fix py_svm_opencv sample

berak · berak · commit 2af63c2bf102 · 2017-05-21T07:39:05.000+02:00
diff --git a/doc/py_tutorials/py_ml/py_svm/py_svm_opencv/py_svm_opencv.markdown b/doc/py_tutorials/py_ml/py_svm/py_svm_opencv/py_svm_opencv.markdown
@@ -17,16 +17,9 @@ vectors.
 
 Here, before finding the HOG, we deskew the image using its second order moments. So we first define
 a function **deskew()** which takes a digit image and deskew it. Below is the deskew() function:
-@code{.py}
-def deskew(img):
-    m = cv2.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 = cv2.warpAffine(img,M,(SZ, SZ),flags=affine_flags)
-    return img
-@endcode
+
+@snippet samples/python/tutorial_code/ml/py_svm_opencv/hogsvm.py deskew
+
 Below image shows above deskew function applied to an image of zero. Left image is the original
 image and right image is the deskewed image.
 
@@ -38,91 +31,15 @@ gradient is quantized to 16 integer values. Divide this image to four sub-square
 sub-square, calculate the histogram of direction (16 bins) weighted with their magnitude. So each
 sub-square gives you a vector containing 16 values. Four such vectors (of four sub-squares) together
 gives us a feature vector containing 64 values. This is the feature vector we use to train our data.
-@code{.py}
-def hog(img):
-    gx = cv2.Sobel(img, cv2.CV_32F, 1, 0)
-    gy = cv2.Sobel(img, cv2.CV_32F, 0, 1)
-    mag, ang = cv2.cartToPolar(gx, gy)
-
-    # quantizing binvalues in (0...16)
-    bins = np.int32(bin_n*ang/(2*np.pi))
-
-    # Divide to 4 sub-squares
-    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)
-    return hist
-@endcode
+
+@snippet samples/python/tutorial_code/ml/py_svm_opencv/hogsvm.py hog
+
 Finally, as in the previous case, we start by splitting our big dataset into individual cells. For
 every digit, 250 cells are reserved for training data and remaining 250 data is reserved for
-testing. Full code is given below:
-@code{.py}
-import cv2
-import numpy as np
-
-SZ=20
-bin_n = 16 # Number of bins
-
-
-affine_flags = cv2.WARP_INVERSE_MAP|cv2.INTER_LINEAR
-
-def deskew(img):
-    m = cv2.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 = cv2.warpAffine(img,M,(SZ, SZ),flags=affine_flags)
-    return img
-
-def hog(img):
-    gx = cv2.Sobel(img, cv2.CV_32F, 1, 0)
-    gy = cv2.Sobel(img, cv2.CV_32F, 0, 1)
-    mag, ang = cv2.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
-
-img = cv2.imread('digits.png',0)
-
-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 = [map(deskew,row) for row in train_cells]
-hogdata = [map(hog,row) for row in deskewed]
-trainData = np.float32(hogdata).reshape(-1,64)
-responses = np.float32(np.repeat(np.arange(10),250)[:,np.newaxis])
-
-svm = cv2.ml.SVM_create()
-svm.setKernel(cv2.ml.SVM_LINEAR)
-svm.setType(cv2.ml.SVM_C_SVC)
-svm.setC(2.67)
-svm.setGamma(5.383)
-
-svm.train(trainData, cv2.ml.ROW_SAMPLE, responses)
-svm.save('svm_data.dat')
-
-######     Now testing      ########################
-
-deskewed = [map(deskew,row) for row in test_cells]
-hogdata = [map(hog,row) for row in deskewed]
-testData = np.float32(hogdata).reshape(-1,bin_n*4)
-result = svm.predict(testData)
-
-#######   Check Accuracy   ########################
-mask = result==responses
-correct = np.count_nonzero(mask)
-print correct*100.0/result.size
-@endcode
+testing. Full code is given below, you also can download it from [here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/ml/py_svm_opencv/hogsvm.py):
+
+@include samples/python/tutorial_code/ml/py_svm_opencv/hogsvm.py
+
 This particular technique gave me nearly 94% accuracy. You can try different values for various
 parameters of SVM to check if higher accuracy is possible. Or you can read technical papers on this
 area and try to implement them.
diff --git a/samples/python/tutorial_code/ml/py_svm_opencv/hogsvm.py b/samples/python/tutorial_code/ml/py_svm_opencv/hogsvm.py
@@ -0,0 +1,71 @@
+import cv2
+import numpy as np
+
+SZ=20
+bin_n = 16 # Number of bins
+
+
+affine_flags = cv2.WARP_INVERSE_MAP|cv2.INTER_LINEAR
+
+## [deskew]
+def deskew(img):
+    m = cv2.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 = cv2.warpAffine(img,M,(SZ, SZ),flags=affine_flags)
+    return img
+## [deskew]
+
+## [hog]
+def hog(img):
+    gx = cv2.Sobel(img, cv2.CV_32F, 1, 0)
+    gy = cv2.Sobel(img, cv2.CV_32F, 0, 1)
+    mag, ang = cv2.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 = cv2.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 = [map(deskew,row) for row in train_cells]
+hogdata = [map(hog,row) for row in deskewed]
+trainData = np.float32(hogdata).reshape(-1,64)
+responses = np.repeat(np.arange(10),250)[:,np.newaxis]
+
+svm = cv2.ml.SVM_create()
+svm.setKernel(cv2.ml.SVM_LINEAR)
+svm.setType(cv2.ml.SVM_C_SVC)
+svm.setC(2.67)
+svm.setGamma(5.383)
+
+svm.train(trainData, cv2.ml.ROW_SAMPLE, responses)
+svm.save('svm_data.dat')
+
+######     Now testing      ########################
+
+deskewed = [map(deskew,row) for row in test_cells]
+hogdata = [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
