for i, (classId, data_path) in enumerate(train_set):
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# 進捗表示
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sys.stdout.write(".")
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# カラーで画像読み込み
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color = cv2.imread(data_path, cv2.IMREAD_COLOR)
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size = (100,100)
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colora = cv2.resize(color,size)
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patches = image.extract_patches_2d(colora, (3, 3))
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patches = patches.astype(np.uint8)
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#print(color.shape, patches.shape, patches.dtype)
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# 特徴点とその特徴を計算
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while x<9604:
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#keypoints, descriptors= detector.detectAndCompute(patches, None)
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keypoints = cv2.KeyPoint(patches[x][1][1].pt,size=9, angele=-1, response=0, octave=0, class_id=-1)
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#keypoints = [cv2.KeyPoint(patches[x][1][1], 1) for x in range(9604)]
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descriptors = detector.compute(patches[x], keypoints)
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#print(patches[x].dtype, keypoints)
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x=x+1
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#descriptors = detector.compute(patches, keypoints)
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# intからfloat32に変換
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descriptors = descriptors.astype(np.float32)
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# 特徴ベクトルをBag Of Visual Words分類器にセット
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bowTrainer.add(descriptors)
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84
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85
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# Bag Of Visual Words分類器で特徴ベクトルを分類
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86
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codebook = bowTrainer.cluster()
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# 訓練完了
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print("train finish")
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90
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"""
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test
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"""
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print("test start")
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# テストデータのパス取得
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test_set = getDataSet("test_img")
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96
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97
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# KNNを使って総当たりでマッチング
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matcher = cv2.BFMatcher()
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99
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# Bag Of Visual Words抽出器
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bowExtractor = cv2.BOWImgDescriptorExtractor(detector, matcher)
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# トレーニング結果をセット
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bowExtractor.setVocabulary(codebook)
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success = 0
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fail = 0
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107
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+
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108
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# 正しく学習できたか検証する
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for i, (classId, data_path) in enumerate(test_set):
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# グレースケールで読み込み
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gray = cv2.imread(data_path, cv2.IMREAD_COLOR)
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# 特徴点と特徴ベクトルを計算
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print(gray.dtype)
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size = (100,100)
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115
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graya = cv2.resize(gray,size)
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patches = image.extract_patches_2d(graya, (3, 3))
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print(patches.shape)
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while x<9604:
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keypoints, descriptors= detector.detectAndCompute(patches[x], None)
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120
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# intからfloat32に変換 特徴量
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descriptors = descriptors.astype(np.float32)
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# Bag Of Visual Wordsの計算 ヒストグラム
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bowDescriptors = bowExtractor.compute(patches[x], keypoints)
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125
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# 結果表示
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126
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className = {"0": "airplane",
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127
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"1": "ferry",
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128
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"2": "laptop"}
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129
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actual = "???"
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if bowDescriptors[0][0] > bowDescriptors[0][1] and bowDescriptors[0][0] > bowDescriptors[0][2]:
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actual = className["0"]
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elif bowDescriptors[0][0] < bowDescriptors[0][1] and bowDescriptors[0][2] < bowDescriptors[0][1]:
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actual = className["1"]
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else:
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actual = className["2"]
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137
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+
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138
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139
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result = ""
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140
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if actual == "???":
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141
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result = " => unknown."
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142
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elif className[classId] == actual:
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result = " => success!!"
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144
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success = success + 1
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+
else:
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146
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result = " => fail"
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147
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fail = fail + 1
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148
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+
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149
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print("expected: ", className[classId], ", actual: ", actual, result)
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150
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+
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151
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print("suceess percentage:", success/(success+fail))
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152
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```
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