这里写目录标题
色彩变换彩色图像平滑和锐化使用彩色分割图像HSI 彩色空间中的分割RGB空间中的分割彩色边缘检测彩色图像中的噪声色彩变换
# 图像颜色分量的显示from PIL import Imageimg_ori = Image.open('DIP_Figures/DIP3E_Original_Images_CH06/Fig0630(01)(strawberries_fullcolor).tif')img_cmyk = img_ori.convert("CMYK")img_temp = np.array(img_cmyk)img_c = img_temp[:, :, 0]img_m = img_temp[:, :, 1]img_y = img_temp[:, :, 2]img_k = img_temp[:, :, 3]plt.figure(figsize=(20, 25))plt.subplot(541), plt.imshow(img_cmyk), plt.title('Original CMYK')# CMYK, seems is CMY, as K is all blackplt.subplot(545), plt.imshow(img_c, 'gray'), plt.title('Cyan')plt.subplot(546), plt.imshow(img_m, 'gray'), plt.title('Magenta')plt.subplot(547), plt.imshow(img_y, 'gray'), plt.title('Yellow')plt.subplot(5, 4, 8), plt.imshow(img_k, 'gray'), plt.title('Black')# Show RGB channelsimg_rgb = np.array(img_ori)plt.subplot(5, 4, 9), plt.imshow(img_rgb[:, :, 0], 'gray'), plt.title('Red')plt.subplot(5, 4, 10), plt.imshow(img_rgb[:, :, 1], 'gray'), plt.title('Green')plt.subplot(5, 4, 11), plt.imshow(img_rgb[:, :, 2], 'gray'), plt.title('Blue')# Show HSI channelsimg_hsi = img_ori.convert("HSV")img_hsi = np.array(img_hsi)plt.subplot(5, 4, 13), plt.imshow(img_hsi[:, :, 0], 'gray'), plt.title('Hue')plt.subplot(5, 4, 14), plt.imshow(img_hsi[:, :, 1], 'gray'), plt.title('Saturation')plt.subplot(5, 4, 15), plt.imshow(img_hsi[:, :, 2], 'gray'), plt.title('Intensity')plt.tight_layout()plt.show()
from PIL import Imageimg_ori = Image.open('DIP_Figures/DIP3E_Original_Images_CH06/Fig0630(01)(strawberries_fullcolor).tif')img_cmyk = img_ori.convert("CMYK")img_temp = np.array(img_cmyk)img_c = img_temp[:, :, 0]img_m = img_temp[:, :, 1]img_y = img_temp[:, :, 2]img_k = img_temp[:, :, 3]plt.figure(figsize=(20, 25))plt.subplot(541), plt.imshow(img_cmyk), plt.title('Original CMYK')# CMYK, seems is CMY, as K is all blackplt.subplot(545), plt.imshow(img_c, 'gray'), plt.title('Cyan')plt.subplot(546), plt.imshow(img_m, 'gray'), plt.title('Magenta')plt.subplot(547), plt.imshow(img_y, 'gray'), plt.title('Yellow')plt.subplot(548), plt.imshow(img_k, 'gray'), plt.title('Black')# change K valueimg_k_new = img_k * 1 + 150img_cmyk_new = np.dstack((img_c, img_m, img_y, img_k_new))plt.subplot(549), plt.imshow(img_cmyk_new, 'gray'), plt.title('New CMYK')plt.tight_layout()plt.show()
def gamma_img(img, c, gamma):img = np.array(img).astype(float)output_img = c * img ** gammaimg_scale = np.uint8((output_img / output_img.max()) * 255)return img_scale
def sigmoid_plot(img, scale):x = np.linspace(img.min(), img.max(), 500)x1 = x - 125y = 1 / (1 + np.exp(-x1 / scale))return x, yplt.plot(x, y)plt.grid()
def sigmoid_transform(img, scale):img = np.array(img).astype(float)img_temp = (img - 125.)img_new = 1 / (1 + np.exp(-img_temp / scale))img_new = np.uint8(normalize(img_new) * 255)return img_new
