def determineMarkerQuality_naive(self, frame_org):
phase = np.exp((self.limitAngleToRange(-self.orientation)) * 1j)
t1_temp = self.kernelComplex * np.power(phase, self.order)
t1 = t1_temp.real > self.threshold
t2_temp = self.kernelComplex * np.power(phase, self.order)
t2 = t2_temp.real < -self.threshold
img_t1_t2_diff = t1.astype(np.float32) - t2.astype(np.float32)
angleThreshold = 3.14 / (2 * self.order)
t3 = np.angle(self.KernelRemoveArmComplex * phase) < angleThreshold
t4 = np.angle(self.KernelRemoveArmComplex * phase) > -angleThreshold
mask = 1 - 2 * (t3 & t4)
template = (img_t1_t2_diff) * mask
template = cv.fromarray(1 - template)
(xm, ym) = self.lastMarkerLocation
y1 = ym - int(math.floor(float(self.kernelSize / 2)))
y2 = ym + int(math.ceil(float(self.kernelSize / 2)))
x1 = xm - int(math.floor(float(self.kernelSize / 2)))
x2 = xm + int(math.ceil(float(self.kernelSize / 2)))
try:
frame = frame_org[y1:y2, x1:x2]
except (TypeError):
self.quality = 0
return
w, h = cv.GetSize(frame)
im_dst = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
cv.Threshold(frame, im_dst, 128, 1, cv.CV_THRESH_BINARY)
matches = 0
blacks = 0
w, h = cv.GetSize(im_dst)
for x in xrange(w):
for y in xrange(h):
if cv.Get2D(im_dst, y, x)[0] == 0: # if pixel is black
blacks += 1
if cv.Get2D(im_dst, y, x)[0] == cv.Get2D(template, y,
x)[0]:
matches += 1
else:
continue
# self.quality = float(matches)/(w*h)
self.quality = float(matches) / blacks
im_dst = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
cv.Threshold(frame, im_dst, 115, 255, cv.CV_THRESH_BINARY)
cv.ShowImage("small_image", im_dst)
cv.ShowImage("temp_kernel", template)
def test_divider(self):
image = cv.LoadImage(
os.path.abspath(os.environ['BORG'] +
'/Brain/data/hog_test/noface.jpg'))
subRect = (0, 0, 250, 187)
subimage = self.divider.crop(image, subRect)
subimage1 = image
result = self.divider.divide(image, 2, 2)
if cv.GetSize(subimage) != cv.GetSize(result[0]):
self.fail(
"The subimage sizes are not correct. Either correctly crop the image manually or check divider function"
)
dif = cv.CreateImage(cv.GetSize(subimage), cv.IPL_DEPTH_8U, 3)
dif2 = cv.CreateImage(cv.GetSize(subimage), cv.IPL_DEPTH_8U, 3)
cv.AbsDiff(subimage, result[0], dif)
cv.Threshold(dif, dif2, 50, 255, cv.CV_THRESH_TOZERO)
for i in range(3):
cv.SetImageCOI(dif2, i + 1)
n_nonzero = cv.CountNonZero(dif2)
if n_nonzero < 400:
threshold = 0
else:
threshold = 1
self.assertEqual(threshold, 0, "The subimages are different")
result = self.divider.divide(image, 4, 4, option="pro")
print len(result)
print result
dif = cv.CreateImage(cv.GetSize(subimage1), cv.IPL_DEPTH_8U, 3)
dif2 = cv.CreateImage(cv.GetSize(subimage1), cv.IPL_DEPTH_8U, 3)
cv.AbsDiff(subimage1, result[0], dif)
cv.Threshold(dif, dif2, 50, 255, cv.CV_THRESH_TOZERO)
for i in range(3):
cv.SetImageCOI(dif2, i + 1)
n_nonzero = cv.CountNonZero(dif2)
if n_nonzero < 400:
threshold = 0
else:
threshold = 1
self.assertEqual(threshold, 0, "The subimages are different")
result = self.divider.divide(image, 4, 4, option="pro", overlap=10)
def preproc_map_img(self, map_img):
""" Preprocesses the map image Soft, erode or whtaever it is necessary to improve the input"""
#Apply threshold to have just black and white
thresh_img=cv.CreateMat(map_img.height, map_img.width, cv.CV_8UC1)
cv.Threshold(map_img, thresh_img, 250, 255, cv.CV_THRESH_BINARY)
#Blur map's thresholded image
soft_img=cv.CreateMat(map_img.height, map_img.width, cv.CV_8UC1)
cv.Smooth(thresh_img, soft_img, cv.CV_GAUSSIAN, 9, 9)
#Dilate the inverse map to get it's skeleton
dilated_img = cv.CreateMat(map_img.height, map_img.width, cv.CV_8UC1)
cv.Dilate(soft_img, dilated_img, iterations=20)
#Create inverse image
# dilated_inverted_img=cv.CreateMat(map_img.height, map_img.width, cv.CV_8UC1)
# for r in range(0,dilated_img.rows):
# for c in range(0,dilated_img.cols):
# dilated_inverted_img[r,c]=255-dilated_img[r,c]
#Enhance image edges for hough transformdilated_img
canny_img=cv.CreateMat(map_img.height, map_img.width, cv.CV_8UC1)
cv.Canny(soft_img, canny_img, 200,220)
preprocessed_map = dilated_img
return preprocessed_map
def post_process_distance_img(self, dist_img):
inverted_img=cv.CreateMat(dist_img.height, dist_img.width, cv.CV_8UC1)
#Blur image
soft_img=cv.CreateMat(dist_img.height, dist_img.width, cv.CV_8UC1)
cv.Smooth(dist_img, soft_img, cv.CV_GAUSSIAN, 21, 21)
#Apply threshold to have just black and white
thresh_img=cv.CreateMat(dist_img.height, dist_img.width, cv.CV_8UC1)
cv.Threshold(soft_img, thresh_img, 1, 255, cv.CV_THRESH_BINARY)#CV_THRESH_OTSU is an adaptive thresholding method
# #Create inverse image
# for r in range(0,thresh_img.rows):
# for c in range(0,thresh_img.cols):
# inverted_img[r,c]=255-thresh_img[r,c]
#Erode the inverse map to get it's skeleton
