Kmeans

Author: JimmyHHua

README.md

@@ -147,4 +147,8 @@ code_105 | [(Detection Case)-HOG+SVM Predict](python/code_105/opencv_105.py)   |
 code_106 | [AKAZE Features and Descriptors](python/code_106)   | ✔️
 code_107 | [Brisk Features and Descriptors](python/code_107)   | ✔️
 code_108 | [GFTT Detector](python/code_108)   | ✔️
-code_109 | [BLOB Feature Analysis](python/code_109)   | ✔️
+code_109 | [BLOB Feature Analysis](python/code_109)   | ✔️
+code_110 | [KMeans Data Classification](python/code_110)   | ✔️
+code_111 | [KMeans Image Segmentation](python/code_111)   | ✔️
+code_112 | [KMeans Background Change](python/code_112)   | ✔️
+code_113 | [KMeans Extract Image Color Card](python/code_113)   | ✔️

README_CN.md

@@ -146,4 +146,8 @@ code_105 | [(检测案例)-HOG+SVM 预测](python/code_105/opencv_105.py)   | 
 code_106 | [AKAZE 特征与描述子](python/code_106)   | ✔️
 code_107 | [Brisk 特征与描述子](python/code_107)   | ✔️
 code_108 | [GFTT关键点检测](python/code_108)   | ✔️
-code_109 | [BLOB 特征分析](python/code_109)   | ✔️
+code_109 | [BLOB 特征分析](python/code_109)   | ✔️
+code_110 | [KMeans 数据分类](python/code_110)   | ✔️
+code_111 | [KMeans 图像分割](python/code_111)   | ✔️
+code_112 | [KMeans 图像替换](python/code_112)   | ✔️
+code_113 | [KMeans 图像色卡提取](python/code_113)   | ✔️

python/code_110/README.md

@@ -0,0 +1,2 @@
+#### More Detail, please check the blog of zhihu as below
+✈️ ✈️ ✈️ [OpenCV图像处理-KMean图像处理](https://zhuanlan.zhihu.com/p/79518281)

python/code_110/opencv_110.py

@@ -0,0 +1,38 @@
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+
+# 读取数据
+def loadDataSet(fileName):
+    data = []
+    with open(fileName) as f:
+        for line in f.readlines():
+            curLine = line.strip().split("\t")
+            fltLine = list(map(float, curLine))  # 转换为float
+            data.append(fltLine)
+    return np.array(data, dtype=np.float32)
+
+# 导入数据
+data = loadDataSet('testSet2.txt')
+
+# 定义停止条件
+criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+
+# kmeans计算
+ret,label,center=cv2.kmeans(data, 3, None, criteria, 2, cv2.KMEANS_RANDOM_CENTERS)
+
+print(len(label))
+print(center)
+
+# 获取不同标签的点
+A = data[label.ravel()==0]
+B = data[label.ravel()==1]
+C = data[label.ravel()==2]
+
+# 可视化
+plt.scatter(A[:,0],A[:,1])
+plt.scatter(B[:,0],B[:,1],c = 'r')
+plt.scatter(C[:,0],C[:,1],c = 'purple')
+plt.scatter(center[:,0],center[:,1],s = 80,c = 'black', marker = '*')
+plt.xlabel('Height'),plt.ylabel('Weight')
+plt.show()

