def on_mouse(event, x, y, flag, params):
global start_draw
global roi_x0
global roi_y0
global roi_x1
global roi_y1
global image2
if (event == cv.CV_EVENT_LBUTTONDOWN):
print("LButton")
if (not start_draw):
roi_x0 = x
roi_y0 = y
start_draw = True
else:
roi_x1 = x
roi_y1 = y
start_draw = False
elif (event == cv.CV_EVENT_MOUSEMOVE and start_draw):
#Redraw ROI selection
image2 = cv.CloneImage(image)
if (len(rect_list) > 0):
for coord in rect_list:
cv.Rectangle(image2, coord[0], coord[1], cv.CV_RGB(255, 0, 0),
5)
cv.Rectangle(image2, (roi_x0, roi_y0), (x, y), cv.CV_RGB(255, 0, 255),
5)
cv.ShowImage(window_name, image2)
def _mixImageAlphaMask(self, wipeSettings, level, image1, image2, image2mask, mixMat):
if(level < 0.99):
wipeMode, wipePostMix, wipeConfig = wipeSettings
if((wipeMode == WipeMode.Fade) or (wipeMode == WipeMode.Default)):
valueCalc = int(256 * (1.0 - level))
rgbColor = cv.CV_RGB(valueCalc, valueCalc, valueCalc)
whiteColor = cv.CV_RGB(255, 255, 255)
cv.Set(mixMat, whiteColor)
cv.Set(mixMat, rgbColor, image2mask)
cv.Mul(image1, mixMat, image1, 0.004)
valueCalc = int(256 * level)
rgbColor = cv.CV_RGB(valueCalc, valueCalc, valueCalc)
cv.Zero(mixMat)
cv.Set(mixMat, rgbColor, image2mask)
cv.Mul(image2, mixMat, image2, 0.004)
cv.Add(image1, image2, image1)
return image1
else:
if(wipePostMix == False):
image2, image2mask = self._wipeImage(wipeMode, wipeConfig, level, image2, image2mask, mixMat, False)
cv.Copy(image2, image1, image2mask)
return image1
else:
cv.Copy(image1, mixMat)
cv.Copy(image2, mixMat, image2mask)
return self._wipeMix(wipeMode, wipeConfig, level, image1, mixMat, image2)
cv.Copy(image2, image1, image2mask)
return image1
def findImageContour(img, frame):
storage = cv.CreateMemStorage()
cont = cv.FindContours(img, storage, cv.CV_RETR_EXTERNAL,
cv.CV_CHAIN_APPROX_NONE, (0, 0))
max_center = [None, 0]
for c in contour_iterator(cont):
# Number of points must be more than or equal to 6 for cv.FitEllipse2
# Use to set minimum size of object to be tracked.
if len(c) >= 60:
# 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)
# Fits ellipse to current contour.
(center, size, angle) = cv.FitEllipse2(PointArray2D32f)
# Only consider location of biggest contour -- adapt for multiple object tracking
if size > max_center[1]:
max_center[0] = center
max_center[1] = size
angle = angle
if True:
# Draw the current contour in gray
gray = cv.CV_RGB(255, 255, 255)
cv.DrawContours(img, c, gray, gray, 0, 1, 8, (0, 0))
if max_center[1] > 0:
# Convert ellipse data from float to integer representation.
center = (cv.Round(max_center[0][0]), cv.Round(max_center[0][1]))
size = (cv.Round(max_center[1][0] * 0.5),
cv.Round(max_center[1][1] * 0.5))
color = cv.CV_RGB(255, 0, 0)
cv.Ellipse(frame, center, size, angle, 0, 360, color, 3, cv.CV_AA, 0)
def processa_frame(imagem):
cv.SetData(imagem_cv, imagem)
if maos:
for id in maos:
cv.PutText(imagem_cv, efeito, maos[id]['atual'] ,fonte_do_texto , cv.CV_RGB(0,0,150))
cv.PutText(imagem_cv, 'Efeito: '+efeito, (10,20) ,fonte_do_texto , cv.CV_RGB(200,0,0))
cv.ShowImage('Video', imagem_cv)
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 draw_common(points):
success, center, radius = cv.MinEnclosingCircle(points)
if success:
cv.Circle(img, roundxy(center), cv.Round(radius),
cv.CV_RGB(255, 255, 0), 1, cv.CV_AA, 0)
box = cv.MinAreaRect2(points)
box_vtx = [roundxy(p) for p in cv.BoxPoints(box)]
cv.PolyLine(img, [box_vtx], 1, cv.CV_RGB(0, 255, 255), 1, cv.CV_AA)
def altera_quadro():
blink = cv.CloneImage(quadro)
if maos:
for id in maos:
cv.Circle(blink, maos[id]['atual'], 10, cv.CV_RGB(0, 0, 150), -1, cv.CV_AA, 0)
if 'anterior' in maos[id]:
if efeito == 'Caneta':
cv.Line(quadro, maos[id]['anterior'], maos[id]['atual'], cv.CV_RGB(0,0,0), 1, cv.CV_AA, 0)
elif efeito == 'Apagador':
cv.Line(quadro, maos[id]['anterior'], maos[id]['atual'], cv.CV_RGB(255,255,255), 30, cv.CV_AA, 0)
cv.ShowImage('Quadro', blink)
def processa_frame(imagem):
cv.SetData(imagem_cv, imagem)
if gesto == False:
cv.PutText(imagem_cv, 'Acene para ser Rastreado!', (80, 50),
fonte_do_texto, cv.CV_RGB(0, 0, 0))
cv.Circle(imagem_cv, centro, 16, cv.CV_RGB(0, 0, 255), 2, cv.CV_AA, 0)
cv.PutText(imagem_cv, 'Real(mm): ' + coordenada_real, (80, 435),
fonte_do_texto, cv.CV_RGB(255, 255, 255))
cv.PutText(imagem_cv, 'Convertido(px): ' + coordenada_projecao, (80, 465),
fonte_do_texto, cv.CV_RGB(255, 255, 255))
cv.ShowImage('Video', imagem_cv)
def processa_frame(imagem):
cv.SetData(imagem_cv, imagem)
if maos:
for id in maos:
cv.PutText(imagem_cv,
', '.join(str(int(e)) for e in maos[id]['real']),
maos[id]['projecao'], fonte_do_texto,
cv.CV_RGB(0, 0, 150))
else:
cv.PutText(imagem_cv, 'Acene para ser Rastreado', (10, 20),
fonte_do_texto, cv.CV_RGB(200, 0, 0))
cv.ShowImage('Video', imagem_cv)
def Color_callibration(capture):
vals = []
bgr = []
mini = [255, 255, 255]
maxi = [0, 0, 0]
cv.NamedWindow("BGR", 0)
print 'Please Put Your color in the circular area.Press ESC to start Callibration:'
while 1:
image = cv.QueryFrame(capture)
cv.Flip(image, image, 1)
cv.Circle(image, (int(200), int(300)), 10, cv.CV_RGB(255, 255, 255), 4)
cv.ShowImage("BGR", image)
c = cv.WaitKey(33)
if c == 27:
break
print 'Starting Callibration...Analyzing the Object...'
