the DocumentFragmentTests now reside in modules/python/test

doc/python_fragments/calchist.py

-# 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)
-

doc/python_fragments/findstereocorrespondence.py

-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)

doc/python_fragments/precornerdetect.py

-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)