eroded_img = cv.CreateMat(dist_img.height, dist_img.width, cv.CV_8UC1)
cv.Erode(inverted_img, eroded_img, iterations=10)
#Create inverse image
for r in range(0,eroded_img.rows):
for c in range(0,eroded_img.cols):
inverted_img[r,c]=255-eroded_img[r,c]
return inverted_img
def preprocessing(im):
gray = cv.CreateImage((im.width, im.height), 8, 1)
out = cv.CreateImage((im.width,im.height),cv.IPL_DEPTH_8U,1)
cv.CvtColor(im,gray,cv.CV_BGR2GRAY)
cv.Threshold(gray,out,110,255,cv.CV_THRESH_BINARY_INV)
return gray
'''
def crack(tocrack):
im = cv.CreateImage(cv.GetSize(tocrack), 8, 1)
cv.Split(tocrack, None, None, im, None)
cv.Threshold(im, im, 250, 255, cv.CV_THRESH_BINARY)
txt = pytesser.iplimage_to_string(im)
return txt[:-2]
def processImage(self, curframe):
cv.Smooth(curframe, curframe) #Remove false positives
if not self.absdiff_frame: #For the first time put values in difference, temp and moving_average
self.absdiff_frame = cv.CloneImage(curframe)
self.previous_frame = cv.CloneImage(curframe)
cv.Convert(
curframe, self.average_frame
) #Should convert because after runningavg take 32F pictures
else:
cv.RunningAvg(curframe, self.average_frame,
0.05) #Compute the average
cv.Convert(self.average_frame,
self.previous_frame) #Convert back to 8U frame
cv.AbsDiff(curframe, self.previous_frame,
self.absdiff_frame) # moving_average - curframe
cv.CvtColor(
self.absdiff_frame, self.gray_frame,
cv.CV_RGB2GRAY) #Convert to gray otherwise can't do threshold
cv.Threshold(self.gray_frame, self.gray_frame, 50, 255,
cv.CV_THRESH_BINARY)
cv.Dilate(self.gray_frame, self.gray_frame, None,
15) #to get object blobs
cv.Erode(self.gray_frame, self.gray_frame, None, 10)
def binary_image(image, min_level, max_level):
new_image = cv.CreateImage((image.width, image.height), image.depth,
image.nChannels)
minVal, maxVal, minLoc, maxLoc = cv.MinMaxLoc(image)
threshold = minVal * min_level + maxVal * max_level
cv.Threshold(image, new_image, threshold, 255, cv.CV_THRESH_BINARY)
return new_image
def difference_image(img1, img2):
print " simg1 = simplify(img1)"
simg1 = simplify(img1)
print " simg2 = simplify(img2)"
simg2 = simplify(img2)
#dbg_image('simg1',simg1)
#dbg_image('simg2',simg2)
#create image buffers
img3 = cv.CreateImage(cv.GetSize(img2), cv.IPL_DEPTH_8U, 1)
simg3 = cv.CloneImage(img3)
bitimage = cv.CreateImage(cv.GetSize(img2), cv.IPL_DEPTH_8U, 1)
eimg3 = cv.CloneImage(bitimage)
#process
print " cv.AbsDiff(simg2,simg1,img3)"
cv.AbsDiff(simg2, simg1, img3)
print " cv.Smooth(img3,simg3)"
cv.Smooth(img3, simg3)
#dbg_image('simg3',simg3)
# these threshold values must be calibrated
#cv.Threshold(simg3,bitimage,16,255,cv.CV_THRESH_TOZERO_INV)
print " cv.Threshold(simg3,bitimage,16,255,cv.CV_THRESH_BINARY)"
cv.Threshold(simg3, bitimage, 50, 255, cv.CV_THRESH_BINARY)
#dbg_image('bitimage',bitimage)
print " cv.Erode(bitimage,eimg3)"
cv.Erode(bitimage, eimg3)
#dbg_image('eimg3',eimg3)
return eimg3
def update_mhi(img, dst, diff_threshold):
global last
global mhi
global storage
global mask
global orient
global segmask
timestamp = time.clock() / CLOCKS_PER_SEC # get current time in seconds
size = cv.GetSize(img) # get current frame size
idx1 = last
if not mhi or cv.GetSize(mhi) != size:
for i in range(N):
buf[i] = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
cv.Zero(buf[i])
mhi = cv.CreateImage(size,cv. IPL_DEPTH_32F, 1)
cv.Zero(mhi) # clear MHI at the beginning
orient = cv.CreateImage(size,cv. IPL_DEPTH_32F, 1)
segmask = cv.CreateImage(size,cv. IPL_DEPTH_32F, 1)
mask = cv.CreateImage(size,cv. IPL_DEPTH_8U, 1)
cv.CvtColor(img, buf[last], cv.CV_BGR2GRAY) # convert frame to grayscale
idx2 = (last + 1) % N # index of (last - (N-1))th frame
last = idx2
silh = buf[idx2]
cv.AbsDiff(buf[idx1], buf[idx2], silh) # get difference between frames
cv.Threshold(silh, silh, diff_threshold, 1, cv.CV_THRESH_BINARY) # and threshold it
cv.UpdateMotionHistory(silh, mhi, timestamp, MHI_DURATION) # update MHI
cv.CvtScale(mhi, mask, 255./MHI_DURATION,
(MHI_DURATION - timestamp)*255./MHI_DURATION)
cv.Zero(dst)
cv.Merge(mask, None, None, None, dst)
cv.CalcMotionGradient(mhi, mask, orient, MAX_TIME_DELTA, MIN_TIME_DELTA, 3)
if not storage:
storage = cv.CreateMemStorage(0)
seq = cv.SegmentMotion(mhi, segmask, storage, timestamp, MAX_TIME_DELTA)
for (area, value, comp_rect) in seq:
if comp_rect[2] + comp_rect[3] > 100: # reject very small components
color = cv.CV_RGB(255, 0,0)
silh_roi = cv.GetSubRect(silh, comp_rect)
mhi_roi = cv.GetSubRect(mhi, comp_rect)
orient_roi = cv.GetSubRect(orient, comp_rect)
mask_roi = cv.GetSubRect(mask, comp_rect)
angle = 360 - cv.CalcGlobalOrientation(orient_roi, mask_roi, mhi_roi, timestamp, MHI_DURATION)
count = cv.Norm(silh_roi, None, cv.CV_L1, None) # calculate number of points within silhouette ROI
if count < (comp_rect[2] * comp_rect[3] * 0.05):
continue
magnitude = 30.
