vehicle-speed-check-master.zip下载

一个监测车辆跟踪的例子 import cv2 import numpy as np import math import matplotlib.pyplot as plt class Hog_descriptor(): def __init__(self, img, cell_size=16, bin_size=8): self.img = img self.img = np.sqrt(img / float(np.max(img))) self.img = self.img * 255 self.cell_size = cell_size self.bin_size = bin_size self.angle_unit = 360 / self.bin_size assert type(self.bin_size) == int, "bin_size should be integer," assert type(self.cell_size) == int, "cell_size should be integer," assert type(self.angle_unit) == int, "bin_size should be divisible by 360" def extract(self): height, width = self.img.shape gradient_magnitude, gradient_angle = self.global_gradient() gradient_magnitude = abs(gradient_magnitude) cell_gradient_vector = np.zeros((height / self.cell_size, width / self.cell_size, self.bin_size)) for i in range(cell_gradient_vector.shape[0]): for j in range(cell_gradient_vector.shape[1]): cell_magnitude = gradient_magnitude[i * self.cell_size:(i + 1) * self.cell_size, j * self.cell_size:(j + 1) * self.cell_size] cell_angle = gradient_angle[i * self.cell_size:(i + 1) * self.cell_size, j * self.cell_size:(j + 1) * self.cell_size] cell_gradient_vector[i][j] = self.cell_gradient(cell_magnitude, cell_angle) hog_image = self.render_gradient(np.zeros([height, width]), cell_gradient_vector) hog_vector = [] for i in range(cell_gradient_vector.shape[0] - 1): for j in range(cell_gradient_vector.shape[1] - 1): block_vector = [] block_vector.extend(cell_gradient_vector[i][j]) block_vector.extend(cell_gradient_vector[i][j + 1]) block_vector.extend(cell_gradient_vector[i + 1][j]) block_vector.extend(cell_gradient_vector[i + 1][j + 1]) mag = lambda vector: math.sqrt(sum(i ** 2 for i in vector)) magnitude = mag(block_vector) if magnitude != 0: normalize = lambda block_vector, magnitude: [element / magnitude for element in block_vector] block_vector = normalize(block_vector, magnitude) hog_vector.append(block_vector) return hog_vector, hog_image def global_gradient(self): gradient_values_x = cv2.Sobel(self.img, cv2.CV_64F, 1, 0, ksize=5) gradient_values_y = cv2.Sobel(self.img, cv2.CV_64F, 0, 1, ksize=5) gradient_magnitude = cv2.addWeighted(gradient_values_x, 0.5, gradient_values_y, 0.5, 0) gradient_angle = cv2.phase(gradient_values_x, gradient_values_y, angleInDegrees=True) return gradient_magnitude, gradient_angle def cell_gradient(self, cell_magnitude, cell_angle): orientation_centers = [0] * self.bin_size for i in range(cell_magnitude.shape[0]): for j in range(cell_magnitude.shape[1]): gradient_strength = cell_magnitude[i][j] gradient_angle = cell_angle[i][j] min_angle, max_angle, mod = self.get_closest_bins(gradient_angle) orientation_centers[min_angle] += (gradient_strength * (1 - (mod / self.angle_unit))) orientation_centers[max_angle] += (gradient_strength * (mod / self.angle_unit)) return orientation_centers def get_closest_bins(self, gradient_angle): idx = int(gradient_angle / self.angle_unit) mod = gradient_angle % self.angle_unit if idx == self.bin_size: return idx - 1, (idx) % self.bin_size, mod return idx, (idx + 1) % self.bin_size, mod def render_gradient(self, image, cell_gradient): cell_width = self.cell_size / 2 max_mag = np.array(cell_gradient).max() for x in range(cell_gradient.shape[0]): for y in range(cell_gradient.shape[1]): cell_grad = cell_gradient[x][y] cell_grad /= max_mag angle = 0 angle_gap = self.angle_unit for magnitude in cell_grad: angle_radian = math.radians(angle) x1 = int(x * self.cell_size + magnitude * cell_width * math.cos(angle_radian)) y1 = int(y * self.cell_size + magnitude * cell_width * math.sin(angle_radian)) x2 = int(x * self.cell_size - magnitude * cell_width * math.cos(angle_radian)) y2 = int(y * self.cell_size - magnitude * cell_width * math.sin(angle_radian)) cv2.line(image, (y1, x1), (y2, x2), int(255 * math.sqrt(magnitude))) angle += angle_gap return image img = cv2.imread('G://1.bmp', cv2.IMREAD_GRAYSCALE) hog = Hog_descriptor(img, cell_size=8, bin_size=8) vector, image = hog.extract() plt.imshow(image, cmap=plt.cm.gray) plt.show()