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Refactoring
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refactoring building of patches
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@auduno
auduno committed Jan 23, 2014
1 parent 6d33b88 commit 0ba3e5e
Showing 1 changed file with 36 additions and 47 deletions.
83 changes: 36 additions & 47 deletions pdm_builder/buildlib/buildpatch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,14 @@

def build_patches(data, c_value=None, gradient=False):
filters = []

if gradient:
patchcrop = [(patch_size+1)/2, (patch_size+3)/2]
new_patch_size = patch_size+2
else:
patchcrop = [(patch_size-1)/2, (patch_size+1)/2]
new_patch_size = patch_size

for r in range(0, num_patches):
print "training patch:"+str(r)
positives = []
Expand All @@ -32,14 +40,14 @@ def build_patches(data, c_value=None, gradient=False):
# TODO : check that there is not missing data:
points = numpy.around(values[r]+(numpy.array(im.size)/2))
points = points.astype(numpy.uint8)
if gradient:
# check whether cropping goes outside image
m_crop = mask.crop((points[0]-((patch_size+1)/2),points[1]-((patch_size+1)/2),points[0]+((patch_size+3)/2),points[1]+((patch_size+3)/2)))
m_crop = numpy.array(m_crop)
if not numpy.all(m_crop == 255):
print "cropping of patch "+str(r)+" in image '"+filename+"' was outside original image bounds. Dropping this patch from training."
else:
p_crop = im.crop((points[0]-((patch_size+1)/2), points[1]-((patch_size+1)/2), points[0]+((patch_size+3)/2), points[1]+((patch_size+3)/2) ))
# check whether cropping goes outside image
m_crop = mask.crop((points[0]-patchcrop[0], points[1]-patchcrop[0], points[0]+patchcrop[1], points[1]+patchcrop[1]))
m_crop = numpy.array(m_crop)
if not numpy.all(m_crop == 255):
print "cropping of patch "+str(r)+" in image '"+filename+"' was outside original image bounds. Dropping this patch from training."
else:
p_crop = im.crop((points[0]-patchcrop[0], points[1]-patchcrop[0], points[0]+patchcrop[1], points[1]+patchcrop[1] ))
if gradient:
p_crop = numpy.array(p_crop).astype('int32')
dx = sobel(p_crop, axis=0, mode="constant")
dy = sobel(p_crop, axis=1, mode="constant")
Expand All @@ -48,30 +56,24 @@ def build_patches(data, c_value=None, gradient=False):
if not numpy.max(p_crop) == 0.:
p_crop *= 255.0 / numpy.max(p_crop)
#Image.fromarray(p_crop.astype('uint8')).save("./pcropped/0svm2"+filename+".bmp")
positives.append(p_crop.flatten())
else:
# check whether cropping goes outside image
m_crop = mask.crop((points[0]-((patch_size-1)/2),points[1]-((patch_size-1)/2),points[0]+((patch_size+1)/2),points[1]+((patch_size+1)/2)))
m_crop = numpy.array(m_crop)
if not numpy.all(m_crop == 255):
print "cropping of patch "+str(r)+" in image '"+filename+"' was outside original image bounds. Dropping this patch from training."
else:
p_crop = im.crop((points[0]-((patch_size-1)/2),points[1]-((patch_size-1)/2),points[0]+((patch_size+1)/2),points[1]+((patch_size+1)/2)))
else:
#p_crop.save("./pcropped/svm"+filename)
positives.append(numpy.array(p_crop).flatten())
p_crop = numpy.array(p_crop)
positives.append(p_crop.flatten())

