need HOG + PCA feature

[fatalfeel]

import glob import numpy as np import time import random import PIL import cv2 from skimage.feature import hog from sklearn import svm from sklearn.decomposition import PCA from sklearn.model_selection import train_test_split

train pos use 96X160H96

if name == 'main': car_paths = glob.glob("./dataset/car_images" + "/*")[:5000] neg_paths = [] pos_images = [] neg_images = []

for class_path in glob.glob("./dataset/natural_images" + "/*"):
    # if class_path != "./dataset/natural_images/car":
    paths = random.choices(glob.glob(class_path + "/*"), k=700)
    neg_paths = paths + neg_paths

for car_path in car_paths:
    img = np.asarray(PIL.Image.open(car_path))
    # We don't have to use RGB channels to extract features, Grayscale is enough.
    img = cv2.cvtColor(cv2.resize(img, (96, 64)), cv2.COLOR_RGB2GRAY)
    img = hog(img,
              orientations=9,
              pixels_per_cell=(16, 16),
              cells_per_block=(2, 2))

    pos_images.append(img)

for neg_path in neg_paths:
    img = np.asarray(PIL.Image.open(neg_path))
    img = cv2.cvtColor(cv2.resize(img, (96, 64)), cv2.COLOR_RGB2GRAY)
    img = hog(img,
              orientations=9,
              pixels_per_cell=(16, 16),
              cells_per_block=(2, 2))

    neg_images.append(img)

pos_labels  = np.ones(len(car_paths))
neg_labels  = np.zeros(len(neg_paths))
x           = np.asarray(pos_images + neg_images)
y           = np.asarray(list(pos_labels) + list(neg_labels))

#########pca enable or disable this section######## pca = PCA(n_components=40) pca.fit(x) x = np.array(pca.fit_transform(x)) #########pca end##########

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)

svc     = svm.SVC()
start   = time.time()
clf     = svc.fit(x_train, y_train)
print(clf.score(x_test, y_test))

processTime = round(time.time() - start, 2)
print("svm has taken {} seconds".format(processTime))

########################test one picture##################### test_list = [] example_image = np.asarray(PIL.Image.open(car_paths[0])) example_image = cv2.cvtColor(cv2.resize(example_image, (96, 64)), cv2.COLOR_RGB2GRAY) example_image = hog(example_image, orientations=9, pixels_per_cell=(16, 16), cells_per_block=(2, 2)) test_list.append(example_image) x = np.asarray(test_list) x = np.array(pca.transform(x)) decision = svc.predict(x) print(decision)

########################refer to py source and do dlib in c++#################### i found an example here https://books.google.com.tw/books?id=ozDnDwAAQBAJ&pg=PA187&lpg=PA187&dq=dlib::vector_normalizer_pca+train&source=bl&ots=4jnTiSl1Mj&sig=ACfU3U23Tm922fkpJNu49oBpjzgZvr6R7w&hl=zh-TW&sa=X&ved=2ahUKEwjUy7G9pd72AhWgsFYBHeNCAMIQ6AF6BAgSEAM#v=onepage&q=dlib%3A%3Avector_normalizer_pca%20train&f=false

fhog_ex.cpp extract_fhog_features(img, hog, sbin);

svm_c_ex.cpp svm_c_trainer trainer;

pca example: https://github.com/PacktPublishing/Hands-On-Machine-Learning-with-CPP/blob/master/Chapter06/dlib/dlib-dr.cc

how to combine hog + pca + svm to detector in dlib c++?

[fatalfeel]

here i make some test for pca~~~ when add pca speed up 2.5~3 times https://github.com/fatalfeel/hog_pca_svm_detection

[fatalfeel]

I do this version for cpp detect 21 fps in old cpu 2012 i7-3770k https://github.com/fatalfeel/hog_pca_svm_slider_nms_cpp

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