answer_97.py

import cv2
import numpy as np

np.random.seed(0)

# 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


# get IoU overlap ratio
def iou(a, b):
	# get area of a
    area_a = (a[2] - a[0]) * (a[3] - a[1])
	# get area of b
    area_b = (b[2] - b[0]) * (b[3] - b[1])

	# get left top x of IoU
    iou_x1 = np.maximum(a[0], b[0])
	# get left top y of IoU
    iou_y1 = np.maximum(a[1], b[1])
	# get right bottom of IoU
    iou_x2 = np.minimum(a[2], b[2])
	# get right bottom of IoU
    iou_y2 = np.minimum(a[3], b[3])

	# get width of IoU
    iou_w = iou_x2 - iou_x1
	# get height of IoU
    iou_h = iou_y2 - iou_y1

	# get area of IoU
    area_iou = iou_w * iou_h
	# get overlap ratio between IoU and all area
    iou = area_iou / (area_a + area_b - area_iou)

    return iou

# resize using bi-linear
def resize(img, h, w):
    # get shape
    _h, _w, _c  = img.shape

    # get resize ratio
    ah = 1. * h / _h
    aw = 1. * w / _w

    # get index of each y
    y = np.arange(h).repeat(w).reshape(w, -1)
    # get index of each x
    x = np.tile(np.arange(w), (h, 1))

    # get coordinate toward x and y of resized image
    y = (y / ah)
    x = (x / aw)

    # transfer to int
    ix = np.floor(x).astype(np.int32)
    iy = np.floor(y).astype(np.int32)

    # clip index
    ix = np.minimum(ix, _w-2)
    iy = np.minimum(iy, _h-2)

    # get distance between original image index and resized image index
    dx = x - ix
    dy = y - iy

    dx = np.tile(dx, [_c, 1, 1]).transpose(1, 2, 0)
    dy = np.tile(dy, [_c, 1, 1]).transpose(1, 2, 0)
    
    # resize
    out = (1 - dx) * (1 - dy) * img[iy, ix] + dx * (1 - dy) * img[iy, ix + 1] + (1 - dx) * dy * img[iy + 1, ix] + dx * dy * img[iy + 1, ix + 1]
    out[out > 255] = 255

    return out

# sliding window
def sliding_window(img, H_size=32):
    # get shape
    H, W, _ = img.shape
    
    # base rectangle [h, w]
    recs = np.array(((42, 42), (56, 56), (70, 70)), dtype=np.float32)

    # sliding window
    for y in range(0, H, 4):
        for x in range(0, W, 4):
            for rec in recs:
                # get half size of ractangle
                dh = int(rec[0] // 2)
                dw = int(rec[1] // 2)

                # get left top x
                x1 = max(x - dw, 0)
                # get left top y
                x2 = min(x + dw, W)
                # get right bottom x
                y1 = max(y - dh, 0)
                # get right bottom y
                y2 = min(y + dh, H)

                # crop region
                region = img[max(y - dh, 0) : min(y + dh, H), max(x - dw, 0) : min(x + dw, W)]

                # resize crop region
                region = resize(region, H_size, H_size)

                # get HOG feature
                region_hog = HOG(region).ravel()



# read detect target image
img = cv2.imread("imori_many.jpg")

sliding_window(img)