conn.send('BUFFER OK')
data = conn.recv(buffer)
print data
flux = data.split(' ')
while True:
image = cv.QueryFrame(video)
faces = detect.FaceDetection.findFrame(image, flux[0], float(flux[1]), int(flux[2]))
if (faces):
finds = '{"faces": ['
index = 0
for ((x, y, w, h), n) in faces:
index += 1
finds += '{"x": "%s", "y": "%s", "w": "%s", "h": "%s"}' % (x, y, w, h)
if index < len(faces):
finds += ','
finds += ']}'
conn.sendall('%s' % finds)
if cv.WaitKey(10) >= 0:
break
except:
message = '%s' % sys.exc_value
message = message.replace('\\', '/').replace('\n', '').replace('\r', '')
conn.send('{"error": "%s"}' % message)
conn.close()
s.close()
video.release()
cv.destroyAllWindows()
''' Referred to example 2.4 in the book "Learning OpenCV: Computer Vision with the OpenCV Library" Example 2-4. Loading and then smoothing an image before it is displayed on the screen Brought to you by Abid.K --mail me at [email protected] ''' ######################################################################################## import cv image=cv.LoadImage("test.jpg") # create windows to show input and output images cv.NamedWindow("Example4-in") cv.NamedWindow("Example4-out") cv.ShowImage("Example4-in",image) # create an image to hold smoothed output out=cv.CreateImage(cv.GetSize(image),cv.IPL_DEPTH_8U,3) # smoothing--> cv.Smooth(input,output,smooth_type,parameter1,parameter2) cv.Smooth(image,out,cv.CV_GAUSSIAN,3,3) cv.ShowImage("Example4-out",out) cv.WaitKey(0) # be clean cv.DestroyWindow("Example4-in") cv.DestroyWindow("Example4-out") #########################################################################################
def loopcv():
cv.NamedWindow("Depth_Map", cv.CV_WINDOW_AUTOSIZE)
cv.SetMouseCallback("Depth_Map", on_mouse, None)
print "Execute loopcv: click 3 pts on mirror, click 4 pts at screen corner"
global mr3
global sn4
global sn4_ref
while 1:
(depth, _) = freenect.sync_get_depth()
im = array2cv(depth.astype(np.uint8))
#[warp]add rgb as img
if pt is not None:
print "=================="
(x_d, y_d) = pt
print "x=", x_d, " ,y=", y_d
#print depth.shape
#Watch out the indexing for depth col,row = 480,640
d_raw = np.array([depth[y_d, x_d]])
u_d = np.array([x_d])
v_d = np.array([y_d])
print "d_raw= ", d_raw
print "u_d= ", u_d
print "v_d= ", v_d
xyz, uv = calibkinect.depth2xyzuv(d_raw, u_d, v_d)
print "XYZ=", xyz
print "XYZonRGBplane=", uv
cv.WaitKey(100)
cv.Circle(im, (x_d, y_d), 4, (0, 0, 255, 0), -1, 8, 0)
cv.Circle(im, (int(uv[0, 0]), int(uv[0, 1])), 2,
(255, 255, 255, 0), -1, 8, 0)
if (mr3 is None):
mr3 = xyz
#print mr3
elif (mr3.shape[0] <= 2): #append "2"+1= 3pts
mr3 = np.append(mr3, xyz, axis=0)
#print "append mr3=",mr3
if (mr3.shape[0] == 3):
print "enough for mirror, click on screen"
#print "mr3.shape= ",mr3.shape
elif (mr3.shape[0] == 3):
if (sn4 is None):
sn4 = xyz
sn4_ref = MirrorReflection(mr3, xyz[0])
elif (sn4.shape[0] <= 3): #append "3"+1= 4pts
sn4 = np.append(sn4, xyz, axis=0)
sn4_ref = np.append(sn4_ref,
MirrorReflection(mr3, xyz[0]),
axis=0)
if (sn4.shape[0] == 4): #sn4 have 4 pts actually
print "Total screen pts before reflection=", sn4
#print "sn4 shape= ",sn4.shape
print "Total screen pts after reflection=", sn4_ref
#print "sn4_ref shape= ",sn4_ref.shape
if (mr3.shape[0] == 3 and sn4.shape[0] == 4):
print "go into Real Game: Virtual Mirror mode"
#print "mr3= ", mr3
#print "sn4_ref[0:3,:]= ", sn4_ref[0:3,:]
else:
print "..."
#for (x,y) in feat:
#print x, y, velx[y,x], vely[y,x]
cv.ShowImage("Depth_Map", im)
if cv.WaitKey(10) == 27:
print "screen size after calibration: "
print "Corner 1-2 = ", np.linalg.norm(sn4_ref[0] - sn4_ref[1])
print "Corner 2-3 = ", np.linalg.norm(sn4_ref[1] - sn4_ref[2])
print "Corner 3-4 = ", np.linalg.norm(sn4_ref[2] - sn4_ref[3])
print "Corner 4-1 = ", np.linalg.norm(sn4_ref[3] - sn4_ref[0])
break
#update()
if (sn4_ref is not None):
cv.DestroyWindow("Depth_Map")
VirtualMirror()
cv.Canny(image_threshed, image_threshed, 10, 50, 3)
current_contour = cv.FindContours(cv.CloneImage(image_threshed),
cv.CreateMemStorage(), cv.CV_RETR_CCOMP,
cv.CV_CHAIN_APPROX_SIMPLE)
largest_contour = current_contour
while True:
current_contour = current_contour.h_next()
if (not current_contour):
break
if (cv.ContourArea(current_contour) > cv.ContourArea(largest_contour)):
largest_contour = current_contour
for c in largest_contour:
cv.Circle(image, c, 1, (0, 255, 0), 1)
#cv.DrawContours(image, contours, (0,255,0), (255,0,0), 1)
moments = cv.Moments(largest_contour, 1)
center = (cv.GetSpatialMoment(moments, 1, 0) /
cv.GetSpatialMoment(moments, 0, 0),
cv.GetSpatialMoment(moments, 0, 1) /
cv.GetSpatialMoment(moments, 0, 0))
cv.Circle(image, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2)
# show the images
print "displaying images [press any key to continue]"
cv.ShowImage('original image', image)
cv.ShowImage('threshed image', image_threshed)
cv.WaitKey(10000)
#!/usr/bin/env python
import opennpy
import cv
import frame_convert
opennpy.align_depth_to_rgb()
cv.NamedWindow('Depth')
cv.NamedWindow('Video')
print('Press ESC in window to stop')
def get_depth():
return frame_convert.pretty_depth_cv(opennpy.sync_get_depth()[0])
def get_video():
return frame_convert.video_cv(opennpy.sync_get_video()[0])
while 1:
cv.ShowImage('Depth', get_depth())
cv.ShowImage('Video', get_video())
if cv.WaitKey(10) == 27:
break
print frame_no
# capture the current frame
frame = cv.QueryFrame(capture)
image_size = cv.GetSize(frame)
if frame is None:
break
is_x, is_y = detect(frame)
if len(is_x) > 0:
print "is_x[5]=" + str(is_x[5])
print "is_y[5]=" + str(is_y[5])
kalman.update(array([is_x[5], frame.height - is_y[5], 1.]))
proj_board(frame, kalman.mu_hat[0], kalman.mu_hat[1],
kalman.mu_hat[2])
show_data(frame, kalman.mu_hat)
# display webcam image
cv.ShowImage('Camera', frame)
# handle events
k = cv.WaitKey(40)
if k == "t":
cvSaveImage('cb-kf-' + str(snap_no) + '.jpg', frame)
snap_no += 1
if k == 27: # ESC
print 'ESC pressed. Exiting ...'
break
x_ini=msg.pose.position.x
y_ini=msg.pose.position.y
z_ini=msg.pose.position.z
img=Imagen()
def camera_callback(data, camera_name):
# Convert image from a ROS image message to a CV image
global img
try:
img.setImg(cv_bridge.CvBridge().imgmsg_to_cv2(data))
except cv_bridge.CvBridgeError, e:
print e
# 3ms wait
cv.WaitKey(3)
# left camera call back function
def left_camera_callback(data):
camera_callback(data, "Left Hand Camera")
# right camera call back function
def right_camera_callback(data):
camera_callback(data, "Right Hand Camera")
# head camera call back function
def head_camera_callback(data):
camera_callback(data, "Head Camera")
def subscribe_to_camera(camera):
if camera == "left":
cv.NamedWindow("Mascara", 0)
cv.NamedWindow("Binario", 0)
cv.NamedWindow("Regiao de Interesse", 1)
cv.MoveWindow("Regiao de Interesse", 1000, 480)
cv.MoveWindow("Mascara", 0, 500)
cv.MoveWindow("Binario", 400, 500)
arquivo = open('Treino.txt', 'a')
mascara = cv.CreateImage((resolucao_largura, resolucao_altura), 8, 3)
cinza = cv.CreateImage((resolucao_largura, resolucao_altura), 8, 1)
while True:
print("Por Favor tire uma foto do fundo estatico do seu video.")
print("Aperte a tecla espaco.")
