def analyzeImage(f, name):
im = Image.open(f)
try:
if (im.size[0] == 1 or im.size[1] == 1):
return
print(name + ' : ' + str(im.size[0]) + ',' + str(im.size[1]))
le = 1
if (type(im.getpixel((0, 0))) == type((1, 2))):
le = len(im.getpixel((0, 0)))
gray = cv.CreateImage(cv.Size(im.size[0], im.size[1]), 8, 1)
edge1 = cv.CreateImage(cv.Size(im.size[0], im.size[1]), 32, 1)
edge2 = cv.CreateImage(cv.Size(im.size[0], im.size[1]), 8, 1)
edge3 = cv.CreateImage(cv.Size(im.size[0], im.size[1]), 32, 3)
for h in range(im.size[1]):
for w in range(im.size[0]):
p = im.getpixel((w, h))
if (type(p) == type(1)):
gray[h][w] = im.getpixel((w, h))
else:
gray[h][w] = im.getpixel((w, h))[0]
cv.CornerHarris(gray, edge1, 5, 5, 0.1)
cv.Canny(gray, edge2, 20, 100)
cv.NamedWindow("win")
cv.ShowImage("win", gray)
cv.NamedWindow("win2")
cv.ShowImage("win2", edge1)
cv.NamedWindow("win3")
cv.ShowImage("win3", edge2)
cv.WaitKey()
f.close()
except Exception, e:
print e
print 'ERROR: problem handling ' + name
def Harris(image):
gray = cv.CreateImage(cv.GetSize(image), 8, 1)
harris = cv.CreateImage(cv.GetSize(image), cv.IPL_DEPTH_32F, 1)
cv.CornerHarris(gray, harris, 5, 5, 0.1)
#for y in range(0, 640):
# for x in range(0, 480):
# harris = cv.Get2D(harris_c, y, x) # get the x,y value
# # check the corner detector response
# if harris[0] > 10e-06:
# # draw a small circle on the original image
# cv.Circle(harris, (x, y), 2, cv.RGB(155, 0, 25))
# I recommend using cvMinMaxLoc to find the min and max values and then use
# cvConvertScale to shift the range [min..max] to [0..255] into an 8bit
# image.
print cv.MinMaxLoc(harris)
gray2 = cv.CreateImage(cv.GetSize(image), 8, 1)
cv.ConvertScale(harris, gray2) # scale/shift?
cv.NamedWindow('Harris')
cv.ShowImage('Harris', gray)
import cv2.cv as cv
import numpy as np
im = cv.LoadImage('/home/pi/Documents/Lab3/foto4.png',
cv.CV_LOAD_IMAGE_GRAYSCALE)
dst_32f = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_32F, 1)
neighbourhood = 3
aperture = 3
k = 0.01
maxStrength = 0.0
threshold = 0.01
nonMaxSize = 3
cv.CornerHarris(im, dst_32f, neighbourhood, aperture, k)
minv, maxv, minl, maxl = cv.MinMaxLoc(dst_32f)
dilated = cv.CloneImage(dst_32f)
cv.Dilate(
dst_32f, dilated
) # By this way we are sure that pixel with local max value will not be changed, and all the others will
localMax = cv.CreateMat(dst_32f.height, dst_32f.width, cv.CV_8U)
cv.Cmp(
dst_32f, dilated, localMax, cv.CV_CMP_EQ
) #compare allow to keep only non modified pixel which are local maximum values which are corners.
threshold = 0.01 * maxv
cv.Threshold(dst_32f, dst_32f, threshold, 255, cv.CV_THRESH_BINARY)
cv.SaveImage("green edge Laplace.png", dst)
cv.WaitKey(0)
enter = raw_input("Mostrar Harrys")
#harrys
dst_32f = cv.CreateImage(cv.GetSize(img), cv.IPL_DEPTH_32F, 1)
neighbourhood = 3
aperture = 3
k = 0.01
maxStrength = 0.0
threshold = 0.01
nonMaxSize = 3
cv.CornerHarris(img, dst_32f, neighbourhood, aperture, k)
minv, maxv, minl, maxl = cv.MinMaxLoc(dst_32f)
dilated = cv.CloneImage(dst_32f)
cv.Dilate(
dst_32f, dilated
) #nos aseguramos que el pixel local su maximo valor no cambia y todos los otros pixeles si
localMax = cv.CreateMat(dst_32f.height, dst_32f.width, cv.CV_8U)
cv.Cmp(
dst_32f, dilated, localMax, cv.CV_CMP_EQ
) #comparamos y guardamos solo los pixeles modificados los que son los maximos valores locales que son esquinas
threshold = 0.01 * maxv
cv.Threshold(dst_32f, dst_32f, threshold, 255, cv.CV_THRESH_BINARY)