if __name__ == "__main__":
args = parse_args()
grid = Image.open(args.image)
cell_w, cell_h = dims.celldims(grid.size, args.rows, args.cols)
answer = []
gold = yaml.load(open(args.gold))
for name, mean_img in gold.items():
if 'path' in mean_img:
gold[name] = Image.open(mean_img['path'])
else:
gold[name] = Image.open(mean_img)
if args.split:
gold[name] = as_black_and_white(gold[name], args.split, contrast=True)
class_errs = [[gold_classify(gold,
grid.crop(bounding_box(
j * cell_w, i * cell_h, cell_w, cell_h)))
for j in range(args.cols)] for i in range(args.rows)]
output = [[min(class_errs[i][j])[1] for j in range(args.cols)] for i in range(args.rows)]
map_list = []
if args.intended_csv:
diff = 0
with open(args.intended_csv) as f:
intended = csv.reader(f)
for answer, guess in zip(intended, output):
map_list.append(answer)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('mean_file_yml')
parser.add_argument('output')
parser.add_argument('-split', type=int)
args = parser.parse_args()
meta_data = yaml.load(open(args.mean_file_yml))
rows = 0
max_w = 0
max_h = 0
for name, data in meta_data.items():
rows += 1
data['image'] = Image.open(data['path'])
data['template'] = Image.open(data['template_path'])
if args.split:
data['image'] = as_black_and_white(data['image'], args.split, contrast=True)
data['template'] = as_black_and_white(data['template'], args.split, contrast=True)
max_w = max(max_w, data['template'].size[0])
max_h = max(max_h, data['template'].size[1])
max_w = max(max_w, data['image'].size[0])
max_h = max(max_h, data['image'].size[1])
width = max_w * 2
height = rows * max_h
pad = 5
out = Image.new('RGBA', (width + 2 * pad, height + 2 * pad), "white")
for i, (name, data) in enumerate(meta_data.items()):
out.paste(data['template'], bb(pad, pad + i * max_h, max_w, max_h))
out.paste(data['image'], bb(max_w + pad, pad + i * max_h, max_w, max_h))
def cal_align(image):
image = black_and_white.as_black_and_white(image)
black = pixel_distribution.black_points(image)
return pixel_distribution.stats(black)
