def bright_object_detection(image):
""" Perform bright object detection on an array image."""
# Store all intermediate steps in a dictionary. Useful for debugging.
steps = dict()
steps['input'] = image
# Reduce noise using a median filter.
med_filter_size = (MED_SIZE, MED_SIZE, MED_SIZE)
steps['median'] = ndimg.median_filter(steps['input'], med_filter_size)
# Convert median filtered image to grayscale.
steps['luminance'] = scikits.image.color.rgb2gray(steps['median']) * 255.
# Compute local pixel average.
k_avg = np.ones((AVG_SIZE, AVG_SIZE)) / AVG_SIZE**2
steps['average'] = ndimg.convolve(steps['luminance'], k_avg)
# Compute local pixel variance.
steps['diff_mean'] = steps['luminance'] - steps['average']
steps['diff_mean_sq'] = steps['diff_mean'] * steps['diff_mean']
steps['variance'] = ndimg.convolve(steps['diff_mean_sq'], k_avg)
# Compute binary threshold image using mahalonobis distance. Use the sign
# of the difference between the pixel and its local mean to ignore dark
# pixels.
steps['maha_sq'] = (steps['diff_mean'] > 0) * steps['diff_mean_sq'] / \
steps['variance']
steps['thresh_maha'] = (steps['maha_sq'] > (NUM_STDDEV * NUM_STDDEV))
# Integrate global illumination effects by taking a top percentage of
# intensities from the detected light regions.
steps['masked_regions_lum'] = steps['thresh_maha'] * steps['luminance']
steps['masked_regions_hist'] = pymorph.histogram(steps['masked_regions_lum'])
steps['global_bright_thresh'] = int((len(steps['masked_regions_hist']) * \
(1.0 - GLOBAL_BRIGHT_PCT)) + 0.5)
steps['thresh_global'] = steps['masked_regions_lum'] >= \
steps['global_bright_thresh']
# Morphological operations on detected blobs.
steps['detect_erode'] = pymorph.erode(steps['thresh_global'])
steps['detect_dilate'] = pymorph.dilate(steps['detect_erode'])
# Count bright objects. Connected components and raw pixels.
steps['detect_labels'] = pymorph.label(steps['detect_dilate'])
steps['bright_blob_count'] = steps['detect_labels'].max()
steps['bright_pixel_count'] = sum(steps['masked_regions_hist']
[steps['global_bright_thresh']:])
return steps
# <demo> auto
print "Detected light regions using maha with {0} "\
"standard deviations:".format(NUM_STDDEV)
plab.imshow(pymorph.overlay(steps['luminance'].astype('uint8'),
steps['thresh_maha']))
# <demo> stop
# <demo> auto
###############################################################################
# Integrate global illumination effects by taking a top percentage of
# intensities from the detected light regions.
steps['masked_regions_lum'] = steps['thresh_maha'] * steps['luminance']
steps['masked_regions_hist'] = pymorph.histogram(steps['masked_regions_lum'])
steps['global_bright_thresh'] = int((len(steps['masked_regions_hist']) * \
(1.0 - GLOBAL_BRIGHT_PCT)) + 0.5)
steps['thresh_global'] = steps['masked_regions_lum'] >= \
steps['global_bright_thresh']
print "Global filtered mask:"
plab.imshow(pymorph.overlay(steps['luminance'].astype('uint8'),
steps['thresh_global']))
###############################################################################
# Morpohological operations on detected blobs.
# <demo> stop
# <demo> auto
steps['detect_erode'] = pymorph.erode(steps['thresh_global'])
def test_histogram():
A = (np.random.rand(200, 300) * 255).astype(np.uint8)
H = pymorph.histogram(A)
for v, c in enumerate(H):
assert (A == v).sum() == c
def test_histogram():
A = (np.random.rand(200,300)*255).astype(np.uint8)
H = pymorph.histogram(A)
for v,c in enumerate(H):
assert (A == v).sum() == c
