fixed test_precornerdetect, test_findstereocorrespondence, test_calchist

diff --git a/tests/python/test.py b/tests/python/test.py
index d944269..9be4b16 100644 (file)
--- a/tests/python/test.py
+++ b/tests/python/test.py
@@ -2107,8 +2107,8 @@ class DocumentFragmentTests(OpenCVTests):
         sys.path.append("../doc/python_fragments")
 
     def test_precornerdetect(self):
-        from precornerdetect import precornerdetect
-        im = self.get_sample("samples/c/right01.jpg", 0)
+        from test_functions import precornerdetect
+        im = self.get_sample("samples/cpp/right01.jpg", 0)
         imf = cv.CreateMat(im.rows, im.cols, cv.CV_32FC1)
         cv.ConvertScale(im, imf)
         (r0,r1) = precornerdetect(imf)
@@ -2117,7 +2117,7 @@ class DocumentFragmentTests(OpenCVTests):
             self.assertEqual(im.rows, r.rows)
 
     def test_findstereocorrespondence(self):
-        from findstereocorrespondence import findstereocorrespondence
+        from test_functions import findstereocorrespondence
         (l,r) = [self.get_sample("doc/pics/tsukuba_%s.png" % c, cv.CV_LOAD_IMAGE_GRAYSCALE) for c in "lr"]
 
         (disparity_left, disparity_right) = findstereocorrespondence(l, r)
@@ -2127,7 +2127,7 @@ class DocumentFragmentTests(OpenCVTests):
         # self.snap(disparity_left_visual)
 
     def test_calchist(self):
-        from calchist import hs_histogram
+        from test_functions import hs_histogram
         i1 = self.get_sample("samples/c/lena.jpg")
         i2 = self.get_sample("doc/pics/building.jpg")
         i3 = cv.CloneMat(i1)

diff --git a/tests/python/test_functions.py b/tests/python/test_functions.py
new file mode 100644 (file)
index 0000000..047f0e8
--- /dev/null
+++ b/tests/python/test_functions.py
@@ -0,0 +1,61 @@
+import sys
+import cv
+
+def hs_histogram(src):
+    # Convert to HSV
+    hsv = cv.CreateImage(cv.GetSize(src), 8, 3)
+    cv.CvtColor(src, hsv, cv.CV_BGR2HSV)
+
+    # Extract the H and S planes
+    h_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
+    s_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
+    cv.Split(hsv, h_plane, s_plane, None, None)
+    planes = [h_plane, s_plane]
+
+    h_bins = 30
+    s_bins = 32
+    hist_size = [h_bins, s_bins]
+    # hue varies from 0 (~0 deg red) to 180 (~360 deg red again */
+    h_ranges = [0, 180]
+    # saturation varies from 0 (black-gray-white) to
+    # 255 (pure spectrum color)
+    s_ranges = [0, 255]
+    ranges = [h_ranges, s_ranges]
+    scale = 10
+    hist = cv.CreateHist([h_bins, s_bins], cv.CV_HIST_ARRAY, ranges, 1)
+    cv.CalcHist([cv.GetImage(i) for i in planes], hist)
+    (_, max_value, _, _) = cv.GetMinMaxHistValue(hist)
+
+    hist_img = cv.CreateImage((h_bins*scale, s_bins*scale), 8, 3)
+
+    for h in range(h_bins):
+        for s in range(s_bins):
+            bin_val = cv.QueryHistValue_2D(hist, h, s)
+            intensity = cv.Round(bin_val * 255 / max_value)
+            cv.Rectangle(hist_img,
+                         (h*scale, s*scale),
+                         ((h+1)*scale - 1, (s+1)*scale - 1),
+                         cv.RGB(intensity, intensity, intensity), 
+                         cv.CV_FILLED)
+    return hist_img
+    
+def precornerdetect(image):
+    # assume that the image is floating-point 
+    corners = cv.CloneMat(image)
+    cv.PreCornerDetect(image, corners, 3)
+    dilated_corners = cv.CloneMat(image)
+    cv.Dilate(corners, dilated_corners, None, 1)
+    corner_mask = cv.CreateMat(image.rows, image.cols, cv.CV_8UC1)
+    cv.Sub(corners, dilated_corners, corners)
+    cv.CmpS(corners, 0, corner_mask, cv.CV_CMP_GE)
+    return (corners, corner_mask)
+
+def findstereocorrespondence(image_left, image_right):
+    # image_left and image_right are the input 8-bit single-channel images
+    # from the left and the right cameras, respectively
+    (r, c) = (image_left.rows, image_left.cols)
+    disparity_left = cv.CreateMat(r, c, cv.CV_16S)
+    disparity_right = cv.CreateMat(r, c, cv.CV_16S)
+    state = cv.CreateStereoGCState(16, 2)
+    cv.FindStereoCorrespondenceGC(image_left, image_right, disparity_left, disparity_right, state, 0)
+    return (disparity_left, disparity_right)