center = ((comp_rect[0] + comp_rect[2] / 2), (comp_rect[1] + comp_rect[3] / 2))
cv.Circle(dst, center, cv.Round(magnitude*1.2), color, 3, cv.CV_AA, 0)
cv.Line(dst,
center,
(cv.Round(center[0] + magnitude * cos(angle * cv.CV_PI / 180)),
cv.Round(center[1] - magnitude * sin(angle * cv.CV_PI / 180))),
color,
3,
cv.CV_AA,
0)
def lines2():
im = cv.LoadImage('roi_edges.jpg', cv.CV_LOAD_IMAGE_GRAYSCALE)
pi = math.pi
x = 0
dst = cv.CreateImage(cv.GetSize(im), 8, 1)
cv.Canny(im, dst, 200, 200)
cv.Threshold(dst, dst, 100, 255, cv.CV_THRESH_BINARY)
color_dst_standard = cv.CreateImage(cv.GetSize(im), 8, 3)
cv.CvtColor(im, color_dst_standard,
cv.CV_GRAY2BGR) #Create output image in RGB to put red lines
lines = cv.HoughLines2(dst, cv.CreateMemStorage(0), cv.CV_HOUGH_STANDARD,
1, pi / 100, 71, 0, 0)
klsum = 0
klaver = 0
krsum = 0
kraver = 0
#global k
#k=0
for (rho, theta) in lines[:100]:
kl = []
kr = []
a = math.cos(theta)
b = math.sin(theta)
x0 = a * rho
y0 = b * rho
pt1 = (cv.Round(x0 + 1000 * (-b)), cv.Round(y0 + 1000 * (a)))
pt2 = (cv.Round(x0 - 1000 * (-b)), cv.Round(y0 - 1000 * (a)))
k = ((y0 - 1000 * (a)) - (y0 + 1000 * (a))) / ((x0 - 1000 * (-b)) -
(x0 + 1000 * (-b)))
if abs(k) < 0.4:
pass
elif k > 0:
kr.append(k)
len_kr = len(kr)
for i in kr:
krsum = krsum + i
kraver = krsum / len_kr
cv.Line(color_dst_standard, pt1, pt2, cv.CV_RGB(255, 0, 0), 2,
4)
elif k < 0:
kr.append(k)
kl.append(k)
len_kl = len(kl)
for i in kl:
klsum = klsum + i
klaver = klsum / len_kl
cv.Line(color_dst_standard, pt1, pt2, cv.CV_RGB(255, 0, 0), 2,
4)
#print k
# cv.Line(color_dst_standard, pt1, pt2, cv.CV_RGB(255, 0, 0), 2, 4)
cv.SaveImage('lane.jpg', color_dst_standard)
print '左车道平均斜率:', klaver, ' 右车道平均斜率:', kraver
cv.ShowImage("Hough Standard", color_dst_standard)
cv.WaitKey(0)
def processImage(self, frame):
cv.CvtColor(frame, self.frame2gray, cv.CV_RGB2GRAY)
#Absdiff to get the difference between to the frames
cv.AbsDiff(self.frame1gray, self.frame2gray, self.res)
#Remove the noise and do the threshold
cv.Smooth(self.res, self.res, cv.CV_BLUR, 5, 5)
cv.MorphologyEx(self.res, self.res, None, None, cv.CV_MOP_OPEN)
cv.MorphologyEx(self.res, self.res, None, None, cv.CV_MOP_CLOSE)
cv.Threshold(self.res, self.res, 10, 255, cv.CV_THRESH_BINARY_INV)
def find_squares4(color_img):
"""
Finds multiple squares in image
Steps:
-Use Canny edge to highlight contours, and dilation to connect
the edge segments.
-Threshold the result to binary edge tokens
-Use cv.FindContours: returns a cv.CvSequence of cv.CvContours
-Filter each candidate: use Approx poly, keep only contours with 4 vertices,
enough area, and ~90deg angles.
Return all squares contours in one flat list of arrays, 4 x,y points each.
"""
#select even sizes only
width, height = (color_img.width & -2, color_img.height & -2)
timg = cv.CloneImage(color_img) # make a copy of input image
gray = cv.CreateImage((width, height), 8, 1)
# select the maximum ROI in the image
cv.SetImageROI(timg, (0, 0, width, height))
# down-scale and upscale the image to filter out the noise
pyr = cv.CreateImage((width / 2, height / 2), 8, 3)
cv.PyrDown(timg, pyr, 7)
cv.PyrUp(pyr, timg, 7)
tgray = cv.CreateImage((width, height), 8, 1)
squares = []
# Find squares in every color plane of the image
# Two methods, we use both:
# 1. Canny to catch squares with gradient shading. Use upper threshold
# from slider, set the lower to 0 (which forces edges merging). Then
# dilate canny output to remove potential holes between edge segments.
# 2. Binary thresholding at multiple levels
N = 11
for c in [0, 1, 2]:
#extract the c-th color plane
cv.SetImageCOI(timg, c + 1)
cv.Copy(timg, tgray, None)
cv.Canny(tgray, gray, 0, 50, 5)
cv.Dilate(gray, gray)
squares = squares + find_squares_from_binary(gray)
# Look for more squares at several threshold levels
for l in range(1, N):
cv.Threshold(tgray, gray, (l + 1) * 255 / N, 255,
cv.CV_THRESH_BINARY)
squares = squares + find_squares_from_binary(gray)
return squares
def process_image(self): """Process the image from the camera in order to remove noise.""" cv.CvtColor(self.colorFrame, self.currentGrayFrame, cv.CV_RGB2GRAY) # remove noise, etc. self.currentGrayFrame = self.reduce_image_noise(self.currentGrayFrame) # find the difference between the current and previous frame cv.AbsDiff(self.currentGrayFrame, self.previousGrayFrame, self.resultImage) # calculate the binary image that shows where there is change in the image cv.Threshold(self.resultImage, self.resultImage, 10, 255, cv.CV_THRESH_BINARY_INV) cv.Copy(self.currentGrayFrame, self.previousGrayFrame)
def process_image(self, slider_pos):
"""
This function finds contours, draws them and their approximation by ellipses.