# 色调和彩色校正from PIL import Imageimg_ori = Image.open('DIP_Figures/DIP3E_Original_Images_CH06/Fig0635(top_ left_flower).tif')plt.figure(figsize=(15, 5))plt.subplot(131), plt.imshow(img_ori), plt.title('Original')img_colour = sigmoid_transform(img_ori, 30)plt.subplot(132), plt.imshow(img_colour), plt.title('Colour Correct')# x, y = sigmoid_plot(np.array(img_ori), 40)# plt.subplot(133), sigmoid_plot(np.array(img_ori), 40), plt.title("Transform")x, y = sigmoid_plot(np.array(img_ori), 40)plt.axes([0.68, 0.15, 0.15, 0.3]), plt.plot(x, y), plt.title("Transform"), plt.grid()# plt.tight_layout()plt.show()
# 色调和彩色校正from PIL import Imageimg_ori = Image.open('DIP_Figures/DIP3E_Original_Images_CH06/Fig0635(middle_row_left_chalk ).tif')plt.figure(figsize=(10, 5))plt.subplot(121), plt.imshow(img_ori), plt.title('Original')img_colour = gamma_img(img_ori, 1, 1.5)plt.subplot(1, 2, 2), plt.imshow(img_colour), plt.title('Colour Correct')plt.tight_layout()plt.show()
# 色调和彩色校正from PIL import Imageimg_ori = Image.open('DIP_Figures/DIP3E_Original_Images_CH06/Fig0635(bottom_left_stream).tif')plt.figure(figsize=(10, 5))plt.subplot(121), plt.imshow(img_ori), plt.title('Original')img_colour = gamma_img(img_ori, 1, 0.5)plt.subplot(1, 2, 2), plt.imshow(img_colour), plt.title('Colour Correct')plt.tight_layout()plt.show()
彩色图像平滑和锐化
import numpy as npdef arithmentic_mean(image, kernel):""":param image: input image:param kernel: input kernel:return: image after convolution"""img_h = image.shape[0]img_w = image.shape[1]m = kernel.shape[0]n = kernel.shape[1]# paddingpadding_h = int((m -1)/2)padding_w = int((n -1)/2)image_pad = np.pad(image.copy(), (padding_h, padding_w), mode="constant", constant_values=0)image_convol = image.copy()for i in range(padding_h, img_h + padding_h):for j in range(padding_w, img_w + padding_w):temp = np.sum(image_pad[i-padding_h:i+padding_h+1, j-padding_w:j+padding_w+1] * kernel)image_convol[i - padding_h][j - padding_w] = 1/(m * n) * tempimage_convol = np.uint8(normalize(image_convol) * 255)return image_convol
# 图像颜色分量的显示from PIL import Image# img_ori = Image.open('DIP_Figures/DIP3E_Original_Images_CH06/Fig0638(a)(lenna_RGB).tif')img_ori = cv2.imread('DIP_Figures/DIP3E_Original_Images_CH06/Fig0638(a)(lenna_RGB).tif')img_ori = img_ori[:, :, ::-1]# Show RGB channelsplt.figure(figsize=(10, 10))img_rgb = np.array(img_ori)plt.subplot(2, 2, 1), plt.imshow(img_rgb), plt.title('RGB')plt.subplot(2, 2, 2), plt.imshow(img_rgb[:, :, 0], 'gray'), plt.title('Red')plt.subplot(2, 2, 3), plt.imshow(img_rgb[:, :, 1], 'gray'), plt.title('Green')plt.subplot(2, 2, 4), plt.imshow(img_rgb[:, :, 2], 'gray'), plt.title('Blue')plt.tight_layout()plt.show()# Show HSI channelsplt.figure(figsize=(15, 5))img_hsi = cv2.cvtColor(np.array(img_ori), cv2.COLOR_RGB2HSV)img_hsi = np.array(img_hsi)plt.subplot(1, 3, 1), plt.imshow(img_hsi[:, :, 0], 'gray'), plt.title('Hue')plt.subplot(1, 3, 2), plt.imshow(img_hsi[:, :, 1], 'gray'), plt.title('Saturation')plt.subplot(1, 3, 3), plt.imshow(img_hsi[:, :, 2], 'gray'), plt.title('Intensity')plt.tight_layout()plt.show()
# 图像平滑mean_kernal = np.ones([5, 5])mean_kernal = mean_kernal / (mean_kernal.size)img_rgb_new = np.zeros(img_rgb.shape, np.uint8)for i in range(3):img_temp = img_rgb[:, :, i]img_dst = arithmentic_mean(img_temp, kernel=mean_kernal)img_rgb_new[:, :, i] = img_dstimg_hsi_new = np.zeros(img_rgb.shape, np.uint8)for i in range(3):if i == 2:img_temp = img_hsi[:, :, i]img_dst = arithmentic_mean(img_temp, kernel=mean_kernal)img_hsi_new[:, :, i] = img_dstelse:img_hsi_new[:, :, i] = img_hsi[:, :, i]img_hsi_rgb = cv2.cvtColor(img_hsi_new, cv2.COLOR_HSV2RGB)img_diff = img_rgb_new - img_hsi_rgbplt.figure(figsize=(15, 5))plt.subplot(1, 3, 1), plt.imshow(img_rgb_new), plt.title('RGB')plt.subplot(1, 3, 2), plt.imshow(img_hsi_rgb), plt.title('HSI RGB')plt.subplot(1, 3, 3), plt.imshow(img_diff), plt.title('Differenc')plt.tight_layout()plt.show()
def laplacian_img(img_gray):# 拉普拉期算子，用于边缘检对于检测图像中的模糊也非常有用kernel_laplacian = np.array(([0,1,0],[1,-4,1],[0,1,0]), np.int8)imgkernel_laplacian = cv2.filter2D(img_gray, -1, kernel_laplacian)laplacian_img = np.uint8(normalize(img_gray + imgkernel_laplacian) * 255)return laplacian_img
# 图像锐化img_rgb_new = np.zeros(img_rgb.shape, np.uint8)for i in range(3):img_temp = img_rgb[:, :, i]img_dst = laplacian_img(img_temp)img_rgb_new[:, :, i] = img_dstimg_hsi_new = np.zeros(img_rgb.shape, np.uint8)for i in range(3):if i == 2:img_temp = img_hsi[:, :, i]img_dst = laplacian_img(img_temp)img_hsi_new[:, :, i] = img_dstelse:img_hsi_new[:, :, i] = img_hsi[:, :, i]img_hsi_rgb = cv2.cvtColor(img_hsi_new, cv2.COLOR_HSV2RGB)img_diff = img_rgb_new - img_hsi_rgbplt.figure(figsize=(15, 5))plt.subplot(1, 3, 1), plt.imshow(img_rgb_new), plt.title('RGB')plt.subplot(1, 3, 2), plt.imshow(img_hsi_rgb), plt.title('HSI RGB')plt.subplot(1, 3, 3), plt.imshow(img_diff), plt.title('Differenc')plt.tight_layout()plt.show()
使用彩色分割图像
HSI 彩色空间中的分割
# HSI彩色图像分割img_ori = cv2.imread('DIP_Figures/DIP3E_Original_Images_CH06/Fig0642(a)(jupiter_moon_original).tif')img_ori = img_ori[:, :, ::-1] # BGR 2 RGB# Show HSI channelsplt.figure(figsize=(14, 20))img_hsi = cv2.cvtColor(np.array(img_ori), cv2.COLOR_RGB2HSV)plt.subplot(4, 2, 1), plt.imshow(img_ori), plt.title('Ori')plt.subplot(4, 2, 2), plt.imshow(img_hsi[:, :, 0], 'gray'), plt.title('Hue')plt.subplot(4, 2, 3), plt.imshow(img_hsi[:, :, 1], 'gray'), plt.title('Saturation')plt.subplot(4, 2, 4), plt.imshow(img_hsi[:, :, 2], 'gray'), plt.title('Intensity')# Thresholdimg_s = normalize(img_hsi[:, :, 1])thresh = 0.255 #0.255 #img_s.max() * 0.1 + 0.233print(thresh)img_thresh = img_s.copy()img_thresh = np.where(img_thresh <= thresh, img_thresh, 1)img_thresh = np.where(img_thresh > thresh, img_thresh, 0)plt.subplot(4, 2, 5), plt.imshow(img_thresh, 'gray'), plt.title('Binary Thred of Saturation')# Threshold X Hueimg_thred_hue = img_hsi[:, :, 0] * img_threshplt.subplot(4, 2, 6), plt.imshow(img_thred_hue, 'gray'), plt.title('Hue X Binary Thred')# Histogramplt.subplot(4, 2, 7), plt.hist(img_thred_hue.flatten(), bins=256), plt.title('Hue X Binary Thred')# Binaryimg_binary = img_thred_hue.copy()img_binary = np.where(img_binary <= 125, img_binary, 255) # >125 为1img_binary = np.where(img_binary > 125, img_binary, 0) # < 125为0plt.subplot(4, 2, 8), plt.imshow(img_binary, 'gray'), plt.title('Binary')plt.tight_layout()plt.show()
0.255