python/code_110/testSet.txt

@@ -0,0 +1,80 @@
+1.658985	4.285136
+-3.453687	3.424321
+4.838138	-1.151539
+-5.379713	-3.362104
+0.972564	2.924086
+-3.567919	1.531611
+0.450614	-3.302219
+-3.487105	-1.724432
+2.668759	1.594842
+-3.156485	3.191137
+3.165506	-3.999838
+-2.786837	-3.099354
+4.208187	2.984927
+-2.123337	2.943366
+0.704199	-0.479481
+-0.392370	-3.963704
+2.831667	1.574018
+-0.790153	3.343144
+2.943496	-3.357075
+-3.195883	-2.283926
+2.336445	2.875106
+-1.786345	2.554248
+2.190101	-1.906020
+-3.403367	-2.778288
+1.778124	3.880832
+-1.688346	2.230267
+2.592976	-2.054368
+-4.007257	-3.207066
+2.257734	3.387564
+-2.679011	0.785119
+0.939512	-4.023563
+-3.674424	-2.261084
+2.046259	2.735279
+-3.189470	1.780269
+4.372646	-0.822248
+-2.579316	-3.497576
+1.889034	5.190400
+-0.798747	2.185588
+2.836520	-2.658556
+-3.837877	-3.253815
+2.096701	3.886007
+-2.709034	2.923887
+3.367037	-3.184789
+-2.121479	-4.232586
+2.329546	3.179764
+-3.284816	3.273099
+3.091414	-3.815232
+-3.762093	-2.432191
+3.542056	2.778832
+-1.736822	4.241041
+2.127073	-2.983680
+-4.323818	-3.938116
+3.792121	5.135768
+-4.786473	3.358547
+2.624081	-3.260715
+-4.009299	-2.978115
+2.493525	1.963710
+-2.513661	2.642162
+1.864375	-3.176309
+-3.171184	-3.572452
+2.894220	2.489128
+-2.562539	2.884438
+3.491078	-3.947487
+-2.565729	-2.012114
+3.332948	3.983102
+-1.616805	3.573188
+2.280615	-2.559444
+-2.651229	-3.103198
+2.321395	3.154987
+-1.685703	2.939697
+3.031012	-3.620252
+-4.599622	-2.185829
+4.196223	1.126677
+-2.133863	3.093686
+4.668892	-2.562705
+-2.793241	-2.149706
+2.884105	3.043438
+-2.967647	2.848696
+4.479332	-1.764772
+-4.905566	-2.911070

python/code_110/testSet2.txt

@@ -0,0 +1,60 @@
+3.275154	2.957587
+-3.344465	2.603513
+0.355083	-3.376585
+1.852435	3.547351
+-2.078973	2.552013
+-0.993756	-0.884433
+2.682252	4.007573
+-3.087776	2.878713
+-1.565978	-1.256985
+2.441611	0.444826
+-0.659487	3.111284
+-0.459601	-2.618005
+2.177680	2.387793
+-2.920969	2.917485
+-0.028814	-4.168078
+3.625746	2.119041
+-3.912363	1.325108
+-0.551694	-2.814223
+2.855808	3.483301
+-3.594448	2.856651
+0.421993	-2.372646
+1.650821	3.407572
+-2.082902	3.384412
+-0.718809	-2.492514
+4.513623	3.841029
+-4.822011	4.607049
+-0.656297	-1.449872
+1.919901	4.439368
+-3.287749	3.918836
+-1.576936	-2.977622
+3.598143	1.975970
+-3.977329	4.900932
+-1.791080	-2.184517
+3.914654	3.559303
+-1.910108	4.166946
+-1.226597	-3.317889
+1.148946	3.345138
+-2.113864	3.548172
+0.845762	-3.589788
+2.629062	3.535831
+-1.640717	2.990517
+-1.881012	-2.485405
+4.606999	3.510312
+-4.366462	4.023316
+0.765015	-3.001270
+3.121904	2.173988
+-4.025139	4.652310
+-0.559558	-3.840539
+4.376754	4.863579
+-1.874308	4.032237
+-0.089337	-3.026809
+3.997787	2.518662
+-3.082978	2.884822
+0.845235	-3.454465
+1.327224	3.358778
+-2.889949	3.596178
+-0.966018	-2.839827
+2.960769	3.079555
+-3.275518	1.577068
+0.639276	-3.412840

python/code_111/README.md

@@ -0,0 +1,2 @@
+#### More Detail, please check the blog of zhihu as below
+✈️ ✈️ ✈️ [OpenCV图像处理-KMean图像处理](https://zhuanlan.zhihu.com/p/79518281)

python/code_111/kmeans-image-demo-t.jpg

@@ -0,0 +1,32 @@
+import numpy as np
+import cv2 as cv
+
+image = cv.imread('toux.jpg')
+
+# 构建图像数据
+data = image.reshape((-1,3))
+data = np.float32(data)
+
+# MAX_ITER最大迭代次数，EPS最高精度
+criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+num_clusters = 4
+ret,label,center=cv.kmeans(data, num_clusters, None, criteria, num_clusters, cv.KMEANS_RANDOM_CENTERS)
+
+center = np.uint8(center)
+
+# 颜色label
+color = np.uint8([[255, 0, 0],
+                  [0, 0, 255],
+                  [128, 128, 128],
+                  [0, 255, 0]])
+
+res = color[label.flatten()]
+print(res.shape)
+# 显示
+result = res.reshape((image.shape))
+cv.imshow('kmeans-image-demo',result)
+cv.imwrite('kmeans-image-demo-t.jpg',np.hstack((image,result)))
+
+
+cv.waitKey(0)
+cv.destroyAllWindows()