"""
stor = cv.CreateMemStorage()
# Create the destination images
image02 = cv.CloneImage(self.source_image)
cv.Zero(image02)
image04 = cv.CreateImage(cv.GetSize(self.source_image),
cv.IPL_DEPTH_8U, 3)
cv.Zero(image04)
# Threshold the source image. This needful for cv.FindContours().
cv.Threshold(self.source_image, image02, slider_pos, 255,
cv.CV_THRESH_BINARY)
# Find all contours.
cont = cv.FindContours(image02, stor, cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_NONE, (0, 0))
for c in contour_iterator(cont):
# Number of points must be more than or equal to 6 for cv.FitEllipse2
if len(c) >= 6:
# Copy the contour into an array of (x,y)s
PointArray2D32f = cv.CreateMat(1, len(c), cv.CV_32FC2)
for (i, (x, y)) in enumerate(c):
PointArray2D32f[0, i] = (x, y)
# Draw the current contour in gray
gray = cv.CV_RGB(100, 100, 100)
cv.DrawContours(image04, c, gray, gray, 0, 1, 8, (0, 0))
# Fits ellipse to current contour.
(center, size, angle) = cv.FitEllipse2(PointArray2D32f)
# Convert ellipse data from float to integer representation.
center = (cv.Round(center[0]), cv.Round(center[1]))
size = (cv.Round(size[0] * 0.5), cv.Round(size[1] * 0.5))
# Draw ellipse in random color
color = cv.CV_RGB(random.randrange(256), random.randrange(256),
random.randrange(256))
cv.Ellipse(image04, center, size, angle, 0, 360, color, 2,
cv.CV_AA, 0)
# Show image. HighGUI use.
cv.ShowImage("Result", image04)
def do_loop(self):
# image processing
if self.config.threshold:
cv.Threshold(self.img_original, self.img_target,
self.config.pix_thresh_min, 0xff, cv.CV_THRESH_BINARY)
cv.And(self.img_target, self.img_mask, self.img_target)
if self.config.dilate:
cv.Dilate(self.img_target,
self.img_target,
iterations=self.config.dilate)
if self.config.erode:
cv.Erode(self.img_target,
self.img_target,
iterations=self.config.erode)
show_image(self)
sys.stdout.write('> ')
sys.stdout.flush()
# keystroke processing
ki = cv.WaitKey(0)
# Simple character value, if applicable
kc = None
# Char if a common char, otherwise the integer code
k = ki
if 0 <= ki < 256:
kc = chr(ki)
k = kc
elif 65506 < ki < 66000 and ki != 65535:
ki2 = ki - 65506 - 30
# modifier keys
if ki2 >= 0:
kc = chr(ki2)
k = kc
if kc:
print '%d (%s)\n' % (ki, kc)
else:
print '%d\n' % ki
if ki > 66000:
return
if ki < 0:
print "Exiting on closed window"
self.running = False
return
on_key(self, k)
def on_trackbar(edge_thresh):
cv.Threshold(gray, edge, float(edge_thresh), float(edge_thresh),
cv.CV_THRESH_BINARY)
#Distance transform
cv.DistTransform(edge, dist, cv.CV_DIST_L2, cv.CV_DIST_MASK_5)
cv.ConvertScale(dist, dist, 5000.0, 0)
cv.Pow(dist, dist, 0.5)
cv.ConvertScale(dist, dist32s, 1.0, 0.5)
cv.AndS(dist32s, cv.ScalarAll(255), dist32s, None)
cv.ConvertScale(dist32s, dist8u1, 1, 0)
cv.ConvertScale(dist32s, dist32s, -1, 0)
cv.AddS(dist32s, cv.ScalarAll(255), dist32s, None)
cv.ConvertScale(dist32s, dist8u2, 1, 0)
cv.Merge(dist8u1, dist8u2, dist8u2, None, dist8u)
cv.ShowImage(wndname, dist8u)
def on_mouse(event, x, y, flags, param):
if (not color_img):
return
if event == cv.CV_EVENT_LBUTTONDOWN:
my_mask = None
seed = (x, y)
if ffill_case == 0:
lo = up = 0
flags = connectivity + (new_mask_val << 8)
else:
lo = lo_diff
up = up_diff
flags = connectivity + (
new_mask_val << 8) + cv.CV_FLOODFILL_FIXED_RANGE
b = random.randint(0, 255)
g = random.randint(0, 255)
r = random.randint(0, 255)
if (is_mask):
my_mask = mask
cv.Threshold(mask, mask, 1, 128, cv.CV_THRESH_BINARY)
if (is_color):
color = cv.CV_RGB(r, g, b)
comp = cv.FloodFill(color_img, seed, color, cv.CV_RGB(lo, lo, lo),
cv.CV_RGB(up, up, up), flags, my_mask)
cv.ShowImage("image", color_img)
else:
brightness = cv.RealScalar((r * 2 + g * 7 + b + 5) / 10)
comp = cv.FloodFill(gray_img, seed, brightness, cv.RealScalar(lo),
cv.RealScalar(up), flags, my_mask)
cv.ShowImage("image", gray_img)
print "%g pixels were repainted" % comp[0]
if (is_mask):
cv.ShowImage("mask", mask)
def motionDetect(self, img):
cv.Smooth(img, img, cv.CV_GAUSSIAN, 3, 0)
cv.RunningAvg(img, self.movingAvg, 0.020, None)
cv.ConvertScale(self.movingAvg, self.tmp, 1.0, 0.0)
cv.AbsDiff(img, self.tmp, self.diff)
cv.CvtColor(self.diff, self.grayImage, cv.CV_RGB2GRAY)
cv.Threshold(self.grayImage, self.grayImage, 70,255, cv.CV_THRESH_BINARY)
cv.Dilate(self.grayImage, self.grayImage, None, 18)#18
cv.Erode(self.grayImage, self.grayImage, None, 10)#10
storage = cv.CreateMemStorage(0)
contour = cv.FindContours(self.grayImage, storage, cv.CV_RETR_CCOMP, cv.CV_CHAIN_APPROX_SIMPLE)
# points = []
while contour:
boundRect = cv.BoundingRect(list(contour))
contour = contour.h_next()
pt1 = (boundRect[0], boundRect[1])
pt2 = (boundRect[0] + boundRect[2], boundRect[1] + boundRect[3])
cv.Rectangle(img, pt1, pt2, cv.CV_RGB(255,255,0), 1)
return img
def processaImagem(self):
"""
Crio uma imagem cinza a partir da atual para o programa ficar mais rapido, crio uma imagem com a
diferenca da imagem anterior e a imagem atual, e binarizo a imagem cinza para filtrar pixels pequenos.
