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samples/python/tutorial_code/ml/py_svm_opencv/hogsvm.py

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+#!/usr/bin/env python
+
+import cv2 as cv
+import numpy as np
+
+SZ=20
+bin_n = 16 # Number of bins
+
+
+affine_flags = cv.WARP_INVERSE_MAP|cv.INTER_LINEAR
+
+## [deskew]
+def deskew(img):
+    m = cv.moments(img)
+    if abs(m['mu02']) < 1e-2:
+        return img.copy()
+    skew = m['mu11']/m['mu02']
+    M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
+    img = cv.warpAffine(img,M,(SZ, SZ),flags=affine_flags)
+    return img
+## [deskew]
+
+## [hog]
+def hog(img):
+    gx = cv.Sobel(img, cv.CV_32F, 1, 0)
+    gy = cv.Sobel(img, cv.CV_32F, 0, 1)
+    mag, ang = cv.cartToPolar(gx, gy)
+    bins = np.int32(bin_n*ang/(2*np.pi))    # quantizing binvalues in (0...16)
+    bin_cells = bins[:10,:10], bins[10:,:10], bins[:10,10:], bins[10:,10:]
+    mag_cells = mag[:10,:10], mag[10:,:10], mag[:10,10:], mag[10:,10:]
+    hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]
+    hist = np.hstack(hists)     # hist is a 64 bit vector
+    return hist
+## [hog]
+
+img = cv.imread('digits.png',0)
+if img is None:
+    raise Exception("we need the digits.png image from samples/data here !")
+
+
+cells = [np.hsplit(row,100) for row in np.vsplit(img,50)]
+
+# First half is trainData, remaining is testData
+train_cells = [ i[:50] for i in cells ]
+test_cells = [ i[50:] for i in cells]
+
+######     Now training      ########################
+
+deskewed = [list(map(deskew,row)) for row in train_cells]
+hogdata = [list(map(hog,row)) for row in deskewed]
+trainData = np.float32(hogdata).reshape(-1,64)
+responses = np.repeat(np.arange(10),250)[:,np.newaxis]
+
+svm = cv.ml.SVM_create()
+svm.setKernel(cv.ml.SVM_LINEAR)
+svm.setType(cv.ml.SVM_C_SVC)
+svm.setC(2.67)
+svm.setGamma(5.383)
+
+svm.train(trainData, cv.ml.ROW_SAMPLE, responses)
+svm.save('svm_data.dat')
+
+######     Now testing      ########################
+
+deskewed = [list(map(deskew,row)) for row in test_cells]
+hogdata = [list(map(hog,row)) for row in deskewed]
+testData = np.float32(hogdata).reshape(-1,bin_n*4)
+result = svm.predict(testData)[1]
+
+#######   Check Accuracy   ########################
+mask = result==responses
+correct = np.count_nonzero(mask)
+print(correct*100.0/result.size)