removed ANN digits recognition

added deskew for SVN and KNearest recognition sample

samples/python2/digits.py

 '''
-Neural network digit recognition sample.
+SVN and KNearest digit recognition.
+
+Sample loads a dataset of handwritten digits from 'digits.png'.
+Then it trains a SVN and KNearest classifiers on it and evaluates
+their accuracy. Moment-based image deskew is used to improve 
+the recognition accuracy.
+
 Usage:
    digits.py
-
-   Sample loads a dataset of handwritten digits from 'digits.png'.
-   Then it trains a neural network classifier on it and evaluates
-   its classification accuracy.
 '''
 
 import numpy as np
 import cv2
-from common import mosaic
-
-def unroll_responses(responses, class_n):
-    '''[1, 0, 2, ...] -> [[0, 1, 0], [1, 0, 0], [0, 0, 1], ...]'''
-    sample_n = len(responses)
-    new_responses = np.zeros((sample_n, class_n), np.float32)
-    new_responses[np.arange(sample_n), responses] = 1
-    return new_responses
-    
+from multiprocessing.pool import ThreadPool
+from common import clock, mosaic
 
 SZ = 20 # size of each digit is SZ x SZ
 CLASS_N = 10
-digits_img = cv2.imread('digits.png', 0)
-
-# prepare dataset
-h, w = digits_img.shape
-digits = [np.hsplit(row, w/SZ) for row in np.vsplit(digits_img, h/SZ)]
-digits = np.float32(digits).reshape(-1, SZ*SZ)
-N = len(digits)
-labels = np.repeat(np.arange(CLASS_N), N/CLASS_N)
-
-# split it onto train and test subsets
-shuffle = np.random.permutation(N)
-train_n = int(0.9*N)
-digits_train, digits_test = np.split(digits[shuffle], [train_n])
-labels_train, labels_test = np.split(labels[shuffle], [train_n])
-
-# train model
-model = cv2.ANN_MLP()
-layer_sizes = np.int32([SZ*SZ, 25, CLASS_N])
-model.create(layer_sizes)
-params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 100, 0.01),
-               train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,
-               bp_dw_scale = 0.001,
-               bp_moment_scale = 0.0 )
-print 'training...'
-labels_train_unrolled = unroll_responses(labels_train, CLASS_N)
-model.train(digits_train, labels_train_unrolled, None, params=params)
-model.save('dig_nn.dat')
-model.load('dig_nn.dat')
-
-def evaluate(model, samples, labels):
-    '''Evaluates classifier preformance on a given labeled samples set.'''
-    ret, resp = model.predict(samples)
-    resp = resp.argmax(-1)
-    error_mask = (resp == labels)
-    accuracy = error_mask.mean()
-    return accuracy, error_mask
-
-# evaluate model
-train_accuracy, _ = evaluate(model, digits_train, labels_train)
-print 'train accuracy: ', train_accuracy
-test_accuracy, test_error_mask = evaluate(model, digits_test, labels_test)
-print 'test accuracy: ', test_accuracy
-
-# visualize test results
-vis = []
-for img, flag in zip(digits_test, test_error_mask):
-    img = np.uint8(img).reshape(SZ, SZ)
+
+def load_digits(fn):
+    print 'loading "%s" ...' % fn
+    digits_img = cv2.imread(fn, 0)
+    h, w = digits_img.shape
+    digits = [np.hsplit(row, w/SZ) for row in np.vsplit(digits_img, h/SZ)]
+    digits = np.array(digits).reshape(-1, SZ, SZ)
+    labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N)
+    return digits, labels
+
+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=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
+    return img
+
+class StatModel(object):
+    def load(self, fn):
+        self.model.load(fn)
+    def save(self, fn):
+        self.model.save(fn)
+
+class KNearest(StatModel):
+    def __init__(self, k = 3):
+        self.k = k
+        self.model = cv2.KNearest()
+
+    def train(self, samples, responses):
+        self.model = cv2.KNearest()
+        self.model.train(samples, responses)
+
+    def predict(self, samples):
+        retval, results, neigh_resp, dists = self.model.find_nearest(samples, self.k)
+        return results.ravel()
+
+class SVM(StatModel):
+    def __init__(self, C = 1, gamma = 0.5):
+        self.params = dict( kernel_type = cv2.SVM_RBF, 
+                            svm_type = cv2.SVM_C_SVC,
+                            C = C,
+                            gamma = gamma )
+        self.model = cv2.SVM()
+
+    def train(self, samples, responses):
+        self.model = cv2.SVM()
+        self.model.train(samples, responses, params = self.params)
+
+    def predict(self, samples):
+        return self.model.predict_all(samples).ravel()
+
+
+def evaluate_model(model, digits, samples, labels):
+    resp = model.predict(samples)
+    err = (labels != resp).mean()
+    print 'error: %.2f %%' % (err*100)
+    
+    confusion = np.zeros((10, 10), np.int32)
+    for i, j in zip(labels, resp):
+        confusion[i, j] += 1
+    print 'confusion matrix:'
+    print confusion
+    print
+
+    vis = []
+    for img, flag in zip(digits, resp == labels):
         img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
         if not flag:
             img[...,:2] = 0
         vis.append(img)
-vis = mosaic(25, vis)
-cv2.imshow('test', vis)
-cv2.waitKey()
+    return mosaic(25, vis)
+
+
+if __name__ == '__main__':
+    print __doc__
+
+    digits, labels = load_digits('digits.png')
+    
+    print 'preprocessing...'
+    # shuffle digits
+    rand = np.random.RandomState(12345)
+    shuffle = rand.permutation(len(digits))
+    digits, labels = digits[shuffle], labels[shuffle]
+    
+    digits2 = map(deskew, digits)
+    samples = np.float32(digits2).reshape(-1, SZ*SZ) / 255.0
+
+    train_n = int(0.9*len(samples))
+    cv2.imshow('test set', mosaic(25, digits[train_n:]))
+    digits_train, digits_test = np.split(digits2, [train_n])
+    samples_train, samples_test = np.split(samples, [train_n])
+    labels_train, labels_test = np.split(labels, [train_n])
+
+    
+    print 'training KNearest...'
+    model = KNearest(k=1)
+    model.train(samples_train, labels_train)
+    vis = evaluate_model(model, digits_test, samples_test, labels_test)
+    cv2.imshow('KNearest test', vis)
+
+    print 'training SVM...'
+    model = SVM(C=4.66, gamma=0.08)
+    model.train(samples_train, labels_train)
+    vis = evaluate_model(model, digits_test, samples_test, labels_test)
+    cv2.imshow('SVM test', vis)
+
+    cv2.waitKey(0)

