建网站需要什么手续,网店推广有哪些新思维,西安网评,删除百度收录网站目录直方图处理直方图处理
令rk,k0,1,2,…,L−1r_k, k0, 1, 2, \dots, L-1rk,k0,1,2,…,L−1表于一幅LLL级灰度数字图像f(x,y)f(x,y)f(x,y)的灰度。fff的非归一化直方图定义为#xff1a; h(rk)nk,k0,1,2,…,L−1(3.6)h(r_{k}) n_{k}, \quad k 0, 1, 2, \dots, L-1 \tag{…
目录直方图处理直方图处理
令rk,k0,1,2,…,L−1r_k, k0, 1, 2, \dots, L-1rk,k0,1,2,…,L−1表于一幅LLL级灰度数字图像f(x,y)f(x,y)f(x,y)的灰度。fff的非归一化直方图定义为
h(rk)nk,k0,1,2,…,L−1(3.6)h(r_{k}) n_{k}, \quad k 0, 1, 2, \dots, L-1 \tag{3.6}h(rk)nk,k0,1,2,…,L−1(3.6)
nk是fn_{k}是fnk是f中灰度为rkr_{k}rk的像素的数量并且细分的灰度级称为直方图容器。
归一化直方图
p(rk)h(rk)MNnkMN(3.7)p(r_{k}) \frac{h(r_{k})}{MN} \frac{n_{k}}{MN} \tag{3.7} p(rk)MNh(rk)MNnk(3.7)
def my_hist(img, bins256, normalizedFalse):create a hist of uint8 image value range[0, 255]param: input img: grayscale image range[0, 255]param: input bins: bins for the image, range[0, 255]return hist and bins for the image, hist - counts for all the values# initializ a list for values and countslist1 list([x, y] for x in np.arange(bins) for y in np.arange(bins) if y 0)data img.flatten()for i in range(img.size):list1[data[i]][1] 1 dst np.array(list1)bins, hist dst[:, 0], dst[:, 1]if normalized:hist hist / img.size #nomalized histreturn hist, bins# 直方图
img_1st cv2.imread(DIP_Figures/DIP3E_Original_Images_CH03/Fig0316(1)(top_left).tif, 0)
img_2nd cv2.imread(DIP_Figures/DIP3E_Original_Images_CH03/Fig0316(2)(2nd_from_top).tif, 0)
img_3rd cv2.imread(DIP_Figures/DIP3E_Original_Images_CH03/Fig0316(3)(third_from_top).tif, 0)
img_4th cv2.imread(DIP_Figures/DIP3E_Original_Images_CH03/Fig0316(4)(bottom_left).tif, 0)img_list [img_1st, img_2nd, img_3rd, img_4th]fig plt.figure(figsize(20, 15))
for i in range(len(img_list)):ax fig.add_subplot(3, 4, i1)ax.imshow(eval(img_list[i]), cmapgray, vmin0, vmax255)ax1 fig.add_subplot(3, 4, i5)hist, bins my_hist(eval(img_list[i]), bins256)ax1.bar(bins, hist), ax1.set_title(My Hist)ax2 fig.add_subplot(3, 4, i9)hist, bins np.histogram(eval(img_list[i]), bins256, range[0, 256])ax2.bar(bins[:-1], hist), ax2.set_title(Numpy Hist) # numpy 的返回bins 比hist多一点数需要去掉最后一个
# ax2.hist(eval(img_list[i]).flatten(), bins256, range[0, 255]), ax2.set_title(Matplotlib Hist)
plt.tight_layout()
plt.show()