if cv.WaitKey(0) % 0x100 == 32:
primeiraImagem = cv.QueryFrame(captura)
fundo = cv.CloneImage(primeiraImagem)
cv.Smooth(fundo, fundo, cv.CV_GAUSSIAN, filtro_de_gauss)
print("Tirou uma Foto !")
break
else:
print "Uma foto do fundo nao foi tirada"
break
while True:
imagem = cv.QueryFrame(captura)
cv.Smooth(imagem, imagem, cv.CV_GAUSSIAN, filtro_de_gauss)
maiorArea = 0
listaContornos = []
sys.exit(1)
input_name = args[0]
if input_name.isdigit():
capture = cv.CreateCameraCapture(int(input_name))
else:
capture = None
cv.NamedWindow("result", 1)
if capture:
frame_copy = None
while True:
frame = cv.QueryFrame(capture)
if not frame:
cv.WaitKey(0)
break
if not frame_copy:
frame_copy = cv.CreateImage((frame.width, frame.height),
cv.IPL_DEPTH_8U, frame.nChannels)
if frame.origin == cv.IPL_ORIGIN_TL:
cv.Copy(frame, frame_copy)
else:
cv.Flip(frame, frame_copy, 0)
detect_and_draw(frame_copy, cascade)
if cv.WaitKey(10) >= 0:
break
else:
image = cv.LoadImage(input_name, 1)
def main(progname, *args):
parser = OptionParser()
parser.add_option("-f",
"--file",
dest="filename",
default=None,
help="analyze a given FILE ending in .jpg or .jpeg",
metavar="FILE")
parser.add_option(
"-i",
"--imageset",
dest="imageset",
default=None,
help="Runs on a predefined set of algorithms (li,chow,china,custom)")
parser.add_option("-d",
"--debug",
dest="debug",
action="store_true",
default=False,
help="Enable visual debugging.")
parser.add_option(
"-t",
"--type",
dest="type",
default="all",
help="Specifies the type of feature to debug. Defaults to all.")
(options, args) = parser.parse_args(list(args))
if options.imageset:
if options.imageset == 'li':
process(li_dir)
return 0
elif options.imageset == 'chow':
process(chow_dir)
return 0
elif options.imageset == 'china':
process(china_dir)
return 0
elif options.imageset == 'custom':
process()
return 0
if not options.filename:
print "Please specify a file (--file) or image set (--imageset)."
return 1
if not options.debug:
process([load_image(options.filename)])
return 0
if options.filename.startswith('data/'):
options.filename = options.filename[len('data/'):]
tdata = load_image(options.filename)
kind = options.type.lower()
size = None #(320,240,'crop') # (0.5, 0.5, 'resize-p')
if size is None:
im = tdata.load()
elif size[-1] == 'crop':
im = image.random_cropped_region(tdata.load(), size[:2])
elif size[-1] == 'resize':
im = tdata.load(size[:2])
elif size[-1] == 'resize-p':
im = image.resize(tdata.load(), by_percent=size[:2])
else:
raise TypeError, "Invalid image sizing type."
image.show(im, "Image")
#l,u,v = image.split(image.rgb2luv(im))
##cv.Set(l, 128)
##cv.EqualizeHist(l, l)
##cv.EqualizeHist(u, u)
##image.show(image.luv2rgb(image.merge(l,u,v)), "test")
#s = cv.GetSize(im)
#t = image.absDiff(u,v)
#image.show(t, "test")
#print "Test Score:", cv.CountNonZero(t) / float(s[0] * s[1])
##image.show(image.threshold(image.And(u,v), threshold=1), "LUV")
# noise
if kind in ('all', 'noise'):
noise_img, score = noise.measure(im, debug=True)
#image.show(noise_img, "Noise Result")
print 'Noise Score:', score, noise.boolean(score)
# contrast
if kind in ('all', 'contrast'):
contrast_img, score = contrast.measure(im, debug=True)
#image.show(contrast_img, "Contrast Result")
print 'Contrast Score:', score, contrast.boolean(score)
# blur
if kind in ('all', 'blur', 'composition'):
focused, score = blur.measure(im, debug=kind in ('all', 'blur'))
#image.show(focused, "Blur Result")
print 'Blur Score:', score, blur.boolean(score)
# composition
if kind in ('all', 'composition'):
composition_img, score = composition.measure(
im, (focused, score, blur.boolean(score)), debug=True)
print 'Composition Score:', score, composition.boolean(score)
if kind in ('faces', ):
result, score = faces.measure(im, debug=True)
print "Face Score:", score, faces.boolean(faces)
#win = CornerTweaker(im)
#win.show()
#_, sat, _ = image.split(image.rgb2hsv(im))
#arr = image.cv2array(sat)
#print arr.mean(), arr.std()
# faces
#im, score = faces.measure(im, debug=True)
#print score, faces.boolean(score)
# composition
#noise_img, score = noise.measure(im, debug=False)
##n = (noise_img, score, noise.boolean(score))
#hulls, score = blur.measure(im, debug=False)
#b = (hulls, score, blur.boolean(score))
#cimg, score = composition.measure(im, b, debug=True)
#print score, composition.boolean(score)
# BLUR
#from time import time
#start = time()
##im2 = image.threshold(image.laplace(im), threshold=75, type=cv.CV_THRESH_TOZERO)
#hulls, score = blur.measure(im, debug=True)
##blur_img, score = blur.measure(im, debug=True)
#end = time()
#print "Time:", (end - start), "seconds"
#image.show(im, "image")
##image.show(noise_img, "Noise Image")
#print score, blur.boolean(score)
#CONTRAST
#_, score = contrast.measure(im, debug=True)
#image.show(im, "Image")
#print score, contrast.boolean(score)
"""
#BLUR
#im2 = image.threshold(image.laplace(im), threshold=75, type=cv.CV_THRESH_TOZERO)
im3, score = blur.measure(im, debug=True)
image.show(im, "image")
image.show(im3, "Focus Mask")
print score, blur.boolean(score)
#plt.show()
"""
#NOISE
#noise_img, score = noise.measure(im, debug=True)
#image.show(noise_img, "Noise")
#print score, noise.boolean(score)
"""
#hwin = ColorHistograms(im)
#hwin.show()
hwin = HistogramWindow(image.rgb2gray(im))
hwin.show()
print cv.GetSize(im), cv.GetSize(im2)
print 'blur', papers.blurry_histogram(im)
#print papers.blurry_histogram(im2)
wind = DerivativeTweaker(im, title="image derivative")
wind.show()
win = EdgeThresholdTweaker(im, title="image edges")
win.show(50)#edge_threshold(im))
#win2 = EdgeThresholdTweaker(im2, title="image resized edges")
#win2.show(edge_threshold(im2))
"""
cv.WaitKey()
cv.DestroyAllWindows()
return 0
image = cv.CreateImage((temp.width, temp.height), cv.IPL_DEPTH_8U, 1)
# Convert original image to B/W - Only because calculations are apparently better
cv.CvtColor(temp, image, cv.CV_BGR2GRAY)
# Use Haar Classifier for face detection (Magic Black Box)
storage = cv.CreateMemStorage()
haar = cv.Load('./haarcascade_frontalface_default.xml')
detected = cv.HaarDetectObjects(image, haar, storage, 1.2, 2,
cv.CV_HAAR_DO_CANNY_PRUNING, (100, 100))
# Returns a list, the first item of which is a tuple
# First element of tuple is x, second is y, third and fourth are width and height
# We pass these params to create a rectangle. If we had not converted a b/w image
# we can have a red border around instead
if detected:
cv.Rectangle(image, (detected[0][0][0], detected[0][0][1]),
(detected[0][0][0] + detected[0][0][2],
detected[0][0][1] + detected[0][0][3]),
cv.RGB(255, 0, 0), 3)
ser.write('Just a really really long string to see something')
# Create a named window and display image in it. Key press kills window and exits
cv.NamedWindow('Nik')
cv.ShowImage('Nik', image)
char = cv.WaitKey(33)
if (char != -1):
if (ord(char) == 27):
loop = False
def run(self):
# Initialize
#log_file_name = "tracker_output.log"
#log_file = file( log_file_name, 'a' )
frame = cv.QueryFrame(self.capture)
frame_size = cv.GetSize(frame)
# Capture the first frame from webcam for image properties
display_image = cv.QueryFrame(self.capture)
