def main(data_file, outdir, image_prefix, cell_images, label):
images_by_class = dict()
image_name = image_prefix + "_y{:02d}_x{:02d}.bmp"
# read csv file
r = csv.DictReader(data_file)
for row in r:
# Create lists of images from each class
cell_image_name = image_name.format(int(row["j"]), int(row["i"]))
if row[label] in images_by_class:
images_by_class[row[label]].append(cell_image_name)
else:
images_by_class[row[label]] = [ cell_image_name ]
for key in images_by_class:
#print key, images_by_class[key], "\n"
dilution = 1.0/float(len(images_by_class[key]))
print key, "at a dilution of:", dilution
# Create an image for each list of images
size = Image.open(images_by_class[key][0]).size
mean_image = Image.new("L", size, "white")
template = None
#print image_by_class[key]
for image_name in images_by_class[key]:
with open(image_name, "rb") as f:
cell = Image.open(f)
if template:
image = align_images.align_to(template, cell)
else:
template = cell
mean_image = Image.blend(mean_image, cell, dilution)
del cell
mean_image.save(os.path.join(outdir,key+".bmp") if outdir else key+".bmp")
def compare_images(A, B, scale=.4, align=True):
if align:
Bmatch = align_images.align_to(A, B).convert('L')
else:
Bmatch = B.convert('L')
A = A.convert('L')
new_shape = (np.array(A.size) * scale).astype(int)
A = A.resize(new_shape, Image.ANTIALIAS)
Bmatch = Bmatch.resize(new_shape, Image.ANTIALIAS)
Bmatch = Bmatch.filter(ImageFilter.BLUR)
A = A.filter(ImageFilter.BLUR)
dataA = np.array(A.getdata())
dataB = np.array(Bmatch.getdata())
diff = abs(dataA - dataB)
return float(diff.sum()) / np.prod(new_shape)
# collect all cells by class name
img_by_class = dict()
for j in range(rows):
for i in range(cols):
name = classes[j][i]
cell = grid.crop(bounding_box(i * width, j * height, width, height))
if name in img_by_class:
img_by_class[name].append(cell)
else:
img_by_class[name] = [cell]
# align all the images to the first
if not args.noalign:
for name, imgs in img_by_class.items():
for i in range(1, len(imgs)):
imgs[i] = align_images.align_to(imgs[0], imgs[i])
# blend all the images
for name, imgs in img_by_class.items():
img_by_class[name] = dict(
template=imgs[0],
image=blend_images(imgs),
n=len(imgs))
# update the meta_data and global means
for name, newdata in img_by_class.items():
if name in meta_data:
data = meta_data[name]
data['n'] += newdata['n']
cell = newdata['image']
if not args.noalign: