梯度直方图(HOG)用于图像多分类和图像推荐

 import os
 import numpy as np
 import pandas as pd
 import cv2 as cv
 from pathlib import Path
 import warnings
 from skimage.feature import hog
 import tqdm
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn import metrics
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import MinMaxScaler
 from sklearn.neighbors import NearestNeighborswarnings.filterwarnings("ignore")
 pd.options.display.max_columns = None

 all_images = []
 #labels = []def load_image(ids,path=image_folder):
     img = cv.imread(image_folder+ids+'.jpg',cv.IMREAD_GRAYSCALE) #load at gray scale
     #img = cv.cvtColor(img, cv.COLOR_BGR2GRAY) #convert to gray scale
     return img,ids#20k samples were taken for modeling
 for ids in tqdm(list(styles.id)[:20000]):
     img,ids = load_image(str(ids))
     if img is not None:
         all_images.append([img,int(ids)])
     #labels.append(ids)
 len(all_images)

 def resize_image(img,ids):
     return cv.resize(img, (60, 80),interpolation =cv.INTER_LINEAR)
     
 all_images_resized = [[resize_image(x,y),y] for x,y in all_images]
 len(all_images_resized)

 ##HOG Descriptor#Returns a 1D vector for an image
 ppcr = 8
 ppcc = 8
 hog_images = []
 hog_features = []
 for image in tqdm(train_images):
  blur = cv.GaussianBlur(image,(5,5),0) #Gaussian Filtering
  fd,hog_image = hog(blur, orientations=8, pixels_per_cell=(ppcr,ppcc),cells_per_block=(2,2),block_norm= ‘L2’,visualize=True)
  hog_images.append(hog_image)
  hog_features.append(fd)
 hog_features = np.array(hog_features)
 hog_features.shape

 #normalization by 'L2-Hys'
 out = block / np.sqrt(np.sum(block ** 2) + eps ** 2)        
 out = np.minimum(out, 0.2)        
 out = out / np.sqrt(np.sum(out ** 2) + eps ** 2)

 X_train, X_test, y_train, y_test = train_test_split(hog_features,df_labels['class'],test_size=0.2,stratify=df_labels['class'])print('Training data and target sizes: \n{}, {}'.format(X_train.shape,y_train.shape))print('Test data and target sizes: \n{}, {}'.format(X_test.shape,y_test.shape))
 =============================================                   Training data and target sizes: 
 (15998, 1728), (15998,)
 Test data and target sizes: 
 (4000, 1728), (4000,)

 test_accuracy = []
 scaler = StandardScaler()
 X_scaled = scaler.fit_transform(X_train)classifier = KNeighborsClassifier(n_neighbors=3,algorithm='brute')
 classifier.fit(X_scaled, y_train)
 test_accuracy = classifier.score(scaler.transform(X_test), y_test)
 print(test_accuracy)

 list_of_categories = categories +['Others']print("Classification Report: \n Target: %s \n Labels: %s \n Classifier: %s:\n%s\n"
       % (target,list_of_categories,classifier, metrics.classification_report(y_test, y_pred)))df_report = pd.DataFrame(metrics.confusion_matrix(y_test, y_pred),columns = list_of_categories )
 df_report.index = [list_of_categories]
 df_report

 #test image with id
 test_data_location = root+'/test/'img = cv.imread(test_data_location+'1570.jpg',cv.IMREAD_GRAYSCALE) #load at gray scale
 image = cv.resize(img, (60, 80),interpolation =cv.INTER_LINEAR)ppcr = 8
 ppcc = 8
 hog_images_test = []
 hog_features_test = []blur = cv.GaussianBlur(image,(5,5),0)
 fd_test,hog_img = hog(blur, orientations=8, pixels_per_cell=(ppcr,ppcc),cells_per_block=(2,2),block_norm= 'L2',visualize=True)
 hog_images_test.append(hog_img)
 hog_features_test.append(fd)hog_features_test = np.array(hog_features_test)
 y_pred_user = classifier.predict(scaler.transform(hog_features_test))
 #print(plt.imshow(hog_images_test))
 print(y_pred_user)
 print("Predicted MaterCategory: ", mapper[mapper['class']==int(y_pred_user)]['masterCategory'])

 scaler_global = MinMaxScaler()
 final_features_scaled = scaler_global.fit_transform(hog_features)
     
 neighbors = NearestNeighbors(n_neighbors=20, algorithm='brute')
 neighbors.fit(final_features_scaled)distance,potential = neighbors.kneighbors(scaler_global.transform(hog_features_test))
 print("Potential Neighbors Found!")
 neighbors = []
 for i in potential[0]:
     neighbors.append(i)recommendation_list = list(df_labels.iloc[neighbors]['id'])
 recommendation_list