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

diff --git a/samples/python2/digits.png b/samples/python2/digits.png
new file mode 100644 (file)
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Binary files /dev/null and b/samples/python2/digits.png differ

diff --git a/samples/python2/digits.py b/samples/python2/digits.py
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+++ b/samples/python2/digits.py
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+import numpy as np\r
+import cv2\r
+import itertools as it\r
+\r
+'''\r
+from scipy.io import loadmat\r
+\r
+m = loadmat('ex4data1.mat')\r
+X = m['X'].reshape(-1, 20, 20)\r
+X = np.transpose(X, (0, 2, 1))\r
+img = np.vstack(map(np.hstack, X.reshape(-1, 100, 20, 20)))\r
+img = np.uint8(np.clip(img, 0, 1)*255)\r
+cv2.imwrite('digits.png', img)\r
+'''\r
+\r
+def unroll_responses(responses, class_n):\r
+    sample_n = len(responses)\r
+    new_responses = np.zeros((sample_n, class_n), np.float32)\r
+    new_responses[np.arange(sample_n), responses] = 1\r
+    return new_responses\r
+    \r
+\r
+SZ = 20\r
+digits_img = cv2.imread('digits.png', 0)\r
+\r
+h, w = digits_img.shape\r
+digits = [np.hsplit(row, w/SZ) for row in np.vsplit(digits_img, h/SZ)]\r
+digits = np.float32(digits).reshape(-1, SZ*SZ)\r
+N = len(digits)\r
+labels = np.repeat(np.arange(10), N/10)\r
+\r
+shuffle = np.random.permutation(N)\r
+train_n = int(0.9*N)\r
+\r
+digits_train, digits_test = np.split(digits[shuffle], [train_n])\r
+labels_train, labels_test = np.split(labels[shuffle], [train_n])\r
+\r
+labels_train_unrolled = unroll_responses(labels_train, 10)\r
+\r
+model = cv2.ANN_MLP()\r
+layer_sizes = np.int32([SZ*SZ, 25, 10])\r
+model.create(layer_sizes)\r
+        \r
+# CvANN_MLP_TrainParams::BACKPROP,0.001\r
+params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 300, 0.01),\r
+               train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,\r
+               bp_dw_scale = 0.001,\r
+               bp_moment_scale = 0.0 )\r
+print 'training...'\r
+model.train(digits_train, labels_train_unrolled, None, params=params)\r
+model.save('dig_nn.dat')\r
+model.load('dig_nn.dat')\r
+\r
+ret, resp = model.predict(digits_test)\r
+resp = resp.argmax(-1)\r
+error_mask = (resp == labels_test)\r
+print error_mask.mean()\r
+\r
+def grouper(n, iterable, fillvalue=None):\r
+    "grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx"\r
+    args = [iter(iterable)] * n\r
+    return it.izip_longest(fillvalue=fillvalue, *args)\r
+\r
+def mosaic(w, imgs):\r
+    imgs = iter(imgs)\r
+    img0 = imgs.next()\r
+    pad = np.zeros_like(img0)\r
+    imgs = it.chain([img0], imgs)\r
+    rows = grouper(w, imgs, pad)\r
+    return np.vstack(map(np.hstack, rows))\r
+\r
+test_img = np.uint8(digits_test).reshape(-1, SZ, SZ)\r
+\r
+def vis_resp(img, flag):\r
+    img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)\r
+    if not flag:\r
+        img[...,:2] = 0\r
+    return img\r
+\r
+test_img = mosaic(25, it.starmap(vis_resp, it.izip(test_img, error_mask)))\r
+cv2.imshow('test', test_img)\r
+cv2.waitKey()\r
+\r
+\r
+\r
+\r
+\r
+\r
+\r