def augment_for_one_image(annotation_line, size):
annotation = annotation_line.strip().split()
img_path = config.root+'/data/%s/'%config.landmark_img_set+annotation[0]
#print img_path
img = cv2.imread(img_path)
width = img.shape[1]
height = img.shape[0]
landmark = np.array(annotation[1:213],dtype=np.float32)
landmark_x = landmark[0::2]
landmark_y = landmark[1::2]
max_x = max(landmark_x)
min_x = min(landmark_x)
max_y = max(landmark_y)
min_y = min(landmark_y)
cx = 0.5*(max_x+min_x)
cy = 0.5*(max_y+min_y)
h = max_x-min_x
w = max_y-min_y
bbox_size = max(h,w)
x1 = int(cx - bbox_size*0.5)
y1 = int(cy - bbox_size*0.5)
w = bbox_size
h = bbox_size
cur_angle = npr.randint(int(config.min_rot_angle),int(config.max_rot_angle)+1)
try_num = 0
cur_sample_num = 0
base_num = 1
force_accept = 0
while cur_sample_num < base_num:
try_num += 1
if try_num > base_num*1000:
force_accept = 1
break
rot_landmark_x,rot_landmark_y = image_processing.rotateLandmark106(cx,cy,landmark_x,landmark_y, cur_angle,1)
cur_size = int(npr.randint(10, 21)*0.1*bbox_size)
up_border_size = int(-cur_size*0.15)
down_border_size = int(-cur_size*0.15)
left_border_size = int(-cur_size*0.15)
right_border_size = int(-cur_size*0.15)
# delta here is the offset of box center
delta_x = npr.randint(-int(w * 0.35), int(w * 0.35)+1)
delta_y = npr.randint(-int(h * 0.35), int(h * 0.35)+1)
nx1 = int(max(x1 + w / 2 + delta_x - cur_size / 2, 0))
ny1 = int(max(y1 + h / 2 + delta_y - cur_size / 2, 0))
nx2 = nx1 + cur_size
ny2 = ny1 + cur_size
if nx2 > width or ny2 > height:
continue
ignore = 0
max_rot_landmark_x = max(rot_landmark_x)
min_rot_landmark_x = min(rot_landmark_x)
max_rot_landmark_y = max(rot_landmark_y)
min_rot_landmark_y = min(rot_landmark_y)
if min_rot_landmark_x < nx1+left_border_size or max_rot_landmark_x >= nx1 + cur_size-right_border_size:
ignore = 1
if min_rot_landmark_y < ny1+up_border_size or max_rot_landmark_y >= ny1 + cur_size-down_border_size:
ignore = 1
if ignore == 1:
continue
landmark_x_dis = max_rot_landmark_x - min_rot_landmark_x
landmark_y_dis = max_rot_landmark_y - min_rot_landmark_y
tmp_dis = landmark_x_dis*landmark_x_dis + landmark_y_dis*landmark_y_dis
if tmp_dis < 0.64*cur_size*cur_size:
continue
offset_x = (rot_landmark_x - nx1+0.5)/float(cur_size)
offset_y = (rot_landmark_y - ny1+0.5)/float(cur_size)
rot_img,_,_ = image_processing.rotateWithLandmark106(img,cx,cy,landmark_x,landmark_y, cur_angle,1)
cropped_im = rot_img[ny1 : ny2, nx1 : nx2, :]
resized_im = cv2.resize(cropped_im, (size, size), interpolation=cv2.INTER_LINEAR)
cur_sample_num += 1
if force_accept == 1:
ny1 = max(0,y1)
ny2 = int(min(height,y1+h))
nx1 = max(0,x1)
nx2 = int(min(width,x1+w))
w = nx2-nx1
h = ny2-ny1
#print ny1,ny2,nx1,nx2
cropped_im = img[ny1 : ny2, nx1 : nx2, :]
resized_im = cv2.resize(cropped_im, (size, size), interpolation=cv2.INTER_LINEAR)
offset_x = (landmark_x - nx1+0.5)/float(cur_size)
