def depthmatrix(leftimage, rightimage, precision=4, mask=0):
"""Returns a 3-channel 32bit floating-point distance matrix. Channels 1,2,3 = x,y,z coordinates of that point.
Precision is the number of times to downsample mask. Downsample is the number of loops to
go through with successively smaller match areas. If mask is set, only pixels in the mask are set."""
info = cv.cvGetSize(leftimage)
width = info.width
height = info.height
precision_pixels = (2**precision)
downsampled_size = cv.cvSize(width/precision_pixels, height/precision_pixels)
print "Precision of", downsampled_size.width, downsampled_size.height, "px"
if mask:
downsampled_mask = cv.cvCreateImage(downsampled_size, 8, 1)
cv.cvResize(mask, downsampled_mask)
matx = cv.cvCreateImage(downsampled_size, 8, 1)
maty = cv.cvCreateImage(downsampled_size, 8, 1)
matz = cv.cvCreateImage(downsampled_size, 8, 1)
for i in xrange(width/precision_pixels):
for j in xrange(height/precision_pixels):
if mask:
if (not cv.cvGetReal2D(downsampled_mask, j, i)):
continue
x = i*precision
y = j*precision
depth = depthmatch(x+precision_pixels/2, y+precision_pixels/2, leftimage, rightimage, roi=precision_pixels, buf=precision_pixels*2)
#print i, j
# fill in result matrix if mask wasn't 0 at this point (X,Y,Z)
cv.cvSetReal2D(matx, j, i, int(depth[0][0]))
cv.cvSetReal2D(maty, j, i, int(depth[0][1]))
cv.cvSetReal2D(matz, j, i, int(depth[0][2]))
return matz
def same2ndValue(frame, x, y):
size = cv.cvGetSize(frame)
if(x >= 0 and x < size.width and y >= 0 and y < size.height):
if(cv.cvGetReal2D(frame, y, x) == 0):
return 0
else:
return 1 #only return 1 if this pixel is also white
else:
return 0
def averageWhitePoints(frame):
xtotal = 0.0
ytotal = 0.0
count = 0
size = cv.cvGetSize(frame)
for x in range(size.width):
for y in range(size.height):
if (cv.cvGetReal2D(frame, y, x) > 200):
xtotal = xtotal + x
ytotal = ytotal + y
count += 1
if count == 0:
return 0, 0
return int(xtotal / count), int(ytotal / count)
def averageWhitePoints(frame):
xtotal = 0.0
ytotal = 0.0
count = 0;
size = cv.cvGetSize(frame)
for x in range(size.width):
for y in range(size.height):
if(cv.cvGetReal2D(frame, y, x) > 200):
xtotal = xtotal + x
ytotal = ytotal + y
count += 1
if count == 0:
return -1, -1
return int(xtotal/count), int(ytotal/count)
def removeErrantPoints(frame):
size = cv.cvGetSize(frame)
for x in range(size.width):
for y in range(size.height):
if(cv.cvGetReal2D(frame, y, x) > 0):
count = 0
count += same2ndValue(frame, x-1, y)
count += same2ndValue(frame, x+1, y)
count += same2ndValue(frame, x, y-1)
count += same2ndValue(frame, x, y+1)
count += same2ndValue(frame, x-1, y-1)
count += same2ndValue(frame, x-1, y+1)
count += same2ndValue(frame, x+1, y-1)
count += same2ndValue(frame, x+1, y+1)
if count == 0:
cv.cvSet2D(frame, y, x, cv.cvScalar(0, 0, 0, 0))
def averageWhitePoints(frame):
xtotal = 0.0
ytotal = 0.0
count = 0.0;
for x in range(cam_width):
for y in range(cam_height):
if(cv.cvGetReal2D(frame, y, x) > 200):
xtotal = xtotal + x
ytotal = ytotal + y
count += 1
#if(xtotal < 0):
# xtotal = 0
#if(xtotal >= cam_width):
# xtotal = cam_width-1
#if(ytotal < 0):
# ytotal = 0
#if(ytotal >= cam_height):
# ytotal = cam_height-1
if(count > 0):
return int(xtotal/count), int(ytotal/count)
return 0, 0
IMGW = 640
IMGH = 400
highgui.cvSetCaptureProperty(cap, highgui.CV_CAP_PROP_FRAME_WIDTH, IMGW)
highgui.cvSetCaptureProperty(cap, highgui.CV_CAP_PROP_FRAME_HEIGHT, IMGH)
tmp = highgui.cvQueryFrame(cap)
# resize mask
size = cv.cvGetSize(tmp)
mask_r = cv.cvCreateImage(size, 8, 3)
cv.cvResize(mask, mask_r)
mask_bw = cv.cvCreateImage(size, 8, 1)
cv.cvCvtColor(mask_r, mask_bw, cv.CV_RGB2GRAY)
total_pixels = size.width * size.height
sample_pixels = 0.0
for x in xrange(size.width):
for y in xrange(size.height):
if cv.cvGetReal2D(mask_bw, y, x) > 0:
sample_pixels = sample_pixels + 1
print "Sample region: %f%%" % (100 * sample_pixels / total_pixels)
del (tmp)
h_bins = 20
h_limit = 180
s_bins = 32
v_bins = 32
v_limit = 255
# create histogram with 30 bins
h_hue = cv.cvCreateHist([h_bins], cv.CV_HIST_ARRAY, [[0, h_limit]], 1)
h_sat = cv.cvCreateHist([s_bins], cv.CV_HIST_ARRAY, [[0, 255]], 1)
h_val = cv.cvCreateHist([v_bins], cv.CV_HIST_ARRAY, [[0, v_limit]], 1)