# Greyscale image, thresholded to create the motion mask:
grey_image = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
# The RunningAvg() function requires a 32-bit or 64-bit image...
running_average_image = cv.CreateImage(cv.GetSize(frame),
cv.IPL_DEPTH_32F, 3)
# ...but the AbsDiff() function requires matching image depths:
running_average_in_display_color_depth = cv.CloneImage(display_image)
# RAM used by FindContours():
mem_storage = cv.CreateMemStorage(0)
# The difference between the running average and the current frame:
difference = cv.CloneImage(display_image)
target_count = 1
last_target_count = 1
last_target_change_t = 0.0
k_or_guess = 1
codebook = []
frame_count = 0
last_frame_entity_list = []
t0 = time.time()
# For toggling display:
image_list = ["camera", "difference", "threshold", "display", "faces"]
image_index = 0 # 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)
###############################
### Face detection stuff
#haar_cascade = cv.Load( 'haarcascades/haarcascade_frontalface_default.xml' )
haar_cascade = cv.Load('haarcascades/haarcascade_frontalface_alt.xml')
#haar_cascade = cv.Load( 'haarcascades/haarcascade_frontalface_alt2.xml' )
#haar_cascade = cv.Load( 'haarcascades/haarcascade_mcs_mouth.xml' )
#haar_cascade = cv.Load( 'haarcascades/haarcascade_eye.xml' )
#haar_cascade = cv.Load( 'haarcascades/haarcascade_frontalface_alt_tree.xml' )
#haar_cascade = cv.Load( 'haarcascades/haarcascade_upperbody.xml' )
#haar_cascade = cv.Load( 'haarcascades/haarcascade_profileface.xml' )
# Set this to the max number of targets to look for (passed to k-means):
max_targets = 3
while True:
# Capture frame from webcam
camera_image = cv.QueryFrame(self.capture)
frame_count += 1
frame_t0 = time.time()
# Create an image with interactive feedback:
display_image = cv.CloneImage(camera_image)
# Create a working "color image" to modify / blur
color_image = cv.CloneImage(display_image)
# Smooth to get rid of false positives
cv.Smooth(color_image, color_image, cv.CV_GAUSSIAN, 19, 0)
# Use the Running Average as the static background
# a = 0.020 leaves artifacts lingering way too long.
# a = 0.320 works well at 320x240, 15fps. (1/a is roughly num frames.)
cv.RunningAvg(color_image, running_average_image, 0.320, None)
# Convert the scale of the moving average.
cv.ConvertScale(running_average_image,
running_average_in_display_color_depth, 1.0, 0.0)
# Subtract the current frame from the moving average.
cv.AbsDiff(color_image, running_average_in_display_color_depth,
difference)
# Convert the image to greyscale.
cv.CvtColor(difference, grey_image, cv.CV_RGB2GRAY)
# Threshold the image to a black and white motion mask:
cv.Threshold(grey_image, grey_image, 2, 255, cv.CV_THRESH_BINARY)
# Smooth and threshold again to eliminate "sparkles"
cv.Smooth(grey_image, grey_image, cv.CV_GAUSSIAN, 19, 0)
cv.Threshold(grey_image, grey_image, 240, 255, cv.CV_THRESH_BINARY)
grey_image_as_array = numpy.asarray(cv.GetMat(grey_image))
non_black_coords_array = numpy.where(grey_image_as_array > 3)
# Convert from numpy.where()'s two separate lists to one list of (x, y) tuples:
non_black_coords_array = zip(non_black_coords_array[1],
non_black_coords_array[0])
points = [
] # Was using this to hold either pixel coords or polygon coords.
bounding_box_list = []
# Now calculate movements using the white pixels as "motion" data
contour = cv.FindContours(grey_image, mem_storage,
cv.CV_RETR_CCOMP,
cv.CV_CHAIN_APPROX_SIMPLE)
while contour:
bounding_rect = cv.BoundingRect(list(contour))
point1 = (bounding_rect[0], bounding_rect[1])
point2 = (bounding_rect[0] + bounding_rect[2],
bounding_rect[1] + bounding_rect[3])
bounding_box_list.append((point1, point2))
polygon_points = cv.ApproxPoly(list(contour), mem_storage,
cv.CV_POLY_APPROX_DP)
# To track polygon points only (instead of every pixel):
#points += list(polygon_points)
# Draw the contours:
###cv.DrawContours(color_image, contour, cv.CV_RGB(255,0,0), cv.CV_RGB(0,255,0), levels, 3, 0, (0,0) )
cv.FillPoly(grey_image, [
list(polygon_points),
], cv.CV_RGB(255, 255, 255), 0, 0)
cv.PolyLine(display_image, [
polygon_points,
], 0, cv.CV_RGB(255, 255, 255), 1, 0, 0)
#cv.Rectangle( display_image, point1, point2, cv.CV_RGB(120,120,120), 1)
contour = contour.h_next()
# Find the average size of the bbox (targets), then
# remove any tiny bboxes (which are prolly just noise).
# "Tiny" is defined as any box with 1/10th the area of the average box.
# This reduces false positives on tiny "sparkles" noise.
box_areas = []
for box in bounding_box_list:
box_width = box[right][0] - box[left][0]
box_height = box[bottom][0] - box[top][0]
box_areas.append(box_width * box_height)
#cv.Rectangle( display_image, box[0], box[1], cv.CV_RGB(255,0,0), 1)
average_box_area = 0.0
if len(box_areas):
average_box_area = float(sum(box_areas)) / len(box_areas)
trimmed_box_list = []
for box in bounding_box_list:
box_width = box[right][0] - box[left][0]
box_height = box[bottom][0] - box[top][0]
# Only keep the box if it's not a tiny noise box:
if (box_width * box_height) > average_box_area * 0.1:
trimmed_box_list.append(box)
# Draw the trimmed box list:
#for box in trimmed_box_list:
# cv.Rectangle( display_image, box[0], box[1], cv.CV_RGB(0,255,0), 2 )
bounding_box_list = merge_collided_bboxes(trimmed_box_list)
# Draw the merged box list:
for box in bounding_box_list:
cv.Rectangle(display_image, box[0], box[1],
cv.CV_RGB(0, 255, 0), 1)
# Here are our estimate points to track, based on merged & trimmed boxes:
estimated_target_count = len(bounding_box_list)
# Don't allow target "jumps" from few to many or many to few.
# Only change the number of targets up to one target per n seconds.
# This fixes the "exploding number of targets" when something stops moving
# and the motion erodes to disparate little puddles all over the place.
if frame_t0 - last_target_change_t < .350: # 1 change per 0.35 secs
estimated_target_count = last_target_count
else:
if last_target_count - estimated_target_count > 1:
estimated_target_count = last_target_count - 1
if estimated_target_count - last_target_count > 1:
estimated_target_count = last_target_count + 1
last_target_change_t = frame_t0
# Clip to the user-supplied maximum:
estimated_target_count = min(estimated_target_count, max_targets)
# The estimated_target_count at this point is the maximum number of targets
# we want to look for. If kmeans decides that one of our candidate
# bboxes is not actually a target, we remove it from the target list below.
# Using the numpy values directly (treating all pixels as points):
points = non_black_coords_array
center_points = []
if len(points):
# If we have all the "target_count" targets from last frame,
# use the previously known targets (for greater accuracy).
k_or_guess = max(estimated_target_count,
1) # Need at least one target to look for.
if len(codebook) == estimated_target_count:
k_or_guess = codebook
#points = vq.whiten(array( points )) # Don't do this! Ruins everything.
codebook, distortion = vq.kmeans(array(points), k_or_guess)
# Convert to tuples (and draw it to screen)
for center_point in codebook:
center_point = (int(center_point[0]), int(center_point[1]))
center_points.append(center_point)
#cv.Circle(display_image, center_point, 10, cv.CV_RGB(255, 0, 0), 2)
#cv.Circle(display_image, center_point, 5, cv.CV_RGB(255, 0, 0), 3)
# Now we have targets that are NOT computed from bboxes -- just
# movement weights (according to kmeans). If any two targets are
# within the same "bbox count", average them into a single target.
#
# (Any kmeans targets not within a bbox are also kept.)
trimmed_center_points = []
removed_center_points = []
for box in bounding_box_list:
# Find the centers within this box:
center_points_in_box = []
for center_point in center_points:
if center_point[0] < box[right][0] and center_point[0] > box[left][0] and \
center_point[1] < box[bottom][1] and center_point[1] > box[top][1] :
# This point is within the box.
center_points_in_box.append(center_point)
# Now see if there are more than one. If so, merge them.
if len(center_points_in_box) > 1:
# Merge them:
x_list = y_list = []
for point in center_points_in_box:
x_list.append(point[0])
y_list.append(point[1])
average_x = int(float(sum(x_list)) / len(x_list))
average_y = int(float(sum(y_list)) / len(y_list))
trimmed_center_points.append((average_x, average_y))
# Record that they were removed:
removed_center_points += center_points_in_box
if len(center_points_in_box) == 1:
trimmed_center_points.append(
center_points_in_box[0]) # Just use it.