# get negative examples from randomization
for nr in range(0,10):
if gradient:
rpoints = random_coord(im.size, patch_size+2, points)
# check whether cropping goes outside image
m_crop = mask.crop((rpoints[0]-((patch_size+1)/2),rpoints[1]-((patch_size+1)/2),rpoints[0]+((patch_size+3)/2),rpoints[1]+((patch_size+3)/2)))
m_crop = numpy.array(m_crop)
if not numpy.all(m_crop == 255):
pass
#print "cropping of a negative for patch "+str(r)+" was outside original image bounds. Dropping this patch from training."
else:
p_crop = im.crop((rpoints[0]-((patch_size+1)/2),rpoints[1]-((patch_size+1)/2),rpoints[0]+((patch_size+3)/2),rpoints[1]+((patch_size+3)/2)))
rpoints = random_coord(im.size, new_patch_size, points)
# check whether cropping goes outside image
m_crop = mask.crop((rpoints[0]-patchcrop[0],rpoints[1]-patchcrop[0],rpoints[0]+patchcrop[1],rpoints[1]+patchcrop[1]))
m_crop = numpy.array(m_crop)
if not numpy.all(m_crop == 255):
pass
#print "cropping of a negative for patch "+str(r)+" was outside original image bounds. Dropping this patch from training."
else:
p_crop = im.crop((rpoints[0]-patchcrop[0], rpoints[1]-patchcrop[0], rpoints[0]+patchcrop[1], rpoints[1]+patchcrop[1]))
if gradient:
#
p_crop = numpy.array(p_crop).astype('int32')
dx = sobel(p_crop, 0)
dy = sobel(p_crop, 1)
Expand All @@ -80,18 +82,9 @@ def build_patches(data, c_value=None, gradient=False):
#p_crop = p_crop.crop((1, 1, patch_size+2, patch_size+1))
if not numpy.max(p_crop) == 0.:
p_crop *= 255.0 / numpy.max(p_crop)
negatives.append(p_crop.flatten())
else:
rpoints = random_coord(im.size, patch_size, points)
# check whether cropping goes outside image
m_crop = mask.crop((rpoints[0]-((patch_size-1)/2),rpoints[1]-((patch_size-1)/2),rpoints[0]+((patch_size+1)/2),rpoints[1]+((patch_size+1)/2)))
m_crop = numpy.array(m_crop)
if not numpy.all(m_crop == 255):
pass
#print "cropping of a negative for patch "+str(r)+" was outside original image bounds. Dropping this patch from training."
else:
p_crop = im.crop((rpoints[0]-((patch_size-1)/2),rpoints[1]-((patch_size-1)/2),rpoints[0]+((patch_size+1)/2),rpoints[1]+((patch_size+1)/2)))
negatives.append(numpy.array(p_crop).flatten())
p_crop = numpy.array(p_crop)
negatives.append(p_crop.flatten())

if i % 1000 == 0:
print i
Expand All @@ -102,17 +95,14 @@ def build_patches(data, c_value=None, gradient=False):
for filename in negfiles:
im = Image.open( join(data_folder, "negatives/", filename) , "r")
im = im.convert("L")
if gradient:
diff = ((patch_size+1)/2)
else:
diff = ((patch_size-1)/2)
diff = patchcrop[0]
for nr in range(0,100):

x = random.randint(1+diff, im.size[0]-diff)
y = random.randint(1+diff, im.size[1]-diff)
rpoints = array([x,y])
p_crop = im.crop((rpoints[0]-patchcrop[0], rpoints[1]-patchcrop[0],rpoints[0]+patchcrop[1],rpoints[1]+patchcrop[1]
if gradient:
p_crop = im.crop((rpoints[0]-((patch_size+1)/2),rpoints[1]-((patch_size+1)/2),rpoints[0]+((patch_size+3)/2),rpoints[1]+((patch_size+3)/2)))
p_crop = numpy.array(p_crop).astype('int32')
dx = sobel(p_crop, 0)
dy = sobel(p_crop, 1)
Expand All @@ -121,10 +111,9 @@ def build_patches(data, c_value=None, gradient=False):
#p_crop = p_crop.crop((1, 1, patch_size+1, patch_size+1))
if not numpy.max(p_crop) == 0.:
p_crop *= 255.0 / numpy.max(p_crop)
negatives.append(p_crop.flatten())
else:
p_crop = im.crop((rpoints[0]-((patch_size-1)/2),rpoints[1]-((patch_size-1)/2),rpoints[0]+((patch_size+1)/2),rpoints[1]+((patch_size+1)/2)))
negatives.append(numpy.array(p_crop).flatten())
p_crop = numpy.array(p_crop)
negatives.append(p_crop.flatten())

# maybe use some other nature photos for negative examples?

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