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samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py

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+import cv2 as cv
+import numpy as np
+
+# Set up training data
+## [setup1]
+labels = np.array([1, -1, -1, -1])
+trainingData = np.matrix([[501, 10], [255, 10], [501, 255], [10, 501]], dtype=np.float32)
+## [setup1]
+
+# Train the SVM
+## [init]
+svm = cv.ml.SVM_create()
+svm.setType(cv.ml.SVM_C_SVC)
+svm.setKernel(cv.ml.SVM_LINEAR)
+svm.setTermCriteria((cv.TERM_CRITERIA_MAX_ITER, 100, 1e-6))
+## [init]
+## [train]
+svm.train(trainingData, cv.ml.ROW_SAMPLE, labels)
+## [train]
+
+# Data for visual representation
+width = 512
+height = 512
+image = np.zeros((height, width, 3), dtype=np.uint8)
+
+# Show the decision regions given by the SVM
+## [show]
+green = (0,255,0)
+blue = (255,0,0)
+for i in range(image.shape[0]):
+    for j in range(image.shape[1]):
+        sampleMat = np.matrix([[j,i]], dtype=np.float32)
+        response = svm.predict(sampleMat)[1]
+
+        if response == 1:
+            image[i,j] = green
+        elif response == -1:
+            image[i,j] = blue
+## [show]
+
+# Show the training data
+## [show_data]
+thickness = -1
+cv.circle(image, (501,  10), 5, (  0,   0,   0), thickness)
+cv.circle(image, (255,  10), 5, (255, 255, 255), thickness)
+cv.circle(image, (501, 255), 5, (255, 255, 255), thickness)
+cv.circle(image, ( 10, 501), 5, (255, 255, 255), thickness)
+## [show_data]
+
+# Show support vectors
+## [show_vectors]
+thickness = 2
+sv = svm.getUncompressedSupportVectors()
+
+for i in range(sv.shape[0]):
+    cv.circle(image, (sv[i,0], sv[i,1]), 6, (128, 128, 128), thickness)
+## [show_vectors]
+
+cv.imwrite('result.png', image) # save the image
+
+cv.imshow('SVM Simple Example', image) # show it to the user
+cv.waitKey()