def gradient(grayscale, aperture_size=3):
"""
Computes the gradient of an image using a Sobel filter.
:param grayscale: A field to derive.
:type grayscale: HxW array float
:param aperture_size: Aperture of the Sobel filter (odd).
:type aperture_size: int,odd,>=1
:return: gx: Gradient in the *x* direction.
:rtype: array(HxW,float)
:return: gy: Gradient in the *y* direction.
:rtype: array(HxW,float)
"""
check_2d_array(grayscale, "grayscale")
im = numpy_to_cv(grayscale)
shape = (im.width, im.height)
sobel = cv.CreateImage(shape, cv.IPL_DEPTH_32F, 1)
cv.Sobel(im, sobel, 1, 0, aperture_size)
gx = cv_to_numpy(sobel).squeeze()
sobel = cv.CreateImage(shape, cv.IPL_DEPTH_32F, 1)
cv.Sobel(im, sobel, 0, 1, aperture_size)
gy = cv_to_numpy(sobel).squeeze()
return gx.astype('float32'), gy.astype('float32')
def integral_img (image,bins):
#numorient=bins;
size = cv.GetSize(image)
gray_img = cv.CreateImage(size, 8, 1)
xsobel = cv.CreateImage(size, 32, 1)
ysobel = cv.CreateImage(size, 32, 1)
# "RGB to grayscale conversion"
cv.CvtColor(image, gray_img, cv.CV_BGR2GRAY)
# "Gradient computation using sobel operators"
cv.Sobel(gray_img, xsobel, 1, 0, 3)
cv.Sobel(gray_img, ysobel, 0, 1, 3)
integral_hog= np.zeros((size[1], size[0], bins))
for y in xrange(0, size[1]-1):
for x in xrange(0, size[0]-1):
angle1= (int(round(180*np.arctan2(ysobel[y,x], xsobel[y,x])/np.pi))+ 180)
if angle1>=0 and angle1<40:
angle=0
elif angle1>=40 and angle1<80:
angle=1
elif angle1>=80 and angle1<120:
angle=2
elif angle1>=120 and angle1<160:
angle=3
elif angle1>=160 and angle1<200:
angle=4
elif angle1>=200 and angle1<240:
angle=5
elif angle1>=240 and angle1<280:
angle=6
elif angle1>=280 and angle1<320:
angle=7
elif angle1>=320 and angle1<=360:
angle=8
grad_m = np.sqrt(xsobel[y,x]*xsobel[y,x]+ysobel[y,x]*ysobel[y,x])
integral_hog[y,x,angle] += grad_m
# "Now we build the integral image"
for x in xrange(1, size[0]-1):
for orient in xrange(bins):
integral_hog[y,x,orient] += integral_hog[y,x-1,orient]
for y in xrange(1, size[1]-1):
for orient in xrange(bins):
integral_hog[y,x,orient] += integral_hog[y-1,x,orient]
for y in xrange(1, size[1]-1):
for x in xrange(1, size[0]-1):
for orient in xrange(bins):
integral_hog[y,x,orient] += integral_hog[y-1,x,orient] + integral_hog[y,x-1,orient] - integral_hog[y-1,x-1,orient]
return integral_hog
def sobel():
cv.Smooth(src_image, dst_image, cv.CV_GAUSSIAN, 3, 3)
src_gray = cv.CreateImage((src_image.width, src_image.height), 8, 1)
dst_gray1 = cv.CreateImage((src_image.width, src_image.height), 8, 1)
dst_gray = cv.CreateImage((src_image.width, src_image.height), 8, 1)
cv.CvtColor(src_image, src_gray, cv.CV_BGR2GRAY)
cv.Sobel(src_gray, dst_gray1, 0, 1, 3)
cv.ConvertScaleAbs(dst_gray1, dst_gray1, 1, 0)
cv.Sobel(src_gray, dst_gray, 1, 0, 3)
cv.ConvertScaleAbs(dst_gray, dst_gray, 1, 0)
cv.AddWeighted(dst_gray, 0.5, dst_gray1, 0.5, 0, dst_gray)
cv.NamedWindow("Destination Image")
cv.ShowImage("Destination Image", dst_gray)
cv.WaitKey(0)
def sobelGradient(self, image):
'''Calculates the gradient in x and y direction using Sobel mask using default 3x3 filter'''
if self.image_check(image) < 0:
return -1
gsimage = cv.CreateImage(cv.GetSize(image), cv.IPL_DEPTH_8U, 1)
if image.channels > 1:
temp = cv.CreateImage(cv.GetSize(image), cv.IPL_DEPTH_8U, 1)
cv.CvtColor(image, temp, cv.CV_BGR2GRAY)
gsimage = temp
else:
gsimage = image
#smoothing image
