def __getitem__(self, item):
img_path = self.images[item]
image = cv2.imread(img_path)
landmark_path = self.landmarks[item]
landmarks = utils.read_mat(landmark_path)
return image, landmarks
def __getitem__(self, item):
image, landmarks = super(AlignDataset, self).__getitem__(item)
al_ldmk = utils.read_mat(self.algin_ldmk[item])
image, _, t = self.aligner(image,
al_ldmk,
noise=np.random.uniform(-self.max_jitter, self.max_jitter, 2),
radian=np.random.uniform(-self.max_radian, self.max_radian))
landmarks = landmarks @ t[0:2, :] + t[2, :]
# start_y = np.random.randint(0, self.aligner.scale[0] - self.shape[0] + 1)
# start_x = np.random.randint(0, self.aligner.scale[1] - self.shape[1] + 1)
# landmarks[:, 0] -= start_x
# landmarks[:, 1] -= start_y
landmarks[:, 0] /= self.shape[1]
landmarks[:, 1] /= self.shape[0]
# image = image[start_y:start_y + self.shape[0], start_x :start_x + self.shape[1]]
if self.phase == 'train':
if self.flip:
image, landmarks = utils.random_flip(image, landmarks, 0.5)
image = utils.random_gamma_trans(image, np.random.uniform(0.8, 1.2, 1))
image = utils.random_color(image)
image = cv2.resize(image, self.shape)
image = np.transpose(image, (2, 0, 1)).astype(np.float32)
landmarks = landmarks[self.ldmk_ids, :]
return image, np.reshape(landmarks, (-1))
def __getitem__(self, item):
img_path = self.images[item]
image = cv2.imread(img_path)
landmark_path = self.landmarks[item]
landmarks = utils.read_mat(landmark_path)
bbox_path = self.bboxes[item]
bbox = utils.read_bbox(bbox_path)
return image, landmarks, np.array(bbox), self.file_list[item]
def __getitem__(self, item):
image, gt_landmarks = super(AlignFusionDataset, self).__getitem__(item)
pr_landmarks = utils.read_mat(self.algin_ldmk[item])
image, pr_landmarks, t = self.aligner(image, pr_landmarks,
noise=np.random.uniform(-self.max_jitter, self.max_jitter, 2))
gt_landmarks = gt_landmarks @ t[0:2, :] + t[2, :]
# start_y = np.random.randint(0, self.aligner.scale[0] - self.shape[0] + 1)
# start_x = np.random.randint(0, self.aligner.scale[1] - self.shape[1] + 1)
# gt_landmarks[:, 0] -= start_x
# gt_landmarks[:, 1] -= start_y
# pr_landmarks[:, 0] -= start_x
# pr_landmarks[:, 1] -= start_y
gt_landmarks[:, 0] /= self.shape[1]
gt_landmarks[:, 1] /= self.shape[0]
# image = image[start_y:start_y + self.shape[0], start_x:start_x + self.shape[1]]
if self.phase == 'train':
if self.flip:
a = np.random.uniform(0, 1, 1)
if a < 0.5:
image = cv2.flip(image, 1)
gt_landmarks = utils.landmark_flip(gt_landmarks)
pr_landmarks = utils.landmark_flip(pr_landmarks, max_x=self.shape[1])
image = utils.random_gamma_trans(image, np.random.uniform(0.8, 1.2, 1))
image = utils.random_color(image)
kernel_size = self.kernel_size
sigma = self.sigma
heatmaps = []
for idx in self.idxs:
img = np.zeros((self.shape[0], self.shape[1]), np.uint8)
draw_curve(img, pr_landmarks, idx)
heatmaps.append(cv2.GaussianBlur(img, (kernel_size, kernel_size), sigma))
inputs = []
for i in range(len(heatmaps)):
heatmaps[i] = heatmaps[i].astype(np.float32) / np.max(heatmaps[i])
inputs.append(image * np.expand_dims(heatmaps[i], -1))
inputs.append(image)
# cv2.imshow(",", image)
# cv2.waitKey(1)
# for i in inputs:
# cv2.imshow("", i.astype(np.uint8))
# cv2.waitKey(0)
inputs = np.concatenate(inputs, axis=-1)
inputs = np.transpose(inputs, [2, 0, 1])
heatmaps = np.stack(heatmaps, axis=2)
heatmaps = np.transpose(heatmaps, [2, 0, 1])
