def getthresholdedimgR4(imhsv):
# A little change here. Creates images for blue and yellow (or whatever color you like).
imgRed = cv.CreateImage(cv.GetSize(imhsv), 8, 1)
imgYellow = cv.CreateImage(cv.GetSize(imhsv), 8, 1)
imgGreen = cv.CreateImage(cv.GetSize(imhsv), 8, 1)
imgthreshold = cv.CreateImage(cv.GetSize(imhsv), 8, 1)
cv.InRangeS(imghsv, cv.Scalar(0, 190, 130), cv.Scalar(18, 255, 190),
imgRed) # Select a range of red color
cv.InRangeS(imghsv, cv.Scalar(100, 100, 100), cv.Scalar(120, 255, 255),
imgYellow) # Select a range of blue color
cv.InRangeS(imghsv, cv.Scalar(67, 103, 46), cv.Scalar(100, 209, 184),
imgGreen) # Select a range of green color
storage = cv.CreateMemStorage(0)
redContour = cv.FindContours(imgRed, storage, cv.CV_RETR_CCOMP,
cv.CV_CHAIN_APPROX_SIMPLE)
points = []
while redContour:
# Draw bounding rectangles
bound_rect = cv.BoundingRect(list(redContour))
#bound_rect = cv.BoundingRect(contour)
# for more details about cv.BoundingRect,see documentation
pt1 = (bound_rect[0], bound_rect[1])
pt2 = (bound_rect[0] + bound_rect[2], bound_rect[1] + bound_rect[3])
points.append(pt1)
points.append(pt2)
cv.Rectangle(imhsv, pt1, pt2, cv.CV_RGB(255, 0, 0), 1)
#Calculating centroids
centroidx = cv.Round((pt1[0] + pt2[0]) / 2)
centroidy = cv.Round((pt1[1] + pt2[1]) / 2)
area = cv.ContourArea(list(redContour))
redContour = redContour.h_next()
while yellowContour:
# Draw bounding rectangles
bound_rect = cv.BoundingRect(list(redContour))
#bound_rect = cv.BoundingRect(contour)
# for more details about cv.BoundingRect,see documentation
pt1 = (bound_rect[0], bound_rect[1])
pt2 = (bound_rect[0] + bound_rect[2], bound_rect[1] + bound_rect[3])
points.append(pt1)
points.append(pt2)
cv.Rectangle(imhsv, pt1, pt2, cv.CV_RGB(255, 0, 0), 1)
#Calculating centroids
centroidx = cv.Round((pt1[0] + pt2[0]) / 2)
centroidy = cv.Round((pt1[1] + pt2[1]) / 2)
area = cv.ContourArea(list(redContour))
redContour = redContour.h_next()
cv.ShowImage("Ceva", imhsv)
cv.Add(imgRed, imgBlue, imgthreshold)
cv.Add(imgGreen, imgGreen, imgthreshold)
return imgthreshold
bestFaceW = w
bestFaceH = h
reScale = 0.3
horScl = int(bestFaceW * reScale)
verScl = int(bestFaceH * reScale)
#rect of middle of face
rect = (bestFaceX + horScl, bestFaceY + verScl,
bestFaceW - (horScl * 2), bestFaceH - (verScl * 2)
) # middle part of face
pt1 = (int(bestFaceX), int(bestFaceY))
pt2 = (int(
(bestFaceX + bestFaceW)), int((bestFaceY + bestFaceH)))
cv.Rectangle(frameSmall, pt1, pt2, cv.RGB(0, 255, 0), 3, 8, 0)
###################################
########calculate histogram########
if biggestFace > 0:
#sets the Region of Interest (face) in HSV image
cv.SetImageROI(frameSmallHSV, rect)
face = cv.CreateImage(cv.GetSize(frameSmallHSV),
frameSmallHSV.depth,
frameSmallHSV.nChannels)
cv.Copy(frameSmallHSV, face)
cv.ResetImageROI(frameSmallHSV)
#get size of face area
faceArea = face.height * face.width
hist = hs_histogram(face)
myHist = hist.getHist(hBins, sBins)
def detect(image, debug=False, display=None):
work_image = cv.CreateImage((image.width, image.height), 8, 1)
cv.CvtColor(image, work_image, cv.CV_BGR2GRAY)
image = work_image
edge = cv.CloneImage(image)
thresholded = cv.CloneImage(image)
v_edges = cv.CloneImage(image)
h_edges = cv.CloneImage(image)
vertical = cv.CreateImage(cv.GetSize(image), cv.IPL_DEPTH_16S, 1)
cv.Sobel(image, vertical, 1, 0, 1)
cv.Abs(vertical, vertical)
cv.Convert(vertical, v_edges)
storage = cv.CreateMemStorage(0)
result = np.asarray(cv.GetMat(v_edges), dtype=np.float)
threshold = 6
rects = []
while len(rects) < 1 and threshold > 0:
rects = []
cv.Convert(vertical, v_edges)
cv.AdaptiveThreshold(v_edges, v_edges, 255,
cv.CV_ADAPTIVE_THRESH_MEAN_C,
cv.CV_THRESH_BINARY_INV, 17, threshold)
threshold -= 1
storage = cv.CreateMemStorage(0)
contour_image = cv.CloneImage(v_edges)
contours = cv.FindContours(contour_image, storage, cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_NONE, (0, 0))
ext.filter_contours(contours, 30, ext.LESSTHAN)
max_size = int(image.width * image.height * 0.1)
# ext.filter_contours(contours, 200**2, ext.GREATERTHAN)
ext.filter_contours(contours, max_size, ext.GREATERTHAN)
if display:
cv.Merge(v_edges, v_edges, v_edges, None, display)
seeds = []
if contours:
seq = contours
rects = []
while seq:
c = ext.as_contour(ext.wrapped(seq))
r = (c.rect.x, c.rect.y, c.rect.width, c.rect.height)
rects.append(r)
if display:
cv.Rectangle(
display, (c.rect.x, c.rect.y),
(c.rect.x + c.rect.width, c.rect.y + c.rect.height),
(0, 0, 255), 1)
seq = seq.h_next()
rects.sort(lambda x, y: cmp(x[0] + x[2] / 2, y[0] + y[2] / 2))
seeds = rects[:]
seeds.sort(lambda x, y: cmp(y[2] * y[3], x[2] * x[3]))
groups = []
skip = False
for seed in seeds:
if seed not in rects:
break
found = False
for group in groups:
if seed in group:
found = True
if found:
continue
r = seed
start = seed
start_index = rects.index(seed)
groups.append([seed])
i = start_index - 1
# delta = max(150, seed[2]/2)
delta = seed[2] * 0.66
if debug:
print "left", seed, delta
col = (randint(0, 255), randint(0, 255), randint(0, 255))
cv.Rectangle(display, (r[0], r[1]), (r[0] + r[2], r[1] + r[3]),
(255, 255, 255), 3)
cv.Rectangle(display, (r[0], r[1]), (r[0] + r[2], r[1] + r[3]),
col, -1)
cv.ShowImage("main", display)
if not skip:
c = cv.WaitKey(0)
if c == ord("a"):
skip = True
# scan left
while 1:
if i < 0:
break
rect = rects[i]
if rect[0] + rect[2] < seed[0] - delta:
if debug:
print "esc1", rect
break
if in_vertical(seed, start, rect):
seed = rect
groups[-1].append(rect)
r = rect
if debug:
print rect
cv.Rectangle(display, (r[0], r[1]),
(r[0] + r[2], r[1] + r[3]), col, -1)
cv.ShowImage("main", display)
if not skip:
c = cv.WaitKey(0)
if c == ord("a"):
skip = True
else:
if debug:
print "rej1", rect
i -= 1
# scan right
seed = start
start_index = rects.index(seed)
i = start_index + 1
if debug:
print
print "right", seed
while 1:
if i >= len(rects):
break
rect = rects[i]
if rect[0] > seed[0] + seed[2] + delta:
if debug:
print "esc2", rect, rect[0] + rect[2] / 2, seed[
0] + seed[2] / 2 + delta
break
if in_vertical(seed, start, rect):
seed = rect
groups[-1].append(rect)
r = rect
if debug:
print rect
cv.Rectangle(display, (r[0], r[1]),
(r[0] + r[2], r[1] + r[3]), col, -1)
cv.ShowImage("main", display)
if not skip:
c = cv.WaitKey(0)
if c == ord("a"):
skip = True
else:
if debug:
print "rej2", rect
i += 1
if debug:
print
# find min and max extent of group
group_rects = []
for group in groups:
min_x, min_y = 1E6, 1E6
max_x, max_y = -1, -1
dev = []
col = (randint(0, 255), randint(0, 255), randint(0, 255))
for rect in group:
r = rect
if display:
if r == group[0]:
cv.Rectangle(display, (r[0], r[1]),
(r[0] + r[2], r[1] + r[3]), (255, 255, 255),
3)
cv.Rectangle(display, (r[0], r[1]), (r[0] + r[2], r[1] + r[3]),
col, -1)
min_x = min(min_x, r[0])
min_y = min(min_y, r[1])
max_x = max(max_x, r[0] + r[2])
max_y = max(max_y, r[1] + r[3])
if display:
cv.Rectangle(display, (min_x, min_y), (max_x, max_y), (0, 255, 0),
1)
width = max_x - min_x
height = max_y - min_y
rect = (min_x, min_y, width, height)
group_rects.append(rect)
return group_rects
freenect.sync_stop()
sys.exit()
color = (100, 255, 100)
cv.NamedWindow('Depth')
cv.SetMouseCallback('Depth', close, param=None)
depthThreshold = Threshold(0)
cv.CreateTrackbar('Depth Threshold', 'Depth', 0, 1200, depthThreshold)
depth_history = []
while 1:
depth, dts = freenect.sync_get_depth_np()
#rgb, rts = freenect.sync_get_rgb_np()
depth = ((depth <= depthThreshold.p).astype(np.uint8) * depth).astype(
np.uint16)
dP = Points(np.argwhere(depth != 0))
avg_dep = 0
#depth = depth.astype(np.uint8)
if dP.points.any():
# Average of the depth values that meet threshold
depth_values = depth[depth != 0]
avg_dep = sum(depth_values) / len(depth_values)
#if len(depth_history) == 5:
depth = array2cv(depth.astype(np.uint8))
bound_Rect = dP.boundingBox()
cv.Rectangle(depth, bound_Rect[0], bound_Rect[1], color, thickness=3)