# If there are any center_points not within a bbox, just use them.
# (It's probably a cluster comprised of a bunch of small bboxes.)
for center_point in center_points:
if (not center_point in trimmed_center_points) and (
not center_point in removed_center_points):
trimmed_center_points.append(center_point)
# Draw what we found:
#for center_point in trimmed_center_points:
# center_point = ( int(center_point[0]), int(center_point[1]) )
# cv.Circle(display_image, center_point, 20, cv.CV_RGB(255, 255,255), 1)
# cv.Circle(display_image, center_point, 15, cv.CV_RGB(100, 255, 255), 1)
# cv.Circle(display_image, center_point, 10, cv.CV_RGB(255, 255, 255), 2)
# cv.Circle(display_image, center_point, 5, cv.CV_RGB(100, 255, 255), 3)
# Determine if there are any new (or lost) targets:
actual_target_count = len(trimmed_center_points)
last_target_count = actual_target_count
# Now build the list of physical entities (objects)
this_frame_entity_list = []
# An entity is list: [ name, color, last_time_seen, last_known_coords ]
for target in trimmed_center_points:
# Is this a target near a prior entity (same physical entity)?
entity_found = False
entity_distance_dict = {}
for entity in last_frame_entity_list:
entity_coords = entity[3]
delta_x = entity_coords[0] - target[0]
delta_y = entity_coords[1] - target[1]
distance = sqrt(pow(delta_x, 2) + pow(delta_y, 2))
entity_distance_dict[distance] = entity
# Did we find any non-claimed entities (nearest to furthest):
distance_list = entity_distance_dict.keys()
distance_list.sort()
for distance in distance_list:
# Yes; see if we can claim the nearest one:
nearest_possible_entity = entity_distance_dict[distance]
# Don't consider entities that are already claimed:
if nearest_possible_entity in this_frame_entity_list:
#print "Target %s: Skipping the one iwth distance: %d at %s, C:%s" % (target, distance, nearest_possible_entity[3], nearest_possible_entity[1] )
continue
#print "Target %s: USING the one iwth distance: %d at %s, C:%s" % (target, distance, nearest_possible_entity[3] , nearest_possible_entity[1])
# Found the nearest entity to claim:
entity_found = True
nearest_possible_entity[
2] = frame_t0 # Update last_time_seen
nearest_possible_entity[
3] = target # Update the new location
this_frame_entity_list.append(nearest_possible_entity)
#log_file.write( "%.3f MOVED %s %d %d\n" % ( frame_t0, nearest_possible_entity[0], nearest_possible_entity[3][0], nearest_possible_entity[3][1] ) )
break
if entity_found == False:
# It's a new entity.
color = (random.randint(0, 255), random.randint(0, 255),
random.randint(0, 255))
name = hashlib.md5(str(frame_t0) +
str(color)).hexdigest()[:6]
last_time_seen = frame_t0
new_entity = [name, color, last_time_seen, target]
this_frame_entity_list.append(new_entity)
#log_file.write( "%.3f FOUND %s %d %d\n" % ( frame_t0, new_entity[0], new_entity[3][0], new_entity[3][1] ) )
# Now "delete" any not-found entities which have expired:
entity_ttl = 1.0 # 1 sec.
for entity in last_frame_entity_list:
last_time_seen = entity[2]
if frame_t0 - last_time_seen > entity_ttl:
# It's gone.
#log_file.write( "%.3f STOPD %s %d %d\n" % ( frame_t0, entity[0], entity[3][0], entity[3][1] ) )
pass
else:
# Save it for next time... not expired yet:
this_frame_entity_list.append(entity)
# For next frame:
last_frame_entity_list = this_frame_entity_list
# Draw the found entities to screen:
for entity in this_frame_entity_list:
center_point = entity[3]
c = entity[1] # RGB color tuple
cv.Circle(display_image, center_point, 20,
cv.CV_RGB(c[0], c[1], c[2]), 1)
cv.Circle(display_image, center_point, 15,
cv.CV_RGB(c[0], c[1], c[2]), 1)
cv.Circle(display_image, center_point, 10,
cv.CV_RGB(c[0], c[1], c[2]), 2)
cv.Circle(display_image, center_point, 5,
cv.CV_RGB(c[0], c[1], c[2]), 3)
#print "min_size is: " + str(min_size)
# Listen for ESC or ENTER key
c = cv.WaitKey(7) % 0x100
if c == 27 or c == 10:
break
# Toggle which image to show
if chr(c) == 'd':
image_index = (image_index + 1) % len(image_list)
image_name = image_list[image_index]
# Display frame to user
if image_name == "camera":
image = camera_image
cv.PutText(image, "Camera (Normal)", text_coord, text_font,
text_color)
elif image_name == "difference":
image = difference
cv.PutText(image, "Difference Image", text_coord, text_font,
text_color)
elif image_name == "display":
image = display_image
cv.PutText(image, "Targets (w/AABBs and contours)", text_coord,
text_font, text_color)
elif image_name == "threshold":
# Convert the image to color.
cv.CvtColor(grey_image, display_image, cv.CV_GRAY2RGB)
image = display_image # Re-use display image here
cv.PutText(image, "Motion Mask", text_coord, text_font,
text_color)
elif image_name == "faces":
# Do face detection
detect_faces(camera_image, haar_cascade, mem_storage)
image = camera_image # Re-use camera image here
cv.PutText(image, "Face Detection", text_coord, text_font,
text_color)
cv.ShowImage("Target", image)
if self.writer:
cv.WriteFrame(self.writer, image)
#log_file.flush()
# If only using a camera, then there is no time.sleep() needed,
# because the camera clips us to 15 fps. But if reading from a file,
# we need this to keep the time-based target clipping correct:
frame_t1 = time.time()
# If reading from a file, put in a forced delay:
if not self.writer:
delta_t = frame_t1 - frame_t0
if delta_t < (1.0 / 15.0): time.sleep((1.0 / 15.0) - delta_t)
t1 = time.time()
time_delta = t1 - t0
processed_fps = float(frame_count) / time_delta
print "Got %d frames. %.1f s. %f fps." % (frame_count, time_delta,
processed_fps)
imageWidth = 1024
imageHeight = 480
cv.NamedWindow("window1", cv.CV_WINDOW_AUTOSIZE)
camera_index = 0
capture = cv.CaptureFromCAM(camera_index)
cv.SetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_WIDTH, imageWidth)
cv.SetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_HEIGHT, imageHeight)
font = cv.InitFont(cv.CV_FONT_HERSHEY_COMPLEX_SMALL, 0.5, 0.5, 0, 1, cv.CV_AA)
while True:
frame = cv.QueryFrame(capture)
cv.Rectangle(frame, (0, 0), (imageWidth, 15), (255, 255, 255),
cv.CV_FILLED, 8, 0)
timeStampString = datetime.datetime.now().strftime("%A %Y-%m-%d %I:%M %p")
cv.PutText(frame, timeStampString, (10, 10), font, (0, 0, 0))
cv.ShowImage("window1", frame)
command = cv.WaitKey(10)
if command == ord('q'):
print("Ending program")
break
elif command == ord('s'):
print("Saving image")
cv.SaveImage("img00.jpg", frame)
def run(self):
frame = cv.QueryFrame(self.capture)
frame_size = cv.GetSize(frame)
grey_image = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
run_mean = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_32F, 3)
diff = None
movement = []
while True:
# Capture frame from webcam
color_image = cv.QueryFrame(self.capture)
cv.Smooth(color_image, color_image, cv.CV_GAUSSIAN, 3, 0)
if not diff:
# Initialize
diff = cv.CloneImage(color_image)
temp = cv.CloneImage(color_image)
cv.ConvertScale(color_image, run_mean, 1.0, 0.0)
else:
cv.RunningAvg(color_image, run_mean, 0.020, None)
# Convert the scale of the moving average.
cv.ConvertScale(run_mean, temp, 1.0, 0.0)
# Minus the current frame from the moving average.
cv.AbsDiff(color_image, temp, diff)