return inputs, np.reshape(gt_landmarks, (-1)), heatmaps
def save_pic_and_lmk(kk):
shuru = kk
if shuru == 0:
e = 1 #return 0 输入为0,说明只有一张图
else:
lmk_dir = '/home/zhang/correctdata/data/landmark/' #注意lmk文件的后缀为.txt
pic_dir = '/home/zhang/correctdata/data/picture/'
bbox_dir = '/home/zhang/correctdata/bbox/'
length = len(shuru)
for i in range(length):
if i % 2 == 0:
filename1 = pic_dir + shuru[i]
'''filename2 = pic_dir+shuru[i+1]'''
lmk1 = utils.read_mat(lmk_dir + shuru[i + 1] + '.txt')
lmk2 = utils.read_mat(lmk_dir + shuru[i] + '.txt')
lmk = 0.5 * lmk1 + 0.5 * lmk2
image = plt.imread(filename1)
#图一的landmark归一化
bbox1 = utils.read_bbox(bbox_dir + shuru[i + 1] + '.rect')
lmk1 = utils.norm_landmarks(lmk1, bbox1)
image = image[bbox1[1]:bbox1[3], bbox1[0]:bbox1[2]]
#图一图二的插值图的landmark归一化
bbox2 = utils.read_bbox(bbox_dir + shuru[i] + '.rect')
lmk = utils.norm_landmarks(lmk, bbox2)
out = pwa.pwa(image.copy(), lmk1.copy(), lmk.copy(),
(256, 256))
# 图片与lmk保存
utils.save_landmarks(
lmk * 255, '/home/zhang/ming/save_for_lmk2/' +
shuru[i][:-5] + "%d" % (200 + i) + '.jpg' + '.txt')
io.imsave(
'/home/zhang/ming/save_for_pic2/' + shuru[i][:-5] + "%d" %
(200 + i) + '.jpg', out)
def __getitem__(self, item):
image, landmarks = super(AlignDataset, self).__getitem__(item)
al_ldmk = utils.read_mat(self.algin_ldmk[item])
image, _, t = self.aligner(image, al_ldmk)
if self.phase == 'train':
if self.flip:
image, landmarks = utils.random_flip(image, landmarks, 0.5)
image = utils.random_gamma_trans(image,
np.random.uniform(0.8, 1.2, 1))
image = utils.random_color(image)
# image = cv2.resize(image, self.shape)
image = np.transpose(image, (2, 0, 1)).astype(np.float32)
# landmarks = landmarks[self.ldmk_ids, :]
return image, np.reshape(landmarks, (-1)), t, self.file_list[item]
def __getitem__(self, i):
img_path = self.images[i]
bbox_path = self.bboxes[i]
landmark_path = self.landmarks[i]
bbox = utils.read_bbox(bbox_path)
landmarks = utils.read_mat(landmark_path)
landmarks = utils.norm_landmarks(landmarks, bbox)
image = cv2.imread(img_path)
# minx, miny, maxx, maxy = bbox
# image = image[miny:maxy+1, minx:maxx+1, :]
# image = cv2.resize(image, self.shape)
# cv2.imshow("t", image)
# cv2.waitKey(0)
image = np.transpose(image, (2, 0, 1)).astype(np.float32)
#print('origin', origin_landmarks)
return image, np.reshape(landmarks,
(-1)), np.reshape(np.array(bbox), (2, 2))
bbox_dir = os.path.join(root, 'bbox')
out_dir = os.path.join(root, 'crop')
if not os.path.exists(out_dir):
os.makedirs(os.path.join(out_dir, 'data/picture'))
os.makedirs(os.path.join(out_dir, 'data/landmark'))
for name in os.listdir(img_dir):
img_name = name
ldmk_name = name + '.txt'
bbox_name = name + '.rect'
img_path = os.path.join(img_dir, img_name)
ldmk_path = os.path.join(ldmk_dir, ldmk_name)
bbox_path = os.path.join(bbox_dir, bbox_name)
img_out_path = img_path.replace(root,
root + 'crop/').replace('.jpg', '.png')
ldmk_out_path = ldmk_path.replace(root, root + 'crop/')
img = plt.imread(img_path)
bbox = utils.read_bbox(bbox_path)
ldmk = utils.read_mat(ldmk_path)
minx, miny, maxx, maxy = bbox
ldmk -= [minx, miny]
img = img[miny:maxy + 1, minx:maxx + 1, :]
plt.imsave(img_out_path, img)
utils.save_landmarks(ldmk, ldmk_out_path)
import numpy as np
import cv2
from data import utils
imgdir = '/data/icme/data/picture/AFW_5452623_1_5.jpg'