#cv.PutText(detph, str(avg_dep), (50,50), ,
cv.Circle(depth, dP.calculateCenter(), 48, color)
cv.ShowImage('Depth', depth)
cv.WaitKey(10)
def track_lk(self, cv_image, face):
feature_box = None
""" Initialize intermediate images if necessary """
if not face.pyramid:
face.grey = cv.CreateImage(cv.GetSize(cv_image), 8, 1)
face.prev_grey = cv.CreateImage(cv.GetSize(cv_image), 8, 1)
face.pyramid = cv.CreateImage(cv.GetSize(cv_image), 8, 1)
face.prev_pyramid = cv.CreateImage(cv.GetSize(cv_image), 8, 1)
face.features = []
""" Create a grey version of the image """
cv.CvtColor(cv_image, face.grey, cv.CV_BGR2GRAY)
""" Equalize the histogram to reduce lighting effects """
cv.EqualizeHist(face.grey, face.grey)
if face.track_box and face.features != []:
""" We have feature points, so track and display them """
""" Calculate the optical flow """
face.features, status, track_error = cv.CalcOpticalFlowPyrLK(
face.prev_grey, face.grey, face.prev_pyramid, face.pyramid,
face.features, (self.win_size, self.win_size), 3,
(cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS, 20, 0.01),
self.flags)
""" Keep only high status points """
face.features = [p for (st, p) in zip(status, face.features) if st]
elif face.track_box and self.is_rect_nonzero(face.track_box):
""" Get the initial features to track """
""" Create a mask image to be used to select the tracked points """
mask = cv.CreateImage(cv.GetSize(cv_image), 8, 1)
""" Begin with all black pixels """
cv.Zero(mask)
""" Get the coordinates and dimensions of the track box """
try:
x, y, w, h = face.track_box
except:
return None
if self.auto_face_tracking:
# """ For faces, the detect box tends to extend beyond the actual object so shrink it slightly """
# x = int(0.97 * x)
# y = int(0.97 * y)
# w = int(1 * w)
# h = int(1 * h)
""" Get the center of the track box (type CvRect) so we can create the
equivalent CvBox2D (rotated rectangle) required by EllipseBox below. """
center_x = int(x + w / 2)
center_y = int(y + h / 2)
roi_box = ((center_x, center_y), (w, h), 0)
""" Create a filled white ellipse within the track_box to define the ROI. """
cv.EllipseBox(mask, roi_box, cv.CV_RGB(255, 255, 255),
cv.CV_FILLED)
else:
""" For manually selected regions, just use a rectangle """
pt1 = (x, y)
pt2 = (x + w, y + h)
cv.Rectangle(mask, pt1, pt2, cv.CV_RGB(255, 255, 255),
cv.CV_FILLED)
""" Create the temporary scratchpad images """
eig = cv.CreateImage(cv.GetSize(self.grey), 32, 1)
temp = cv.CreateImage(cv.GetSize(self.grey), 32, 1)
if self.feature_type == 0:
""" Find keypoints to track using Good Features to Track """
face.features = cv.GoodFeaturesToTrack(
face.grey,
eig,
temp,
self.max_count,
self.quality,
self.good_feature_distance,
mask=mask,
blockSize=self.block_size,
useHarris=self.use_harris,
k=0.04)
elif self.feature_type == 1:
""" Get the new features using SURF """
(surf_features, descriptors) = cv.ExtractSURF(
face.grey, mask, cv.CreateMemStorage(0),
(0, self.surf_hessian_quality, 3, 1))
for feature in surf_features:
face.features.append(feature[0])
#
if self.auto_min_features:
""" Since the detect box is larger than the actual face
or desired patch, shrink the number of features by 10% """
face.min_features = int(len(face.features) * 0.9)
face.abs_min_features = int(0.5 * face.min_features)
""" Swapping the images """
face.prev_grey, face.grey = face.grey, face.prev_grey
face.prev_pyramid, face.pyramid = face.pyramid, face.prev_pyramid
""" If we have some features... """
if len(face.features) > 0:
""" The FitEllipse2 function below requires us to convert the feature array
into a CvMat matrix """
try:
self.feature_matrix = cv.CreateMat(1, len(face.features),
cv.CV_32SC2)
except:
pass
""" Draw the points as green circles and add them to the features matrix """
i = 0
for the_point in face.features:
if self.show_features:
cv.Circle(self.marker_image,
(int(the_point[0]), int(the_point[1])), 2,
(0, 255, 0, 0), cv.CV_FILLED, 8, 0)
try:
cv.Set2D(self.feature_matrix, 0, i,
(int(the_point[0]), int(the_point[1])))
except:
pass
i = i + 1
""" Draw the best fit ellipse around the feature points """
if len(face.features) > 6:
feature_box = cv.FitEllipse2(self.feature_matrix)
else:
feature_box = None
""" Publish the ROI for the tracked object """
# try:
# (roi_center, roi_size, roi_angle) = feature_box
# except:
# logger.info("Patch box has shrunk to zeros...")
# feature_box = None
# if feature_box and not self.drag_start and self.is_rect_nonzero(face.track_box):
# self.ROI = RegionOfInterest()
# self.ROI.x_offset = min(self.image_size[0], max(0, int(roi_center[0] - roi_size[0] / 2)))
# self.ROI.y_offset = min(self.image_size[1], max(0, int(roi_center[1] - roi_size[1] / 2)))
# self.ROI.width = min(self.image_size[0], int(roi_size[0]))
# self.ROI.height = min(self.image_size[1], int(roi_size[1]))
# self.pubROI.publish(self.ROI)
if feature_box is not None and len(face.features) > 0:
return feature_box
else:
return None
def draw_rectangle(self, x, y, width, height):
cv.Rectangle(self.image, (int(x), int(y)),
(int(x + width), int(y + height)),
cv.Scalar(255, 255, 255, 1.0))
if face:
((hx, hy, hw, hh), hn) = face
cv.SetImageROI(image, (int(hx), int(hy), hw, hh))
template = cv.CloneImage(image)
cv.ResetImageROI(image)
w, h = cv.GetSize(template)
width = W - w + 1
height = H - h + 1
result = cv.CreateImage((width, height), 32, 1)
#calculate center
centerX = int(hx) + cv.Round(hw / 2)
centerY = int(hy) + cv.Round(hh / 2)
if centerX > centerScreenX + 200:
#print centerX-centerScreenX
directSentry(4)
elif centerX < centerScreenX - 200:
#print centerScreenX-centerX
directSentry(2)
if centerY > centerScreenY + 200:
#print centerY - centerScreenY
directSentry(1)
elif centerY < centerScreenY - 200:
#print centerScreenY - centerY
directSentry(3)
#white box for face detection
cv.Rectangle(image, (int(hx), int(hy)),
(int(hx) + hw, int(hy) + hh), (255, 255, 255), 3, 0)
cv.ShowImage('tempwindow', image)
if cv.WaitKey(5) >= 0:
break
def findHoughLines():
""" Uses the Hough transformation to find lines from the sensor
readings and displays them
"""
global D
# initialize lists
D.lines = []
D.theta = []
D.distance = []
D.midpoint = []
# sensitivity options for Hough transformation
threshold = 20
min_line_len = 10
max_gap_len = 30
line_width = 1
# source for Hough transformation has dots instead of lines
src = D.hough
# prepare image and destination for Hough transformation
dst = cv.CreateImage(cv.GetSize(src), 8, 1)
D.color_dst = cv.CreateImage(cv.GetSize(src), 8, 3)
storage = cv.CreateMemStorage(0)
lines = 0
cv.Canny(src, dst, 50, 200, 3)
cv.CvtColor(dst, D.color_dst, cv.CV_GRAY2BGR)
# apply Hough transformation to find walls
# For more information, see:
# http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.html
lines = cv.HoughLines2(dst, storage, cv.CV_HOUGH_PROBABILISTIC, line_width, \
pi / 180, threshold, min_line_len, max_gap_len)
# draw the danger zone
cv.Rectangle(D.image, (CENTER - 30, CENTER - 90),
(CENTER + 30, CENTER - 30), cv.RGB(25, 25, 112), 2, 8)
for line in lines:
cv.Line(D.color_dst, line[0], line[1], cv.CV_RGB(0, 255, 0), 1, 8)
# storing the lines and their distances
D.lines.append((line[0], line[1]))
x1 = float(line[0][0])
y1 = float(line[0][1])
x2 = float(line[1][0])
y2 = float(line[1][1])
x3 = float(CENTER)
y3 = float(CENTER)
# find the midpoint, the angle, and the distance to center
midpoint = (int((x1 + x2) / 2), int((y1 + y2) / 2))
theta = atan2((y2 - y1), (x2 - x1)) / pi * 180
if (x2 - x1) != 0:
slope = (y2 - y1) / (x2 - x1)
intercept = (x2 * y1 - x1 * y2) / (x2 - x1)
distance = abs(y3 - slope * x3 - intercept) / sqrt(slope**2 + 1)
else:
distance = abs(x2 - x3)
cv.Line(D.image, line[0], line[1], cv.CV_RGB(0, 255, 0), 1, 8)
cv.Line(D.image, midpoint, midpoint, cv.RGB(255, 255, 255), 4, 8)
# add data to the list
D.theta.append(theta)
D.distance.append(distance)
