def _crop_image(self, src, cw, ch):
CX, CY = 0, 0
cw_px = int(cw * self.pxpermm)
ch_px = int(ch * self.pxpermm)
w, h = get_size(src)
x = int((w - cw_px) / 2 + CX)
y = int((h - ch_px) / 2 + CY)
# print w / self.pxpermm, cw_px / self.pxpermm
# ra = 1
# print self.pxpermm * ra
# print w / float(cw)
# self.cpxpermm = w / float(cw) / 2.
# print h / float(ch), w / float(cw)
# print self.pxpermm * float(w) / cw_px
# self.cpxpermm = self.pxpermm * w / cw
# print self.cpxpermm, w / cw
# print w, cw_px
# print cw, w / (cw * self.pxpermm)
# self.croppixels = (cw_px, ch_px)
# self.croprect = (x, y, cw_px, ch_px)
# cw_px = ch_px = 107
r = 4 - cw_px % 4
cw_px = ch_px = cw_px + r
return asarray(crop(src, x, y, cw_px, ch_px))
def _calculate_spacing(self, im):
h, w, d = im.shape
# cw, ch = 600, 600
cw, ch = 300, 300
cx = (w - cw) / 2
cy = (h - ch) / 2
im = crop(im, cx, cy, cw, ch)
# d = self.test_image.plotdata.get_data('imagedata000')
d = grayspace(im)
# d /= 255.
# edges = filter.canny(d, sigma=3,
# # low_threshold=0,
# # high_threshold=
# )
# edges = edges.astype('uint8')
# edges = vsobel(d)
edges = sobel(d)
nim = zeros_like(edges)
nim[edges > 0.1] = 255
edges = nim
self.test_image.set_image(edges)
hspace, angles, dists = hough_line(edges)
self.test_image.set_image(hspace, 1)
_hspace, angles, dists = hough_peaks(
hspace,
angles,
dists,
)
nim = zeros_like(edges)
h, w, d = im.shape
xs = []
for ti, di in zip(angles, dists):
ai = math.degrees(ti) + 180
di = abs(int(round(di)))
aa = abs(ai - 90) < 1
bb = abs(ai - 270) < 1
if aa or bb:
adi = abs(di)
coords = line(0, adi, h - 1, adi)
nim[coords] = 200
xs.append(di)
self.test_image.set_image(nim, 2)
xs.sort()
# compute difference between each pair
dxs = diff(xs)
print dxs
dd = sorted(dxs)[1:]
print dd
while len(dd):
if std(dd) < 3:
print dd
return mean(dd) * 4 # each bar =0.25mm
else:
dd = dd[:-1]
def _calculate_spacing(self, im):
h, w, d = im.shape
# cw, ch = 600, 600
cw, ch = 300, 300
cx = (w - cw) / 2
cy = (h - ch) / 2
im = crop(im, cx, cy, cw, ch)
# d = self.test_image.plotdata.get_data('imagedata000')
d = grayspace(im)
# d /= 255.
# edges = filter.canny(d, sigma=3,
# # low_threshold=0,
# # high_threshold=
# )
# edges = edges.astype('uint8')
# edges = vsobel(d)
edges = sobel(d)
nim = zeros_like(edges)
nim[edges > 0.1] = 255
edges = nim
self.test_image.set_image(edges)
hspace, angles, dists = hough_line(edges)
self.test_image.set_image(hspace, 1)
_hspace, angles, dists = hough_peaks(hspace, angles, dists,
)
nim = zeros_like(edges)
h, w, d = im.shape
xs = []
for ti, di in zip(angles, dists):
ai = math.degrees(ti) + 180
di = abs(int(round(di)))
aa = abs(ai - 90) < 1
bb = abs(ai - 270) < 1
if aa or bb :
adi = abs(di)
coords = line(0, adi, h - 1, adi)
nim[coords] = 200
xs.append(di)
self.test_image.set_image(nim, 2)
xs.sort()
# compute difference between each pair
dxs = diff(xs)
print dxs
dd = sorted(dxs)[1:]
print dd
while len(dd):
if std(dd) < 3:
print dd
return mean(dd) * 4 # each bar =0.25mm
else:
dd = dd[:-1]
def crop(self, src, cw, ch, ox=0, oy=0):
cw_px = int(cw * self.pxpermm)
ch_px = int(ch * self.pxpermm)