offset_y = (landmark_y - ny1+0.5)/float(cur_size)
if config.landmark106_select23:
select_landmark=[0 for i in range(46)]
select_landmark[0:16:2] = offset_x[66:74]
select_landmark[1:17:2] = offset_y[66:74]
select_landmark[16] = offset_x[104]
select_landmark[17] = offset_y[104]
select_landmark[18:34:2] = offset_x[75:83]
select_landmark[19:35:2] = offset_y[75:83]
select_landmark[34] = offset_x[105]
select_landmark[35] = offset_y[105]
select_landmark[36] = offset_x[60]
select_landmark[37] = offset_y[60]
select_landmark[38] = offset_x[84]
select_landmark[39] = offset_y[84]
select_landmark[40] = offset_x[96]
select_landmark[41] = offset_y[96]
select_landmark[42] = offset_x[90]
select_landmark[43] = offset_y[90]
select_landmark[44] = offset_x[100]
select_landmark[45] = offset_y[100]
landmark = select_landmark
else:
landmark[0::2] = offset_x
landmark[1::2] = offset_y
if config.enable_blur:
#kernel_size = npr.randint(-5,4)*2+1
kernel_size = npr.randint(-5,13)*2+1
if kernel_size >= 3:
blur_im = cv2.GaussianBlur(resized_im,(kernel_size,kernel_size),0)
resized_im = blur_im
return resized_im,landmark
def augment_for_one_image(annotation_line, size):
annotation = annotation_line.strip().split()
img_path = config.root + '/data/%s/' % config.landmark_img_set + annotation[
0]
#print img_path
img = cv2.imread(img_path)
width = img.shape[1]
height = img.shape[0]
landmark = np.array(annotation[1:213], dtype=np.float32)
landmark_x = landmark[0::2]
landmark_y = landmark[1::2]
max_x = max(landmark_x)
min_x = min(landmark_x)
max_y = max(landmark_y)
min_y = min(landmark_y)
cx = 0.5 * (max_x + min_x)
cy = 0.5 * (max_y + min_y)
h = max_x - min_x
w = max_y - min_y
bbox_size = max(h, w)
x1 = int(cx - bbox_size * 0.5)
y1 = int(cy - bbox_size * 0.5)
w = bbox_size
h = bbox_size
cur_angle = npr.randint(int(config.min_rot_angle),
int(config.max_rot_angle) + 1)
try_num = 0
cur_sample_num = 0
base_num = 1
force_accept = 0
while cur_sample_num < base_num:
try_num += 1
if try_num > base_num * 1000:
force_accept = 1
break
rot_landmark_x, rot_landmark_y = image_processing.rotateLandmark106(
cx, cy, landmark_x, landmark_y, cur_angle, 1)
#cur_size = int(npr.randint(10, 21)*0.1*bbox_size)
cur_size = int(npr.randint(10, 16) * 0.1 * bbox_size)
up_border_size = int(-cur_size * 0.15)
down_border_size = int(-cur_size * 0.15)
left_border_size = int(-cur_size * 0.15)
right_border_size = int(-cur_size * 0.15)
# delta here is the offset of box center
#delta_x = npr.randint(-int(w * 0.35), int(w * 0.35)+1)
#delta_y = npr.randint(-int(h * 0.35), int(h * 0.35)+1)
delta_x = npr.randint(-int(w * 0.20), int(w * 0.20) + 1)
delta_y = npr.randint(-int(h * 0.20), int(h * 0.20) + 1)
nx1 = int(max(x1 + w / 2 + delta_x - cur_size / 2, 0))
ny1 = int(max(y1 + h / 2 + delta_y - cur_size / 2, 0))
nx2 = nx1 + cur_size
ny2 = ny1 + cur_size
if nx2 > width or ny2 > height:
continue
ignore = 0
max_rot_landmark_x = max(rot_landmark_x)