for i in range(0, 100):
image = cv.QueryFrame(capture)
cv.Flip(image, image, 1)
cv.Smooth(image, image, cv.CV_MEDIAN, 3, 0)
imagehsv = cv.CreateImage(cv.GetSize(image), 8, 3)
cv.CvtColor(image, imagehsv, cv.CV_BGR2YCrCb)
vals = cv.Get2D(imagehsv, 300, 200)
font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 0.5, 1, 0, 2, 8)
cv.PutText(
image,
" " + str(vals[0]) + "," + str(vals[1]) + "," + str(vals[2]),
(200, 300), font, (55, 25, 255))
for j in range(0, 3):
if (vals[j] < mini[j]): mini[j] = vals[j]
if (vals[j] > maxi[j]): maxi[j] = vals[j]
cv.Circle(image, (int(200), int(300)), 10, cv.CV_RGB(255, 255, 255), 4)
cv.ShowImage("BGR", image)
c = cv.WaitKey(33)
if c == 27:
break
print 'Analyzation Completed'
mini[0] -= 35
mini[1] -= 15
mini[2] -= 15
maxi[0] += 35
maxi[1] += 15
maxi[2] += 15
for i in range(0, 3):
if (mini[i] < 0):
mini[i] = 0
if (maxi[i] > 255):
maxi[i] = 255
cv.DestroyWindow("BGR")
bgr = (mini, maxi)
return bgr
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 drawBoundingRect(self, frame, b_rect, smallSize = None): bounding_rect = [0,0,0,0] if smallSize != None: size = cv.GetSize(frame) scale = size[0] / float(smallSize[0]) bounding_rect[0] = int(b_rect[0] * scale) bounding_rect[1] = int(b_rect[1] * scale) bounding_rect[2] = int(b_rect[2] * scale) bounding_rect[3] = int(b_rect[3] * scale) l = 10 c = cv.CV_RGB(200,120,120) point1 = ( bounding_rect[0] - self._bRectOffset, bounding_rect[1] - self._bRectOffset ) point2 = ( bounding_rect[0] - self._bRectOffset, bounding_rect[1] + bounding_rect[3] + self._bRectOffset ) point3 = ( bounding_rect[0] + bounding_rect[2] + self._bRectOffset, bounding_rect[1] - self._bRectOffset ) point4 = ( bounding_rect[0] + bounding_rect[2] + self._bRectOffset, bounding_rect[1] + bounding_rect[3] + self._bRectOffset) cv.Line(frame, point1, (point1[0] + l, point1[1]) , c) cv.Line(frame, point1, (point1[0], point1[1] + l) , c) cv.Line(frame, point2, (point2[0] + l, point2[1]) , c) cv.Line(frame, point2, (point2[0], point2[1] - l) , c) cv.Line(frame, point3, (point3[0] - l, point3[1]) , c) cv.Line(frame, point3, (point3[0], point3[1] + l) , c) cv.Line(frame, point4, (point4[0] - l, point4[1]) , c) cv.Line(frame, point4, (point4[0], point4[1] - l) , c)
def draw_rects(img, rects, color):
if rects:
for i in rects:
# 画一个绿色的矩形框
cv.Rectangle(img, (int(rects[0][0]), int(rects[0][1])),
(int(rects[0][2]), int(rects[0][3])),
cv.CV_RGB(0, 255, 0), 1, 8, 0)
def add_keypoints(self, roi, ignore_distance=False):
# Begin with a mask of all black pixels
mask = np.zeros_like(self.grey)
# Get the coordinates and dimensions of the currently ROI
try:
((x, y), (w, h), a) = roi
except:
try:
x, y, w, h = roi
a = 0
except:
rospy.loginfo("ROI has shrunk to zero...")
return
x = int(x)
y = int(y)
# Expand the ROI 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 mask 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):
if ignore_distance:
self.keypoints.append((x, y))
else:
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))
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 run(self):
hist = cv.CreateHist([180], cv.CV_HIST_ARRAY, [(0, 180)], 1)
backproject_mode = True
while True:
frame = cv.QueryFrame(self.capture)
# Convert to HSV and keep the hue
hsv = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.CvtColor(frame, hsv, cv.CV_BGR2HSV)
self.hue = cv.CreateImage(cv.GetSize(frame), 8, 1)
cv.Split(hsv, self.hue, None, None, None)
# Compute back projection
backproject = cv.CreateImage(cv.GetSize(frame), 8, 1)
cv.CalcArrBackProject([self.hue], backproject, hist)
# Run the cam-shift (if the a window is set and != 0)
if self.track_window and is_rect_nonzero(self.track_window):
crit = (cv.CV_TERMCRIT_EPS | cv.CV_TERMCRIT_ITER, 10, 1)
(iters, (area, value, rect),
track_box) = cv.CamShift(backproject, self.track_window,
crit) #Call the camshift !!
