目錄

• 直方圖處理

直方圖處理

令$r_k,k=0,1,2,\dots ,L?1$表于一幅$L$級灰度數字圖像$f(x,y)$的灰度。$f$的非歸一化直方圖定義為：

$$h(r_k)=n_k,k=0,1,2,\dots ,L?1\text{\hspace{1em}(3.6)}$$

$n_k是f$中灰度為$r_k$的像素的數量，并且細分的灰度級稱為直方圖容器。

歸一化直方圖

$$p(r_k)=\frac{h(r_k)}{MN}=\frac{n_k}{MN}\text{\hspace{1em}(3.7)}$$

def my_hist(img, bins=256, normalized=False):"""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 # 直方圖 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, i+1)ax.imshow(eval(img_list[i]), cmap='gray', vmin=0, vmax=255)ax1 = fig.add_subplot(3, 4, i+5)hist, bins = my_hist(eval(img_list[i]), bins=256)ax1.bar(bins, hist), ax1.set_title('My Hist')ax2 = fig.add_subplot(3, 4, i+9)hist, bins = np.histogram(eval(img_list[i]), bins=256, range=[0, 256])ax2.bar(bins[:-1], hist), ax2.set_title('Numpy Hist') # numpy 的返回bins 比hist多一點數，需要去掉最后一個 # ax2.hist(eval(img_list[i]).flatten(), bins=256, range=[0, 255]), ax2.set_title('Matplotlib Hist') plt.tight_layout() plt.show()

總結

以上是生活随笔為你收集整理的第3章 Python 数字图像处理(DIP) - 灰度变换与空间滤波7 - 直方图处理 - 直方图、归一化直方图的全部內容，希望文章能夠幫你解決所遇到的問題。

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