RGB空间中的分割
欧氏距离协方差矩阵边界盒def rgb_segment(img_rgb, img_roi, d0):"""RGB spatial domain sementation base of ROIparam: img_rgb: input image, RGB channelparam: img_roi: region of interesting of the image where you want to be seperatedparam: d0: the Euculidean distance of the ROI region against othersreturn: img_dst, a mask image range [0, 1] """mean = np.mean(img_roi, axis=(0, 1))sigma = np.std(img_roi, axis=(0, 1))img_dst = np.zeros(img_rgb.shape[:2])height, width = img_dst.shapefor h in range(height):for w in range(width):temp = img_rgb[h, w]if np.linalg.norm(temp - mean) <= d0:img_dst[h, w] = 1else:img_dst[h, w] = 0return img_dst
# RGB彩色图像分割img_ori = cv2.imread('DIP_Figures/DIP3E_Original_Images_CH06/Fig0642(a)(jupiter_moon_original).tif')# img_ori = img_ori[:, :, ::-1] # BGR 2 RGBimg_rgb = cv2.cvtColor(img_ori, cv2.COLOR_BGR2RGB)plt.figure(figsize=(14, 20))plt.subplot(4, 2, 1), plt.imshow(img_rgb), plt.title('Ori')# draw rectangle# img_rect = cv2.rectangle(img_rgb, (60, 240), (98, 315), (255, 255, 255), 2)# plt.subplot(4, 2, 2), plt.imshow(img_rect), plt.title('ROI')# show ROIroi = img_rgb[240:315, 60:98, :]mean = np.mean(roi, axis=(0, 1))sigma = np.std(roi, axis=(0, 1))print(f"RGB mean -> {mean}")print(f"RGB sigma -> {sigma}")plt.subplot(4, 2, 3), plt.imshow(roi), plt.title('ROI')img_dst = rgb_segment(img_rgb, roi, d0=38)plt.subplot(4, 2, 4), plt.imshow(img_dst, 'gray'), plt.title('Segment')plt.tight_layout()plt.show()
RGB mean -> [146.81298246 40.47473684 42.62385965]RGB sigma -> [23.60878011 25.67369246 17.97835714]
彩色边缘检测
img1_r = cv2.imread('DIP_Figures/DIP3E_Original_Images_CH06/Fig0645(a)(RGB1-red).tif', -1)img1_g = cv2.imread('DIP_Figures/DIP3E_Original_Images_CH06/Fig0645(b)(RGB1-green).tif', -1)img1_b = cv2.imread('DIP_Figures/DIP3E_Original_Images_CH06/Fig0645(c)(RGB1-blue).tif', -1)img1_rgb = np.dstack((img1_r, img1_g, img1_b))img2_r = cv2.imread('DIP_Figures/DIP3E_Original_Images_CH06/Fig0645(e)(RGB2_red).tif', -1)img2_g = cv2.imread('DIP_Figures/DIP3E_Original_Images_CH06/Fig0645(f)(RGB2_green).tif', -1)img2_b = cv2.imread('DIP_Figures/DIP3E_Original_Images_CH06/Fig0645(g)(RGB2_blue).tif', -1)img2_rgb = np.dstack((img2_r, img2_g, img2_b))plt.figure(figsize=(20, 10))plt.subplot(2, 4, 1), plt.imshow(img1_r, 'gray'), plt.title('R channel'), plt.xticks([]), plt.yticks([])plt.subplot(2, 4, 2), plt.imshow(img1_g, 'gray'), plt.title('G channel'), plt.xticks([]), plt.yticks([])plt.subplot(2, 4, 3), plt.imshow(img1_b, 'gray'), plt.title('B channel'), plt.xticks([]), plt.yticks([])plt.subplot(2, 4, 4), plt.imshow(img1_rgb), plt.title('RGB'), plt.xticks([]), plt.yticks([])plt.subplot(2, 4, 5), plt.imshow(img2_r, 'gray'), plt.title('R channel'), plt.xticks([]), plt.yticks([])plt.subplot(2, 4, 6), plt.imshow(img2_g, 'gray'), plt.title('G channel'), plt.xticks([]), plt.yticks([])plt.subplot(2, 4, 7), plt.imshow(img2_b, 'gray'), plt.title('B channel'), plt.xticks([]), plt.yticks([])plt.subplot(2, 4, 8), plt.imshow(img2_rgb), plt.title('RGB'), plt.xticks([]), plt.yticks([])plt.tight_layout()plt.show()