D.midpoint.append(midpoint)
# create the wanted images
import cv
image=cv.LoadImage('picture.png', cv.CV_LOAD_IMAGE_COLOR)
grey=cv.CreateImage((100,100),8,1)
eig = cv.CreateImage (cv.GetSize (grey), 32, 1)
temp = cv.CreateImage (cv.GetSize (grey), 32, 1)
# the default parameters
quality = 0.01
min_distance = 10
# search the good points
features = cv.GoodFeaturesToTrack (
grey, eig, temp,
1000,
quality, min_distance, None, 3, 0, 0.04)
for (x,y) in features:
x) + ',' + str(y)
cv.Circle (image, (int(x), int(y)), 3, (0, 255, 0), -1, 8, 0)
cv.ResetImageROI(image)
W,H=cv.GetSize(image)
w,h=cv.GetSize(template)
width=W-w+1
height=H-h+1
result=cv.CreateImage((width,height),32,1)
cv.MatchTemplate(frame,template, result,cv.CV_TM_SQDIFF)
(min_x,max_y,minloc,maxloc)=cv.MinMaxLoc(result)
(x,y)=minloc
cv.Rectangle(image2,(int(x),int(y)),(int(x)+w,int(y)+h),(255,255,255),1,0)
def red_eye(self):
self.load_cascade_file()
faces = [face for face in self.context.request.focal_points if face.origin == 'Face Detection']
if faces:
engine = self.context.modules.engine
mode, data = engine.image_data_as_rgb()
mode = mode.lower()
sz = engine.size
image = cv.CreateImageHeader(sz, cv.IPL_DEPTH_8U, 3)
cv.SetData(image, data)
for face in faces:
face_x = int(face.x - face.width / 2)
face_y = int(face.y - face.height / 2)
face_roi = (
int(face_x),
int(face_y),
int(face.width),
int(face.height)
)
cv.SetImageROI(image, face_roi)
eyes = cv.HaarDetectObjects(
image,
self.cascade,
cv.CreateMemStorage(0),
HAAR_SCALE,
MIN_NEIGHBORS,
HAAR_FLAGS,
MIN_SIZE)
for (x, y, w, h), other in self.filter_eyes(eyes):
# Set the image Region of interest to be the eye area [this reduces processing time]
cv.SetImageROI(image, (face_x + x, face_y + y, w, h))
if self.context.request.debug:
cv.Rectangle(
image,
(0, 0),
(w, h),
cv.RGB(255, 255, 255),
2,
8,
0
)
for pixel in self.get_pixels(image, w, h, mode):
green_blue_avg = (pixel['g'] + pixel['b']) / 2
if not green_blue_avg:
red_intensity = RED_THRESHOLD
else:
# Calculate the intensity compared to blue and green average
red_intensity = pixel['r'] / green_blue_avg
# If the red intensity is greater than 2.0, lower the value
if red_intensity >= RED_THRESHOLD:
new_red_value = (pixel['g'] + pixel['b']) / 2
# Insert the new red value for the pixel to the image
cv.Set2D(
image,
pixel['y'],
pixel['x'],
cv.RGB(new_red_value, pixel['g'], pixel['b'])
)
# Reset the image region of interest back to full image
cv.ResetImageROI(image)
self.context.modules.engine.set_image_data(image.tostring())
storage = cv.CreateMemStorage(0)
contour = cv.FindContours(thresh_img, storage, cv.CV_RETR_CCOMP,
cv.CV_CHAIN_APPROX_NONE)
points = []
while contour:
# Draw bounding rectangles
bound_rect = cv.BoundingRect(list(contour))
if (cv.ContourArea(contour) > 200):
pt1 = (bound_rect[0], bound_rect[1])
pt2 = (bound_rect[0] + bound_rect[2],
bound_rect[1] + bound_rect[3])
points.append(pt1)
points.append(pt2)
cv.Rectangle(frame, pt1, pt2, cv.CV_RGB(255, 0, 0), 1)
obj_mid = bound_rect[0] + (bound_rect[2] / 2)
frame_mid = frame.width / 2
mid = frame_mid - obj_mid
# only move if not near middle
offset = abs(mid)
if offset > 20:
pandir = (mid / offset)
else:
pandir = 0
contour = contour.h_next()
(leftmost, rightmost, topmost, bottommost) = getpositions(thresh_img)
def run(self):
# Capture first frame to get size
frame = cv.QueryFrame(self.capture)
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)
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, 0.020, None)
# 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 to grayscale.
cv.CvtColor(difference, grey_image, cv.CV_RGB2GRAY)
# Convert the image to black and white.
cv.Threshold(grey_image, grey_image, 70, 255, cv.CV_THRESH_BINARY)
# Dilate and erode to get people blobs
cv.Dilate(grey_image, grey_image, None, 18)
cv.Erode(grey_image, grey_image, None, 10)
storage = cv.CreateMemStorage(0)
contour = cv.FindContours(grey_image, storage, cv.CV_RETR_CCOMP,
cv.CV_CHAIN_APPROX_SIMPLE)
points = []
while contour:
bound_rect = cv.BoundingRect(list(contour))
contour = contour.h_next()
pt1 = (bound_rect[0], bound_rect[1])
pt2 = (bound_rect[0] + bound_rect[2],
bound_rect[1] + bound_rect[3])
points.append(pt1)
points.append(pt2)
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)
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, center_point, 10, cv.CV_RGB(255, 100, 0), 1)
print center_point
cv.ShowImage("Target", color_image)
# Listen for ESC key
c = cv.WaitKey(7) % 0x100
if c == 27:
break
# copy some pixels from the original image to the second one
(minx, miny) = (150, 100)
(maxx, maxy) = (300, 250)
for x in range(minx, maxx):
for y in range(miny, maxy):
value = cv.Get2D(image, y, x)
cv.Set2D(empty_image, y, x, value)
# display both images
print "showing original image plus a copied section (press any key to continue)"
cv.ShowImage('window1', image)
cv.ShowImage('window2', empty_image)
cv.WaitKey(10000)
# draw a blue rectangle around the copied section
cv.Rectangle(empty_image, (minx, miny), (maxx, maxy), (255, 0, 0), 4)
# draw green circles on the vertices
cv.Circle(empty_image, (minx, miny), 8, (0, 255, 0), 2)
cv.Circle(empty_image, (maxx, miny), 8, (0, 255, 0), 2)
cv.Circle(empty_image, (minx, maxy), 8, (0, 255, 0), 2)
cv.Circle(empty_image, (maxx, maxy), 8, (0, 255, 0), 2)
# display both images
print "showing original image plus a copied section with shapes (press any key to continue)"
cv.ShowImage('window1', image)
cv.ShowImage('window2', empty_image)
cv.WaitKey(10000)
# save the new image to the filesystem
cv.SaveImage('image.png', empty_image)
def show_mask(name, m, rect):
im = cv.CreateImage((m.shape[1], m.shape[0]), 32, 3)
cv.SetData(im, np.ascontiguousarray(np.dstack(3 * [m])))
(t, l), (b, r) = rect
cv.Rectangle(im, (t, l), (b, r), (255, 255, 0))
cv.ShowImage(name, im)
def process(args):
'''process a set of files'''
global slipmap, mosaic
scan_count = 0
files = []
for a in args:
if os.path.isdir(a):
files.extend(glob.glob(os.path.join(a, '*.pgm')))
else:
files.append(a)
files.sort()
num_files = len(files)
print("num_files=%u" % num_files)
region_count = 0
joes = []
if opts.mavlog:
mpos = mav_position.MavInterpolator(gps_lag=opts.gps_lag)
mpos.set_logfile(opts.mavlog)
else:
mpos = None
if opts.boundary:
boundary = cuav_util.polygon_load(opts.boundary)
else:
boundary = None
if opts.mosaic:
slipmap = mp_slipmap.MPSlipMap(service='GoogleSat', elevation=True, title='Map')
icon = slipmap.icon('planetracker.png')
slipmap.add_object(mp_slipmap.SlipIcon('plane', (0,0), icon, layer=3, rotation=0,
follow=True,
trail=mp_slipmap.SlipTrail()))
C_params = cam_params.CameraParams(lens=opts.lens)
path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..',
'cuav', 'data', 'chameleon1_arecont0.json')
C_params.load(path)
mosaic = cuav_mosaic.Mosaic(slipmap, C=C_params)
if boundary is not None:
mosaic.set_boundary(boundary)
if opts.joe:
joes = cuav_util.polygon_load(opts.joe)
if boundary:
for i in range(len(joes)):
joe = joes[i]
if cuav_util.polygon_outside(joe, boundary):
print("Error: joe outside boundary", joe)
return
icon = slipmap.icon('flag.png')
slipmap.add_object(mp_slipmap.SlipIcon('joe%u' % i, (joe[0],joe[1]), icon, layer=4))
joelog = cuav_joe.JoeLog('joe.log')
if opts.view:
viewer = mp_image.MPImage(title='Image')
frame_time = 0
for f in files:
if mpos:
frame_time = cuav_util.parse_frame_time(f)
try:
if opts.roll_stabilised:
roll = 0
else:
roll = None
pos = mpos.position(frame_time, opts.max_deltat,roll=roll)
slipmap.set_position('plane', (pos.lat, pos.lon), rotation=pos.yaw)
except mav_position.MavInterpolatorException as e:
print e
pos = None
else:
pos = None
# check for any events from the map
if opts.mosaic:
slipmap.check_events()
mosaic.check_events()
if f.endswith('.pgm'):
pgm = cuav_util.PGM(f)
im = pgm.array
if pgm.eightbit:
im_8bit = im
else:
im_8bit = numpy.zeros((960,1280,1),dtype='uint8')