# Convert the image to grayscale.
cv.CvtColor(diff, 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 object blobs
cv.Dilate(grey_image, grey_image, None, 18)
cv.Erode(grey_image, grey_image, None, 10)
# Calculate movements
store_movement = cv.CreateMemStorage(0)
contour = cv.FindContours(grey_image, store_movement, cv.CV_RETR_CCOMP, cv.CV_CHAIN_APPROX_SIMPLE)
points = []
while contour:
# Draw rectangles
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)
num_points = len(points)
if num_points:
x = 0
for point in points:
x += point[0]
x /= num_points
movement.append(x)
if len(movement) > 0 and numpy.average(numpy.diff(movement[-30:-1])) > 0:
print 'Left'
else:
print 'Right'
# Display frame to user
cv.ShowImage("Object Direction of Motion", color_image)
# Listen for ESC or ENTER key
c = cv.WaitKey(7) % 0x100
if (0xFF & c == 27):
break
img = cv.CreateImage((200, 200), 8, 1)
col = (128, 128, 128)
p3 = [(0, 0), (65, 65), (200, 0)]
p1 = [(0, 0), (120, 65), (200, 0)]
p2 = [(0, 200), (120, 100), (200, 0)]
c1 = Corner(p1, 1)
#print det((0,0),(0,1) , (1,0)), det((0,0),(1,0),(0,1))
c1.measure()
c2 = Corner(p2, 1, imgtime=2)
c2.measure()
c3 = Corner(p3, 1, imgtime=2)
c3.measure()
c1.computeChange(c3, 1)
c2.computeChange(c1, 1)
c3.draw(img)
c1.draw(img)
c2.draw(img)
points = c2.get_bounding_points(1)
for p in points:
cv.Circle(img, p, 2, (255, 255, 255))
cv.Line(img, c2.p, points[0], (255, 255, 255))
rect = c2.get_rectangle(1)
#print rect
cv.Rectangle(img, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]), (64, 64, 64))
cv.NamedWindow('a')
cv.ShowImage('a', img)
cv.WaitKey(120000)
import roslib
roslib.load_manifest('hai_sandbox')
import cv
import sys
import hai_sandbox.features as fea
if __name__ == '__main__':
fname = sys.argv[1]
image = cv.LoadImage(fname)
image_gray = cv.CreateImage((640, 480), cv.IPL_DEPTH_8U, 1)
cv.CvtColor(image, image_gray, cv.CV_BGR2GRAY)
star_keypoints = fea.star(image)
surf_keypoints, surf_descriptors = fea.surf(image_gray)
harris_keypoints = fea.harris(image_gray)
cv.NamedWindow('surf', 1)
cv.NamedWindow('harris', 1)
cv.NamedWindow('star', 1)
while True:
cv.ShowImage('surf', fea.draw_surf(image, surf_keypoints, (255, 0, 0)))
cv.ShowImage('harris',
fea.draw_harris(image, harris_keypoints, (0, 255, 0)))
cv.ShowImage('star', fea.draw_star(image, star_keypoints, (0, 0, 255)))
k = cv.WaitKey(33)
if k == 27:
break
#Canny(image, edges, threshold1, threshold2, aperture_size=3) => None
def wait():
while True:
k = cv.WaitKey(0) % 0x100
if k == 27:
break
class video_processor:
def __init__(self):
self.sub = rospy.Subscriber('usb_cam/image_raw', Image, self.callback)
self.pub = rospy.Publisher('heading', Twist)
self.speed = float(1)
self.bridge = CvBridge()
cv.NamedWindow("Input Video")
#cv.NamedWindow("Blur Video")
#cv.NamedWindow("HSV Video")
#cv.NamedWindow("Hue Video")
#cv.NamedWindow("Saturation Video")
#cv.NamedWindow("Value Video")
cv.NamedWindow("Red-Orange Video")
cv.NamedWindow("White Video")
cv.NamedWindow("Red-Orange and White Video")
#cv.WaitKey(0)
def callback(self, image_in):
try:
input_image = self.bridge.imgmsg_to_cv(image_in,"bgr8")
except CvBridgeError, e:
print e
cv.ShowImage("Input Video", input_image)
blur_image = cv.CreateMat(input_image.rows,input_image.cols,cv.CV_8UC3)
cv.Smooth(input_image,blur_image,cv.CV_BLUR, 10, 10)
#cv.ShowImage("Blur Video", proc_image)
proc_image = cv.CreateMat(input_image.rows,input_image.cols,cv.CV_8UC3)
cv.CvtColor(blur_image, proc_image, cv.CV_BGR2HSV)
#cv.ShowImage("HSV Video", proc_image)
split_image = [cv.CreateMat(input_image.rows,input_image.cols,cv.CV_8UC1),cv.CreateMat(input_image.rows,input_image.cols,cv.CV_8UC1),cv.CreateMat(input_image.rows,input_image.cols,cv.CV_8UC1)]
cv.Split(proc_image, split_image[0],split_image[1],split_image[2], None )
#hue = cv.CreateMat(input_image.rows,input_image.cols,cv.CV_8UC1)
#sat = cv.CreateMat(input_image.rows,input_image.cols,cv.CV_8UC1)
#val = cv.CreateMat(input_image.rows,input_image.cols,cv.CV_8UC1)
#cv.Split(proc_image, hue,sat,val, None )
#cv.ShowImage("Hue Video", hue)
#cv.ShowImage("Saturation Video", sat)
#cv.ShowImage("Value Video", val)
thresh_0 = cv.CreateMat(input_image.rows,input_image.cols,cv.CV_8UC1)
thresh_1 = cv.CreateMat(input_image.rows,input_image.cols,cv.CV_8UC1)
thresh_2 = cv.CreateMat(input_image.rows,input_image.cols,cv.CV_8UC1)
red_orange = cv.CreateMat(input_image.rows,input_image.cols,cv.CV_8UC1)
cv.Threshold(split_image[1],thresh_0, 128,255,cv.CV_THRESH_BINARY) # > 50% saturation
cv.Threshold(split_image[0],thresh_1, 220,255,cv.CV_THRESH_BINARY) # > Purple
cv.Threshold(split_image[0],thresh_2, 10, 255,cv.CV_THRESH_BINARY_INV) # < Yellow-Orange
cv.Add(thresh_1,thresh_2,red_orange)
cv.And(red_orange,thresh_0,red_orange)
cv.ShowImage("Red-Orange Video",red_orange)
cv.CvtColor(blur_image, proc_image, cv.CV_BGR2HLS)
cv.Split(proc_image, split_image[0], split_image[1],split_image[2], None )
cv.Threshold(split_image[1],thresh_0, 204,255,cv.CV_THRESH_BINARY) # > 80% Lum
cv.ShowImage("White Video",thresh_0)
cv.Or(red_orange, thresh_0, thresh_0)
cv.ShowImage("Red-Orange and White Video",thresh_0)
cv.WaitKey(30)
ang_z = 0
x = 0
for i in range(input_image.rows):
y = -(input_image.cols / 2)
row = cv.GetRow(thresh_0,i)
for j in row.tostring():
ang_z = ang_z + (x * y *ord(j))
y = y + 1
x = x + 1
ang_z = (ang_z * pi * 2 * 2 * 4 / 255 / input_image.rows / input_image.rows / input_image.cols / input_image.cols)
p = Twist()
p.linear.x = self.speed
p.angular.z = ang_z
self.pub.publish(p)
def main():
if len(sys.argv) == 1:
print 'Usage: %s [inputfile]' % sys.argv[0]
sys.exit(1)
# initialize window
cv.NamedWindow('video', cv.CV_WINDOW_AUTOSIZE)
cv.MoveWindow('video', 10, 10)
cv.NamedWindow('threshold', cv.CV_WINDOW_AUTOSIZE)
cv.MoveWindow('threshold', 10, 500)
cv.NamedWindow('flow', cv.CV_WINDOW_AUTOSIZE)
cv.MoveWindow('flow', 500, 10)
cv.NamedWindow('edges', cv.CV_WINDOW_AUTOSIZE)
cv.MoveWindow('edges', 500, 500)
cv.NamedWindow('combined', cv.CV_WINDOW_AUTOSIZE)
cv.MoveWindow('combined', 1000, 10)
capture = cv.CreateFileCapture(sys.argv[1])
if not capture:
print 'Error opening capture'
sys.exit(1)
# Load bg image
bg = cv.LoadImage('bg.png')
# Discard some frames
for i in xrange(2300):
cv.GrabFrame(capture)
frame = cv.QueryFrame(capture)
frame_size = cv.GetSize(frame)
# vars for playback
fps = 25
play = True
velx = cv.CreateImage(frame_size, cv.IPL_DEPTH_32F, 1)
vely = cv.CreateImage(frame_size, cv.IPL_DEPTH_32F, 1)
combined = cv.CreateImage(frame_size, cv.IPL_DEPTH_8U, 1)
prev = cv.CreateImage(frame_size, cv.IPL_DEPTH_8U, 1)
curr = cv.CreateImage(frame_size, cv.IPL_DEPTH_8U, 1)
frame_sub = cv.CreateImage(frame_size, cv.IPL_DEPTH_8U, 3)
edges = cv.CreateImage(frame_size, cv.IPL_DEPTH_8U, 1)
prev_edges = None
storage = cv.CreateMemStorage(0)
blob_mask = cv0.cvCreateImage(frame_size, cv.IPL_DEPTH_8U, 1)
cv0.cvSet(blob_mask, 1)
hough_in = cv.CreateImage(frame_size, cv.IPL_DEPTH_8U, 1)
hough_storage = cv.CreateMat(100, 1, cv.CV_32FC3)
'''
cv.CvtColor(frame, prev, cv.CV_BGR2GRAY)
frame = cv.QueryFrame(capture)
cv.CvtColor(frame, curr, cv.CV_BGR2GRAY)
# winSize can't have even numbers
cv.CalcOpticalFlowLK(prev, curr, (3,3), velx, vely)
cv.ShowImage('video', frame)
cv.ShowImage('flow', velx)
cv.WaitKey(0)
'''
while True:
if play:
frame = cv.QueryFrame(capture)
cv.Sub(frame, bg, frame_sub)
'''#detect people
found = list(cv.HOGDetectMultiScale(frame, storage, win_stride=(8,8),
padding=(32,32), scale=1.05, group_threshold=2))
for r in found:
(rx, ry), (rw, rh) = r
tl = (rx + int(rw*0.1), ry + int(rh*0.07))
br = (rx + int(rw*0.9), ry + int(rh*0.87))
cv.Rectangle(frame, tl, br, (0, 255, 0), 3)
'''
#color thresholding
hsv = cv.CreateImage(frame_size, cv.IPL_DEPTH_8U, 3)
cv.CvtColor(frame, hsv, cv.CV_BGR2HSV)
mask = cv.CreateMat(frame_size[1], frame_size[0], cv.CV_8UC1)
cv.InRangeS(hsv, (0.06 * 256, 0.2 * 256, 0.6 * 256, 0),
(0.16 * 256, 1.0 * 256, 1.0 * 256, 0), mask)
cv.ShowImage('threshold', mask)
#optical flow method
# store previous frame
prev, curr = curr, prev
# convert next frame to single channel grayscale
cv.CvtColor(frame_sub, curr, cv.CV_BGR2GRAY)
#cv.CalcOpticalFlowLK(prev, curr, (3,3), velx, vely)
#cv.Threshold(velx, velx, 8.0, 0, cv.CV_THRESH_TOZERO)
cv.CalcOpticalFlowHS(prev, curr, 1, velx, vely, 0.5,
(cv.CV_TERMCRIT_ITER, 10, 0))
cv.Threshold(velx, velx, 0.5, 0, cv.CV_THRESH_TOZERO)
cv.Threshold(vely, vely, 0.5, 0, cv.CV_THRESH_TOZERO)
cv.Erode(
vely, vely,
cv.CreateStructuringElementEx(2, 2, 0, 0, cv.CV_SHAPE_ELLIPSE))
cv.Add(vely, velx, vely)
cv.ShowImage('flow', vely)
#edge detection
cv.Canny(curr, edges, 50, 100)
cv.Dilate(
edges, edges,
cv.CreateStructuringElementEx(7, 7, 0, 0, cv.CV_SHAPE_ELLIPSE))
cv.ShowImage('edges', edges)
if prev_edges:
cv.CalcOpticalFlowHS(prev_edges, edges, 1, velx, vely, 0.5,
(cv.CV_TERMCRIT_ITER, 10, 0))
cv.Threshold(velx, velx, 0.5, 0, cv.CV_THRESH_TOZERO)
cv.Threshold(vely, vely, 0.5, 0, cv.CV_THRESH_TOZERO)
cv.ShowImage('flow', vely)
prev_edges = edges
cv.Threshold(vely, combined, 0.5, 255, cv.CV_THRESH_BINARY)
cv.Min(combined, edges, combined)
cv.ShowImage('combined', combined)
# blobs
myblobs = CBlobResult(edges, blob_mask, 100, False)
myblobs.filter_blobs(10, 10000)
blob_count = myblobs.GetNumBlobs()
for i in range(blob_count):
my_enumerated_blob = myblobs.GetBlob(i)
# print "%d: Area = %d" % (i, my_enumerated_blob.Area())
my_enumerated_blob.FillBlob(frame,
hsv2rgb(i * 180.0 / blob_count), 0,
0)
cv.ShowImage('video', frame)
''' crashes
#hough transform on dilated image
#http://wiki.elphel.com/index.php?