min_rot_landmark_x = min(rot_landmark_x)
max_rot_landmark_y = max(rot_landmark_y)
min_rot_landmark_y = min(rot_landmark_y)
if min_rot_landmark_x < nx1 + left_border_size or max_rot_landmark_x >= nx1 + cur_size - right_border_size:
ignore = 1
if min_rot_landmark_y < ny1 + up_border_size or max_rot_landmark_y >= ny1 + cur_size - down_border_size:
ignore = 1
if ignore == 1:
continue
landmark_x_dis = max_rot_landmark_x - min_rot_landmark_x
landmark_y_dis = max_rot_landmark_y - min_rot_landmark_y
tmp_dis = landmark_x_dis * landmark_x_dis + landmark_y_dis * landmark_y_dis
#if tmp_dis < 0.64*cur_size*cur_size:
if tmp_dis < 1.00 * cur_size * cur_size:
continue
offset_x = (rot_landmark_x - nx1 + 0.5) / float(cur_size)
offset_y = (rot_landmark_y - ny1 + 0.5) / float(cur_size)
rot_img, _, _ = image_processing.rotateWithLandmark106(
img, cx, cy, landmark_x, landmark_y, cur_angle, 1)
cropped_im = rot_img[ny1:ny2, nx1:nx2, :]
resized_im = cv2.resize(cropped_im, (size, size),
interpolation=cv2.INTER_LINEAR)
cur_sample_num += 1
if force_accept == 1:
ny1 = max(0, y1)
ny2 = int(min(height, y1 + h))
nx1 = max(0, x1)
nx2 = int(min(width, x1 + w))
w = nx2 - nx1
h = ny2 - ny1
#print ny1,ny2,nx1,nx2
cropped_im = img[ny1:ny2, nx1:nx2, :]
resized_im = cv2.resize(cropped_im, (size, size),
interpolation=cv2.INTER_LINEAR)
offset_x = (landmark_x - nx1 + 0.5) / float(cur_size)
offset_y = (landmark_y - ny1 + 0.5) / float(cur_size)
if config.landmark106_migu_weighting:
landmark[0:66:2] = offset_x[0:33]
landmark[1:67:2] = offset_y[0:33]
landmark[66:86:2] = offset_x[33:43] * 2 #eyebrow up
landmark[67:87:2] = offset_y[33:43] * 2 #eyebrow up
landmark[86:128:2] = offset_x[43:64]
landmark[87:129:2] = offset_y[43:64]
landmark[128:144:2] = offset_x[64:72] * 2 #eyebrow down
landmark[129:145:2] = offset_y[64:72] * 2 #eyebrow down
landmark[144:168:2] = offset_x[72:84]
landmark[145:169:2] = offset_y[72:84]
landmark[168:208:2] = offset_x[84:104] * 2 #lips
landmark[169:209:2] = offset_y[84:104] * 2 #lips
landmark[208:212:2] = offset_x[104:106]
landmark[209:213:2] = offset_y[104:106]
else:
landmark[0::2] = offset_x
landmark[1::2] = offset_y
if config.enable_blur:
#kernel_size = npr.randint(-5,4)*2+1
kernel_size = npr.randint(-5, 13) * 2 + 1
if kernel_size >= 3:
blur_im = cv2.GaussianBlur(resized_im, (kernel_size, kernel_size),
0)
resized_im = blur_im
return resized_im, landmark
def augment_for_one_image(annotation_line, size):
annotation = annotation_line.strip().split()
img_path = config.root + '/data/%s/' % config.landmark_img_set + annotation[
0]
#print img_path
img = cv2.imread(img_path)
width = img.shape[1]
height = img.shape[0]
landmark = np.array(annotation[1:213], dtype=np.float32)
landmark_x = landmark[0::2]
landmark_y = landmark[1::2]
max_x = max(landmark_x)
min_x = min(landmark_x)
max_y = max(landmark_y)
min_y = min(landmark_y)
cx = 0.5 * (max_x + min_x)
cy = 0.5 * (max_y + min_y)
w = max_x - min_x
h = max_y - min_y