if opts.gamma != 0:
scanner.gamma_correct(im, im_8bit, opts.gamma)
else:
scanner.reduce_depth(im, im_8bit)
im_full = numpy.zeros((960,1280,3),dtype='uint8')
scanner.debayer(im_8bit, im_full)
im_640 = numpy.zeros((480,640,3),dtype='uint8')
scanner.downsample(im_full, im_640)
else:
im_orig = cv.LoadImage(f)
(w,h) = cuav_util.image_shape(im_orig)
im_full = im_orig
im_640 = cv.CreateImage((640, 480), 8, 3)
cv.Resize(im_full, im_640, cv.CV_INTER_NN)
im_640 = numpy.ascontiguousarray(cv.GetMat(im_640))
im_full = numpy.ascontiguousarray(cv.GetMat(im_full))
count = 0
total_time = 0
img_scan = im_640
t0=time.time()
for i in range(opts.repeat):
if opts.fullres:
regions = scanner.scan(im_full)
regions = cuav_region.RegionsConvert(regions, 1280, 960)
else:
regions = scanner.scan(img_scan)
regions = cuav_region.RegionsConvert(regions)
count += 1
t1=time.time()
if opts.filter:
regions = cuav_region.filter_regions(im_full, regions, frame_time=frame_time, min_score=opts.minscore,
filter_type=opts.filter_type)
scan_count += 1
# optionally link all the images with joe into a separate directory
# for faster re-running of the test with just joe images
if pos and opts.linkjoe and len(regions) > 0:
cuav_util.mkdir_p(opts.linkjoe)
if not cuav_util.polygon_outside((pos.lat, pos.lon), boundary):
joepath = os.path.join(opts.linkjoe, os.path.basename(f))
if os.path.exists(joepath):
os.unlink(joepath)
os.symlink(f, joepath)
if pos and len(regions) > 0:
joelog.add_regions(frame_time, regions, pos, f, width=1280, height=960, altitude=opts.altitude)
if boundary:
regions = cuav_region.filter_boundary(regions, boundary, pos)
region_count += len(regions)
if opts.mosaic and len(regions) > 0:
composite = cuav_mosaic.CompositeThumbnail(cv.GetImage(cv.fromarray(im_full)), regions)
thumbs = cuav_mosaic.ExtractThumbs(composite, len(regions))
mosaic.add_regions(regions, thumbs, f, pos)
if opts.compress:
jpeg = scanner.jpeg_compress(im_full, opts.quality)
jpeg_filename = f[:-4] + '.jpg'
if os.path.exists(jpeg_filename):
print('jpeg %s already exists' % jpeg_filename)
continue
chameleon.save_file(jpeg_filename, jpeg)
if opts.view:
if opts.fullres:
img_view = im_full
else:
img_view = img_scan
mat = cv.fromarray(img_view)
for r in regions:
(x1,y1,x2,y2) = r.tuple()
(w,h) = cuav_util.image_shape(img_view)
x1 = x1*w//1280
x2 = x2*w//1280
y1 = y1*h//960
y2 = y2*h//960
cv.Rectangle(mat, (max(x1-2,0),max(y1-2,0)), (x2+2,y2+2), (255,0,0), 2)
cv.CvtColor(mat, mat, cv.CV_BGR2RGB)
viewer.set_image(mat)
total_time += (t1-t0)
if t1 != t0:
print('%s scan %.1f fps %u regions [%u/%u]' % (
f, count/total_time, region_count, scan_count, num_files))
def closest_point(self, pt):
pos0, dpos = zip(*self.objects)
est_pts = np.asarray(pos0)+np.asarray(dpos)
min_dist = np.sum( est_pts
def run(self):
# Capture first frame to get size
frame = cv.QueryFrame(self.capture)
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)
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, 0.020, None)
# 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 to grayscale.
cv.CvtColor(difference, grey_image, cv.CV_RGB2GRAY)
# Convert the image to black and white.
cv.Threshold(grey_image, grey_image, 70, 255, cv.CV_THRESH_BINARY)
# Dilate and erode to get people blobs
cv.Dilate(grey_image, grey_image, None, 18)
cv.Erode(grey_image, grey_image, None, 10)
storage = cv.CreateMemStorage(0)
contour = cv.FindContours(grey_image, storage, cv.CV_RETR_CCOMP, cv.CV_CHAIN_APPROX_SIMPLE)
objects = []
while contour:
bound_rect = cv.BoundingRect(list(contour))
contour = contour.h_next()
pt1 = (bound_rect[0], bound_rect[1])
pt2 = (bound_rect[0] + bound_rect[2], bound_rect[1] + bound_rect[3])
cv.Rectangle(color_image, pt1, pt2, cv.CV_RGB(255,0,0), 1)
center_point = ((pt1[0] + pt2[0]) / 2, (pt1[1] + pt2[1]) / 2), points)
if len(objects):
closest
self.eye_cascade = cv2.CascadeClassifier(
'/usr/local/Cellar/opencv/2.4.7.1/share/OpenCV/haarcascades/haarcascade_eye.xml'
)
# used for storing and recalling the face image history:
self.storage = cv.CreateMemStorage(0)
self.last_face_position = None
self.detect_times = []
self.eye_pair_history = []
self.xamount_histories = [[], []]
self.yamount_histories = [[], []]
self.xpos_history = []
self.ypos_history = []
def drawrect(self, image, (x, y, w, h), color=(0, 0, 255)):
cv.Rectangle(image, (x, y), (x + w, y + h), cv.RGB(*color), 3, 8, 0)
# TODO: put draw pupil, draw_plus, and maybe drawrect in the same function and request parameters when called
def draw_pupil(self,
face_img,
pupcoord_normalized,
(ex, ey, ew, eh),
rad=5,
color=(0, 0, 255)):
pupcoord = ((pupcoord_normalized[0] + 1) / 2 * ew + ex,
(pupcoord_normalized[1] + 1) / 2 * eh + ey)
pupcoord = tuple([int(round(p)) for p in pupcoord])
cv2.circle(numpy.asarray(face_img[:, :]), pupcoord, rad, color)
def draw_plus(self,
image,
def show(area):
cv.Rectangle(img,(area[0][0],area[0][1]),
(area[0][0]+area[0][2],area[0][1]+area[0][3]),
(255,0,0),2)
storage = cv.CreateMemStorage(0) contour = cv.FindContours(imgyellowthresh, storage, cv.CV_RETR_CCOMP, cv.CV_CHAIN_APPROX_SIMPLE) points = [] # This is the new part here. ie Use of cv.BoundingRect() while contour: # Draw bounding rectangles bound_rect = cv.BoundingRect(list(contour)) contour = contour.h_next() print contour # for more details about cv.BoundingRect,see documentation pt1 = (bound_rect[0], bound_rect[1]) pt2 = (bound_rect[0] + bound_rect[2], bound_rect[1] + bound_rect[3]) points.append(pt1) points.append(pt2) cv.Rectangle(color_image, pt1, pt2, cv.CV_RGB(255,0,0), 1) # Calculating centroids centroidx=cv.Round((pt1[0]+pt2[0])/2) centroidy=cv.Round((pt1[1]+pt2[1])/2) # Identifying if blue or yellow blobs and adding centroids to corresponding lists if (20<cv.Get2D(imghsv,centroidy,centroidx)[0]<30): yellow.append((centroidx,centroidy)) elif (100<cv.Get2D(imghsv,centroidy,centroidx)[0]<120): blue.append((centroidx,centroidy)) # Now drawing part. Exceptional handling is used to avoid IndexError. After drawing is over, centroid from previous part is # removed from list by pop. So in next frame,centroids in this frame become initial points of line to draw. try: cv.Circle(imdraw,yellow[1],5,(0,255,255))
while True:
video = kinect.get_video()
width = video.width
height = video.height
rcenter = (width / 4, height / 4, width / 2, height * 3 / 4)
bincount = 4
mask = cv.CreateImage((width, height), 8, 1)
cv.Zero(mask)
cv.SetImageROI(mask, rcenter)
cv.Set(mask, 1)
cv.ResetImageROI(mask)
cv.Rectangle(video, (width / 4, height / 4), (width * 3 / 4, height), 255)
# define our 'control region'
xleft = int(leftLimit * width)
xright = int(rightLimit * width)
faces = cv.HaarDetectObjects(video, hc, ms, 1.25, 2, 0, (50, 50))
# move the robot toward the first face it sees
for (x, y, w, h), n in faces:
cv.Rectangle(video, (x, y), (x + w, y + h), 255)
if x + w > xright:
bot.moveBot("right", 50)
print "right"
# sleep(0.2)
# bot.stopBot()
elif x < xleft:
bot.moveBot("left", 50)
boxWidth = box[right][0] - box[left][0]
boxHeight = box[bottom][0] - box[top][0]
boxAreas.append(boxWidth * boxHeight)
averageBoxArea = 0.0
if len(boxAreas):
averageBoxArea = float(sum(boxAreas) / len(boxAreas))
trimmedBoxList = []
for box in boundingBoxList:
boxWidth = box[right][0] - box[left][0]
boxHeight = box[bottom][0] - box[top][0]
# remove the box if it's smaller than our noise threshold
if (boxWidth * boxHeight) > 0.05 * averageBoxArea:
trimmedBoxList.append(box)
boundingBoxList = mergeOverlappingBoxes(trimmedBoxList)
#Draw the boxes
for box in boundingBoxList:
cv.Rectangle(displayImage, box[0], box[1], cv.CV_RGB(0, 255, 0), 1)
estimatedTargetCount = len(boundingBoxList)
cv.ShowImage("Target", displayImage)
frameCount += 1
print "FPS: %f" % (frameCount / (time.time() - t0))
cv.DestroyAllWindows()
def draw_from_points(cv_image, points):
"""Takes the cv_image and points and draws a rectangle based on the points.