samples/python2/digits2.py

-import numpy as np
-import cv2
-from multiprocessing.pool import ThreadPool
-
-SZ = 20 # size of each digit is SZ x SZ
-CLASS_N = 10
-
-def load_base(fn):
-    print 'loading "%s" ...' % fn
-    digits_img = cv2.imread(fn, 0)
-    h, w = digits_img.shape
-    digits = [np.hsplit(row, w/SZ) for row in np.vsplit(digits_img, h/SZ)]
-    digits = np.array(digits).reshape(-1, SZ, SZ)
-    digits = np.float32(digits).reshape(-1, SZ*SZ) / 255.0
-    labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N)
-    return digits, labels
-
-def cross_validate(model_class, params, samples, labels, kfold = 4, pool = None):
-    n = len(samples)
-    folds = np.array_split(np.arange(n), kfold)
-    def f(i):
-        model = model_class(**params)
-        test_idx = folds[i]
-        train_idx = list(folds)
-        train_idx.pop(i)
-        train_idx = np.hstack(train_idx)
-        train_samples, train_labels = samples[train_idx], labels[train_idx]
-        test_samples, test_labels = samples[test_idx], labels[test_idx]
-        model.train(train_samples, train_labels)
-        resp = model.predict(test_samples)
-        score = (resp != test_labels).mean()
-        print ".",
-        return score
-    if pool is None:
-        scores = map(f, xrange(kfold))
-    else:
-        scores = pool.map(f, xrange(kfold))
-    return np.mean(scores)
-
-class StatModel(object):
-    def load(self, fn):
-        self.model.load(fn)
-    def save(self, fn):
-        self.model.save(fn)
-
-class KNearest(StatModel):
-    def __init__(self, k = 3):
-        self.k = k
-
-    @staticmethod
-    def adjust(samples, labels):
-        print 'adjusting KNearest ...'
-        best_err, best_k = np.inf, -1
-        for k in xrange(1, 11):
-            err = cross_validate(KNearest, dict(k=k), samples, labels)
-            if err < best_err:
-                best_err, best_k = err, k
-            print 'k = %d, error: %.2f %%' % (k, err*100)
-        best_params = dict(k=best_k)
-        print 'best params:', best_params
-        return best_params
-
-    def train(self, samples, responses):
-        self.model = cv2.KNearest()
-        self.model.train(samples, responses)
-
-    def predict(self, samples):
-        retval, results, neigh_resp, dists = self.model.find_nearest(samples, self.k)
-        return results.ravel()
-
-class SVM(StatModel):
-    def __init__(self, C = 1, gamma = 0.5):
-        self.params = dict( kernel_type = cv2.SVM_RBF, 
-                            svm_type = cv2.SVM_C_SVC,
-                            C = C,
-                            gamma = gamma )
-
-    @staticmethod
-    def adjust(samples, labels):
-        Cs = np.logspace(0, 5, 10, base=2)
-        gammas = np.logspace(-7, -2, 10, base=2)
-        scores = np.zeros((len(Cs), len(gammas)))
-        scores[:] = np.nan
-
-        print 'adjusting SVM (may take a long time) ...'
-        def f(job):
-            i, j = job
-            params = dict(C = Cs[i], gamma=gammas[j])
-            score = cross_validate(SVM, params, samples, labels)
-            scores[i, j] = score
-            nready = np.isfinite(scores).sum()
-            print '%d / %d (best error: %.2f %%, last: %.2f %%)' % (nready, scores.size, np.nanmin(scores)*100, score*100)
-
-        pool = ThreadPool(processes=cv2.getNumberOfCPUs())
-        pool.map(f, np.ndindex(*scores.shape))
-        print scores
-
-        i, j = np.unravel_index(scores.argmin(), scores.shape)
-        best_params = dict(C = Cs[i], gamma=gammas[j])
-        print 'best params:', best_params
-        print 'best error: %.2f %%' % (scores.min()*100)
-        return best_params
-
-    def train(self, samples, responses):
-        self.model = cv2.SVM()
-        self.model.train(samples, responses, params = self.params)
-
-    def predict(self, samples):
-        return self.model.predict_all(samples).ravel()
-
-def main_adjustSVM(samples, labels):
-    params = SVM.adjust(samples, labels)
-    print 'training SVM on all samples ...'
-    model = SVN(**params)
-    model.train(samples, labels)
-    print 'saving "digits_svm.dat" ...'
-    model.save('digits_svm.dat')
-
-def main_adjustKNearest(samples, labels):
-    params = KNearest.adjust(samples, labels)
-
-def main_showSVM(samples, labels):
-    from common import mosaic
-
-    train_n = int(0.9*len(samples))
-    digits_train, digits_test = np.split(samples[shuffle], [train_n])
-    labels_train, labels_test = np.split(labels[shuffle], [train_n])
-
-    print 'training SVM ...'
-    model = SVM(C=2.16, gamma=0.0536)
-    model.train(digits_train, labels_train)
-
-    train_err = (model.predict(digits_train) != labels_train).mean()
-    resp_test = model.predict(digits_test)
-    test_err = (resp_test != labels_test).mean()
-    print 'train errors: %.2f %%' % (train_err*100)
-    print 'test errors: %.2f %%' % (test_err*100)
-
-    
-    # visualize test results
-    vis = []
-    for img, flag in zip(digits_test, resp_test == labels_test):
-        img = np.uint8(img*255).reshape(SZ, SZ)
-        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-        if not flag:
-            img[...,:2] = 0
-        vis.append(img)
-    vis = mosaic(25, vis)
-    cv2.imshow('test', vis)
-    cv2.waitKey()
-    
-
-
-if __name__ == '__main__':
-    samples, labels = load_base('digits.png')
-    shuffle = np.random.permutation(len(samples))
-    samples, labels = samples[shuffle], labels[shuffle]
-    
-    #main_adjustSVM(samples, labels)
-    #main_adjustKNearest(samples, labels)
-    main_showSVM(samples, labels)