Opencv 可视化特征量|极客教程

方向梯度直方图（HOG）第四步：可视化特征量

在这里我们将得到的特征量可视化。

如果将特征量叠加在灰度化后的imori.jpg上，可以很容易看到（蝾螈的）外形。

一个好的可视化的方法是这样的，为 cell 内的每个 index 的方向画一条线段，并且值越大，线段越白，值越小，线段越黑。

python实现：

import cv2
import numpy as np
import matplotlib.pyplot as plt

# get HOG
def HOG(img):
    # Grayscale
    def BGR2GRAY(img):
        gray = 0.2126 * img[..., 2] + 0.7152 * img[..., 1] + 0.0722 * img[..., 0]
        return gray

    # Magnitude and gradient
    def get_gradXY(gray):
        H, W = gray.shape

        # padding before grad
        gray = np.pad(gray, (1, 1), 'edge')

        # get grad x
        gx = gray[1:H+1, 2:] - gray[1:H+1, :W]
        # get grad y
        gy = gray[2:, 1:W+1] - gray[:H, 1:W+1]
        # replace 0 with 
        gx[gx == 0] = 1e-6

        return gx, gy

    # get magnitude and gradient
    def get_MagGrad(gx, gy):
        # get gradient maginitude
        magnitude = np.sqrt(gx ** 2 + gy ** 2)

        # get gradient angle
        gradient = np.arctan(gy / gx)

        gradient[gradient < 0] = np.pi / 2 + gradient[gradient < 0] + np.pi / 2

        return magnitude, gradient

    # Gradient histogram
    def quantization(gradient):
        # prepare quantization table
        gradient_quantized = np.zeros_like(gradient, dtype=np.int)

        # quantization base
        d = np.pi / 9

        # quantization
        for i in range(9):
            gradient_quantized[np.where((gradient >= d * i) & (gradient <= d * (i + 1)))] = i

        return gradient_quantized


    # get gradient histogram
    def gradient_histogram(gradient_quantized, magnitude, N=8):
        # get shape
        H, W = magnitude.shape

        # get cell num
        cell_N_H = H // N
        cell_N_W = W // N
        histogram = np.zeros((cell_N_H, cell_N_W, 9), dtype=np.float32)

        # each pixel
        for y in range(cell_N_H):
            for x in range(cell_N_W):
                for j in range(N):
                    for i in range(N):
                        histogram[y, x, gradient_quantized[y * 4 + j, x * 4 + i]] += magnitude[y * 4 + j, x * 4 + i]

        return histogram

        # histogram normalization
    def normalization(histogram, C=3, epsilon=1):
        cell_N_H, cell_N_W, _ = histogram.shape
        ## each histogram
        for y in range(cell_N_H):
            for x in range(cell_N_W):
            #for i in range(9):
                histogram[y, x] /= np.sqrt(np.sum(histogram[max(y - 1, 0) : min(y + 2, cell_N_H),
                                                            max(x - 1, 0) : min(x + 2, cell_N_W)] ** 2) + epsilon)

        return histogram

    # 1. BGR -> Gray
    gray = BGR2GRAY(img)

    # 1. Gray -> Gradient x and y
    gx, gy = get_gradXY(gray)

    # 2. get gradient magnitude and angle
    magnitude, gradient = get_MagGrad(gx, gy)

    # 3. Quantization
    gradient_quantized = quantization(gradient)

    # 4. Gradient histogram
    histogram = gradient_histogram(gradient_quantized, magnitude)

    # 5. Histogram normalization
    histogram = normalization(histogram)

    return histogram


# draw HOG
def draw_HOG(img, histogram):
    # Grayscale
    def BGR2GRAY(img):
        gray = 0.2126 * img[..., 2] + 0.7152 * img[..., 1] + 0.0722 * img[..., 0]
        return gray

    def draw(gray, histogram, N=8):
        # get shape
        H, W = gray.shape
        cell_N_H, cell_N_W, _ = histogram.shape

        ## Draw
        out = gray[1 : H + 1, 1 : W + 1].copy().astype(np.uint8)

        for y in range(cell_N_H):
            for x in range(cell_N_W):
                cx = x * N + N // 2
                cy = y * N + N // 2
                x1 = cx + N // 2 - 1
                y1 = cy
                x2 = cx - N // 2 + 1
                y2 = cy

                h = histogram[y, x] / np.sum(histogram[y, x])
                h /= h.max()

                for c in range(9):
                    #angle = (20 * c + 10 - 90) / 180. * np.pi
                    # get angle
                    angle = (20 * c + 10) / 180. * np.pi
                    rx = int(np.sin(angle) * (x1 - cx) + np.cos(angle) * (y1 - cy) + cx)
                    ry = int(np.cos(angle) * (x1 - cx) - np.cos(angle) * (y1 - cy) + cy)
                    lx = int(np.sin(angle) * (x2 - cx) + np.cos(angle) * (y2 - cy) + cx)
                    ly = int(np.cos(angle) * (x2 - cx) - np.cos(angle) * (y2 - cy) + cy)

                    # color is HOG value
                    c = int(255. * h[c])

                    # draw line
                    cv2.line(out, (lx, ly), (rx, ry), (c, c, c), thickness=1)

        return out


    # get gray
    gray = BGR2GRAY(img)

    # draw HOG
    out = draw(gray, histogram)

    return out

# Read image
img = cv2.imread("imori.jpg").astype(np.float32)

# get HOG
histogram = HOG(img)

# draw HOG
out = draw_HOG(img, histogram)


# Save result
cv2.imwrite("out.jpg", out)
cv2.imshow("result", out)
cv2.waitKey(0)
cv2.destroyAllWindows()

输入(imori.jpg)：

Opencv 可视化特征量