"""
# Remove os falsos positivos.
cv.Smooth(self.imagem_atual, self.imagem_atual)
# Aqui eu coloco um tempo de execucao entre as imagens.
cv.RunningAvg(self.imagem_atual, self.imagem_auxiliar, 0.05)
# Covertendo de volta a imagem para poder trabalhar.
cv.Convert(self.imagem_auxiliar, self.imagem_anterior)
# Cria uma nova imagem com a diferenca entre a imagem anterior e a atual.
cv.AbsDiff(self.imagem_atual, self.imagem_anterior, self.imagem_diferenca)
# Converte a imagem atual em escala de cinza.
cv.CvtColor(self.imagem_diferenca, self.imagem_cinza, cv.CV_RGB2GRAY)
# Binariza a imagem. Para poder filtrar pixels pequenos.
cv.Threshold(self.imagem_cinza, self.imagem_cinza, 50, 255, cv.CV_THRESH_BINARY)
def process_image(self, slider_pos):
global cimg, source_image1, ellipse_size, maxf, maxs, eoc, lastcx, lastcy, lastr
"""
This function finds contours, draws them and their approximation by ellipses.
"""
stor = cv.CreateMemStorage()
# Create the destination images
cimg = cv.CloneImage(self.source_image)
cv.Zero(cimg)
image02 = cv.CloneImage(self.source_image)
cv.Zero(image02)
image04 = cv.CreateImage(cv.GetSize(self.source_image),
cv.IPL_DEPTH_8U, 3)
cv.Zero(image04)
# Threshold the source image. This needful for cv.FindContours().
cv.Threshold(self.source_image, image02, slider_pos, 255,
cv.CV_THRESH_BINARY)
# Find all contours.
cont = cv.FindContours(image02, stor, cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_NONE, (0, 0))
maxf = 0
maxs = 0
size1 = 0
for c in contour_iterator(cont):
if len(c) > ellipse_size:
PointArray2D32f = cv.CreateMat(1, len(c), cv.CV_32FC2)
for (i, (x, y)) in enumerate(c):
PointArray2D32f[0, i] = (x, y)
# Draw the current contour in gray
gray = cv.CV_RGB(100, 100, 100)
cv.DrawContours(image04, c, gray, gray, 0, 1, 8, (0, 0))
if iter == 0:
strng = segF + '/' + 'contour1.png'
cv.SaveImage(strng, image04)
color = (255, 255, 255)
(center, size, angle) = cv.FitEllipse2(PointArray2D32f)
# Convert ellipse data from float to integer representation.
center = (cv.Round(center[0]), cv.Round(center[1]))
size = (cv.Round(size[0] * 0.5), cv.Round(size[1] * 0.5))
if iter == 1:
if size[0] > size[1]:
size2 = size[0]
else:
size2 = size[1]
if size2 > size1:
size1 = size2
size3 = size
# Fits ellipse to current contour.
if eoc == 0 and iter == 2:
rand_val = abs((lastr - ((size[0] + size[1]) / 2)))
if rand_val > 20 and float(max(size[0], size[1])) / float(
min(size[0], size[1])) < 1.5:
lastcx = center[0]
lastcy = center[1]
lastr = (size[0] + size[1]) / 2
if rand_val > 20 and float(max(size[0], size[1])) / float(
min(size[0], size[1])) < 1.4:
cv.Ellipse(cimg, center, size, angle, 0, 360, color, 2,
cv.CV_AA, 0)
cv.Ellipse(source_image1, center, size, angle, 0, 360,
color, 2, cv.CV_AA, 0)
elif eoc == 1 and iter == 2:
(int, cntr, rad) = cv.MinEnclosingCircle(PointArray2D32f)
cntr = (cv.Round(cntr[0]), cv.Round(cntr[1]))
rad = (cv.Round(rad))
if maxf == 0 and maxs == 0:
cv.Circle(cimg, cntr, rad, color, 1, cv.CV_AA, shift=0)
cv.Circle(source_image1,
cntr,
rad,
color,
2,
cv.CV_AA,
shift=0)
maxf = rad
elif (maxf > 0 and maxs == 0) and abs(rad - maxf) > 30:
cv.Circle(cimg, cntr, rad, color, 2, cv.CV_AA, shift=0)
cv.Circle(source_image1,
cntr,
rad,
color,
2,
cv.CV_AA,
shift=0)
maxs = len(c)
if iter == 1:
temp3 = 2 * abs(size3[1] - size3[0])
if (temp3 > 40):
eoc = 1
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)
cornerMap = cv.CreateMat(dst_32f.height, dst_32f.width, cv.CV_8U)
cv.Convert(dst_32f, cornerMap) #Convert to make the and
cv.And(cornerMap, localMax, cornerMap) #Delete all modified pixels
radius = 3
thickness = 2
l = []
for x in range(
cornerMap.height
): #Create the list of point take all pixel that are not 0 (so not black)
for y in range(cornerMap.width):
if cornerMap[x, y]:
l.append((y, x))
h = frame2gray.height
nb_pixels = frame2gray.width * frame2gray.height
while True:
frame2 = cv.QueryFrame(capture)
cv.CvtColor(frame2, frame2gray, cv.CV_RGB2GRAY)
cv.AbsDiff(frame1gray, frame2gray, res)
cv.ShowImage("After AbsDiff", res)
cv.Smooth(res, res, cv.CV_BLUR, 5, 5)
element = cv.CreateStructuringElementEx(5 * 2 + 1, 5 * 2 + 1, 5, 5,
cv.CV_SHAPE_RECT)
cv.MorphologyEx(res, res, None, None, cv.CV_MOP_OPEN)
cv.MorphologyEx(res, res, None, None, cv.CV_MOP_CLOSE)
cv.Threshold(res, res, 10, 255, cv.CV_THRESH_BINARY_INV)
cv.ShowImage("Image", frame2)
cv.ShowImage("Res", res)
# -----------
nb = 0
for y in range(h):
for x in range(w):
if res[y, x] == 0.0:
nb += 1
avg = (nb * 100.0) / nb_pixels
# print "Average: ",avg, "%\r",
if avg >= 5:
print
"Something is moving !"