self.track_window = rect #Put the current rectangle as the tracked area
# If mouse is pressed, highlight the current selected rectangle and recompute histogram
if self.drag_start and is_rect_nonzero(self.selection):
sub = cv.GetSubRect(frame, self.selection) #Get specified area
#Make the effect of background shadow when selecting a window
save = cv.CloneMat(sub)
cv.ConvertScale(frame, frame, 0.5)
cv.Copy(save, sub)
#Draw temporary rectangle
x, y, w, h = self.selection
cv.Rectangle(frame, (x, y), (x + w, y + h), (255, 255, 255))
#Take the same area but in hue image to calculate histogram
sel = cv.GetSubRect(self.hue, self.selection)
cv.CalcArrHist([sel], hist, 0)
#Used to rescale the histogram with the max value (to draw it later on)
(_, max_val, _, _) = cv.GetMinMaxHistValue(hist)
if max_val != 0:
cv.ConvertScale(hist.bins, hist.bins, 255. / max_val)
elif self.track_window and is_rect_nonzero(
self.track_window): #If window set draw an elipseBox
cv.EllipseBox(frame, track_box, cv.CV_RGB(255, 0, 0), 3,
cv.CV_AA, 0)
cv.ShowImage("CamShiftDemo", frame)
cv.ShowImage("Backprojection", backproject)
cv.ShowImage("Histogram", self.hue_histogram_as_image(hist))
c = cv.WaitKey(7) % 0x100
if c == 27:
break
def show_liner(self):
size = cv.GetSize(self.image)
# B&W background but color highlighting
gray_img = cv.CreateImage(size, 8, 1)
draw_img = cv.CreateImage(size, 8, 3)
#cv.Copy(self.image, draw_img)
cv.CvtColor(self.image, gray_img, cv.CV_BGR2GRAY)
cv.CvtColor(gray_img, draw_img, cv.CV_GRAY2BGR)
'''Takes in list of (contour, color) tuples where contour is iterable for (x, y) tuples'''
cv.PolyLine(draw_img, [self.best_contour], True, cv.CV_RGB(255, 0, 0))
cv.PolyLine(draw_img, [self.ref_polygon], True, cv.CV_RGB(0, 0, 255))
print(self.line)
cv.PolyLine(draw_img, [self.line], True, cv.CV_RGB(0, 255, 0))
cv.ShowImage("Contours", draw_img)
cv.WaitKey()
sys.exit(1)
def initialize(self, frame):
# Initialize
# log_file_name = "tracker_output.log"
# log_file = file( log_file_name, 'a' )
print str(type(frame))
print "resize to ::: " + str(cv.GetSize(frame)) + " " + str(type(frame))
(w, h) = cv.GetSize(frame)
# gray = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
size = (w, h) #cv.GetSize(frame)#(300 , 300)
self.thumbnail = cv.CreateImage(size, cv.IPL_DEPTH_8U, 3)
