work on added digits.py sample (neural network for handwritten digit recognition)

samples/python2/digits.py

@@ -0,0 +1,89 @@
+import numpy as np
+import cv2
+import itertools as it
+
+'''
+from scipy.io import loadmat
+
+m = loadmat('ex4data1.mat')
+X = m['X'].reshape(-1, 20, 20)
+X = np.transpose(X, (0, 2, 1))
+img = np.vstack(map(np.hstack, X.reshape(-1, 100, 20, 20)))
+img = np.uint8(np.clip(img, 0, 1)*255)
+cv2.imwrite('digits.png', img)
+'''
+
+def unroll_responses(responses, class_n):
+    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
+    
+
+SZ = 20
+digits_img = cv2.imread('digits.png', 0)
+
+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(10), N/10)
+
+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])
+
+labels_train_unrolled = unroll_responses(labels_train, 10)
+
+model = cv2.ANN_MLP()
+layer_sizes = np.int32([SZ*SZ, 25, 10])
+model.create(layer_sizes)
+        
+# CvANN_MLP_TrainParams::BACKPROP,0.001
+params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 300, 0.01),
+               train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,
+               bp_dw_scale = 0.001,
+               bp_moment_scale = 0.0 )
+print 'training...'
+model.train(digits_train, labels_train_unrolled, None, params=params)
+model.save('dig_nn.dat')
+model.load('dig_nn.dat')
+
+ret, resp = model.predict(digits_test)
+resp = resp.argmax(-1)
+error_mask = (resp == labels_test)
+print error_mask.mean()
+
+def grouper(n, iterable, fillvalue=None):
+    "grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx"
+    args = [iter(iterable)] * n
+    return it.izip_longest(fillvalue=fillvalue, *args)
+
+def mosaic(w, imgs):
+    imgs = iter(imgs)
+    img0 = imgs.next()
+    pad = np.zeros_like(img0)
+    imgs = it.chain([img0], imgs)
+    rows = grouper(w, imgs, pad)
+    return np.vstack(map(np.hstack, rows))
+
+test_img = np.uint8(digits_test).reshape(-1, SZ, SZ)
+
+def vis_resp(img, flag):
+    img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+    if not flag:
+        img[...,:2] = 0
+    return img
+
+test_img = mosaic(25, it.starmap(vis_resp, it.izip(test_img, error_mask)))
+cv2.imshow('test', test_img)
+cv2.waitKey()
+
+
+
+
+
+
+