File size: 3,027 Bytes
e9fe176 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | #!/usr/local/bin/python
import os;
import numpy as np
import json
import glob
import sys
from StringIO import StringIO
prefix = sys.argv[1];
margin_ratio = 0.15
rectified_size = 50;
put_annotation = False
def make_square(box):
cx = box[0] + box[2] / 2;
cy = box[1] + box[3] / 2;
half_side = min(box[2], box[3]) / 2;
return [cx - half_side, cy - half_side, 2 * half_side + 1, 2 * half_side + 1]
def convert_img(filename, box, r, annotation=None):
if annotation is not None:
annotation_command = "-fill '#0008'"
annotation_command += \
"".join([" -draw 'rectangle %d,%d,%d,%d'" % (x - 2, y - 2, x + 2, y + 2) for x, y in zip(annotation["x"], annotation["y"])])
else:
annotation_command = "";
command = "convert %s.png -crop %dx%d+%d+%d -resize %dx%d %s %s%s.png" % (filename, int(box[2]), int(box[3]), int(box[0]), int(box[1]), r, r, annotation_command, prefix, filename)
# print(command)
if any(x < 0 for x in box) or r < 0:
print command;
else:
os.system(command)
num_landmarks = 68;
# Indices start from zero.
selected_indices = [v - 1 for v in [37, 40, 43, 46, 34, 49, 55]]
annotation = {
"num_landmarks" : num_landmarks
};
count = 0;
for filename in glob.glob("*.pts"):
lm_data = os.popen("sed -n '4,71p' " + filename).read();
# print(lm_data)
landmarks = np.loadtxt(StringIO(lm_data));
if landmarks.shape[0] != num_landmarks: continue;
# Expand it
imgname, ext = os.path.splitext(filename)
w, h = os.popen('identify -format "%wx%h" ' + imgname + '.png').read().split("x")
w, h = int(w), int(h)
max_limits = np.amax(landmarks, axis=0);
min_limits = np.amin(landmarks, axis=0);
w_win, h_win = max_limits[0] - min_limits[0], max_limits[1] - min_limits[1]
w_margin, h_margin = w_win * margin_ratio, h_win * margin_ratio
max_limits[0] = min(max_limits[0] + w_margin, w - 1)
max_limits[1] = min(max_limits[1] + h_margin, h - 1)
min_limits[0] = max(min_limits[0] - w_margin, 0)
min_limits[1] = max(min_limits[1] - h_margin, 0)
# print(max_limits)
# print(min_limits)
box = [ min_limits[0], min_limits[1],
max_limits[0] - min_limits[0], max_limits[1] - min_limits[1]]
# Make the box square.
box = make_square(box)
scale = float(rectified_size) / box[2];
# if box[2] > box[3]:
# scale = float(rectified_size) / box[3];
# r = [int(box[2] * scale), rectified_size]
# else:
# scale = float(rectified_size) / box[2];
# r = [rectified_size, int(box[3] * scale)]
# Crop and resize.
# Then open the file and only crop the image.
anno = {
"x" : [int((v - box[0]) * scale + 0.5) for v in landmarks[selected_indices, 0]],
"y" : [int((v - box[1]) * scale + 0.5) for v in landmarks[selected_indices, 1]],
}
if put_annotation:
convert_img(imgname, box, rectified_size, annotation=anno)
else:
convert_img(imgname, box, rectified_size)
annotation.update({ prefix + imgname + ".png" : anno })
count += 1;
annotation.update({ "num_images" : count })
json.dump(annotation, open("landmarks.json", "w"), sort_keys=True, indent=4);
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