self.grey_average_image = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
self.grey_original_image = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
# cv.CvtColor(display_image, gray, cv.CV_RGB2GRAY)
# prev_image = gray
# Greyscale image, thresholded to create the motion mask:
self.grey_image = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
# The RunningAvg() function requires a 32-bit or 64-bit image...
self.running_average_image = cv.CreateImage(size, cv.IPL_DEPTH_32F, 3)
# ...but the AbsDiff() function requires matching image depths:
self.running_average_in_display_color_depth = cv.CloneImage(self.thumbnail)
# RAM used by FindContours():
self.mem_storage = cv.CreateMemStorage(0)
# The difference between the running average and the current frame:
self.difference = cv.CloneImage(self.thumbnail)
self.target_count = 1
self.last_target_count = 1
self.last_target_change_t = 0.0
self.k_or_guess = 1
self.codebook = []
self.last_frame_entity_list = []
self.frame_count = 0
# 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):
self.max_targets = 5
def run(self):
hist = cv.CreateHist([180], cv.CV_HIST_ARRAY, [(0, 180)], 1)
backproject_mode = False
while True:
frame = cv.QueryFrame(self.capture)
cv.Flip(frame, frame, 0)
# Convert to HSV and keep the hue
hsv = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.CvtColor(frame, frame, cv.CV_RGB2BGR)
cv.CvtColor(frame, hsv, cv.CV_BGR2HSV)
#cv.CvtColor(frame, hsv, cv.CV_RGB2HSV)
self.hue = cv.CreateImage(cv.GetSize(frame), 8, 1)
cv.Split(hsv, self.hue, None, None, None)
# Compute back projection
backproject = cv.CreateImage(cv.GetSize(frame), 8, 1)
# Run the cam-shift
cv.CalcArrBackProject([self.hue], backproject, hist)
if self.track_window and is_rect_nonzero(self.track_window):
crit = (cv.CV_TERMCRIT_EPS | cv.CV_TERMCRIT_ITER, 10, 1)
(iters, (area, value, rect),
track_box) = cv.CamShift(backproject, self.track_window, crit)
self.track_window = rect
# If mouse is pressed, highlight the current selected rectangle
# and recompute the histogram
if self.drag_start and is_rect_nonzero(self.selection):
sub = cv.GetSubRect(frame, self.selection)
save = cv.CloneMat(sub)
cv.ConvertScale(frame, frame, 0.5)
cv.Copy(save, sub)
x, y, w, h = self.selection
cv.Rectangle(frame, (x, y), (x + w, y + h), (255, 255, 255))
sel = cv.GetSubRect(self.hue, self.selection)
cv.CalcArrHist([sel], hist, 0)
(_, max_val, _, _) = cv.GetMinMaxHistValue(hist)
if max_val != 0:
cv.ConvertScale(hist.bins, hist.bins, 255. / max_val)
elif self.track_window and is_rect_nonzero(self.track_window):
cv.EllipseBox(frame, track_box, cv.CV_RGB(255, 0, 0), 3,
cv.CV_AA, 0)
if not backproject_mode:
cv.ShowImage("CamShiftDemo", frame)
else:
cv.ShowImage("CamShiftDemo", backproject)
cv.ShowImage("Histogram", self.hue_histogram_as_image(hist))
c = cv.WaitKey(7) % 0x100
if c == 27:
break
elif c == ord("b"):
backproject_mode = not backproject_mode
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 minarea_seq(img, count, storage):
points = [(randint(img.width / 4, img.width * 3 / 4),
randint(img.height / 4, img.height * 3 / 4))
for i in range(count)]
cv.Zero(img)
for p in points:
cv.Circle(img, roundxy(p), 2, cv.CV_RGB(255, 0, 0), cv.CV_FILLED,
cv.CV_AA, 0)
draw_common(points)
def transformation(imCalRGB, calData, tx1, ty1, tx2, ty2, tx3, ty3, tx4, ty4):
points = calData.points
## sectors are sometimes different -> make accessible
# used when line rectangle intersection at specific segment is used for transformation:
newtop = destinationPoint(calData.dstpoints[0], calData)
newbottom = destinationPoint(calData.dstpoints[1], calData)
newleft = destinationPoint(calData.dstpoints[2], calData)
newright = destinationPoint(calData.dstpoints[3], calData)
# get a fresh new image
new_image = imCalRGB.copy()
# create transformation matrix
src = np.array([(points[0][0] + tx1, points[0][1] + ty1),
(points[1][0] + tx2, points[1][1] + ty2),
(points[2][0] + tx3, points[2][1] + ty3),
(points[3][0] + tx4, points[3][1] + ty4)], np.float32)
dst = np.array([newtop, newbottom, newleft, newright], np.float32)
transformation_matrix = cv2.getPerspectiveTransform(src, dst)
new_image = cv2.warpPerspective(new_image, transformation_matrix,
(800, 800))
# draw image
drawBoard = Draw()
new_image = drawBoard.drawBoard(new_image, calData)
cv2.circle(new_image, (int(newtop[0]), int(newtop[1])), 2,
cv.CV_RGB(255, 255, 0), 2, 4)
cv2.circle(new_image, (int(newbottom[0]), int(newbottom[1])), 2,
cv.CV_RGB(255, 255, 0), 2, 4)
cv2.circle(new_image, (int(newleft[0]), int(newleft[1])), 2,
cv.CV_RGB(255, 255, 0), 2, 4)
cv2.circle(new_image, (int(newright[0]), int(newright[1])), 2,
cv.CV_RGB(255, 255, 0), 2, 4)
cv2.imshow('manipulation', new_image)
return transformation_matrix
def create(src, id, pos, time):
global hands
ponto = depth.to_projective([pos])
centro = (int(ponto[0][0]), int(ponto[0][1]))
hands[id] = {
'current_position': centro,
'drawing': False,
'color': {
'name': 'Choose a Color',
'cv': cv.CV_RGB(255, 255, 255)
}
}
def minarea_array(img, count):
pointMat = cv.CreateMat(count, 1, cv.CV_32SC2)
for i in range(count):
pointMat[i, 0] = (randint(img.width / 4, img.width * 3 / 4),
randint(img.height / 4, img.height * 3 / 4))
cv.Zero(img)
for i in range(count):
cv.Circle(img, roundxy(pointMat[i, 0]), 2, cv.CV_RGB(255, 0, 0),
cv.CV_FILLED, cv.CV_AA, 0)
draw_common(pointMat)
def get_features(Img):
img = cv2.imread(Img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 读取灰度图
ret, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
(Center_x, Center_y) = img_center(binary, 0, 0) # 整幅图的重心
leng_diagonal = math.sqrt(gray.shape[0] * gray.shape[0] +
gray.shape[1] * gray.shape[1])
# print "Center_x",Center_x
List = Canny(Img) # 返回轮廓信息，(轮廓矩形框信息，边界点集合)
# List = check_merge(Img)
# for i in List:
# print "i",i
# raw_input("i")
feature = []
# print "Img",Img
# print "LIst from canny is",List
for iterm in List:
'''
[
(1, 33, 386, 34), (1, 33, 385, 1), array([ [ [ 1, 33] ],
[ [385, 33] ] ])
]
'''
cv2.rectangle(gray, iterm[0][:2], iterm[0][2:], cv.CV_RGB(255, 0, 0),
2)
# print "iterm[]",iterm[1],iterm[0][1],iterm[0][3]
# 截取子图像矩阵 #~~here
SubImg = gray[iterm[0][1]:iterm[0][3], iterm[0][0]:iterm[0][2]]
SubBin = binary[iterm[0][1]:iterm[0][3],
iterm[0][0]:iterm[0][2]] # 截取二值子图像矩阵
Sub_x, Sub_y = img_center(SubBin, iterm[0][0], iterm[0][1]) # 子图像重心坐标
# print "Sub_x,Sub_y is",Sub_x,Sub_y
Distance = math.sqrt(
(Sub_x - Center_x) * (Sub_x - Center_x) + (Sub_y - Center_y) *
(Sub_y - Center_y)) # 分块重心与整幅图像重心间的距离
# print "Distance is",Distance,Distance/leng_diagonal
# 与重心间距离相对对角线的比例
angle = img_angle(Sub_y, Sub_x) # 子图重心相对整幅图像顶点的夹角
# print "tan is",angle
Sub_entory = Entory(SubImg) # 计算分块子图的信息熵
Sub_eccentricity = img_eccentricity(SubBin) # 计算分块的偏心率
# print "iterm[2] is",iterm
Sub_circularity = Subimg_circularity(Sub_x, Sub_y, iterm[2]) # 分块的圆形性
Sub_hu = img_hu(SubBin) # 分块的Hu矩
feature.append([])
feature[-1] = (iterm[1], (Sub_x, Sub_y), Distance, angle,
Distance / leng_diagonal, Sub_entory, Sub_eccentricity,
Sub_circularity, Sub_hu)
return feature
def detect_and_draw(self, imgmsg):
if self.pause:
return
# frame = cv.QueryFrame( self.capture )
frame = self.br.imgmsg_to_cv(imgmsg, "bgr8")
# Convert to HSV and keep the hue
hsv = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.CvtColor(frame, hsv, cv.CV_BGR2HSV)
self.hue = cv.CreateImage(cv.GetSize(frame), 8, 1)
cv.Split(hsv, self.hue, None, None, None)
# Compute back projection
backproject = cv.CreateImage(cv.GetSize(frame), 8, 1)
# Run the cam-shift
cv.CalcArrBackProject([self.hue], backproject, self.hist)
if self.track_window and is_rect_nonzero(self.track_window):
crit = (cv.CV_TERMCRIT_EPS | cv.CV_TERMCRIT_ITER, 10, 1)
(iters, (area, value, rect),
track_box) = cv.CamShift(backproject, self.track_window, crit)
self.track_window = rect
x, y, w, h = rect
self.bbpub.publish(RegionOfInterest(x, y, w, h, False))
proba_msg = self.br.cv_to_imgmsg(backproject)
proba_msg.header = imgmsg.header
self.bppub.publish(proba_msg)
# If mouse is pressed, highlight the current selected rectangle
# and recompute the histogram
if self.drag_start and is_rect_nonzero(self.selection):
sub = cv.GetSubRect(frame, self.selection)
save = cv.CloneMat(sub)
cv.ConvertScale(frame, frame, 0.5)
cv.Copy(save, sub)
x, y, w, h = self.selection
cv.Rectangle(frame, (x, y), (x + w, y + h), (255, 255, 255))
sel = cv.GetSubRect(self.hue, self.selection)
cv.CalcArrHist([sel], self.hist, 0)
(_, max_val, _, _) = cv.GetMinMaxHistValue(self.hist)
if max_val != 0:
cv.ConvertScale(self.hist.bins, self.hist.bins, 255. / max_val)
elif self.track_window and is_rect_nonzero(self.track_window):
cv.EllipseBox(frame, track_box, cv.CV_RGB(255, 0, 0), 3, cv.CV_AA,
0)
self.frame = frame
self.backproject = backproject
def camshift(x, y, w, h, selection):
print "Performing camshift with x:{} y:{} w:{} h:{}".format(x, y, w, h)
print selection
hist = cv.CreateHist([180], cv.CV_HIST_ARRAY, [(0, 180)], 1)
while True:
print "entered loop"
#camshift termination criteria (10 iterations without movement of 1 pixel ends camshift)
frame = cv.QueryFrame(cap)
cv.Flip(frame, frame, 1)
#print "switching to HSV"
hsv = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.CvtColor(frame, hsv, cv.CV_BGR2HSV)
hue = cv.CreateImage(cv.GetSize(frame), 8, 1)
cv.Split(hsv, hue, None, None, None)
#compute back projection
# print "back projection"
backproject = cv.CreateImage(cv.GetSize(frame), 8, 1)
cv.CalcArrBackProject([hue], backproject, hist)