#Creating empty images for dx,dy and temporary 16 bit dx16 and dy16
dy16 = cv.CreateImage(cv.GetSize(gsimage), cv.IPL_DEPTH_16S, 1)
dx16 = cv.CreateImage(cv.GetSize(gsimage), cv.IPL_DEPTH_16S, 1)
dx = cv.CreateImage(cv.GetSize(gsimage), cv.IPL_DEPTH_8U, 1)
dy = cv.CreateImage(cv.GetSize(gsimage), cv.IPL_DEPTH_8U, 1)
#Convolving sobel mask to get gradient of dx and dy
cv.Sobel(gsimage, dy16, 0, 1, apertureSize=3)
cv.Sobel(gsimage, dx16, 1, 0, apertureSize=3)
cv.ConvertScaleAbs(dx16, dx)
cv.ConvertScaleAbs(dy16, dy)
if self.visualize:
while True:
cv.NamedWindow("Original")
cv.ShowImage("Original", gsimage)
cv.NamedWindow("Sobelx")
cv.ShowImage("Sobelx", dx)
cv.NamedWindow("Sobely")
cv.ShowImage("Sobely", dy)
c = cv.WaitKey(5)
if c > 0:
break
cv.DestroyAllWindows()
return (dx16, dy16)
def get_gradient_img(self, input_img):
# Generate grad_x and grad_y to use Sobel
grad_x=cv.CreateMat(input_img.height, input_img.width, input_img.type)
grad_y=cv.CreateMat(input_img.height, input_img.width, input_img.type)
abs_grad_x=cv.CreateMat(input_img.height, input_img.width, input_img.type)
abs_grad_y=cv.CreateMat(input_img.height, input_img.width, input_img.type)
grad =cv.CreateMat(input_img.height, input_img.width, input_img.type)
#Gradient X
#Scharr( src_gray, grad_x, ddepth, 1, 0, scale, delta, BORDER_DEFAULT );#for small kernels use this instead of sobel
cv.Sobel( input_img, grad_x, 1, 0)
cv.Abs(grad_x, abs_grad_x )
#Gradient Y
#Scharr( src_gray, grad_y, ddepth, 0, 1, scale, delta, BORDER_DEFAULT );
cv.Sobel( input_img, grad_y, 0, 1)
cv.Abs( grad_y, abs_grad_y )
#/// Total Gradient (approximate)
cv.AddWeighted( abs_grad_x, 0.5, abs_grad_y, 0.5, 0, grad )
return grad
def sobel_image(image, x, y):
sobel_image = cv.CreateImage((image.width, image.height), image.depth,
image.nChannels)
cv.Sobel(image, sobel_image, x, y)
return sobel_image
def find(self, img):
started = time.time()
gray = self.Cached('gray', img.height, img.width, cv.CV_8UC1)
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
sobel = self.Cached('sobel', img.height, img.width, cv.CV_16SC1)
sobely = self.Cached('sobely', img.height, img.width, cv.CV_16SC1)
cv.Sobel(gray, sobel, 1, 0)
cv.Sobel(gray, sobely, 0, 1)
cv.Add(sobel, sobely, sobel)
sobel8 = self.Cached('sobel8', sobel.height, sobel.width, cv.CV_8UC1)
absnorm8(sobel, sobel8)
cv.Threshold(sobel8, sobel8, 128.0, 255.0, cv.CV_THRESH_BINARY)
sobel_integral = self.Cached('sobel_integral', img.height + 1,
img.width + 1, cv.CV_32SC1)
cv.Integral(sobel8, sobel_integral)
d = 16
_x1y1 = cv.GetSubRect(
sobel_integral,
(0, 0, sobel_integral.cols - d, sobel_integral.rows - d))
_x1y2 = cv.GetSubRect(
sobel_integral,
(0, d, sobel_integral.cols - d, sobel_integral.rows - d))
_x2y1 = cv.GetSubRect(
sobel_integral,
(d, 0, sobel_integral.cols - d, sobel_integral.rows - d))
_x2y2 = cv.GetSubRect(
sobel_integral,
(d, d, sobel_integral.cols - d, sobel_integral.rows - d))
summation = cv.CloneMat(_x2y2)
cv.Sub(summation, _x1y2, summation)
cv.Sub(summation, _x2y1, summation)
cv.Add(summation, _x1y1, summation)
sum8 = self.Cached('sum8', summation.height, summation.width,
cv.CV_8UC1)
absnorm8(summation, sum8)
cv.Threshold(sum8, sum8, 32.0, 255.0, cv.CV_THRESH_BINARY)
cv.ShowImage("sum8", sum8)
seq = cv.FindContours(sum8, cv.CreateMemStorage(), cv.CV_RETR_EXTERNAL)
subimg = cv.GetSubRect(img, (d / 2, d / 2, sum8.cols, sum8.rows))
t_cull = time.time() - started
seqs = []
while seq:
seqs.append(seq)
seq = seq.h_next()
started = time.time()
found = {}
print 'seqs', len(seqs)
for seq in seqs:
area = cv.ContourArea(seq)
if area > 1000:
rect = cv.BoundingRect(seq)
edge = int((14 / 14.) * math.sqrt(area) / 2 + 0.5)
candidate = cv.GetSubRect(subimg, rect)
sym = self.dm.decode(
candidate.width,
candidate.height,
buffer(candidate.tostring()),
max_count=1,
#min_edge = 6,
#max_edge = int(edge) # Units of 2 pixels
)
if sym:
onscreen = [(d / 2 + rect[0] + x, d / 2 + rect[1] + y)
for (x, y) in self.dm.stats(1)[1]]
found[sym] = onscreen
else:
print "FAILED"
t_brute = time.time() - started
print "cull took", t_cull, "brute", t_brute
return found
from pylab import *
import glob
import os
i = 0
for files in glob.glob('D:/pic/car/*.jpg'):
filepath,filename = os.path.split(files)
image = cv2.imread(filepath + '/' + filename)
# image = cv2.imread('D:/pic/car2.jpg')
h,w = image.shape[:2]
#灰度化
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
grayIPimage = cv.GetImage(cv.fromarray(gray))
sobel = cv.CreateImage((w, h),cv2.IPL_DEPTH_16S, 1) #创建一张深度为16位有符号（-65536~65535）的的图像区域保持处理结果
cv.Sobel(grayIPimage,sobel,2,0,7) # 进行x方向的sobel检测
temp = cv.CreateImage(cv.GetSize(sobel),cv2.IPL_DEPTH_8U, 1) #图像格式转换回8位深度已进行下一步处理
cv.ConvertScale(sobel, temp,0.00390625, 0)
cv.Threshold(temp, temp, 0, 255, cv2.THRESH_OTSU)
kernal = cv.CreateStructuringElementEx(3,1, 1, 0, 0)
cv.Dilate(temp, temp,kernal,2)
cv.Erode(temp, temp,kernal,4)
cv.Dilate(temp, temp,kernal,2)
# cv.ShowImage('1', temp)
kernal = cv.CreateStructuringElementEx(1,3, 0, 1, 0)
cv.Erode(temp, temp,kernal,1)
cv.Dilate(temp, temp,kernal,3)
# cv.ShowImage('2', temp)
temp = np.asarray(cv.GetMat(temp))
contours, heirs = cv2.findContours(temp,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
import cv2.cv as cv
im = cv.LoadImage('meinv.jpg', cv.CV_LOAD_IMAGE_GRAYSCALE)
sobx = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_16S, 1)
cv.Sobel(im, sobx, 1, 0, 3) #Sobel with x-order=1
soby = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_16S, 1)
cv.Sobel(im, soby, 0, 1, 3) #Sobel withy-oder=1
cv.Abs(sobx, sobx)
cv.Abs(soby, soby)
result = cv.CloneImage(im)
cv.Add(sobx, soby, result) #Add the two results together.
cv.Threshold(result, result, 100, 255, cv.CV_THRESH_BINARY_INV)
cv.ShowImage('Image', im)
cv.ShowImage('Result', result)
cv.WaitKey(0)
dst = cv.CreateImage(cv.GetSize(r), 8, 1)
cv.Canny(r, dst, 100, 200)
cv.Threshold(dst, dst, 100, 255, cv.CV_THRESH_BINARY)
color_dst_standard = cv.CreateImage(cv.GetSize(r), 8, 3)
cv.CvtColor(r, color_dst_standard, cv.CV_GRAY2BGR)
lines = cv.HoughLines2(dst, cv.CreateMemStorage(0), cv.CV_HOUGH_STANDARD, 1,
pi / 180, 100, 0, 0)
cv.ShowImage("red edges canny", dst)
cv.SaveImage("red edges canny.png", dst)
cv.WaitKey(0)
enter = raw_input("Mostrar Sobel")
# sobel
#imagen en gris
sobx = cv.CreateImage(cv.GetSize(img), cv.IPL_DEPTH_16S, 1)
cv.Sobel(img, sobx, 1, 0, 3) #Sobel with x-order=1
soby = cv.CreateImage(cv.GetSize(img), cv.IPL_DEPTH_16S, 1)
cv.Sobel(img, soby, 0, 1, 3) #Sobel withy-oder=1
cv.Abs(sobx, sobx)
cv.Abs(soby, soby)
result = cv.CloneImage(img)
cv.Add(sobx, soby, result) #Add the two results together.
cv.Threshold(result, result, 100, 255, cv.CV_THRESH_BINARY_INV)
cv.ShowImage('Grayscale edge Sobel', result)
cv.SaveImage("Grayscale edge Sobel.png", dst)
cv.WaitKey(0)
#azul