# title=OpenCV_Tennis_balls_recognizing_tutorial&redirect=no
cv.Copy(edges, hough_in)
cv.Smooth(hough_in, hough_in, cv.CV_GAUSSIAN, 15, 15, 0, 0)
cv.HoughCircles(hough_in, hough_storage, cv.CV_HOUGH_GRADIENT,
4, frame_size[1]/10, 100, 40, 0, 0)
print hough_storage
'''
k = cv.WaitKey(1000 / fps)
if k == 27: # ESC key
break
elif k == 'p': # play/pause
play = not play
def getFrameData(self, files):
depthFilename = []
rgbFilename = []
skelFilename = []
# pdb.set_trace()
# Get filenames
if len(files) > 0:
for i in files:
if i[-5:] == 'depth':
depthFilename = i
self.depthFilename = depthFilename
if i[-3:] == 'rgb':
rgbFilename = i
self.rgbFilename = rgbFilename
if self.skelsEnabled:
if i[-4:] == 'skel':
skelFilename = i
self.skelFilename = skelFilename
displayUsers = []
skels = []
if len(skelFilename) > 0:
users = SR.readUserData(skelFilename)
skels = SR.readSkeletonData(skelFilename)
for i in users:
displayUsers.append(users[i]['Img'])
#eliminate bad skeleton data (joint data is linked to old data)
# pdb.set_trace()
deleteInd = []
for i in users.keys():
# print users[i]['Img']
if users[i]['Img'][2] == 0:
deleteInd.append(i)
deleteInd.sort(reverse=1)
for i in deleteInd:
# print "Del", i
del users[i]
del skels[i]
del displayUsers[i - 1]
if len(depthFilename) > 0:
## 1
# depthRaw = open(depthFilename, 'rb').read().split()
# depthRaw = np.fromfile(depthFilename, dtype=np.uint16, sep=" ")
# self.depthData = np.array(depthRaw, dtype=int).reshape([480,640])[:,::-1]
# self.depthData = self.depthData[:,::-1]
## 2
# self.depthData = np.fromfile(depthFilename, dtype=np.uint16, sep=" ").reshape([480, 640])[:,::-1]
# self.depthDataRaw = self.depthData
## 3
x = open(depthFilename).read()
self.depthDataRaw = np.fromstring(x, dtype=np.uint16,
sep=" ").reshape([480, 640
])[:, ::-1]
self.depthIm = self.depthDataRaw
if self.constrain != []:
self.depthIm8 = constrain(self.depthIm, self.constrain[0],
self.constrain[1])
# print "User count: ", len(displayUsers)
# print displayUsers
if self.viz:
self.depthData = constrain(self.depthDataRaw)
try:
for i in displayUsers:
if i[0] != 0:
for x in xrange(-10, 10):
for j in xrange(-1, 1):
self.depthData[480 - i[1] + j,
640 - i[0] + x] = 30
for y in xrange(-10, 10):
for j in xrange(-1, 1):
self.depthData[480 - i[1] + y,
640 - i[0] + j] = 30
except:
print "Error adding cross at", i
# self.depthData = cv.fromarray(np.array(self.depthData, dtype=np.uint8))
# print "Skels: ", len(skels)
# print skels
if skels != [] and self.vizSkel:
self.depthData = cv.fromarray(
np.array(self.depthData, dtype=np.uint8))
self.depthData = SR.displaySkeleton_CV(
self.depthData, skels)
if self.viz:
self.depthData = cv.fromarray(
np.array(self.depthData, dtype=np.uint8))
cv2.imshow(self.windowName,
np.array(self.depthData, dtype=np.uint8))
if len(rgbFilename) > 0:
imgData = np.fromfile(rgbFilename, dtype=np.uint8)
imgData = imgData.reshape([480, 640, 3])
if self.viz:
cv2.imshow("RGB", imgData)
cv.WaitKey(1)
#!/usr/bin/env python2
import cv
# ESC = 1048603
ESC = 27
def WaitKey(delay=0):
c = cv.WaitKey(delay)
if c == -1:
ret = -1
else:
ret = c & ~0b100000000000000000000
return ret
if __name__ == '__main__':
cv.NamedWindow("janela", cv.CV_WINDOW_AUTOSIZE)
cv.ShowImage("janela", None)
c = cv.WaitKey()
print "%d - %d" % (c & ~0b100000000000000000000, c)
# If faces are found
if 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
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 5, 8, 0)
return image
#----------
# M A I N
#----------
capture = cv.CaptureFromCAM(0)
#capture = cv.CaptureFromFile("test.avi")
#faceCascade = cv.Load("haarcascades/haarcascade_frontalface_default.xml")
#faceCascade = cv.Load("haarcascades/haarcascade_frontalface_alt2.xml")
faceCascade = cv.Load("haarcascades/haarcascade_frontalface_alt.xml")
#faceCascade = cv.Load("haarcascades/haarcascade_frontalface_alt_tree.xml")
while (cv.WaitKey(15)==-1):
img = cv.QueryFrame(capture)
image = DetectFace(img, faceCascade)
cv.ShowImage("face detection test", image)
cv.ReleaseCapture(capture)
class image_converter:
def __init__(self):
self.image_pub = rospy.Publisher("image_converter1", Image)
cv.NamedWindow("Image window", 1)
self.bridge = CvBridge()
self.image_sub = rospy.Subscriber("ardrone/image_raw", Image,
self.callback)
def callback(self, data):
try:
cv_image = self.bridge.imgmsg_to_cv(data, "bgr8")
except CvBridgeError, e:
print e
#(cols,rows) = cv.GetSize(cv_image)
#if cols > 60 and rows > 60 :
# cv.Circle(cv_image, (50,50), 10, 255)
#######################################
#img1=cv.CreateImage((150,150),8,3)
#cv.Rectangle(cv_image, (60,60),(70,90), (255,0,255))
#sub1=cv.GetSubRect(cv_image,(60,60,150,150))
#save1=cv.CloneMat(sub1)
#sub2=cv.GetSubRect(img1,(0,0,150,150))
#cv.Copy(save1,sub2)
storage = cv.CreateMemStorage(0)
img = cv.CreateImage(
(cv.GetSize(cv_image)[1], cv.GetSize(cv_image)[1]), 8, 3)
img1 = cv.CreateImage(
(cv.GetSize(cv_image)[1], cv.GetSize(cv_image)[1]), 8, 3)
#img=cv.CreateImage(cv.GetSize(cv_image),8,3)
img_r = cv.CreateImage(cv.GetSize(img), 8, 1)
img_g = cv.CreateImage(cv.GetSize(img), 8, 1)
img_b = cv.CreateImage(cv.GetSize(img), 8, 1)
img_g1 = cv.CreateImage(cv.GetSize(img), 8, 1)
img_g2 = cv.CreateImage(cv.GetSize(img), 8, 1)
img2 = cv.LoadImage("/home/petin/catkin_ws/src/vp_ardrone2/ris1.jpg",
cv.CV_LOAD_IMAGE_GRAYSCALE)
sub1 = cv.GetSubRect(
cv_image, (0, 0, cv.GetSize(cv_image)[1], cv.GetSize(cv_image)[1]))
save1 = cv.CloneMat(sub1)
sub2 = cv.GetSubRect(
img, (0, 0, cv.GetSize(cv_image)[1], cv.GetSize(cv_image)[1]))
cv.Copy(save1, sub2)
#cv.CvtColor(img, img1, cv.CV_BGR2HSV)
#cv.CvtPixToPlane(img1,img_h,img_s,img_v,None)
#cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
#cv.Smooth(gray,gray,cv.CV_GAUSSIAN,5,5)
cv.Split(img, img_b, img_g, img_r, None)
#
#cv.ShowImage("Image window1", img)
#cv.ShowImage("Image windowb", img_b)
#cv.ShowImage("Image windowg", img_g)
#cv.ShowImage("Image windowr", img_r)
#
cv.InRangeS(img_g, cv.Scalar(180), cv.Scalar(255), img_g1)
#cv.InRangeS(img_s, cv.Scalar(135), cv.Scalar(255), img_s1);
#cv.InRangeS(img_b, cv.Scalar(0), cv.Scalar(61), img_b1);
#cv.Invert(img_g1,img_g2,cv.CV_SVD)
cv.Smooth(img2, img2, cv.CV_GAUSSIAN, 9, 9)
#
cv.ShowImage("Image windowh1", img_g1)
#cv.ShowImage("Image windowg1", img_h1)
#cv.ShowImage("Image windowr1", img_r1)
#cv.ShowImage("Image gray", gray)