def run(self):
# Capture first frame to get size
frame = cv.QueryFrame(self.capture)
frame_size = cv.GetSize(frame)
width = frame.width
height = frame.height
surface = width * height # Surface area of the image
cursurface = 0 # Hold the current surface that have changed
grey_image = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
moving_average = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_32F, 3)
difference = None
while True:
color_image = cv.QueryFrame(self.capture)
cv.Smooth(color_image, color_image, cv.CV_GAUSSIAN, 3,
0) # Remove false positives
if not difference: # For the first time put values in difference, temp and moving_average
difference = cv.CloneImage(color_image)
temp = cv.CloneImage(color_image)
cv.ConvertScale(color_image, moving_average, 1.0, 0.0)
else:
cv.RunningAvg(color_image, moving_average, 0.020,
None) # Compute the average
# Convert the scale of the moving average.
cv.ConvertScale(moving_average, temp, 1.0, 0.0)
# Minus the current frame from the moving average.
cv.AbsDiff(color_image, temp, difference)
# Convert the image so that it can be thresholded
cv.CvtColor(difference, grey_image, cv.CV_RGB2GRAY)
cv.Threshold(grey_image, grey_image, 70, 255, cv.CV_THRESH_BINARY)
cv.Dilate(grey_image, grey_image, None, 18) # to get object blobs
cv.Erode(grey_image, grey_image, None, 10)
# Find contours
storage = cv.CreateMemStorage(0)
contours = cv.FindContours(grey_image, storage,
cv.CV_RETR_EXTERNAL,
cv.CV_CHAIN_APPROX_SIMPLE)
backcontours = contours # Save contours
while contours: # For all contours compute the area
cursurface += cv.ContourArea(contours)
contours = contours.h_next()
avg = (
cursurface * 100
) / surface # Calculate the average of contour area on the total size
if avg > self.ceil:
print("Something is moving !")
ring = IntrusionAlarm()
ring.run()
# print avg,"%"
cursurface = 0 # Put back the current surface to 0
# Draw the contours on the image
_red = (0, 0, 255)
# Red for external contours
_green = (0, 255, 0)
# Gren internal contours
levels = 1 # 1 contours drawn, 2 internal contours as well, 3 ...
cv.DrawContours(color_image, backcontours, _red, _green, levels, 2,
cv.CV_FILLED)
cv.ShowImage("Virtual Eye", color_image)
# Listen for ESC or ENTER key
c = cv.WaitKey(7) % 0x100
if c == 27 or c == 10:
break
elif c == 99:
cv2.destroyWindow('Warning!!!')
def run(self):
# Capture first frame to get size
frame = cv.QueryFrame(self.capture)
#nframes =+ 1
frame_size = cv.GetSize(frame)
color_image = cv.CreateImage(cv.GetSize(frame), 8, 3)
grey_image = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
moving_average = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_32F, 3)
def totuple(a):
try:
return tuple(totuple(i) for i in a)
except TypeError:
return a
first = True
while True:
closest_to_left = cv.GetSize(frame)[0]
closest_to_right = cv.GetSize(frame)[1]
color_image = cv.QueryFrame(self.capture)
# Smooth to get rid of false positives
cv.Smooth(color_image, color_image, cv.CV_GAUSSIAN, 3, 0)
if first:
difference = cv.CloneImage(color_image)
temp = cv.CloneImage(color_image)
cv.ConvertScale(color_image, moving_average, 1.0, 0.0)
first = False
else:
cv.RunningAvg(color_image, moving_average, .1, None)
cv.ShowImage("BG", moving_average)
# Convert the scale of the moving average.
cv.ConvertScale(moving_average, temp, 1, 0.0)
# Minus the current frame from the moving average.
cv.AbsDiff(color_image, temp, difference)
#cv.ShowImage("BG",difference)
# Convert the image to grayscale.
cv.CvtColor(difference, grey_image, cv.CV_RGB2GRAY)
cv.ShowImage("BG1", grey_image)