landmarks = utils.read_mat('/data/icme/data/landmark/AFW_5452623_1_5.jpg.txt')
bbox = utils.read_bbox('/data/icme/bbox/AFW_5452623_1_5.jpg.rect')
img = cv2.imread(imgdir)
minx, miny, maxx, maxy = bbox
img = img[miny:maxy+1, minx:maxx+1, :]
landmarks = utils.norm_landmarks(landmarks, bbox)
img, landmarks = utils.random_flip(img, landmarks, 1)
img = np.transpose(img, (2, 0, 1))
img = utils.draw_landmarks(img, landmarks, (255, 255, 255))
img = np.transpose(img, (1, 2, 0))
cv2.imshow('', img)
cv2.waitKey(0)
def main():
if not os.path.exists('cache'):
os.system('mkdir cache')
# 我偷懒了，所以最好不要写成'/home/yqi/data/icme/'
root_dir = '/data/icme'
lamdmark_dir = os.path.join(root_dir, 'data/landmark')
image_dir = os.path.join(root_dir, 'data/picture')
bbox_dir = os.path.join(root_dir, 'bbox')
try:
filenames = joblib.load('cache/filenames.pkl')
norm_landmarks = joblib.load('cache/norm_landmarks.pkl')
mean_landmarks = joblib.load('cache/mean_landmarks.pkl')
bboxes = joblib.load('cache/bboxes.pkl')
split = joblib.load('cache/split.pkl')
except:
filenames = os.listdir(image_dir)
norm_landmarks = []
bboxes = []
split = {}
for filename in filenames:
id = get_id(filename)
if np.random.uniform(0, 1) < 0.8:
split[id] = 'train'
landmark_path = os.path.join(lamdmark_dir, filename + '.txt')
bbox_path = os.path.join(bbox_dir, filename + '.rect')
bbox = utils.read_bbox(bbox_path)
landmarks = utils.read_mat(landmark_path)
landmarks = utils.norm_landmarks(landmarks, bbox)
norm_landmarks.append(landmarks)
else:
split[id] = 'valid'
bbox_path = os.path.join(bbox_dir, filename + '.rect')
bbox = utils.read_bbox(bbox_path)
bboxes.append(bbox)
norm_landmarks = np.stack(norm_landmarks, axis=0)
mean_landmarks = np.mean(norm_landmarks, axis=0)
joblib.dump(norm_landmarks, 'cache/norm_landmarks.pkl', compress=3)
joblib.dump(mean_landmarks, 'cache/mean_landmarks.pkl', compress=3)
joblib.dump(filenames, 'cache/filenames.pkl', compress=3)
joblib.dump(bboxes, 'cache/bboxes.pkl', compress=3)
joblib.dump(split, 'cache/split.pkl', compress=3)
# for i in range(106):
# plt.scatter(mean_landmarks[i, 0], mean_landmarks[i, 1])
# plt.show()
try:
transform_matrix = joblib.load('cache/transform_matrix.pkl')
aligned = joblib.load('cache/aligned.pkl')
except:
transform_matrix = []
aligned = []
i = -1
for filename in filenames:
if split[get_id(filename)] == 'valid':
continue
i += 1
curr = norm_landmarks[i, :]
one = np.ones(shape=(106, 1))
curr = np.concatenate((curr, one), axis=1)
t = procrustes(curr, mean_landmarks)
transform_matrix.append(t)
aligned.append(np.reshape(curr@t, (-1)))
joblib.dump(transform_matrix, 'cache/transform_matrix.pkl', compress=3)
joblib.dump(aligned, 'cache/aligned.pkl', compress=3)
temp = (aligned - np.mean(aligned, axis=0))
covariance = 1.0 / len(aligned) * temp.T.dot(temp)
U, S, V = np.linalg.svd(covariance)
joblib.dump(U, 'cache/u.pkl', compress=3)
pc = temp.dot(U[:, 0])
plt.hist(pc,bins=11)
plt.show()
for i, filename in enumerate(filenames):
img_path = os.path.join(image_dir, filename)
if pc[i] > 0.793:
n = '1'
elif pc[i] > 0.615:
n = '2'
elif pc[i] > 0.44:
n = '3'
elif pc[i] > 0.26:
n = '4'
elif pc[i] > 0.087:
n = '5'
elif pc[i] > -0.0913:
n = '6'
elif pc[i] > -0.264:
n = '7'
elif pc[i] > -0.448:
n = '8'
elif pc[i] > -0.62:
n = '9'
elif pc[i] > -0.79:
n = '10'
else:
n = '11'
id = get_id(filename)
cmd = 'ln -s %s %s/%s/%s/%s' % (img_path, root_dir, split[id], n, filename)
os.system(cmd)
import matplotlib.pyplot as plt
import numpy as np
from data import utils
import os
lmk_input = '/home/zhang/ming/save_for_lmk/'
pic_input = '/home/zhang/ming/save_for_pic/'
pic_filename = os.listdir(pic_input)
lmk_filename = os.listdir(lmk_input)
for img_name in pic_filename:
img = plt.imread(pic_input + img_name)
out = (np.transpose(img, (2, 0, 1))).astype(np.uint8)
lmk = utils.read_mat(lmk_input + img_name + '.txt')
output = utils.draw_landmarks(out, lmk / 255, (255, 0, 0))
output = np.transpose(output, (1, 2, 0))
plt.imshow(output)
plt.show()
src = np.concatenate([src, add], axis=0)
dst = np.concatenate([dst, add], axis=0)
tform = PiecewiseAffineTransform()
tform.estimate(dst, src)
# out_rows ,out_cols = shape
out_rows = image.shape[0]
out_cols = image.shape[1]
out = warp(image, tform, output_shape=(out_rows, out_cols))
return out
if __name__ == '__main__':
import matplotlib.pyplot as plt
image = plt.imread('/data/icme/data/picture/AFW_134212_1_0.jpg')
bbox = utils.read_bbox('/data/icme/bbox/AFW_134212_1_0.jpg.rect')
src = utils.read_mat('/data/icme/data/landmark/AFW_134212_1_0.jpg.txt')
src = utils.norm_landmarks(src, bbox)
image = image[bbox[1]:bbox[3], bbox[0]:bbox[2]]
bbox = utils.read_bbox('/data/icme/bbox/AFW_134212_1_3.jpg.rect')
dst = utils.read_mat('/data/icme/data/landmark/AFW_134212_1_3.jpg.txt')
dst = utils.norm_landmarks(dst, bbox)
out = pwa(image, src, dst, (128, 128))
plt.subplot(1, 2, 1)
plt.imshow(image)
plt.subplot(1, 2, 2)
plt.imshow(out)
# plt.plot(tform.inverse(src)[:, 0], tform.inverse(src)[:, 1], '.b')
metrics = Metrics().add_nme().add_auc()
model_name = 'nose-model-8400.pth'
saver.load(net, model_name)
all_pr = []
all_gt = []
save_dir = '/data/icme/data/pred_landmark_align_nose'
base_dir = '/data/icme/data/pred_landmark_align'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
batch_iterator = iter(DataLoader(a, batch_size=1, shuffle=False, num_workers=4))
for i in range(len(a)):
image, gt, t, name = next(batch_iterator)
pr = utils.read_mat(os.path.join(base_dir, name[0] + '.txt'))
image = image.cuda()
with torch.no_grad():
nose = net.forward(image)
nose = nose.cpu().data.numpy()
gt = gt.data.numpy()
t = t.data.numpy()
nose = np.reshape(nose, (-1, 2))
nose[:, 0] *= a.shape[1]
nose[:, 1] *= a.shape[0]
nose = a.aligner.inverse(nose, t[0])
pr[ids] = nose
gt = np.reshape(gt, (-1, 2))
all_pr.append(pr)
all_gt.append(gt)
img_dir = '/data/icme/data/picture'
bbox_dir = '/data/icme/bbox'
landmark_dir = '/data/icme/data/landmark'
aligner = alignment.Align('cache/mean_landmarks.pkl')
idx = np.argsort(pc)
for i in range(5000, len(idx) - 5):
id = list(idx[i:i + 5])
names = filenames[id]
for j in range(1, 5):
src = names[0]
dst = names[j]
image = plt.imread(os.path.join(img_dir, src))
bbox = utils.read_bbox(os.path.join(bbox_dir, src + '.rect'))
src = utils.read_mat(os.path.join(landmark_dir, src + '.txt'))
image, src, _ = aligner(image, src, bbox)
src[:, 0] /= aligner.scale[0]
src[:, 1] /= aligner.scale[1]
image2 = plt.imread(os.path.join(img_dir, dst))
bbox = utils.read_bbox(os.path.join(bbox_dir, dst + '.rect'))
dst = utils.read_mat(os.path.join(landmark_dir, dst + '.txt'))
image2, dst, _ = aligner(image2, dst, bbox)
dst[:, 0] /= aligner.scale[0]
dst[:, 1] /= aligner.scale[1]
dst[0:34, :] = src[0:34, :]
out = pwa.pwa(image, src, dst, (128, 128))
plt.subplot(1, 3, 1)
plt.imshow(image)