# search circle
storage = cv.CreateMat(img2.width, 1, cv.CV_32FC3)
cv.ShowImage("Image window1", img2)
cv.HoughCircles(img2, storage, cv.CV_HOUGH_GRADIENT, 2, 100, 100, 50,
10, 400)
#rospy.loginfo(storage.width)
for i in xrange(storage.width - 1):
radius = storage[i, 2]
center = (storage[i, 0], storage[i, 1])
rospy.loginfo('444')
cv.Circle(cv_image, center, radius, (0, 0, 255), 3, 10, 200)
#search_circle=cv.HoughCircles(img_g1,np.asarray(storage),3,10,150)
#for res in search_circles:
# p = float (cv.GetSeqElem(res))
# pt = cv.Point( cv.Round( p[0] ), cv.Round( p[1] ) );
# cv.Circle( cv_image, pt, cv.Round( p[2] ), 255 );
#
#cv.And(img_g,img_r,img_a)
#cv.And(img_a,img_b,img_a)
#
cv.ShowImage("Image window", cv_image)
cv.WaitKey(3)
try:
self.image_pub.publish(self.bridge.cv_to_imgmsg(cv_image, "bgr8"))
except CvBridgeError, e:
print e
def mainLoop():
input_video_fn = get_input_video_filename()
print 'input video filename:', input_video_fn
# Setting up the window objects and environment
proc_win_name = "Processing window"
cam_win_name = "Capture from camera"
proc_win = cv.NamedWindow(proc_win_name, 1)
cam_win = cv.NamedWindow(cam_win_name, 1)
if input_video_fn:
cam = cv.CaptureFromFile(input_video_fn)
else:
cam = cv.CaptureFromCAM(0)
cv.SetMouseCallback(proc_win_name, handle_mouse)
cv.SetMouseCallback(cam_win_name, handle_mouse)
msdelay = 3
initHueThreshold = 42
initIntensityThreshold = 191
skin_detector = skin.SkinDetector()
skin_detector.setHueThreshold(initHueThreshold)
skin_detector.setIntensityThreshold(initIntensityThreshold)
cv.CreateTrackbar('hueThreshold',
proc_win_name,
initHueThreshold,
255,
skin_detector.setHueThreshold)
cv.CreateTrackbar('intensityThreshold',
proc_win_name,
initIntensityThreshold,
255,
skin_detector.setIntensityThreshold)
session = ImageProcessSession(skin_detector)
ga = gesture.GestureAnalyzer()
grammar = Grammar()
gfn = get_grammar_filename()
if not gfn:
print 'usage: python GestureLock.py -g grammar_file.gmr'
exit(0)
answer_grammer = read_grammar(gfn)
im_orig_writer = ImageWriter(output_folder=get_output_folder())
im_contour_writer = ImageWriter(output_folder='out2')
prev = []
while True:
k = cv.WaitKey(msdelay)
k = chr(k) if k > 0 else 0
if handle_keyboard(k) < 0:
break
bgrimg = cv.QueryFrame(cam)
if not bgrimg:
break
im_orig_writer.write(bgrimg)
cv.Flip(bgrimg, None, 1)
contours = session.process(bgrimg)
img = cv.CreateImage((bgrimg.width, bgrimg.height), 8, 3)
if contours:
ges, area, depth = ga.recognize(contours)
x, y, r, b = im.find_max_rectangle(contours)
cv.Rectangle(img, (x,y), (r, b), im.color.RED)
cv.DrawContours(img, contours, im.color.RED, im.color.GREEN, 1,
thickness=3)
print ges
currentInput = grammar.instantGes(ges)
print currentInput
if len(prev)>=2:
for i,g in enumerate(currentInput):
im.puttext(prev[0], str(g), 30, 70+40*i)
im_contour_writer.write(prev[0])
prev.append( img )
prev.pop(0)
else:
prev.append( img )
if grammar == answer_grammer:
for i,g in enumerate(currentInput):
im.puttext(prev[0], str(g), 30, 70+40*i)
im_contour_writer.write(prev[0])
im.puttext(prev[0], 'AUTHENTICATED!', 30, 70+40*len(currentInput))
im_contour_writer.write(prev[0])
print 'AUTHENTICATED!!!!'
break
cv.ShowImage(proc_win_name, 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)
cv.ShowImage("Target", color_image)
# Listen for ESC key
c = cv.WaitKey(7) % 0x100
if c == 27:
break
+ struct.pack('b', bearingToGoal) \
+ struct.pack('B', 0)
cyril.write(output)
#Data Logging
if (cyril.inWaiting() > 0):
logdata = cyril.read(cyril.inWaiting())
a = 0
b = 0
for c in logdata:
if c == '\n':
datalog.write(
str(datetime.now().time()) + "," + logdata[a:b] + "\n")
a = b + 1
b = b + 1
cv.ShowImage('cam', img)
cv.ShowImage('unwrapped', cropped)
cv.ShowImage('target', cones)
key = cv.WaitKey(10) # THIS REQUIRES AT LEAST ONE WINDOW
#print "key ",key
if key > 0:
break
cv.DestroyAllWindows()
cyril.close()
datalog.write("\n~~~=== Rambler Data Log Closed, " + str(datetime.now()) +
" ===~~~\n")
datalog.close()
import cv
import numpy as np
cv.NamedWindow('Leak')
while 1:
leak = np.random.random((480, 640)) * 255
cv.ShowImage('Leak', leak.astype(np.uint8))
cv.WaitKey(10)
def rangeGUI():
""" creates and displays a GUI for the range finder data
Ranges window: shows range finder values as red lines
coming from the center of the range finder
HoughLines window: shows the result of using a Hough
transformation on image containing the range values as points.
"""
global D
init_GUI() # initialize images and windows
# initialize the lists and variables
D.dangerList = [] # Set up the danger point list
D.ahead = []
D.behind = []
D.left = []
D.right = []
D.broadcast = []
D.send_counter = 0 # counter to turn on the 'send' switch
D.corner_type = 0 # counter to send the corner type
D.send = True # at first the send switch turns on
D.text = "Wait for information" # Text on the display window
D.waiting = 0 # Counter for waiting map direction
D.next_dir = ""
D.default_dir = "Right"
# main loop
while rospy.is_shutdown() == False:
# loop through every angle
for angle in range(REV):
magnitude = MAG_SCALE * D.ranges[angle]
x = int(CENTER + magnitude * cos(pi / 180 * (angle + ANGLE_OFFSET))
) # find x and y coordinates based on the angle
y = int(CENTER - magnitude *
sin(pi / 180 *
(angle + ANGLE_OFFSET))) # and the length of the line
# put the danger points into the list
if x > CENTER - 30 and x < CENTER + 30 and y < CENTER - 30 and y > CENTER - 90:
D.dangerList.append((x, y))
# check the points in the cross zone and put them in four lists
if x < CENTER - 20 and y > CENTER - 20 and y < CENTER + 10:
D.left.append((x, y))
elif x > CENTER + 20 and y > CENTER - 20 and y < CENTER + 10:
D.right.append((x, y))
elif y < CENTER - 30 and y > 60 and x < CENTER + 10 and x > CENTER - 10:
D.ahead.append((x, y))
elif y > CENTER + 10 and x < CENTER + 10 and x > CENTER - 10:
D.behind.append((x, y))
if 0 < magnitude < MAX_MAG: # if data is within "good data" range
# add line to the ranges image
cv.Line(D.image, (CENTER, CENTER), (x, y), cv.RGB(255, 0, 0))
# add dot to image being used in Hough transformation
cv.Line(D.hough, (x, y), (x, y), cv.RGB(255, 0, 0))
# wait and check for quit request
key_code = cv.WaitKey(1) & 255
key_press = chr(key_code)
if key_code == 27 or key_press == 'q': # if ESC or 'q' was pressed
rospy.signal_shutdown("Quitting...")