# Convert the image to black and white.
cv.Threshold(grey_image, grey_image, 40, 255, cv.CV_THRESH_BINARY)
#cv.ShowImage("BG2", grey_image)
# Dilate and erode to get people blobs
cv.Dilate(grey_image, grey_image, None, 8)
cv.Erode(grey_image, grey_image, None, 3)
cv.ShowImage("BG3", grey_image)
storage = cv.CreateMemStorage(0)
global contour
contour = cv.FindContours(grey_image, storage, cv.CV_RETR_CCOMP,
cv.CV_CHAIN_APPROX_SIMPLE)
points = []
while contour:
global bound_rect
bound_rect = cv.BoundingRect(list(contour))
polygon_points = cv.ApproxPoly(list(contour), storage,
cv.CV_POLY_APPROX_DP)
contour = contour.h_next()
global pt1, pt2
pt1 = (bound_rect[0], bound_rect[1])
pt2 = (bound_rect[0] + bound_rect[2],
bound_rect[1] + bound_rect[3])
#size control
if (bound_rect[0] - bound_rect[2] >
10) and (bound_rect[1] - bound_rect[3] > 10):
points.append(pt1)
points.append(pt2)
#points += list(polygon_points)
global box, box2, box3, box4, box5
box = cv.MinAreaRect2(polygon_points)
box2 = cv.BoxPoints(box)
box3 = np.int0(np.around(box2))
box4 = totuple(box3)
box5 = box4 + (box4[0], )
cv.FillPoly(grey_image, [
list(polygon_points),
], cv.CV_RGB(255, 255, 255), 0, 0)
cv.PolyLine(color_image, [
polygon_points,
], 0, cv.CV_RGB(255, 255, 255), 1, 0, 0)
cv.PolyLine(color_image, [list(box5)], 0, (0, 0, 255), 2)
#cv.Rectangle(color_image, pt1, pt2, cv.CV_RGB(255,0,0), 1)
if len(points):
#center_point = reduce(lambda a, b: ((a[0] + b[0]) / 2, (a[1] + b[1]) / 2), points)
center1 = (pt1[0] + pt2[0]) / 2
center2 = (pt1[1] + pt2[1]) / 2
#print center1, center2, center_point
#cv.Circle(color_image, center_point, 40, cv.CV_RGB(255, 255, 255), 1)
#cv.Circle(color_image, center_point, 30, cv.CV_RGB(255, 100, 0), 1)
#cv.Circle(color_image, center_point, 20, cv.CV_RGB(255, 255, 255), 1)
cv.Circle(color_image, (center1, center2), 5,
cv.CV_RGB(0, 0, 255), -1)
cv.ShowImage("Target", color_image)
# Listen for ESC key
c = cv.WaitKey(7) % 0x100
if c == 27:
#cv.DestroyAllWindows()
break
def store_proba(self, proba):
# print "Got Image"
if not self.info:
return
# print "Processing"
self.timestamp = proba.header.stamp
I = self.br.imgmsg_to_cv(proba, "8UC1")
self.proba = cv.CloneMat(I)
cv.Threshold(I, self.proba, 0xFE, 0xFE, cv.CV_THRESH_TRUNC)
try:
# (trans,rot) = self.listener.lookupTransform(proba.header.frame_id, '/world', proba.header.stamp)
self.listener.waitForTransform(proba.header.frame_id,
self.target_frame,
proba.header.stamp,
rospy.Duration(1.0))
trans = numpy.mat(
self.listener.asMatrix(self.target_frame, proba.header))
# print "Transformation"
# print trans
dstdir = [trans * v for v in self.dirpts3d]
# print "Destination dir"
# print dstdir
origin = trans * self.origin
origin = origin / origin[3, 0]
# origin = numpy.matrix([0.0, 0.0, origin[2,0] / origin[3,0], 1.0]).T
# print "Origin"
# print origin
self.dstpts2d = cv.CreateMat(4, 2, cv.CV_32F)
for i in range(4):
self.dstpts2d[i, 0] = self.x_floor + (origin[0, 0] - dstdir[i][
0, 0] * origin[2, 0] / dstdir[i][2, 0]) * self.floor_scale
self.dstpts2d[i, 1] = self.y_floor - (origin[1, 0] - dstdir[i][
1, 0] * origin[2, 0] / dstdir[i][2, 0]) * self.floor_scale
# print numpy.asarray(self.dstpts2d)
# print "Source points"
# print numpy.asarray(self.srcpts2d)
# print "Dest points"
# print numpy.asarray(self.dstpts2d)
self.H = cv.CreateMat(3, 3, cv.CV_32F)
cv.FindHomography(self.srcpts2d, self.dstpts2d, self.H)
# print "Homography"
# print numpy.asarray(self.H)
cv.WarpPerspective(cv.GetSubRect(
self.proba, (0, self.horizon_offset, self.proba.width,
self.proba.height - self.horizon_offset)),
self.floor_map,
self.H,
flags=cv.CV_INTER_NN + cv.CV_WARP_FILL_OUTLIERS,
fillval=0xFF)
msg = self.br.cv_to_imgmsg(self.floor_map)
msg.header.stamp = proba.header.stamp
msg.header.frame_id = self.target_frame
self.pub.publish(msg)
# print "Publishing image"
except (tf.LookupException, tf.ConnectivityException,
tf.ExtrapolationException):
print "Exception while looking for transform"
return
#CONTOUR MAKING CODE
# create the image where we want to display results
image = cv.CreateImage((_SIZE, _SIZE), 8, 1)
# start with an empty image
cv.SetZero(image)
im = cv.LoadImage("C:\\3d-Model\\bin\\segmentation_files\\pic_seg.jpg",
cv.CV_LOAD_IMAGE_COLOR)
image = cv.CreateImage((im.width, im.height), 8, 1)
cv.CvtColor(im, image, cv.CV_BGR2GRAY)
threshold = 51
colour = 255
cv.Threshold(image, image, threshold, colour, cv.CV_THRESH_BINARY)
# create the window for the contours
cv.NamedWindow("contours", cv.CV_WINDOW_NORMAL)
# create the trackbar, to enable the change of the displayed level
cv.CreateTrackbar("levels+3", "contours", 3, 7, on_contour)
# create the storage area for contour image
storage = cv.CreateMemStorage(0)
# find the contours
contours = cv.FindContours(image, storage, cv.CV_RETR_TREE,
cv.CV_CHAIN_APPROX_SIMPLE, (0, 0))
# polygon approxomation
def run(self):
# Initialize
# log_file_name = "tracker_output.log"
# log_file = file( log_file_name, 'a' )
print "hello"
frame = cv.QueryFrame(self.capture)
frame_size = cv.GetSize(frame)
# Capture the first frame from webcam for image properties
display_image = cv.QueryFrame(self.capture)
