copied helper modules from doc to modules/python/test

modules/python/test/calchist.py

@@ -0,0 +1,53 @@
+# Calculating and displaying 2D Hue-Saturation histogram of a color image
+
+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
+
+if __name__ == '__main__':
+    src = cv.LoadImageM(sys.argv[1])
+    cv.NamedWindow("Source", 1)
+    cv.ShowImage("Source", src)
+
+    cv.NamedWindow("H-S Histogram", 1)
+    cv.ShowImage("H-S Histogram", hs_histogram(src))
+
+    cv.WaitKey(0)
+

modules/python/test/findstereocorrespondence.py

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+import sys
+import cv
+
+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)
+
+
+if __name__ == '__main__':
+
+    (l, r) = [cv.LoadImageM(f, cv.CV_LOAD_IMAGE_GRAYSCALE) for f in sys.argv[1:]]
+
+    (disparity_left, disparity_right) = findstereocorrespondence(l, r)
+
+    disparity_left_visual = cv.CreateMat(l.rows, l.cols, cv.CV_8U)
+    cv.ConvertScale(disparity_left, disparity_left_visual, -16)
+    cv.SaveImage("disparity.pgm", disparity_left_visual)

modules/python/test/precornerdetect.py

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+import cv
+
+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)