def set_image(self, arr, idx=0):
arr = asarray(grayspace(arr))
self.plotdata.set_data('imagedata{:03d}'.format(idx), arr)
# invoke_in_main_thread(self.container.invalidate_and_redraw)
# self.container.invalidate_and_redraw()
self.container.request_redraw()
def _preprocess(self, frame, stretch_intensity=True, blur=1, denoise=0):
"""
1. convert frame to grayscale
2. remove noise from frame. increase denoise value for more noise filtering
3. stretch contrast
"""
if len(frame.shape) != 2:
frm = grayspace(frame) * 255
else:
frm = frame / self.pixel_depth * 255
frm = frm.astype('uint8')
# self.preprocessed_frame = frame
# if denoise:
# frm = self._denoise(frm, weight=denoise)
# print 'gray', frm.shape
if blur:
frm = gaussian(frm, blur) * 255
frm = frm.astype('uint8')
# frm1 = gaussian(self.preprocessed_frame, blur,
# multichannel=True) * 255
# self.preprocessed_frame = frm1.astype('uint8')
if stretch_intensity:
frm = rescale_intensity(frm)
# frm = self._contrast_equalization(frm)
# self.preprocessed_frame = self._contrast_equalization(self.preprocessed_frame)
return frm
def _preprocess(self, frame, contrast=True, blur=1, denoise=0):
"""
1. convert frame to grayscale
2. remove noise from frame. increase denoise value for more noise filtering
3. stretch contrast
"""
frm = grayspace(frame) * 255
frm = frm.astype('uint8')
self.preprocessed_frame = frame
# if denoise:
# frm = self._denoise(frm, weight=denoise)
# print 'gray', frm.shape
if blur:
frm = gaussian_filter(frm, blur) * 255
frm = frm.astype('uint8')
frm1 = gaussian_filter(
self.preprocessed_frame, blur, multichannel=True) * 255
self.preprocessed_frame = frm1.astype('uint8')
if contrast:
frm = self._contrast_equalization(frm)
self.preprocessed_frame = self._contrast_equalization(
self.preprocessed_frame)
return frm
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 set_image(self, arr, idx=0):
arr = asarray(grayspace(arr))
self.plotdata.set_data('imagedata{:03d}'.format(idx), arr)
# invoke_in_main_thread(self.container.invalidate_and_redraw)
# self.container.invalidate_and_redraw()
self.container.request_redraw()
def _preprocess(self, frame,
contrast=True, blur=1, denoise=0):
"""
1. convert frame to grayscale
2. remove noise from frame. increase denoise value for more noise filtering
3. stretch contrast
"""
frm = grayspace(frame) * 255
frm = frm.astype('uint8')
self.preprocessed_frame = frame
# if denoise:
# frm = self._denoise(frm, weight=denoise)
# print 'gray', frm.shape
if blur:
frm = gaussian_filter(frm, blur) * 255
frm = frm.astype('uint8')
frm1 = gaussian_filter(self.preprocessed_frame, blur,
multichannel=True) * 255
self.preprocessed_frame = frm1.astype('uint8')
if contrast:
frm = self._contrast_equalization(frm)
self.preprocessed_frame = self._contrast_equalization(
self.preprocessed_frame)
return frm
def _preprocess(self, frame, stretch_intensity=True, blur=1, denoise=0):
"""
1. convert frame to grayscale
2. remove noise from frame. increase denoise value for more noise filtering
3. stretch contrast
"""
if len(frame.shape) != 2:
frm = grayspace(frame)
else:
frm = frame / self.pixel_depth * 255
frm = frm.astype('uint8')
# self.preprocessed_frame = frame
# if denoise:
# frm = self._denoise(frm, weight=denoise)
# print 'gray', frm.shape
if blur:
frm = gaussian(frm, blur) * 255
frm = frm.astype('uint8')
# frm1 = gaussian(self.preprocessed_frame, blur,
# multichannel=True) * 255
# self.preprocessed_frame = frm1.astype('uint8')
if stretch_intensity:
frm = rescale_intensity(frm)
# frm = self._contrast_equalization(frm)
# self.preprocessed_frame = self._contrast_equalization(self.preprocessed_frame)
return frm
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 _find_targets(self,
image,
frame,
dim,
n=20,
w=10,
start=None,
step=1,
preprocess=False,
filter_targets=True,
depth=0,
set_image=True):
'''
use a segmentor to segment the image
'''
if preprocess:
src = self._preprocess(frame)
else:
src = grayspace(frame)
# pychron = array(pychron)
# self.test_image.setup_images(1, (640, 480))
# self.test_image.set_image(pychron)
seg = RegionSegmenter(use_adaptive_threshold=False)
if start is None:
start = int(array(src).mean()) - 3 * w
fa = self._get_filter_target_area(dim)
for i in range(n):
seg.threshold_low = max((0, start + i * step - w))
seg.threshold_high = max((1, min((255, start + i * step + w))))
seg.block_size += 5
nsrc = seg.segment(src)
nf = colorspace(nsrc)
# nf = array(colorspace(nsrc))
# draw contours
targets = self._find_polygon_targets(nsrc, frame=nf)
if targets:
if set_image:
image.set_frame(nf)
# filter targets
if filter_targets:
targets = self._filter_targets(image, frame, dim, targets,
fa)
if targets:
return targets
def _find_targets(self, image, frame, dim, n=20, w=10, start=None, step=1,
preprocess=False,
filter_targets=True,
depth=0,
set_image=True):
'''
use a segmentor to segment the image
'''
if preprocess:
src = self._preprocess(frame)
else:
src = grayspace(frame)
# pychron = array(pychron)
# self.test_image.setup_images(1, (640, 480))
# self.test_image.set_image(pychron)
seg = RegionSegmenter(use_adaptive_threshold=False)
if start is None:
start = int(array(src).mean()) - 3 * w
fa = self._get_filter_target_area(dim)
for i in range(n):
seg.threshold_low = max((0, start + i * step - w))
seg.threshold_high = max((1, min((255, start + i * step + w))))
seg.block_size += 5
nsrc = seg.segment(src)
nf = colorspace(nsrc)
# nf = array(colorspace(nsrc))
# draw contours
targets = self._find_polygon_targets(nsrc,
frame=nf
)
if targets:
if set_image:
image.set_frame(nf)
# filter targets
if filter_targets:
targets = self._filter_targets(image, frame, dim, targets, fa)
if targets:
return targets
def _test2(self):
self.test_image.setup_images(
3,
# (475, 613)
(640, 480))
root = '/Users/ross/Pychrondata_demo/data/snapshots/scan6'
p = os.path.join(root, '008.jpg')
im = load_image(p)
im = grayspace(im)
self.test_image.set_image(im)
nim = zeros_like(im)
nim[((im > 100) & (im < 200))] = 255
self.test_image.set_image(nim, 1)
def _test2(self):
self.test_image.setup_images(3,
# (475, 613)
(640, 480)
)
root = '/Users/ross/Pychrondata_demo/data/snapshots/scan6'
p = os.path.join(root, '008.jpg')
im = load_image(p)
im = grayspace(im)
self.test_image.set_image(im)
nim = zeros_like(im)
nim[((im > 100) & (im < 200))] = 255
self.test_image.set_image(nim, 1)
def _preprocess(self, frame, contrast=True, denoise=0):
'''
1. convert frame to grayscale
2. remove noise from frame. increase denoise value for move noise filtering
3. stretch contrast
'''
frm = grayspace(frame) * 255
frm = frm.astype('uint8')
# # preprocess
if denoise:
frm = self._denoise(frm, weight=denoise)
# # contrast = False
if contrast:
frm = self._contrast_equalization(frm)
return frm
def _preprocess(self, frame,
contrast=True, denoise=0):
'''
1. convert frame to grayscale
2. remove noise from frame. increase denoise value for move noise filtering
3. stretch contrast
'''
frm = grayspace(frame) * 255
frm = frm.astype('uint8')
# # preprocess
if denoise:
frm = self._denoise(frm, weight=denoise)
# # contrast = False
if contrast:
frm = self._contrast_equalization(frm)
return frm
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)
def _gray_image(self, src):
return grayspace(src)
def _find_targets(self,
image,
frame,
dim,
shape='circle',
search=None,
preprocess=True,
filter_targets=True,
convexity_filter=False,
mask=False,
set_image=True,
inverted=False):
"""