# find walls and add to image using Hough transformation
findHoughLines()
# call wall following algorithm
wallFollow()
# show image with range finder data and calculated walls
cv.ShowImage("Ranges", D.image)
# show image used in Hough transformation if option is selected
if SHOW_HOUGH:
cv.ShowImage("HoughLines", D.color_dst)
# clear the images for next loop
cv.Set(D.image, cv.RGB(0, 0, 0))
cv.Set(D.hough, cv.RGB(0, 0, 0))
# clear all the lists for next loop
D.dangerList = []
D.ahead = []
D.behind = []
D.left = []
D.right = []
D.tank(0, 0) # stops the robot
print "Quitting..."
def VirtualMirror():
cv.NamedWindow("RGB_remap", cv.CV_WINDOW_NORMAL)
cv.NamedWindow("Depth_remap", cv.CV_WINDOW_AUTOSIZE)
cv.NamedWindow('dst', cv.CV_WINDOW_NORMAL)
cv.SetMouseCallback("Depth_remap", on_mouse, None)
print "Virtual Mirror"
print "Calibrated 4 Screen corner= ", sn4_ref
print "Corner 1-2 = ", np.linalg.norm(sn4_ref[0] - sn4_ref[1])
print "Corner 2-3 = ", np.linalg.norm(sn4_ref[1] - sn4_ref[2])
print "Corner 3-4 = ", np.linalg.norm(sn4_ref[2] - sn4_ref[3])
print "Corner 4-1 = ", np.linalg.norm(sn4_ref[3] - sn4_ref[0])
global head_pos
global head_virtual
global scene4_cross
head_pos = np.array([-0.2, -0.2, 1.0]) #Head_detect()
while 1:
(depth, _) = freenect.sync_get_depth()
(rgb, _) = freenect.sync_get_video()
#print type(depth)
img = array2cv(rgb[:, :, ::-1])
im = array2cv(depth.astype(np.uint8))
#modulize this part for update_on() and loopcv()
#q = depth
X, Y = np.meshgrid(range(640), range(480))
d = 2 #downsampling if need
projpts = calibkinect.depth2xyzuv(depth[::d, ::d], X[::d, ::d],
Y[::d, ::d])
xyz, uv = projpts
if tracking == 0:
#*********************************
if pt is not None:
print "=================="
(x_d, y_d) = pt
print "x=", x_d, " ,y=", y_d
#print depth.shape
#Watch out the indexing for depth col,row = 480,640
d_raw = np.array([depth[y_d, x_d]])
u_d = np.array([x_d])
v_d = np.array([y_d])
print "d_raw= ", d_raw
print "u_d= ", u_d
print "v_d= ", v_d
head3D, head2D = calibkinect.depth2xyzuv(d_raw, u_d, v_d)
print "XYZ=", head3D
print "XYZonRGBplane=", head2D
head_pos = head3D[0]
#print "head_pos.shape",head_pos.shape
print "head_pos= ", head_pos
cv.WaitKey(100)
cv.Circle(im, (x_d, y_d), 4, (0, 0, 255, 0), -1, 8, 0)
cv.Circle(im, (int(head2D[0, 0]), int(head2D[0, 1])), 2,
(255, 255, 255, 0), -1, 8, 0)
#*********************************
elif tracking == 1:
#find the nearest point (nose) as reference for right eye position
print "nose"
inds = np.nonzero(xyz[:, 2] > 0.5)
#print xyz.shape
new_xyz = xyz[inds]
#print new_xyz.shape
close_ind = np.argmin(new_xyz[:, 2])
head_pos = new_xyz[close_ind, :] + (0.03, 0.04, 0.01)
#print head_pos.shape
#print head_pos
elif tracking == 2:
#find the closest point as eye posiiton
print "camera"
inds = np.nonzero(xyz[:, 2] > 0.5)
#print xyz.shape
new_xyz = xyz[inds]
#print new_xyz.shape
close_ind = np.argmin(new_xyz[:, 2])
head_pos = new_xyz[close_ind, :]
#print head_pos.shape
#print head_pos
else:
print "please select a tracking mode"
head_virtual = MirrorReflection(sn4_ref[0:3, :], head_pos)
print "head_virtual= ", head_virtual
rgbK = np.array([[520.97092069697146, 0.0, 318.40565581396697],
[0.0, 517.85544366622719, 263.46756370601804],
[0.0, 0.0, 1.0]])
rgbD = np.array([[0.22464481251757576], [-0.47968370787671893], [0.0],
[0.0]])
irK = np.array([[588.51686020601733, 0.0, 320.22664144213843],
[0.0, 584.73028132692866, 241.98395817513071],
[0.0, 0.0, 1.0]])
irD = np.array([[-0.1273506872313161], [0.36672476189160591], [0.0],
[0.0]])
mapu = cv.CreateImage((640, 480), cv.IPL_DEPTH_32F, 1)
mapv = cv.CreateImage((640, 480), cv.IPL_DEPTH_32F, 1)
mapx = cv.CreateImage((640, 480), cv.IPL_DEPTH_32F, 1)
mapy = cv.CreateImage((640, 480), cv.IPL_DEPTH_32F, 1)
cv.InitUndistortMap(rgbK, rgbD, mapu, mapv)
cv.InitUndistortMap(irK, irD, mapx, mapy)
if 1:
rgb_remap = cv.CloneImage(img)
cv.Remap(img, rgb_remap, mapu, mapv)
depth_remap = cv.CloneImage(im)
cv.Remap(im, depth_remap, mapx, mapy)
scene4_cross = Cross4Pts.CrossPts(xyz, uv, head_pos, head_virtual,
sn4_ref)
#[warp] Add whole warpping code here
#[warp] points = Scene4Pts() as warpping 4 pts
#Flip the dst image!!!!!!!!!
#ShowImage("rgb_warp", dst)
#Within/out of the rgb range
#Mapping Destination (width, height)=(x,y)
#Warning: the order of pts in clockwise: pt1(L-T),pt2(R-T),pt3(R-B),pt4(L-B)
#points = [(test[0,0],test[0,1]), (630.,300.), (700.,500.), (400.,470.)]
points = [(scene4_cross[0, 0], scene4_cross[0, 1]),
(scene4_cross[1, 0], scene4_cross[1, 1]),
(scene4_cross[2, 0], scene4_cross[2, 1]),
(scene4_cross[3, 0], scene4_cross[3, 1])]
#Warping the image without flipping (camera image)
#npoints = [(0.,0.), (640.,0.), (640.,480.), (0.,480.)]
#Warping the image with flipping (mirror flip image)
npoints = [(640., 0.), (0., 0.), (0., 480.), (640., 480.)]
mat = cv.CreateMat(3, 3, cv.CV_32FC1)
cv.GetPerspectiveTransform(points, npoints, mat)
#src = cv.CreateImage( cv.GetSize(img), cv.IPL_DEPTH_32F, 3 )
src = cv.CreateImage(cv.GetSize(rgb_remap), cv.IPL_DEPTH_32F, 3)
#cv.ConvertScale(img,src,(1/255.00))
cv.ConvertScale(rgb_remap, src, (1 / 255.00))
dst = cv.CloneImage(src)
cv.Zero(dst)
cv.WarpPerspective(src, dst, mat)
#************************************************************************
#Remap the rgb and depth image
#Warping will use remap rgb image as src
if 1:
cv.ShowImage("RGB_remap", rgb_remap) #rgb[200:440,300:600,::-1]
cv.ShowImage("Depth_remap", depth_remap)
cv.ShowImage("dst", dst) #warp rgb image
if cv.WaitKey(5) == 27:
cv.DestroyWindow("RGB_remap")
cv.DestroyWindow("Depth_remap")
cv.DestroyWindow("dst")
break
cv.DrawContours (orig, contours, cv.RGB(0,255,0), cv.RGB(255,0,0), 2, 3, cv.CV_AA, (0, 0))
def contour_iterator(contour):
while contour:
yield contour
contour = contour.h_next()
for c in contour_iterator(contours):
# 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)
# 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
cv.Ellipse(orig, center, size, angle, 0, 360, cv.RGB(255,0,0), 2,cv.CV_AA, 0)
# show images
cv.ShowImage("image - press 'q' to quit", orig)
#cv.ShowImage("post-process", processed)
cv.WaitKey(-1)