bbox_size = max(h, w)
x1 = int(cx - bbox_size * 0.5)
y1 = int(cy - bbox_size * 0.5)
w = bbox_size
h = bbox_size
init_rot = 0
if config.landmark106_migu_init_rot:
eye_cx = 0.25 * (landmark_x[52] + landmark_x[55] + landmark_x[58] +
landmark_x[61])
eye_cy = 0.25 * (landmark_y[52] + landmark_y[55] + landmark_y[58] +
landmark_y[61])
mouth_cx = 0.25 * (landmark_x[84] + landmark_x[96] + landmark_x[100] +
landmark_x[90])
mouth_cy = 0.25 * (landmark_y[84] + landmark_y[96] + landmark_y[100] +
landmark_y[90])
dir_x = mouth_cx - eye_cx
dir_y = mouth_cy - eye_cy
init_rot = 90 - math.atan2(dir_y, dir_x) / math.pi * 180
cur_angle = npr.randint(int(config.min_rot_angle - init_rot),
int(config.max_rot_angle - init_rot) + 1)
try_num = 0
cur_sample_num = 0
base_num = 1
force_accept = 0
while cur_sample_num < base_num:
try_num += 1
if try_num > base_num * 1000:
force_accept = 1
break
rot_landmark_x, rot_landmark_y = image_processing.rotateLandmark106(
cx, cy, landmark_x, landmark_y, cur_angle, 1)
rot_max_x = max(rot_landmark_x)
rot_min_x = min(rot_landmark_x)
rot_max_y = max(rot_landmark_y)
rot_min_y = min(rot_landmark_y)
rot_cx = 0.5 * (rot_max_x + rot_min_x)
rot_cy = 0.5 * (rot_max_y + rot_min_y)
rot_w = rot_max_x - rot_min_x
rot_h = rot_max_y - rot_min_y
rot_bbox_size = max(rot_h, rot_w)
rot_x1 = int(rot_cx - rot_bbox_size * 0.5)
rot_y1 = int(rot_cy - rot_bbox_size * 0.5)
rot_w = rot_bbox_size
rot_h = rot_bbox_size
#cur_size = int(npr.randint(10, 21)*0.1*rot_bbox_size)
cur_size = int(npr.randint(10, 16) * 0.1 * rot_bbox_size)
#cur_size = int(npr.randint(110, 126)*0.01*rot_bbox_size)
up_border_size = int(-cur_size * 0.15)
down_border_size = int(-cur_size * 0.15)
left_border_size = int(-cur_size * 0.15)
right_border_size = int(-cur_size * 0.15)
#up_border_size = int(cur_size*0.05)
#down_border_size = int(cur_size*0.05)
#left_border_size = int(cur_size*0.05)
#right_border_size = int(cur_size*0.05)
# delta here is the offset of box center
#delta_x = npr.randint(-int(rot_w * 0.35), int(rot_w * 0.35)+1)
#delta_y = npr.randint(-int(rot_h * 0.35), int(rot_h * 0.35)+1)
delta_x = npr.randint(-int(rot_w * 0.20), int(rot_w * 0.20) + 1)
delta_y = npr.randint(-int(rot_h * 0.20), int(rot_h * 0.20) + 1)
#delta_x = npr.randint(-int(rot_w * 0.02), int(rot_w * 0.02)+1)
#delta_y = npr.randint(-int(rot_h * 0.02), int(rot_h * 0.02)+1)
nx1 = int(max(rot_x1 + rot_w / 2 + delta_x - cur_size / 2, 0))
ny1 = int(max(rot_y1 + rot_h / 2 + delta_y - cur_size / 2, 0))
nx2 = nx1 + cur_size
ny2 = ny1 + cur_size
if nx2 > width or ny2 > height:
continue
ignore = 0
max_rot_landmark_x = max(rot_landmark_x)
min_rot_landmark_x = min(rot_landmark_x)
max_rot_landmark_y = max(rot_landmark_y)
min_rot_landmark_y = min(rot_landmark_y)
if min_rot_landmark_x < nx1 + left_border_size or max_rot_landmark_x >= nx1 + cur_size - right_border_size:
ignore = 1
if min_rot_landmark_y < ny1 + up_border_size or max_rot_landmark_y >= ny1 + cur_size - down_border_size:
ignore = 1
if ignore == 1:
continue
landmark_x_dis = max_rot_landmark_x - min_rot_landmark_x
landmark_y_dis = max_rot_landmark_y - min_rot_landmark_y
tmp_dis = landmark_x_dis * landmark_x_dis + landmark_y_dis * landmark_y_dis
#if tmp_dis < 0.64*cur_size*cur_size:
if tmp_dis < 1.00 * cur_size * cur_size:
continue
offset_x = (rot_landmark_x - nx1 + 0.5) / float(cur_size)
offset_y = (rot_landmark_y - ny1 + 0.5) / float(cur_size)
rot_img, _, _ = image_processing.rotateWithLandmark106(
img, cx, cy, landmark_x, landmark_y, cur_angle, 1)
cropped_im = rot_img[ny1:ny2, nx1:nx2, :]
resized_im = cv2.resize(cropped_im, (size, size),
interpolation=cv2.INTER_LINEAR)
cur_sample_num += 1
if force_accept == 1:
ny1 = max(0, y1)
ny2 = int(min(height, y1 + h))
nx1 = max(0, x1)
nx2 = int(min(width, x1 + w))
w = nx2 - nx1
h = ny2 - ny1
#print ny1,ny2,nx1,nx2
cropped_im = img[ny1:ny2, nx1:nx2, :]
resized_im = cv2.resize(cropped_im, (size, size),
interpolation=cv2.INTER_LINEAR)
offset_x = (landmark_x - nx1 + 0.5) / float(cur_size)
offset_y = (landmark_y - ny1 + 0.5) / float(cur_size)
if config.landmark106_migu_random_flip:
flip_val = npr.randint(0, 2)
if flip_val == 1:
tmp_arr1_x, tmp_arr1_y = offset_x[0:33], offset_y[0:33]
offset_x[0:33], offset_y[
0:33] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y = offset_x[33:43], offset_y[33:43]
offset_x[33:43], offset_y[
33:43] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y = offset_x[43:47], offset_y[43:47]
offset_x[43:47], offset_y[43:47] = 1 - tmp_arr1_x, tmp_arr1_y
tmp_arr1_x, tmp_arr1_y = offset_x[47:52], offset_y[47:52]
offset_x[47:52], offset_y[
47:52] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y, tmp_arr2_x, tmp_arr2_y = offset_x[
52:56], offset_y[52:56], offset_x[58:62], offset_y[58:62]
offset_x[52:56], offset_y[52:56], offset_x[58:62], offset_y[
58:
62] = 1 - tmp_arr2_x[::
-1], tmp_arr2_y[::
-1], 1 - tmp_arr1_x[::
-1], tmp_arr1_y[::
-1]
tmp_arr1_x, tmp_arr1_y, tmp_arr2_x, tmp_arr2_y = offset_x[
56:58], offset_y[56:58], offset_x[62:64], offset_y[62:64]
offset_x[56:58], offset_y[56:58], offset_x[62:64], offset_y[
62:
64] = 1 - tmp_arr2_x[::
-1], tmp_arr2_y[::
-1], 1 - tmp_arr1_x[::
-1], tmp_arr1_y[::
-1]
tmp_arr1_x, tmp_arr1_y = offset_x[64:72], offset_y[64:72]
offset_x[64:72], offset_y[
64:72] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y, tmp_arr2_x, tmp_arr2_y = offset_x[
72:75], offset_y[72:75], offset_x[75:78], offset_y[75:78]
offset_x[72:75], offset_y[72:75], offset_x[75:78], offset_y[
75:78] = 1 - tmp_arr2_x, tmp_arr2_y, 1 - tmp_arr1_x, tmp_arr1_y
offset_x[78], offset_y[78], offset_x[79], offset_y[
79] = 1 - offset_x[79], offset_y[
79], 1 - offset_x[78], offset_y[78]
offset_x[80], offset_y[80], offset_x[81], offset_y[
81] = 1 - offset_x[81], offset_y[
81], 1 - offset_x[80], offset_y[80]
offset_x[82], offset_y[82], offset_x[83], offset_y[
83] = 1 - offset_x[83], offset_y[
83], 1 - offset_x[82], offset_y[82]
tmp_arr1_x, tmp_arr1_y = offset_x[84:91], offset_y[84:91]
offset_x[84:91], offset_y[
84:91] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y = offset_x[91:96], offset_y[91:96]
offset_x[91:96], offset_y[
91:96] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y = offset_x[96:101], offset_y[96:101]
offset_x[96:101], offset_y[
96:101] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y = offset_x[101:104], offset_y[101:104]
offset_x[101:104], offset_y[
101:104] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
offset_x[104], offset_y[104], offset_x[105], offset_y[
105] = 1 - offset_x[105], offset_y[
105], 1 - offset_x[104], offset_y[104]
resized_im = resized_im[:, ::-1, :]
if config.landmark106_migu_weighting:
landmark[0:66:2] = offset_x[0:33]
landmark[1:67:2] = offset_y[0:33]
landmark[66:86:2] = offset_x[33:43] * 2 #eyebrow up
landmark[67:87:2] = offset_y[33:43] * 2 #eyebrow up
landmark[86:128:2] = offset_x[43:64]
landmark[87:129:2] = offset_y[43:64]
landmark[128:144:2] = offset_x[64:72] * 2 #eyebrow down
landmark[129:145:2] = offset_y[64:72] * 2 #eyebrow down
landmark[144:168:2] = offset_x[72:84]
landmark[145:169:2] = offset_y[72:84]
landmark[168:208:2] = offset_x[84:104] * 2 #lips
landmark[169:209:2] = offset_y[84:104] * 2 #lips
landmark[208:212:2] = offset_x[104:106]
landmark[209:213:2] = offset_y[104:106]
else:
landmark[0::2] = offset_x
landmark[1::2] = offset_y
if config.enable_blur:
#kernel_size = npr.randint(-5,4)*2+1
kernel_size = npr.randint(-5, 13) * 2 + 1
if kernel_size >= 3:
blur_im = cv2.GaussianBlur(resized_im, (kernel_size, kernel_size),
0)
resized_im = blur_im
if config.enable_black_border:
black_size = npr.randint(0, int(size * 0.5))
if npr.randint(0, 2) == 0:
resized_im[:, 0:black_size, :] = 128
else:
resized_im[:, (size - black_size):size, :] = 128
return resized_im, landmark
def gen_landmark_for_one_image(size, idx, img, landmark_save_dir, landmarks, base_num = 1):
landmark_names = list()
landmark_num = 0
width = img.shape[1]
height = img.shape[0]
landmark_x = landmarks[0::2]
landmark_y = landmarks[1::2]
max_x = max(landmark_x)
min_x = min(landmark_x)
max_y = max(landmark_y)
min_y = min(landmark_y)
cx = 0.5*(max_x+min_x)
cy = 0.5*(max_y+min_y)
w = max_x-min_x
h = max_y-min_y
bbox_size = max(h,w)
x1 = int(cx - bbox_size*0.5)
y1 = int(cy - bbox_size*0.5)
w = bbox_size
h = bbox_size
init_rot = 0
if config.landmark106_migu_init_rot:
eye_cx = 0.25*(landmark_x[52]+landmark_x[55]+landmark_x[58]+landmark_x[61])
eye_cy = 0.25*(landmark_y[52]+landmark_y[55]+landmark_y[58]+landmark_y[61])
mouth_cx = 0.25*(landmark_x[84]+landmark_x[96]+landmark_x[100]+landmark_x[90])
mouth_cy = 0.25*(landmark_y[84]+landmark_y[96]+landmark_y[100]+landmark_y[90])
dir_x = mouth_cx - eye_cx
dir_y = mouth_cy - eye_cy
init_rot = 90 - math.atan2(dir_y, dir_x)/math.pi*180
cur_angle = npr.randint(int(config.min_rot_angle - init_rot),int(config.max_rot_angle - init_rot)+1)
try_num = 0
force_accept = 0
while landmark_num < base_num:
try_num += 1
if try_num > base_num*1000:
force_accept = 1
break
rot_landmark_x,rot_landmark_y = image_processing.rotateLandmark106(cx,cy,landmark_x,landmark_y, cur_angle,1)
rot_max_x = max(rot_landmark_x)
rot_min_x = min(rot_landmark_x)
rot_max_y = max(rot_landmark_y)
rot_min_y = min(rot_landmark_y)
rot_cx = 0.5*(rot_max_x+rot_min_x)
rot_cy = 0.5*(rot_max_y+rot_min_y)
rot_w = rot_max_x-rot_min_x
rot_h = rot_max_y-rot_min_y
rot_bbox_size = max(rot_h,rot_w)
rot_x1 = int(rot_cx - rot_bbox_size*0.5)
rot_y1 = int(rot_cy - rot_bbox_size*0.5)
rot_w = rot_bbox_size
rot_h = rot_bbox_size
#cur_size = int(npr.randint(10, 21)*0.1*rot_bbox_size)
#cur_size = int(npr.randint(10, 16)*0.1*rot_bbox_size)
cur_size = int(npr.randint(110, 126)*0.01*rot_bbox_size)
#up_border_size = int(-cur_size*0.15)
#down_border_size = int(-cur_size*0.15)
#left_border_size = int(-cur_size*0.15)
#right_border_size = int(-cur_size*0.15)
up_border_size = int(cur_size*0.05)
down_border_size = int(cur_size*0.05)
left_border_size = int(cur_size*0.05)
right_border_size = int(cur_size*0.05)
# delta here is the offset of box center
#delta_x = npr.randint(-int(rot_w * 0.35), int(rot_w * 0.35)+1)
#delta_y = npr.randint(-int(rot_h * 0.35), int(rot_h * 0.35)+1)
#delta_x = npr.randint(-int(rot_w * 0.20), int(rot_w * 0.20)+1)
#delta_y = npr.randint(-int(rot_h * 0.20), int(rot_h * 0.20)+1)
delta_x = npr.randint(-int(rot_w * 0.02), int(rot_w * 0.02)+1)
delta_y = npr.randint(-int(rot_h * 0.02), int(rot_h * 0.02)+1)
nx1 = int(max(x1 + rot_w / 2 + delta_x - cur_size / 2, 0))
ny1 = int(max(y1 + rot_h / 2 + delta_y - cur_size / 2, 0))
nx2 = nx1 + cur_size
ny2 = ny1 + cur_size
if nx2 > width or ny2 > height:
continue
ignore = 0
max_rot_landmark_x = max(rot_landmark_x)
min_rot_landmark_x = min(rot_landmark_x)
max_rot_landmark_y = max(rot_landmark_y)
min_rot_landmark_y = min(rot_landmark_y)
if min_rot_landmark_x < nx1+left_border_size or max_rot_landmark_x >= nx1 + cur_size-right_border_size:
ignore = 1
if min_rot_landmark_y < ny1+up_border_size or max_rot_landmark_y >= ny1 + cur_size-down_border_size:
ignore = 1
if ignore == 1:
continue
landmark_x_dis = max_rot_landmark_x - min_rot_landmark_x
landmark_y_dis = max_rot_landmark_y - min_rot_landmark_y
tmp_dis = landmark_x_dis*landmark_x_dis + landmark_y_dis*landmark_y_dis
#if tmp_dis < 0.64*cur_size*cur_size:
if tmp_dis < 1.00*cur_size*cur_size:
continue
offset_x = (rot_landmark_x - nx1+0.5)/float(cur_size)
offset_y = (rot_landmark_y - ny1+0.5)/float(cur_size)
rot_img,_,_ = image_processing.rotateWithLandmark106(img,cx,cy,landmark_x,landmark_y, cur_angle,1)
cropped_im = rot_img[ny1 : ny2, nx1 : nx2, :]
resized_im = cv2.resize(cropped_im, (size, size), interpolation=cv2.INTER_LINEAR)
if config.landmark106_migu_random_flip:
flip_val = npr.randint(0,2)