python/code_111/kmeans-image-demo.jpg

@@ -0,0 +1,2 @@
+#### More Detail, please check the blog of zhihu as below
+✈️ ✈️ ✈️ [OpenCV图像处理-KMean图像处理](https://zhuanlan.zhihu.com/p/79518281)

python/code_111/opencv_111.py

@@ -0,0 +1,59 @@
+import numpy as np
+import cv2 as cv
+
+image = cv.imread('toux.jpg')
+cv.imshow("input", image)
+h, w ,ch = image.shape
+# 构建图像数据
+data = image.reshape((-1,3))
+data = np.float32(data)
+
+# 图像分割
+criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+num_clusters = 4
+ret,label,center=cv.kmeans(data, num_clusters, None, criteria, num_clusters, cv.KMEANS_RANDOM_CENTERS)
+
+# 生成mask区域
+index = label[0][0]
+center = np.uint8(center)
+color = center[0]
+mask = np.ones((h, w), dtype=np.uint8)*255.
+label = np.reshape(label, (h, w))
+# alpha图
+mask[label == index] = 0
+
+# 高斯模糊
+se = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
+# 膨胀，防止背景出现
+cv.erode(mask, se, mask)
+#边缘模糊
+mask = cv.GaussianBlur(mask, (5, 5), 0)
+cv.imshow('background-mask',mask)
+
+# 白色背景
+bg = np.ones(image.shape, dtype=np.float)*255.
+
+# 粉丝背景
+purle = np.array([255, 0, 255])
+bg_color = np.tile(purle, (image.shape[0], image.shape[1], 1))
+
+alpha = mask.astype(np.float32) / 255.
+fg = alpha[..., None] * image
+new_image = fg + (1 - alpha[..., None])*bg
+new_image_purle = fg + (1 - alpha[..., None])*bg_color
+
+# # blend image
+# result = np.zeros((h, w, ch), dtype=np.uint8)
+# for row in range(h):
+#     for col in range(w):
+#         w1 = mask[row, col] / 255.0
+#         b, g, r = image[row, col]
+#         b = w1 * 255.0 + b * (1.0 - w1)
+#         g = w1 * 0 + g * (1.0 - w1)
+#         r = w1 * 255 + r * (1.0 - w1)
+#         result[row, col] = (b, g, r)
+cv.imshow("background substitution", bg_color.astype(np.uint8))
+cv.imwrite("white.jpg", np.hstack((image, new_image.astype(np.uint8))))
+cv.imwrite("purle.jpg", np.hstack((image, new_image_purle.astype(np.uint8))))
+cv.waitKey(0)
+cv.destroyAllWindows()

python/code_111/toux.jpg

@@ -0,0 +1,2 @@
+#### More Detail, please check the blog of zhihu as below
+✈️ ✈️ ✈️ [OpenCV图像处理-KMean图像处理](https://zhuanlan.zhihu.com/p/79518281)

python/code_112/README.md

@@ -0,0 +1,45 @@
+import numpy as np
+import cv2 as cv
+
+image = cv.imread('result1.jpg')
+image2 = cv.imread('result2.jpg')
+img = cv.resize(image,(300,510))
+img2 = cv.resize(image2,(300,510))
+cv.imshow("input", img)
+cv.imwrite('result1_1.jpg',img)
+cv.imwrite('result2_2.jpg',img2)
+# h, w ,ch = image.shape
+# print(image.shape)
+# # 构建图像数据
+# data = image.reshape((-1,3))
+# data = np.float32(data)
+
+# # 图像分割
+# criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+# num_clusters = 5
+# ret,label,center=cv.kmeans(data, num_clusters, None, criteria, num_clusters, cv.KMEANS_RANDOM_CENTERS)
+# print(label[300])
+
+# # 生成主色彩条形卡片
+# card = np.zeros((50, w, 3), dtype=np.uint8)
+# clusters = np.zeros([5], dtype=np.int32)
+# # 统计每一类的数目
+# for i in range(len(label)):
+#     clusters[label[i]] += 1
+# # 比重
+# clusters = np.float32(clusters) / float(h*w)
+# center = np.int32(center)
+# x_offset = 0
+
+# # 绘制色卡
+# for c in range(num_clusters):
+#     dx = np.int(clusters[c] * w)
+#     b = center[c][0]
+#     g = center[c][1]
+#     r = center[c][2]
+#     cv.rectangle(card, (x_offset, 0), (x_offset+dx, 50), (int(b), int(g), int(r)), -1)
+#     x_offset += dx
+
+# cv.imshow("color table", card)
+cv.waitKey(0)
+cv.destroyAllWindows()