def redraw():
global draging
global has_roi
global roi_x0
global roi_y0
global cur_mouse_x
global cur_mouse_y
#Redraw ROI selection
image2 = cv.CloneImage(current_image)
# redraw old rect
pen_width = 4
if rect_table.has_key(current_img_file_name):
rects_in_table = rect_table[current_img_file_name]
for r in rects_in_table:
cv.Rectangle(image2, (r[0], r[1]), (r[0] + r[2], r[1] + r[3]),
cv.CV_RGB(0, 255, 0), pen_width)
# redraw new rect
if has_roi:
cv.Rectangle(image2, (roi_x0, roi_y0), (cur_mouse_x, cur_mouse_y),
cv.CV_RGB(255, 0, 255), pen_width)
# draw background
if current_img_file_name in background_files:
cv.Line(image2, (0, 0), (image2.width, image2.height),
cv.CV_RGB(255, 0, 0))
cv.Line(image2, (0, image2.height), (image2.width, 0),
cv.CV_RGB(255, 0, 0))
cv.ShowImage(window_name, image2)
def getIris(frame): iris = [] copyImg = cv.CloneImage(frame) resImg = cv.CloneImage(frame) grayImg = cv.CreateImage(cv.GetSize(frame), 8, 1) mask = cv.CreateImage(cv.GetSize(frame), 8, 1) storage = cv.CreateMat(frame.width, 1, cv.CV_32FC3) cv.CvtColor(frame,grayImg,cv.CV_BGR2GRAY) cv.Canny(grayImg, grayImg, 5, 70, 3) cv.Smooth(grayImg,grayImg,cv.CV_GAUSSIAN, 7, 7) circles = getCircles(grayImg) iris.append(resImg) for circle in circles: rad = int(circle[0][2]) global radius radius = rad cv.Circle(mask, centroid, rad, cv.CV_RGB(255,255,255), cv.CV_FILLED) cv.Not(mask,mask) cv.Sub(frame,copyImg,resImg,mask) x = int(centroid[0] - rad) y = int(centroid[1] - rad) w = int(rad * 2) h = w cv.SetImageROI(resImg, (x,y,w,h)) cropImg = cv.CreateImage((w,h), 8, 3) cv.Copy(resImg,cropImg) cv.ResetImageROI(resImg) return(cropImg) return (resImg)
#---- Standard ----
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 / 180, 100, 0, 0)
for (rho, theta) in lines[:100]:
a = math.cos(theta) #Calculate orientation in order to print them
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)))
cv.Line(color_dst_standard, pt1, pt2, cv.CV_RGB(255, 0, 0), 2,
4) #Draw the line
#---- Probabilistic ----
color_dst_proba = cv.CreateImage(cv.GetSize(im), 8, 3)
cv.CvtColor(im, color_dst_proba, cv.CV_GRAY2BGR) # idem
rho = 1
theta = pi / 180
thresh = 50
minLength = 120 # Values can be changed approximately to fit your image edges
maxGap = 20
lines = cv.HoughLines2(dst, cv.CreateMemStorage(0), cv.CV_HOUGH_PROBABILISTIC,
rho, theta, thresh, minLength, maxGap)
for line in lines:
def add_keypoints(self, track_box):
# Look for any new keypoints around the current keypoints
# Begin with a mask of all black pixels
mask = np.zeros_like(self.grey)
# Get the coordinates and dimensions of the current track box
try:
((x, y), (w, h), a) = track_box
except:
try:
x, y, w, h = track_box
x = x + w / 2
y = y + h / 2
a = 0
except:
rospy.loginfo("Track box has shrunk to zero...")
return
x = int(x)
y = int(y)
# Expand the track box to look for new keypoints
w_new = int(self.expand_roi * w)
h_new = int(self.expand_roi * h)
pt1 = (x - int(w_new / 2), y - int(h_new / 2))
pt2 = (x + int(w_new / 2), y + int(h_new / 2))
mask_box = ((x, y), (w_new, h_new), a)
# Display the expanded ROI with a yellow rectangle
if self.show_add_drop:
cv2.rectangle(self.marker_image, pt1, pt2, cv.RGB(255, 255, 0))
# Create a filled white ellipse within the track_box to define the ROI
cv2.ellipse(mask, mask_box, cv.CV_RGB(255, 255, 255), cv.CV_FILLED)
if self.keypoints is not None:
# Mask the current keypoints
for x, y in [np.int32(p) for p in self.keypoints]:
cv2.circle(mask, (x, y), 5, 0, -1)
new_keypoints = cv2.goodFeaturesToTrack(self.grey,
mask=mask,
**self.gf_params)
# Append new keypoints to the current list if they are not
# too far from the current cluster
if new_keypoints is not None:
for x, y in np.float32(new_keypoints).reshape(-1, 2):
distance = self.distance_to_cluster((x, y), self.keypoints)
if distance > self.add_keypoint_distance:
self.keypoints.append((x, y))
# Briefly display a blue disc where the new point is added
if self.show_add_drop:
cv2.circle(self.marker_image, (x, y), 3,
(255, 255, 0, 0), cv.CV_FILLED, 2, 0)
# Remove duplicate keypoints
self.keypoints = list(set(self.keypoints))
trainloss, _ = sess.run(
[loss, optimizer],
feed_dict={pt_in: tpoints.astype('float32')})
trainloss, trainmatch = sess.run(
[loss, match], feed_dict={pt_in: tpoints.astype('float32')})
#trainmatch=trainmatch.transpose((0,2,1))
show = np.zeros((400, 400, 3), dtype='uint8') ^ 255
trainmypoints = sess.run(mypoints)
for i in range(len(tpoints[0])):
u = np.random.choice(range(len(trainmypoints[0])),
p=trainmatch[0].T[i])
cv2.line(show, (int(tpoints[0][i, 1] * 100 + 200),
int(tpoints[0][i, 0] * 100 + 200)),
(int(trainmypoints[0][u, 1] * 100 + 200),
int(trainmypoints[0][u, 0] * 100 + 200)),
cv2.CV_RGB(0, 255, 0))
for x, y, z in tpoints[0]:
cv2.circle(show, (int(y * 100 + 200), int(x * 100 + 200)), 2,
cv2.cv.CV_RGB(255, 0, 0))
for x, y, z in trainmypoints[0]:
cv2.circle(show, (int(y * 100 + 200), int(x * 100 + 200)), 3,
cv2.cv.CV_RGB(0, 0, 255))
cost = ((tpoints[0][:, None, :] - np.repeat(
trainmypoints[0][None, :, :], 4, axis=1))**2).sum(axis=2)**0.5
#trueloss=bestmatch.bestmatch(cost)[0]
print(trainloss) #,trueloss
cv2.imshow('show', show)
cmd = cv2.waitKey(10) % 256
if cmd == ord('q'):
break
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