Merged the trunk r8547:8574, r8587

samples/android/tutorial-4-mixed/jni/Android.mk

@@ -3,6 +3,8 @@ LOCAL_PATH := $(call my-dir)
 include $(CLEAR_VARS)
 
 OPENCV_CAMERA_MODULES:=off
+OPENCV_INSTALL_MODULES:=on
+#OPENCV_LIB_TYPE:=SHARED <- this is default
 
 include ../includeOpenCV.mk
 ifeq ("$(wildcard $(OPENCV_MK_PATH))","")

samples/cpp/tutorial_code/Histograms_Matching/compareHist_Demo.cpp

@@ -44,8 +44,8 @@ int main( int argc, char** argv )
     int histSize[] = { h_bins, s_bins };
 
     // hue varies from 0 to 256, saturation from 0 to 180
-    float h_ranges[] = { 0, 256 };
-    float s_ranges[] = { 0, 180 };
+    float s_ranges[] = { 0, 256 };
+    float h_ranges[] = { 0, 180 };
 
     const float* ranges[] = { h_ranges, s_ranges };
 

samples/python2/common.py

@@ -2,6 +2,7 @@ import numpy as np
 import cv2
 import os
 from contextlib import contextmanager
+import itertools as it
 
 image_extensions = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.pbm', '.pgm', '.ppm']
 
@@ -170,3 +171,22 @@ class RectSelector:
             return
         x0, y0, x1, y1 = self.drag_rect
         cv2.rectangle(vis, (x0, y0), (x1, y1), (0, 255, 0), 2)
+
+
+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):
+    '''Make a grid from images. 
+
+    w    -- number of grid columns
+    imgs -- images (must have same size and format)
+    '''
+    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))

samples/python2/digits.py

@@ -0,0 +1,78 @@
+'''
+Neural network digit recognition sample.
+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
+    
+
+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)
+    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()