if flip_val == 1:
tmp_arr1_x, tmp_arr1_y = offset_x[0:33], offset_y[0:33]
offset_x[0:33], offset_y[0:33] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y = offset_x[33:43], offset_y[33:43]
offset_x[33:43], offset_y[33:43] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y = offset_x[43:47], offset_y[43:47]
offset_x[43:47], offset_y[43:47] = 1 - tmp_arr1_x, tmp_arr1_y
tmp_arr1_x, tmp_arr1_y = offset_x[47:52], offset_y[47:52]
offset_x[47:52], offset_y[47:52] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y, tmp_arr2_x, tmp_arr2_y = offset_x[52:56], offset_y[52:56], offset_x[58:62], offset_y[58:62]
offset_x[52:56], offset_y[52:56], offset_x[58:62], offset_y[58:62] = 1 - tmp_arr2_x[::-1], tmp_arr2_y[::-1], 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y, tmp_arr2_x, tmp_arr2_y = offset_x[56:58], offset_y[56:58], offset_x[62:64], offset_y[62:64]
offset_x[56:58], offset_y[56:58], offset_x[62:64], offset_y[62:64] = 1 - tmp_arr2_x[::-1], tmp_arr2_y[::-1], 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y = offset_x[64:72], offset_y[64:72]
offset_x[64:72], offset_y[64:72] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y, tmp_arr2_x, tmp_arr2_y = offset_x[72:75], offset_y[72:75], offset_x[75:78], offset_y[75:78]
offset_x[72:75], offset_y[72:75], offset_x[75:78], offset_y[75:78] = 1 - tmp_arr2_x, tmp_arr2_y, 1 - tmp_arr1_x, tmp_arr1_y
offset_x[78], offset_y[78], offset_x[79], offset_y[79] = 1 - offset_x[79], offset_y[79], 1 - offset_x[78], offset_y[78]
offset_x[80], offset_y[80], offset_x[81], offset_y[81] = 1 - offset_x[81], offset_y[81], 1 - offset_x[80], offset_y[80]
offset_x[82], offset_y[82], offset_x[83], offset_y[83] = 1 - offset_x[83], offset_y[83], 1 - offset_x[82], offset_y[82]
tmp_arr1_x, tmp_arr1_y = offset_x[84:91], offset_y[84:91]
offset_x[84:91], offset_y[84:91] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y = offset_x[91:96], offset_y[91:96]
offset_x[91:96], offset_y[91:96] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y = offset_x[96:101], offset_y[96:101]
offset_x[96:101], offset_y[96:101] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
tmp_arr1_x, tmp_arr1_y = offset_x[101:104], offset_y[101:104]
offset_x[101:104], offset_y[101:104] = 1 - tmp_arr1_x[::-1], tmp_arr1_y[::-1]
offset_x[104], offset_y[104], offset_x[105], offset_y[105] = 1 - offset_x[105], offset_y[105], 1 - offset_x[104], offset_y[104]
resized_im = resized_im[:,::-1,:]
if config.enable_black_border:
black_size = npr.randint(0,int(size*0.5))
if npr.randint(0,2) == 0:
resized_im[:,0:black_size,:] = 128
else:
resized_im[:,(size-black_size):size,:] = 128
save_file = '%s/%d_%d.jpg'%(landmark_save_dir,idx,landmark_num)
if cv2.imwrite(save_file, resized_im):
line = '%s/%d_%d'%(landmark_save_dir,idx,landmark_num)
for j in range(106):
line = line + ' %.7f %.7f'%(offset_x[j],offset_y[j])
landmark_names.append(line)
landmark_num += 1
return landmark_names