Returns a cv_image."""
for (x, y, w, h), n in points:
cv.Rectangle(cv_image, (x, y), (x + w, y + h), 255)
return cv_image
def meet(IP, PORT, fighter):
global motionProxy
global post
global sonarProxy
global memoryProxy
global cameraProxy
global videoClient
# work ! set current to servos
stiffnesses = 1.0
time.sleep(0.5)
# TEMP
pNames = "Body"
pStiffnessLists = 0.0
pTimeLists = 1.0
motionProxy.stiffnessInterpolation(pNames, pStiffnessLists, pTimeLists)
# Get a camera image.
# image[6] contains the image data passed as an array of ASCII chars.
naoImage = cameraProxy.getImageRemote(videoClient)
imageWidth = naoImage[0]
imageHeight = naoImage[1]
found = True
posx = 0
posy = 0
mem = cv.CreateMemStorage(0)
i = 0
cv.NamedWindow("Real")
cv.MoveWindow("Real", 0, 0)
cv.NamedWindow("Threshold")
cv.MoveWindow("Real", imageWidth + 100, 0)
error = 0.0
nframe = 0.0
closing = 3
tstp, tu = 0, 0
K = 2
try:
while found:
nframe = nframe + 1
# Get current image (top cam)
naoImage = cameraProxy.getImageRemote(videoClient)
# Get the image size and pixel array.
imageWidth = naoImage[0]
imageHeight = naoImage[1]
array = naoImage[6]
# Create a PIL Image from our pixel array.
pilImg = Image.fromstring("RGB", (imageWidth, imageHeight), array)
# Convert Image to OpenCV
cvImg = cv.CreateImageHeader((imageWidth, imageHeight),
cv.IPL_DEPTH_8U, 3)
cv.SetData(cvImg, pilImg.tostring())
cvImg2 = cv.CreateImageHeader((imageWidth, imageHeight),
cv.IPL_DEPTH_8U, 3)
cv.SetData(cvImg2, pilImg.tostring())
cv.CvtColor(cvImg, cvImg, cv.CV_RGB2BGR)
hsv_img = cv.CreateImage(cv.GetSize(cvImg), 8, 3)
cv.CvtColor(cvImg, hsv_img, cv.CV_BGR2HSV)
thresholded_img = cv.CreateImage(cv.GetSize(hsv_img), 8, 1)
thresholded_img2 = cv.CreateImage(cv.GetSize(hsv_img), 8, 1)
imcv2 = cv2.cvtColor(np.asarray(pilImg), cv2.COLOR_RGB2BGR)
# Get the orange on the image
cv.InRangeS(hsv_img, (0, 150, 150), (40, 255, 255),
thresholded_img)
cv.InRangeS(hsv_img, (0, 150, 150), (40, 255, 255),
thresholded_img2)
storage = cv.CreateMemStorage(0)
contour = cv.FindContours(thresholded_img, storage,
cv.CV_RETR_CCOMP,
cv.CV_CHAIN_APPROX_SIMPLE)
cv.Smooth(thresholded_img, thresholded_img, cv.CV_GAUSSIAN, 5, 5)
cv.Erode(thresholded_img, thresholded_img, None, closing)
cv.Dilate(thresholded_img, thresholded_img, None, closing)
storage = cv.CreateMemStorage(0)
contour = cv.FindContours(thresholded_img, storage,
cv.CV_RETR_CCOMP,
cv.CV_CHAIN_APPROX_SIMPLE)
points = []
d = []
data = []
while contour:
# Draw bounding rectangles
bound_rect = cv.BoundingRect(list(contour))
contour = contour.h_next()
# for more details about cv.BoundingRect,see documentation
pt1 = (bound_rect[0], bound_rect[1])
pt2 = (bound_rect[0] + bound_rect[2],
bound_rect[1] + bound_rect[3])
points.append(pt1)
points.append(pt2)
cv.Rectangle(cvImg2, pt1, pt2, cv.CV_RGB(255, 0, 0), 1)
lastx = posx
lasty = posy
posx = cv.Round((pt1[0] + pt2[0]) / 2)
posy = cv.Round((pt1[1] + pt2[1]) / 2)
data.append([posx, posy])
d.append(math.sqrt(pt1[0]**2 + pt2[0]**2))
d.append(math.sqrt(pt1[1]**2 + pt2[1]**2))
cvImg2, error, centroid, labels = clustering(
data, cvImg2, nframe, error, K)
# Update the closing size towards the number of found labels
if labels.size < 2:
closing = 1
if labels.size < 6:
closing = 2
if labels.size > 10:
closing = 3
if closing < 1:
closing = 0
if centroid.size != 0:
uh = 0
try:
x = int(centroid[0][0])
y = int(centroid[0][1])
except:
print "NaN"
l = memoryProxy.getData(
"Device/SubDeviceList/US/Left/Sensor/Value")
r = memoryProxy.getData(
"Device/SubDeviceList/US/Right/Sensor/Value")
# Commande proportionnelles pour le cap et la distance
kh = 20.0 / (imageWidth / 2)
km = 0.25
uh = -kh * (x - imageWidth / 2)
um = km * (math.sqrt(l**2 + r**2) - 0.6)
if (uh > 4 or uh < -4):
motionProxy.moveTo(0.0, 0.0, uh * almath.TO_RAD)
tu = time.time()
if (um > 0.03 or um < -0.03) and (l > 0.6 and r > 0.6):
motionProxy.moveTo(um, 0.0, 0.0)
tu = time.time()
else:
# quand il est proche mettre K=1, on admet qu il n y a plus de parasites, et que les zones oranges sont assez
# grosse pour leur appliquer un coef ce fermeture eleve
# print "-------------------------"
# print "K = 1"
K = 1
closing = 5
tstp = time.time()
# print "l",l
# print "r",r
# print "um ",um
#Temps de repos du Nao
tact = tstp - tu
#S'il attend plus de 5sec, il admet qu'il est devant sa cible
if tact > 5:
found = False
if fighter == "dark":
#tts.say("I've been waiting for you, Obi-Wan. We meet again, at last. The circle is now complete. When I left you, I was but the learner; now I am the master.")
