opencv_python圖像處理——HOG特徵描述算子-行人檢測

參考鏈接：
Datawhale 計算機視覺基礎-圖像處理（下）-Task04 HOG特徵描述算子-行人檢測

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt

if __name__ == '__main__':
    src = cv.imread("img/winner.jpg")

    hog = cv.HOGDescriptor()

    cv.imshow("input", src)
    hog.setSVMDetector(cv.HOGDescriptor_getDefaultPeopleDetector())
    # Detect people in the image
    (rects, weights) = hog.detectMultiScale(src,
                                            winStride=(2, 4),
                                            padding=(8, 8),
                                            scale=1.2,
                                            useMeanshiftGrouping=False)
    for (x, y, w, h) in rects:
        cv.rectangle(src, (x, y), (x + w, y + h), (0, 255, 0), 2)

    cv.imshow("hog-detector", src)
    cv.imwrite("hog-detector.jpg", src)
    cv.waitKey(0)
    cv.destroyAllWindows()

原圖：

結果圖：

可視化hog特徵檢測：
注意參考鏈接裏feature_hog()的visualse=True參數拼寫應該爲visualize。

from skimage import feature, exposure
from matplotlib import pyplot as plt
import cv2
image = cv2.imread('img/cartoons1.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

fd, hog_image = feature.hog(image, orientations=9, pixels_per_cell=(8, 8),
                    cells_per_block=(2, 4), visualize=True)

# Rescale histogram for better display
hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 10))

cv2.namedWindow("img",cv2.WINDOW_NORMAL)
cv2.imshow('img', image)
cv2.namedWindow("hog",cv2.WINDOW_NORMAL)
cv2.imshow('hog', hog_image_rescaled)
cv2.waitKey(0)==ord('q')

原圖：

結果圖：