# Greyscale image, thresholded to create the motion mask:
grey_image = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
# The RunningAvg() function requires a 32-bit or 64-bit image...
running_average_image = cv.CreateImage(cv.GetSize(frame),
cv.IPL_DEPTH_32F, 3)
# ...but the AbsDiff() function requires matching image depths:
running_average_in_display_color_depth = cv.CloneImage(display_image)
# RAM used by FindContours():
mem_storage = cv.CreateMemStorage(0)
# The difference between the running average and the current frame:
difference = cv.CloneImage(display_image)
target_count = 1
last_target_count = 1
last_target_change_t = 0.0
k_or_guess = 1
codebook = []
frame_count = 0
last_frame_entity_list = []
t0 = time.time()
# For toggling display:
image_list = ["camera", "difference", "threshold", "display", "faces"]
image_index = 3 # Index into image_list
# Prep for text drawing:
text_font = cv.InitFont(cv.CV_FONT_HERSHEY_COMPLEX, .5, .5, 0.0, 1,
cv.CV_AA)
text_coord = (5, 15)
text_color = cv.CV_RGB(255, 255, 255)
# Set this to the max number of targets to look for (passed to k-means):
max_targets = 5
while True:
# Capture frame from webcam
camera_image = cv.QueryFrame(self.capture)
frame_count += 1
frame_t0 = time.time()
# Create an image with interactive feedback:
display_image = cv.CloneImage(camera_image)
# Create a working "color image" to modify / blur
color_image = cv.CloneImage(display_image)
# Smooth to get rid of false positives
cv.Smooth(color_image, color_image, cv.CV_GAUSSIAN, 19, 0)
# Use the Running Average as the static background
# a = 0.020 leaves artifacts lingering way too long.
# a = 0.320 works well at 320x240, 15fps. (1/a is roughly num frames.)
cv.RunningAvg(color_image, running_average_image, 0.320, None)
# Convert the scale of the moving average.
cv.ConvertScale(running_average_image,
running_average_in_display_color_depth, 1.0, 0.0)
# Subtract the current frame from the moving average.
cv.AbsDiff(color_image, running_average_in_display_color_depth,
difference)
cv.ShowImage("difference ", difference)
# Convert the image to greyscale.
cv.CvtColor(difference, grey_image, cv.CV_RGB2GRAY)
# Threshold the image to a black and white motion mask:
cv.Threshold(grey_image, grey_image, 2, 255, cv.CV_THRESH_BINARY)
# Smooth and threshold again to eliminate "sparkles"
cv.Smooth(grey_image, grey_image, cv.CV_GAUSSIAN, 19, 0)
cv.Threshold(grey_image, grey_image, 240, 255, cv.CV_THRESH_BINARY)
grey_image_as_array = numpy.asarray(cv.GetMat(grey_image))
non_black_coords_array = numpy.where(grey_image_as_array > 3)
# Convert from numpy.where()'s two separate lists to one list of (x, y) tuples:
non_black_coords_array = zip(non_black_coords_array[1],
non_black_coords_array[0])
# cv.SegmentMotion(non_black_coords_array, None, storage, timestamp, seg_thresh)
# print "min_size is: " + str(min_size)
# Listen for ESC or ENTER key
c = cv.WaitKey(7) % 0x100
if c == 27 or c == 10:
break
# Toggle which image to show
# if chr(c) == 'd':
# image_index = ( image_index + 1 ) % len( image_list )
#
# image_name = image_list[ image_index ]
#
# # Display frame to user
# if image_name == "camera":
# image = camera_image
# cv.PutText( image, "Camera (Normal)", text_coord, text_font, text_color )
# elif image_name == "difference":
# image = difference
# cv.PutText( image, "Difference Image", text_coord, text_font, text_color )
# elif image_name == "display":
# image = display_image
# cv.PutText( image, "Targets (w/AABBs and contours)", text_coord, text_font, text_color )
# elif image_name == "threshold":
# # Convert the image to color.
# cv.CvtColor( grey_image, display_image, cv.CV_GRAY2RGB )
# image = display_image # Re-use display image here
# cv.PutText( image, "Motion Mask", text_coord, text_font, text_color )
# elif image_name == "faces":
# # Do face detection
# detect_faces( camera_image, haar_cascade, mem_storage )
# image = camera_image # Re-use camera image here
# cv.PutText( image, "Face Detection", text_coord, text_font, text_color )
# cv.ShowImage( "Target", image )
image1 = display_image
cv.ShowImage("Target 1", image1)
# if self.writer:
# cv.WriteFrame( self.writer, image );
# log_file.flush()
# If only using a camera, then there is no time.sleep() needed,
# because the camera clips us to 15 fps. But if reading from a file,
# we need this to keep the time-based target clipping correct:
frame_t1 = time.time()
# If reading from a file, put in a forced delay:
if not self.writer:
delta_t = frame_t1 - frame_t0
if delta_t < (1.0 / 15.0): time.sleep((1.0 / 15.0) - delta_t)
t1 = time.time()
time_delta = t1 - t0
processed_fps = float(frame_count) / time_delta
print "Got %d frames. %.1f s. %f fps." % (frame_count, time_delta,
processed_fps)
import cv2.cv as cv
image = cv.LoadImage('../img/build.png', cv.CV_LOAD_IMAGE_GRAYSCALE)
#Get edges
morphed = cv.CloneImage(image)
cv.MorphologyEx(image, morphed, None, None,
cv.CV_MOP_GRADIENT) # Apply a dilate - Erode
cv.Threshold(morphed, morphed, 30, 255, cv.CV_THRESH_BINARY_INV)
cv.ShowImage("Image", image)
cv.ShowImage("Morphed", morphed)
cv.WaitKey(0)
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