#time.sleep(1.0)
tts.say("I see you")
if fighter == "obi":
#time.sleep(15.0)
#tts.say("Only a master of evil, Darth.")
tts.say("I see you")
cv.ShowImage("Real", cvImg)
#cv.ShowImage("Threshold",thresholded_img2)
cv.ShowImage("Threshold", thresholded2)
cv.WaitKey(1)
except KeyboardInterrupt:
print
print "Interrupted by user, shutting down"
end(IP, PORT)
def main():
captura = cv.CreateCameraCapture(
1) ##guardamos la imagen de la camara web usb
global arra ##cargamos el arreglo de los objetos
font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 1, 1, 0, 3,
3) #creamos el fondo para las letras
proses = 0
sumaa = 0
while True:
img = cv.QueryFrame(captura)
#cv.Resize(img,img,cv.CV_INTER_CUBIC)
#tiempoi = time.time()
#draw = ImageDraw.Draw(img)
anch, alt = cv.GetSize(img) ##obtenemos las dimensiones
k = cv.WaitKey(10)
##esperemos para cualquier incombeniente
#cv.SaveImage("test.jpg",img)
cv.Smooth(img, img, cv.CV_GAUSSIAN, 9,
9) ##aplicamos filtro para reducir el ruido
#cv.SaveImage("sruido.jpg",img)
grey = cv.CreateImage(cv.GetSize(img), 8,
1) ##creamos una imagen en blanco
bn = cv.CreateImage(cv.GetSize(img), 8, 1)
##creamos imagen en blanco
cv.CvtColor(
img, grey, cv.CV_BGR2GRAY
) ###pasamos la imagen a escala de grises y la guardamos en la imagen ne blanco
#cv.SaveImage("gris.jpg",grey)
cv.ConvertImage(img, bn, 0)
##convertimos la imagen a blancos
threshold = 40 ##umbral 1 para binarizacion
colour = 255 ## umbral 2 para binarizacion
cv.Threshold(grey, grey, threshold, colour,
cv.CV_THRESH_BINARY) ##aplicamos binarizacion
cv.Canny(
grey, bn, 1, 1, 3
) ##preparamos para obtener contornos, esto nos muestra la imagen con los contornos
#cv.SaveImage("cont.jpg",bn)
cv.SaveImage("grey.jpg", grey) ##guardamos la imagen
cambio("grey.jpg") ##invertimos la imagen y discretisamos
imgg = cv.LoadImage(
'ngrey.jpg',
cv.CV_LOAD_IMAGE_GRAYSCALE) ##cargamos nuevamente la imagen
storage = cv.CreateMemStorage(
0) ##para guardar los puntos y no saturar la memoria
contours = cv.FindContours(
imgg, storage, cv.CV_RETR_TREE, cv.CV_CHAIN_APPROX_SIMPLE,
(0, 0)) ##obtener los puntos de los contornos
puntos = [
] ##para guardar los diferente centros de los objetos y verificarlas posteriormente
while contours: ##leemos los contornos
nx, ny = contours[
0] ##para verificar donde se encuentra los centros de la figura u bojeto
mx, my = contours[0] ##
ancho, altura = cv.GetSize(img) ##obtenemos el tama de la imagen
for i in range(len(contours)): ##verificamos las esquinas
xx, yy = contours[i]
if xx > mx:
mx = xx
if xx < nx:
nx = xx
if yy > my:
my = yy
if yy < ny:
ny = yy
a, b, c, d = random.randint(0, 255), random.randint(
0,
255), random.randint(0,
255), random.randint(0,
255) ##para el color
if len(contours
) >= 50: ##si son mas de 50 puntos es tomada como figura
cv.Rectangle(img, (nx, ny), (mx, my), cv.RGB(0, 255, 0), 1, 8,
0) ##pintamos el rectangulo con las esquinas
#are = abs(mx-nx)*abs(my-ny)
puntos.append((abs(mx + nx) / 2,
abs(my + ny) / 2)) ##agregamos los centros
#are = abs(mx-nx)*abs(my-ny)
contours = contours.h_next(
) #pasamos con los siguientes puntos unidos
nuevo = de(
puntos, anch, alt
) ##verificamos los objetos y obtenemos los centros de los mismos
for i in range(len(nuevo)): ## pintamos la direccin de los mismos
x, y, z = nuevo[i]
cv.PutText(img, "" + z + "", (x, y), font, 255)
tiempof = time.time() ##verificar rendimiento
cv.ShowImage('img', img)
#cv.SaveImage("final.jpg",img)
#tiempoa = tiempof - tiempoi
#proses += 1
#sumaa = sumaa + tiempoa
#print float(sumaa)/float(proses)
#f.write(""+str(proses)+" "+str(tiempoa)+"\n")
##verificar rendimientp
if k == 'f': ##si se preciona f se sale
break
for (x, y, w, h), n in detected:
faces.append((x, y, w, h))
return faces
if __name__ == "__main__":
cv.NamedWindow("Video", cv.CV_WINDOW_AUTOSIZE)
capture = cv.CaptureFromCAM(CAMERA_INDEX)
storage = cv.CreateMemStorage()
cascade = cv.Load(HAAR_CASCADE_PATH)
faces = []
c = -1
i = 0
while (c == -1):
image = cv.QueryFrame(capture)
# Only run the Detection algorithm every 5 frames to improve performance
if i % 5 == 0:
faces = detect_faces(image)
for (x, y, w, h) in faces:
cv.Rectangle(image, (x, y), (x + w, y + h), 255)
cv.PutText(image, "Hello Suresh", (x + 20, y - 20), font, 255)
#cv.Rectangle(image, (50,50), (100,100), 255)
cv.ShowImage("Video", image)
i += 1
c = cv.WaitKey(10)
def run(self):
# Capture first frame to get size
frame = cv.QueryFrame(self.capture)
frame_size = cv.GetSize(frame)
new_size = ( frame_size[0] / 2, frame_size[1] / 2)
color_image = cv.CreateImage(new_size, 8, 3)
grey_image = cv.CreateImage(new_size, cv.IPL_DEPTH_8U, 1)
moving_average = cv.CreateImage(new_size, cv.IPL_DEPTH_32F, 3)
font = cv.InitFont(cv.CV_FONT_HERSHEY_COMPLEX_SMALL, 1, 1, 0, 1, 1)
first = True
k = 0
while True:
k+=1
captured_image = cv.QueryFrame(self.capture)
color_image = cv.CreateImage(new_size, captured_image.depth, captured_image.nChannels)
cv.Resize(captured_image, color_image)
# 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, 0.020, None)
# 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 to grayscale.
cv.CvtColor(difference, grey_image, cv.CV_RGB2GRAY)
# Convert the image to black and white.
cv.Threshold(grey_image, grey_image, 70, 255, cv.CV_THRESH_BINARY)
# Dilate and erode to get people blobs
cv.Dilate(grey_image, grey_image, None, 18)
cv.Erode(grey_image, grey_image, None, 10)
storage = cv.CreateMemStorage(0)
contour = cv.FindContours(grey_image, storage, cv.CV_RETR_CCOMP, cv.CV_CHAIN_APPROX_TC89_KCOS)
points = []
#cv.DrawContours(color_image, contour, cv.CV_RGB(255,0,0), cv.CV_RGB(255,0,255), 2, 1, 8, (0, 0))
i = 0
while contour:
self.observed_occupancy = True
bound_rect = cv.BoundingRect(list(contour))
center_x = bound_rect[0] + (bound_rect[2]/2)
center_y = bound_rect[1] + (bound_rect[3]/2)
#if center_y < 200:
# continue
i+=1
closest_distance = 10000
closest_object = None
for to in self.tracked_objects:
current_distance = math.hypot(to.latest_position[0] - center_x, to.latest_position[1] - center_y)
closest_distance = min(closest_distance, current_distance)
#print "DISTANCES: ", str(closest_distance), str(current_distance)
if current_distance == closest_distance:
closest_object = to
if closest_object is None:
#print "OBJECT IS NEW"
self.tracked_objects.append(TrackedObject((center_x, center_y), [(center_x, center_y)], "new"))
else:
#print "CLOSEST OBJECT: ", closest_object.latest_position
closest_object.movement_vector.append((center_x, center_y))
closest_object.latest_position = (center_x, center_y)
#print "AMOUNT OF OBJECTS: ", str(len(self.tracked_objects))
if closest_object is not None:
cv.Line(color_image, closest_object.latest_position, (center_x, center_y), cv.CV_RGB(0,255,0))
#closest_x = min(closest_x, to.latest_position[0])
#closest_y = min(closest_y, to.latest_position[0])
contour = contour.h_next()
pt1 = (bound_rect[0], bound_rect[1])
pt2 = (bound_rect[0] + bound_rect[2], bound_rect[1] + bound_rect[3])
points.append(pt1)
points.append(pt2)
cv.Rectangle(color_image, pt1, pt2, cv.CV_RGB(0,0,255), 1)
cv.PutText(color_image, str(i), pt1, font, cv.CV_RGB(255,0,255))
cv.Circle(color_image, (center_x, center_y), 2, cv.CV_RGB(255,0,255), 2, 8, 0)
#print "LEN ", len(self.tracked_objects)
#if len(self.tracked_objects) > 0 and self.tracked_objects[0] is not None:
# #print "ENTRE"
# obj_vector = self.tracked_objects[0].movement_vector
# print "MVV LEN ", len(obj_vector)
# for index in range(0, len(obj_vector)-2):
# try:
# print "Index ", index, "len(obj_vector) ", len(obj_vector)
# cv.Line(color_image, obj_vector[index], obj_vector[index+1], cv.CV_RGB(0,255,0))
#
# except: print "oops"
#print "Iteration ", k, " Vector: ", vectors["1"]
cv.ShowImage("Target", color_image)
time_passed = time.time() - self.last_request
request_threshold = 60
if time_passed > request_threshold:
self.send_occupancy()
self.send_image(color_image)
#Listen for ESC key
c = cv.WaitKey(10)
#c = cv.WaitKey(7) % 0x100
if c == 27:
break
def segment_rect(image,
rect,
debug=False,
display=None,
target_size=None,
group_range=(3, 25)):
global next
skip = False
best_chars = []
best_threshold = None
thresholded = cv.CloneImage(image)
contour_image = cv.CloneImage(image)
edges = cv.CloneImage(image)
min_x, min_y, width, height = rect
# cv.SetImageROI(thresholded, rect)
cv.SetImageROI(contour_image, rect)
cv.SetImageROI(image, rect)
cv.SetImageROI(edges, rect)
horizontal = cv.CreateImage(cv.GetSize(image), cv.IPL_DEPTH_16S, 1)
magnitude32f = cv.CreateImage(cv.GetSize(image), cv.IPL_DEPTH_32F, 1)
vertical = cv.CloneImage(horizontal)
magnitude = cv.CloneImage(horizontal)
cv.Sobel(image, horizontal, 0, 1, 3)
cv.Sobel(image, vertical, 1, 0, 3)
cv.Pow(horizontal, horizontal, 2)
cv.Pow(vertical, vertical, 2)
cv.Add(vertical, horizontal, magnitude)
cv.Convert(magnitude, magnitude32f)
cv.Pow(magnitude32f, magnitude32f, 0.5)
cv.Convert(magnitude32f, edges)
original_rect = rect
if display:
cv.SetImageROI(display, rect)
for threshold in range(1, 20, 1):
cv.SetImageROI(thresholded, original_rect)
#for i in range(30, 60, 1):
if display:
cv.Merge(image, image, image, None, display)
cv.Copy(image, thresholded)
#cv.Threshold(thresholded, thresholded, i, 255, cv.CV_THRESH_BINARY_INV)
cv.AdaptiveThreshold(thresholded, thresholded, 255,
cv.CV_ADAPTIVE_THRESH_MEAN_C,
cv.CV_THRESH_BINARY_INV, 17, threshold)
#cv.AdaptiveThreshold(thresholded, thresholded, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY_INV, 5, i)
# skip rects greater than 50% thresholded
summed = cv.Norm(thresholded, None, cv.CV_L1,
None) / 255 / thresholded.width / thresholded.height
if summed > 0.5:
continue
if debug:
cv.ShowImage("edge", thresholded)
storage = cv.CreateMemStorage(0)
cv.Copy(thresholded, contour_image)
contours = cv.FindContours(contour_image, storage, cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_SIMPLE, (0, 0))
ext.filter_contours(contours, 20, ext.LESSTHAN)
groups = []
rects = []
edge_counts = []
overlappings = {}
if contours:
seq = contours
while seq:
c = ext.as_contour(ext.wrapped(seq))
r = (c.rect.x, c.rect.y, c.rect.width, c.rect.height)
rects.append(r)
seq = seq.h_next()
similarity = 0.45 #0.3
rects.sort(lambda x, y: cmp(y[2] * y[3], x[2] * x[3]))
for rect in rects:
if debug:
print
print "R", rect, len(groups)
cv.SetImageROI(edges,
(original_rect[0] + rect[0],
original_rect[1] + rect[1], rect[2], rect[3]))
edge_count = cv.Sum(edges)[0] / 255 / (rect[2] * rect[3])
edge_counts.append(edge_count)
# cv.ShowImage("edges", edges)
# cv.WaitKey(0)
if debug and target_size:
print "X", target_size, rect
print(target_size[0] - rect[2]) / target_size[0]
print(target_size[1] - rect[3]) / target_size[1]
if rect[2] > rect[3] or float(rect[3])/rect[2] < 3./3 or edge_count < 0.1\
or (rect[2] == image.width and rect[3] == image.height) \
or (target_size and not 0 < (target_size[0] - rect[2]) / target_size[0] < 0.3 \
and not 0 < (target_size[1] - rect[3]) / target_size[1] < 0.05):
if debug:
print "rej", rect[2], ">", rect[3], "edge=", edge_count
cv.Rectangle(display, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]),
(0, 0, 255), 1)
cv.ShowImage("main", display)
if not skip and not next:
c = cv.WaitKey(0)
if c == ord("a"):
skip = True
if c == ord("z"):
next = True
continue
added = False
for group_id, group in enumerate(groups):
avg_width, avg_height, avg_y = 0, 0, 0
overlap = None
c = 0
for r in group:
avg_y += r[1] + r[3] / 2.0
avg_width += r[2]
avg_height += r[3]
irect = intersect(r, rect)
if irect[2] * irect[3] > 0.2 * r[2] * r[3]:
overlappings.setdefault(group_id,
[]).append([r, rect])
avg_y /= float(len(group))
avg_width /= float(len(group))
avg_height /= float(len(group))
if debug:
print group
if (abs(avg_width - rect[2]) / avg_width < similarity or \
(rect[2] < avg_width)) and \
abs(avg_height - rect[3])/ avg_height < similarity and \
abs(avg_y - (rect[1] + rect[3]/2.0)) / avg_y < similarity:
group.append(rect)
added = True
else:
pass
if not added:
# first char in group
groups.append([rect])
if debug:
print "now:"
for g in groups:
print g
cv.Rectangle(display, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]),
(255, 0, 0), 1)
cv.ShowImage("main", display)
if not skip and not next:
c = cv.WaitKey(0)
if c == ord("a"):
skip = True
if c == ord("z"):
next = True
if groups:
#handle overlapping regions, default to average width match
for group_id, over in overlappings.items():
group = groups[group_id]
avg_width = 0
avg_height = 0
for r in group:
avg_width += r[2]
avg_height += r[3]
avg_width /= float(len(group))
avg_height /= float(len(group))
for r1, r2 in over:
if r2 not in group or r1 not in group:
continue
if debug:
print "over", r1, r2, r1[2] * r1[3], r2[2] * r2[
3], avg_width
d1 = abs(r1[2] - avg_width) + abs(r1[3] - avg_height)
d2 = abs(r2[2] - avg_width) + abs(r2[3] - avg_height)
if d1 < d2:
group.remove(r2)
else:
group.remove(r1)
#group = max(groups, key=len)
# from longest groups, find largest area
groups.sort(key=len)
groups.reverse()
max_area = 0
mad_index = -1
for i, g in enumerate(groups[:5]):
area = 0
for r in g:
area += r[2] * r[3]
if area > max_area:
max_area = area
max_index = i
group = groups[max_index]
# vertical splitting
avg_width, avg_height, avg_y = 0, 0, 0
if debug:
print "G", group
for r in group:
avg_y += r[1] + r[3] / 2.0
avg_width += r[2]
avg_height += r[3]
avg_y /= float(len(group))
avg_width /= float(len(group))
avg_height /= float(len(group))
band_rects = []
bound = bounding_rect(group)
for i, rect in enumerate(rects):
if edge_counts[i] < 0.1:
continue
if (abs(avg_width - rect[2]) / avg_width < similarity or \
(rect[2] < avg_width)) and \
(abs(avg_height - rect[3]) / avg_height < similarity or \
(rect[3] < avg_height)) and \
abs(avg_y - (rect[1] + rect[3]/2.0)) < avg_height/2:
band_rects.append(rect)
band_rects.sort(lambda x, y: cmp(y[2] * y[3], x[2] * x[3]))
for i, rect_a in enumerate(band_rects[:-1]):
if rect_a[2] * rect_a[3] < 0.2 * avg_width * avg_height:
continue
merge_rects = []
for rect_b in band_rects[i + 1:]:
w = avg_width
m1 = rect_a[0] + rect_a[2] / 2
m2 = rect_b[0] + rect_b[2] / 2
if abs(m1 - m2) < w:
merge_rects.append(rect_b)
if debug:
print "M", merge_rects
if merge_rects:
merge_rects.append(rect_a)
rect = bounding_rect(merge_rects)
area = 0
for r in merge_rects:
area += r[2] * r[3]
if (abs(avg_width - rect[2]) / avg_width < similarity or \
(rect[2] < avg_width)) and \
abs(avg_height - rect[3])/ avg_height < similarity and \
area > 0.5*(avg_width*avg_height) and \
abs(avg_y - (rect[1] + rect[3]/2.0)) / avg_y < similarity:
for r in merge_rects:
if r in group:
group.remove(r)
# merge into group
new_group = []
merged = False
for gr in group:
area2 = max(gr[2] * gr[3], rect[2] * rect[3])
isect = intersect(gr, rect)
if isect[2] * isect[3] > 0.4 * area2:
x = min(gr[0], rect[0])
y = min(gr[1], rect[1])
x2 = max(gr[0] + gr[2], rect[0] + rect[2])
y2 = max(gr[1] + gr[3], rect[1] + rect[3])
new_rect = (x, y, x2 - x, y2 - y)
new_group.append(new_rect)
merged = True
else:
new_group.append(gr)
if not merged:
new_group.append(rect)
group = new_group
cv.Rectangle(display, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]),
(255, 0, 255), 2)
# avoid splitting
split = False
# select higher threshold if innovates significantly
best_width = 0.0
if best_chars:
best_area = 0.0
for rect in best_chars:
best_area += rect[2] * rect[3]
best_width += rect[2]
best_width /= len(best_chars)
area = 0.0
overlapped = 0.0
avg_width = 0.0
avg_height = 0.0
for rect in group:
area += rect[2] * rect[3]
avg_width += rect[2]
avg_height += rect[3]
for char in best_chars:
section = intersect(rect, char)
if section[2] * section[3] > 0:
overlapped += section[2] * section[3]
avg_width /= len(group)
avg_height /= len(group)
quotient = overlapped / area
quotient2 = (area - overlapped) / best_area
if debug:
print area, overlapped, best_area
print group
print "QUO", quotient
print "QUO2", quotient2
else:
quotient = 0
quotient2 = 1
best_area = 0
group.sort(lambda x, y: cmp(x[0] + x[2] / 2, y[0] + y[2] / 2))
best_chars.sort(lambda x, y: cmp(x[0] + x[2] / 2, y[0] + y[2] / 2))
if group_range[0] <= len(group) <= group_range[1] and avg_width > 5 and avg_height > 10 and \
((quotient2 > 0.05 and (best_area == 0 or abs(area - best_area)/best_area < 0.4))
or (quotient2 > 0.3 and area > best_area)):
if debug:
print "ASSIGNED", group
best_chars = group
best_threshold = threshold #get_patch(thresholded, original_rect)
else:
if debug:
print "not", quotient2, len(
group), avg_width, avg_height, area, best_area
# best_chars = groups
if debug:
for rect in best_chars:
cv.Rectangle(display, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]),
(0, 255, 0), 1)
cv.ShowImage("main", display)
if not skip and not next:
c = cv.WaitKey(0)
if c == ord("a"):
skip = True
if c == ord("z"):
next = True
best_chars.sort(lambda x, y: cmp(x[0], y[0]))
cv.ResetImageROI(thresholded)
cv.ResetImageROI(contour_image)
cv.ResetImageROI(image)
cv.ResetImageROI(edges)
if display:
cv.ResetImageROI(display)
return best_chars, best_threshold
def blockfacemask(bgrimg):
global lastrects
rects = detect_faces(bgrimg)
rects = addheights(rects)
newimg = im.newgray(bgrimg)
cv.Set(newimg, 255)
if rects:
lastrects = rects
else:
rects = lastrects
if rects:
for rect in rects:
cv.SetImageROI(newimg, rect)
cv.Set(newimg, 0)
cv.ResetImageROI(newimg)
return newimg
if __name__ == '__main__':
cam = cv.CaptureFromCAM(0)
while True:
img = cv.QueryFrame(cam)
img = im.resize(img, width=400)
faces = detect_faces(img)
for f in faces:
cv.Rectangle(img, (f[0], f[1]), (f[0] + f[2], f[1] + f[3]),
im.color.RED,
thickness=3)
cv.Flip(img, None, 1)
cv.ShowImage('face', img)
def recognize(image, display=None, visualize=None, clean=False):
rects = detect(image, debug=0, display=display)
candidates = segment(image, rects, debug=0, display=display)
# return
font = cv.InitFont(cv.CV_FONT_VECTOR0, 0.4, 0.4, 0.0, 0, 0)
source = cv.CreateImage((image.width, image.height), 8, 1)
source2 = cv.CreateImage((image.width, image.height), 8, 1)
mask = cv.CloneImage(source)
cv.CvtColor(image, source, cv.CV_BGR2GRAY)
for i, c in enumerate(candidates):
window_name = "candidate%s" % i
rect = c["rect"]
invert = c["invert"]
if visualize:
cv.SetImageROI(source, rect)
cv.SetImageROI(source2, rect)
cv.SetImageROI(mask, rect)
bound_rect = bounding_rect(c["chars"])
rects_to_mask([bound_rect], mask, value=255)
cv.Zero(source2)
edge = edge_threshold(source)
cv.Copy(edge, source2, mask)
text1, conf1 = run_tesseract(source)
text2, conf2 = run_tesseract(source2)
cv.ShowImage("source", source)
cv.ShowImage("thresholded", source2)
cv.ShowImage("edge", edge)
cv.ShowImage("mask", mask)
cv.WaitKey(5)
gray = (150, 150, 150)
col1, col2, col3 = gray, gray, gray
k = 70
if np.mean(conf1) >= np.mean(conf2):
if any(conf1 > k):
if any(conf2 > k) and len(text2) > len(text1) * 2:
col2 = (0, 255, 0)
else:
col1 = (0, 255, 0)
col3 = (0, 255, 0)
else:
if any(conf2 > k):
if any(conf1 > k) and len(text1) > len(text2) * 2:
col1 = (0, 255, 0)
else:
col2 = (0, 255, 0)
col3 = (0, 255, 0)
if clean:
if col1 != gray and text1 is not None:
cv.PutText(visualize, text1, (rect[0], rect[1] - 5), font,
col1)
if col2 != gray and text2 is not None:
cv.PutText(visualize, text2, (rect[0], rect[1] - 5), font,
col2)
else:
if text1 is not None:
cv.PutText(visualize, text1, (rect[0], rect[1] - 5), font,
col1)
if text2 is not None:
cv.PutText(visualize, text2, (rect[0], rect[1] - 15), font,
col2)
cv.Rectangle(visualize, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]), col3, 1)
cv.ResetImageROI(source)
cv.ResetImageROI(source2)
cv.ResetImageROI(mask)
def DetectRedEyes(image, faceCascade, smileCascade):
min_size = (20, 20)
image_scale = 2
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
# Allocate the temporary images
gray = cv.CreateImage((image.width, image.height), 8, 1)
smallImage = cv.CreateImage((cv.Round(
image.width / image_scale), cv.Round(image.height / image_scale)), 8,
1)
# Convert color input image to grayscale
cv.CvtColor(image, gray, cv.CV_BGR2GRAY)
# Scale input image for faster processing
cv.Resize(gray, smallImage, cv.CV_INTER_LINEAR)
# Equalize the histogram
cv.EqualizeHist(smallImage, smallImage)
# Detect the faces
faces = cv.HaarDetectObjects(smallImage, faceCascade,
cv.CreateMemStorage(0), haar_scale,
min_neighbors, haar_flags, min_size)
# If faces are found
if faces:
print faces
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
print "face"
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
# print pt1
# print pt2
cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 1, 8, 0)
cv.PutText(image, "face", pt1, font, cv.RGB(255, 0, 0))
face_region = cv.GetSubRect(image,
(x, int(y + (h / 4)), w, int(h / 2)))
#split face
cv.Rectangle(image,
(pt1[0], (pt1[1] + (abs(pt1[1] - pt2[1]) / 2))), pt2,
cv.RGB(0, 255, 0), 1, 8, 0)
cv.PutText(image, "lower",
(pt1[0], (pt1[1] + (abs(pt1[1] - pt2[1]) / 2))), font,
cv.RGB(0, 255, 0))
cv.SetImageROI(
image, (pt1[0],
(pt1[1] + (abs(pt1[1] - pt2[1]) / 2)), pt2[0] - pt1[0],
int((pt2[1] - (pt1[1] + (abs(pt1[1] - pt2[1]) / 2))))))
smiles = cv.HaarDetectObjects(image, smileCascade,
cv.CreateMemStorage(0), 1.1, 5, 0,
(15, 15))
if smiles:
#print smiles
for smile in smiles:
cv.Rectangle(
image, (smile[0][0], smile[0][1]),
(smile[0][0] + smile[0][2], smile[0][1] + smile[0][3]),
cv.RGB(0, 0, 255), 1, 8, 0)
cv.PutText(image, "smile", (smile[0][0], smile[0][1]),
font, cv.RGB(0, 0, 255))
cv.PutText(image, str(smile[1]),
(smile[0][0], smile[0][1] + smile[0][3]), font,
cv.RGB(0, 0, 255))
#print ((abs(smile[0][1] - smile[0][2]) / abs(pt1[0] - pt2[0])) * 100)
global smileness
smileness = smile[1]
cv.ResetImageROI(image)
#if smile[1] > 90:
# mqttc.publish("smiles", "got smile", 1)
# time.sleep(5)
#eyes = cv.HaarDetectObjects(image, eyeCascade,
#cv.CreateMemStorage(0),
#haar_scale, min_neighbors,
#haar_flags, (15,15))
#if eyes:
# For each eye found
#print eyes
#for eye in eyes:
# Draw a rectangle around the eye
# cv.Rectangle(image,
# (eye[0][0],
# eye[0][1]),
# (eye[0][0] + eye[0][2],
# eye[0][1] + eye[0][3]),
# cv.RGB(255, 0, 0), 1, 8, 0)
cv.ResetImageROI(image)
return image