#run the camshift
#print "camshift"
print "Selection"
#pdb.set_trace()
print selection
crit = (cv.CV_TERMCRIT_EPS | cv.CV_TERMCRIT_ITER, 10, 1)
(iters, (area, value, rect),
track_box) = cv.CamShift(backproject, selection, crit)
print "rect"
print rect
if rect[0] > 0 and rect[1] > 0:
selection = rect
print "SelectionNew"
print selection
print "track_box"
print track_box
#draw the surrounding ellipse
# print "ellipse"
cv.EllipseBox(frame, track_box, cv.CV_RGB(255, 0, 0), 3, cv.CV_AA, 0)
#draw image
#print "drawing image"
cv.ShowImage("CamShift", frame)
if cv.WaitKey(1) & 0xFF == ord('q'):
break
def hog_visualize(hog_image_v, integral_img, cell):
hog_feat= [0] * 9
width_img,height_img = cv.GetSize(hog_image_v)
halfcell = cell/2
num_cells_w,num_cells_h = width_img/cell,height_img/cell
norient = integral_img.shape[2]
mid = norient/2
for y in xrange(num_cells_h-1):
for x in xrange(num_cells_w-1):
px,py=x*cell,y*cell
#features = integral_hog_window(integral_img, (px,py,max(px+8, width_img-1),max(py+8, height_img-1)))
features = integral_hog_window(integral_img, (px, py, px+cell, py+cell))
hog_feat = hog_feat + list(features)
px += halfcell
py += halfcell
#L1-norm, nice for visualization
total = np.sum(features)
maximum_value_feature = np.max(features)
if total > 1e-3:
normalized = features/maximum_value_feature
N = norient
final = []
for i in xrange(N):
maximum_orient = normalized.argmax()
valmax = normalized[maximum_orient]
x1 = int(round(valmax*halfcell*np.sin(np.deg2rad(45*(maximum_orient-4)))))
y1 = int(round(valmax*halfcell*np.cos(np.deg2rad(45*(maximum_orient-4)))))
gradient_val = int(round(255*features[maximum_orient]/total))
#print "values of x1 =",x1,"and y1=",y1, "and gv=",gradient_val
#don't draw if less than a threshold
if gradient_val < 30:
break
final.insert(0, (x1,y1,gradient_val))
normalized[maximum_orient] = 0.
#draw from smallest to highest gradient magnitude
for i in xrange(len(final)):
x1,y1,gradient_val = final[i]
cv.Line(hog_image_v, (px-x1,py+y1), (px+x1,py-y1), cv.CV_RGB(gradient_val, gradient_val, gradient_val), 1, 8)
else:
#don't draw if there's no reponse
pass
return hog_feat
def drawSquares(img, squares):
cpy = cv.CloneImage(img)
# read 4 sequence elements at a time (all vertices of a square)
i=0
while i<squares.total:
pt = []
# read 4 vertices
pt.append(squares[i])
pt.append(squares[i+1])
pt.append(squares[i+2])
pt.append(squares[i+3])
# draw the square as a closed polyline
cv.PolyLine(cpy, [pt], 1, cv.CV_RGB(0, 255, 0), 3, cv. CV_AA, 0)
i+=4
# show the resultant image
cv.ShowImage(wndname, cpy)
def update_video_with(image):
cv.SetData(cv_image, image)
if hands:
if hands[1]['drawing']:
update_notification_with('Click to Stop Drawing')
else:
update_notification_with('Click to Start Drawing')
for id in hands:
cv.PutText(cv_image, hands[id]['color']['name'],
hands[id]['current_position'], text_font,
cv.CV_RGB(255, 255, 255))
else:
update_notification_with('Wave to Interact')
for button in buttons:
cv.Rectangle(cv_image, buttons[button]['start'],
buttons[button]['end'], buttons[button]['color'], -1,
cv.CV_AA, 0)
cv.ShowImage('Video', cv_image)