w, h = get_size(src)
x = int((w - cw_px) / 2. + ox)
y = int((h - ch_px) / 2. + oy)
# r = 4 - cw_px % 4
# cw_px = ch_px = cw_px + r
return asarray(crop(src, x, y, cw_px, ch_px))
def crop(self, src, cw, ch, ox=0, oy=0):
cw_px = int(cw * self.pxpermm)
ch_px = int(ch * self.pxpermm)
w, h = get_size(src)
x = int((w - cw_px) / 2. + ox)
y = int((h - ch_px) / 2. + oy)
# r = 4 - cw_px % 4
# cw_px = ch_px = cw_px + r
return asarray(crop(src, x, y, cw_px, ch_px))
def _crop_image(self, src):
ccx, ccy = 0, 0
cw_px, ch_px = self.get_frame_size()
# cw_px = int(cw)# * self.pxpermm)
# ch_px = int(ch)# * self.pxpermm)
w, h = get_size(src)
x = int((w - cw_px) / 2 + ccx)
y = int((h - ch_px) / 2 + ccy)
r = 4 - cw_px % 4
cw_px += r
return asarray(crop(src, x, y, cw_px, cw_px))
def _crop_image(self, src):
ccx, ccy = 0, 0
cw_px, ch_px = self.get_frame_size()
# cw_px = int(cw)# * self.pxpermm)
# ch_px = int(ch)# * self.pxpermm)
w, h = get_size(src)
x = int((w - cw_px) / 2 + ccx)
y = int((h - ch_px) / 2 + ccy)
r = 4 - cw_px % 4
cw_px += r
return asarray(crop(src, x, y, cw_px, cw_px))
def crop(self, src, cw, ch, ox=0, oy=0, verbose=True):
cw_px = int(cw * self.pxpermm)
ch_px = int(ch * self.pxpermm)
w, h = get_size(src)
x = int((w - cw_px) / 2. + ox)
y = int((h - ch_px) / 2. - oy)
# r = 4 - cw_px % 4
# cw_px = ch_px = cw_px + r
if verbose:
self.debug('Crop: x={},y={}, cw={}, ch={}, '
'width={}, height={}, ox={}, oy={}'.format(x, y, cw_px, ch_px, w, h, ox, oy))
return asarray(crop(src, x, y, cw_px, ch_px))
def crop(self, src, cw, ch, ox=0, oy=0, verbose=True):
cw_px = int(cw * self.pxpermm)
ch_px = int(ch * self.pxpermm)
w, h = get_size(src)
x = int((w - cw_px) / 2. + ox)
y = int((h - ch_px) / 2. - oy)
# r = 4 - cw_px % 4
# cw_px = ch_px = cw_px + r
if verbose:
self.debug('Crop: x={},y={}, cw={}, ch={}, '
'width={}, height={}, ox={}, oy={}'.format(
x, y, cw_px, ch_px, w, h, ox, oy))
return asarray(crop(src, x, y, cw_px, ch_px))
def _calculate_focus_measure(self, src, operator, roi):
'''
see
IMPLEMENTATION OF A PASSIVE AUTOMATIC FOCUSING ALGORITHM
FOR DIGITAL STILL CAMERA
DOI 10.1109/30.468047
and
http://cybertron.cg.tu-berlin.de/pdci10/frankencam/#autofocus
'''
# need to resize to 640,480. this is the space the roi is in
# s = resize(grayspace(pychron), 640, 480)
src = grayspace(src)
v = crop(src, *roi)
di = dict(var=lambda x:variance(x),
laplace=lambda x: get_focus_measure(x, 'laplace'),
sobel=lambda x: ndsum(generic_gradient_magnitude(x, sobel, mode='nearest'))
)
func = di[operator]
return func(v)
def _calculate_focus_measure(self, src, operator, roi):
'''
see
IMPLEMENTATION OF A PASSIVE AUTOMATIC FOCUSING ALGORITHM
FOR DIGITAL STILL CAMERA
DOI 10.1109/30.468047
and
http://cybertron.cg.tu-berlin.de/pdci10/frankencam/#autofocus
'''
# need to resize to 640,480. this is the space the roi is in
# s = resize(grayspace(pychron), 640, 480)
src = grayspace(src)
v = crop(src, *roi)
di = dict(var=lambda x: variance(x),
laplace=lambda x: get_focus_measure(x, 'laplace'),
sobel=lambda x: ndsum(
generic_gradient_magnitude(x, sobel, mode='nearest')))
func = di[operator]
return func(v)