彩色图像中的噪声
# RGB channel merge to RGB imageimg1_r = cv2.imread('DIP_Figures/DIP3E_Original_Images_CH06/Fig0648(a)(lenna-noise-R-gauss-mean0-var800).tif', 0)img1_g = cv2.imread('DIP_Figures/DIP3E_Original_Images_CH06/Fig0648(b)(lenna-noise-G-gauss-mean0-var800).tif', 0)img1_b = cv2.imread('DIP_Figures/DIP3E_Original_Images_CH06/Fig0648(c)(lenna-noise-B-gauss-mean0-var800).tif', 0)img1_rgb = np.dstack((img1_r, img1_g, img1_b))plt.figure(figsize=(10, 10))plt.subplot(2, 2, 1), plt.imshow(img1_r, 'gray'), plt.title('R channel'), plt.xticks([]), plt.yticks([])plt.subplot(2, 2, 2), plt.imshow(img1_g, 'gray'), plt.title('G channel'), plt.xticks([]), plt.yticks([])plt.subplot(2, 2, 3), plt.imshow(img1_b, 'gray'), plt.title('B channel'), plt.xticks([]), plt.yticks([])plt.subplot(2, 2, 4), plt.imshow(img1_rgb), plt.title('RGB'), plt.xticks([]), plt.yticks([])plt.tight_layout()plt.show()
# convert RGB to HSI, noise affect all channelsimg1_hsi = cv2.cvtColor(img1_rgb, cv2.COLOR_RGB2HSV_FULL)plt.figure(figsize=(15, 5))plt.subplot(1, 3, 1), plt.imshow(img1_hsi[:, :, 0], 'gray'), plt.title('Hue'), plt.xticks([]), plt.yticks([])plt.subplot(1, 3, 2), plt.imshow(img1_hsi[:, :, 1], 'gray'), plt.title('Saturation'), plt.xticks([]), plt.yticks([])plt.subplot(1, 3, 3), plt.imshow(img1_hsi[:, :, 2], 'gray'), plt.title('Intensity'), plt.xticks([]), plt.yticks([])plt.tight_layout()plt.show()
# RGB image, only green channel affect noise, but convert to HSI, all channel affectimg1_ori = cv2.imread('DIP_Figures/DIP3E_Original_Images_CH06/Fig0650(a)(rgb_image_G_saltpep_pt05).tif')img1_rgb = img1_ori[:, :, ::-1]plt.figure(figsize=(20, 10))img1_hsi = cv2.cvtColor(img1_rgb, cv2.COLOR_RGB2HSV_FULL)plt.subplot(2, 4, 1), plt.imshow(img1_rgb), plt.title('RGB'), plt.xticks([]), plt.yticks([])plt.subplot(2, 4, 2), plt.imshow(img1_hsi[:, :, 0], 'gray'), plt.title('Hue'), plt.xticks([]), plt.yticks([])plt.subplot(2, 4, 3), plt.imshow(img1_hsi[:, :, 1], 'gray'), plt.title('Saturation'), plt.xticks([]), plt.yticks([])plt.subplot(2, 4, 4), plt.imshow(img1_hsi[:, :, 2], 'gray'), plt.title('Intensity'), plt.xticks([]), plt.yticks([])plt.subplot(2, 4, 5), plt.imshow(img1_rgb[:, :, 0], 'gray'), plt.title('R channel'), plt.xticks([]), plt.yticks([])plt.subplot(2, 4, 6), plt.imshow(img1_rgb[:, :, 1], 'gray'), plt.title('G channel'), plt.xticks([]), plt.yticks([])plt.subplot(2, 4, 7), plt.imshow(img1_rgb[:, :, 2], 'gray'), plt.title('B channel'), plt.xticks([]), plt.yticks([])plt.tight_layout()plt.show()
第6章 Python 数字图像处理(DIP) - 彩色图像处理3 -色彩变换 彩色校正 彩色图像平滑和锐化 HSI彩色空间中的分割 RGB空间中的分割 彩色边缘检测
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