use a segmentor to segment the image
"""
if search is None:
search = {}
if preprocess:
if not isinstance(preprocess, dict):
preprocess = {}
src = self._preprocess(frame, **preprocess)
else:
src = grayspace(frame)
if src is None:
print('Locator: src is None')
return
if mask:
self._mask(src, mask)
if inverted:
src = invert(src)
start = search.get('start')
if start is None:
w = search.get('width', 10)
start = int(mean(
src[src > 0])) - search.get('start_offset_scalar', 3) * w
step = search.get('step', 2)
n = search.get('n', 20)
blocksize_step = search.get('blocksize_step', 5)
seg = RegionSegmenter(use_adaptive_threshold=search.get(
'use_adaptive_threshold', False),
blocksize=search.get('blocksize', 20))
fa = self._get_filter_target_area(shape, dim)
phigh, plow = None, None
for j in range(n):
ww = w * (j + 1)
self.debug('start intensity={}, width={}'.format(start, ww))
for i in range(n):
low = max((0, start + i * step - ww))
high = max((1, min((255, start + i * step + ww))))
if inverted:
low = 255 - low
high = 255 - high
seg.threshold_low = low
seg.threshold_high = high
if seg.threshold_low == plow and seg.threshold_high == phigh:
break
plow = seg.threshold_low
phigh = seg.threshold_high
nsrc = seg.segment(src)
seg.blocksize += blocksize_step
nf = colorspace(nsrc)
# draw contours
targets = self._find_polygon_targets(nsrc, frame=nf)
if set_image and image is not None:
image.set_frame(nf)
if targets:
# filter targets
if filter_targets:
targets = self._filter_targets(image, frame, dim,
targets, fa)
elif convexity_filter:
# for t in targets:
# print t.convexity, t.area, t.min_enclose_area, t.perimeter_convexity
targets = [
t for t in targets
if t.perimeter_convexity > convexity_filter
]
if targets:
return sorted(targets,
key=attrgetter('area'),
reverse=True)
def _find_targets(self, image, frame, dim,
search=None, preprocess=True,
filter_targets=True,
convexity_filter=False,
mask=False,
set_image=True, inverted=False):
"""
use a segmentor to segment the image
"""
if search is None:
search = {}
if preprocess:
if not isinstance(preprocess, dict):
preprocess = {}
src = self._preprocess(frame, **preprocess)
else:
src = grayspace(frame)
if src is None:
print('Locator: src is None')
return
if mask:
self._mask(src, mask)
if inverted:
src = invert(src)
start = search.get('start')
if start is None:
# n=20, w=10, start=None, step=2
w = search.get('width', 10)
start = int(median(src)) - search.get('start_offset_scalar', 3) * w
# start = 2*w
# start = 20
step = search.get('step', 2)
n = search.get('n', 20)
blocksize_step = search.get('blocksize_step', 5)
seg = RegionSegmenter(use_adaptive_threshold=search.get('use_adaptive_threshold', False),
blocksize=search.get('blocksize', 20))
fa = self._get_filter_target_area(dim)
phigh, plow = None, None
for j in range(n):
ww = w * (j + 1)
for i in range(n):
seg.threshold_low = max((0, start + i * step - ww))
seg.threshold_high = max((1, min((255, start + i * step + ww))))
if seg.threshold_low == plow and seg.threshold_high == phigh:
break
plow = seg.threshold_low
phigh = seg.threshold_high
nsrc = seg.segment(src)
seg.blocksize += blocksize_step
nf = colorspace(nsrc)
# print(i, seg.threshold_high, seg.threshold_low)
# draw contours
targets = self._find_polygon_targets(nsrc, frame=nf)
# print('tasfdas', targets)
if set_image and image is not None:
image.set_frame(nf)
if targets:
# filter targets
if filter_targets:
targets = self._filter_targets(image, frame, dim, targets, fa)
elif convexity_filter:
# for t in targets:
# print t.convexity, t.area, t.min_enclose_area, t.perimeter_convexity
targets = [t for t in targets if t.perimeter_convexity > convexity_filter]
if targets:
return sorted(targets, key=attrgetter('area'), reverse=True)