img_dir = '/data/icme/data/picture'
bbox_dir = '/data/icme/bbox'
landmark_dir = '/data/icme/data/landmark'
out_dir = '/data/expand/'
N = 7 #每次选择的目标landmark数目，src_nums : dst_nums = 1 : N
stop = len(idx) - N
for i in range(0, stop):
print('Nums:', stop - 1, 'Done:', i)
id = list(idx[i:i + N + 1]) #取N + 1张图片
names = filenames[id]
src_name = names[0]
src_image = plt.imread(os.path.join(img_dir, src_name))
src_landmark = utils.read_mat(os.path.join(landmark_dir,
src_name + '.txt'))
ali_src_image, ali_src_landmark, _ = aligner(src_image.copy(),
src_landmark.copy())
ali_src_landmark[:, 0] /= aligner.scale[0]
ali_src_landmark[:, 1] /= aligner.scale[1]
for j in range(1, N + 1):
dst_name = names[j]
dst_image = plt.imread(os.path.join(img_dir, dst_name))
dst_landmark = utils.read_mat(
os.path.join(landmark_dir, dst_name + '.txt'))
ali_dst_image, ali_dst_landmark, _ = aligner(dst_image.copy(),
dst_landmark.copy())
ali_dst_landmark[:, 0] /= aligner.scale[0]
ali_dst_landmark[:, 1] /= aligner.scale[1]
feature_params = dict(maxCorners=100,
qualityLevel=0.3,
minDistance=7,
blockSize=7)
# Parameters for lucas kanade optical flow
lk_params = dict(winSize=(15, 15),
maxLevel=2,
criteria=(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10,
0.03))
# Create some random colors
# color = np.random.randint(0,255,(106,3))
# Take first frame and find corners in it
old_frame = cv.imread('/data/icme/data/picture/AFW_70037463_1_2.jpg')
old_gray = cv.cvtColor(old_frame, cv.COLOR_BGR2GRAY)
# p0 = cv.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
p0 = utils.read_mat('/data/icme/data/landmark/AFW_70037463_1_2.jpg.txt')
p0 = np.expand_dims(p0, 1)
for new in p0:
a, b = new.ravel()
# c, d = old.ravel()
# mask = cv.line(mask, (a,b),(c,d), color[i].tolist(), 2)
frame = cv.circle(old_frame, (a, b), 2, (0, 0, 255), 1)
cv.imshow("frame", frame)
cv.waitKey(0)
# Create a mask image for drawing purposes
mask = np.zeros_like(old_frame)
for i in [3, 4]:
name = '/data/icme/data/picture/AFW_70037463_1_%d.jpg' % i
frame = cv.imread(name)
frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
# calculate optical flow
def main():
def get_id(name):
t = name.split('_')[0:2]
return t[0] + t[1]
root_dir = 'D:\icmedata\correctdata\\'
lamdmark_dir = os.path.join(root_dir, 'data/landmark')
image_dir = os.path.join(root_dir, 'data/picture')
bbox_dir = os.path.join(root_dir, 'bbox')
filenames = os.listdir(image_dir)
norm_landmarks = []
bboxes = []
split = {}
for filename in filenames:
id = get_id(filename)
if np.random.uniform(0, 1) < 0.8:
split[id] = 'train'
else:
split[id] = 'valid'
landmark_path = os.path.join(lamdmark_dir, filename + '.txt')
bbox_path = os.path.join(bbox_dir, filename + '.rect')
bbox = utils.read_bbox(bbox_path)
landmarks = utils.read_mat(landmark_path)
landmarks = utils.norm_landmarks(landmarks, bbox)
norm_landmarks.append(landmarks)
bboxes.append(bbox)
norm_landmarks = np.stack(norm_landmarks, axis=0)
mean_landmarks = np.mean(norm_landmarks, axis=0)
# for i in range(106):
# plt.scatter(mean_landmarks[i, 0], mean_landmarks[i, 1])
# plt.show()
target=[]
for i, filename in enumerate(filenames):
curr = norm_landmarks[i, :]
y = curr[1::2]
y_max = np.max(y)
y_min = np.min(y)
x = curr[::2]
x_max = np.max(x)
x_min = np.min(x)
chang = x_max - x_min
kuan = y_max - y_min
Slandmark = chang * kuan
#print((Slandmark))
#print(bboxes[i])
# bbox_tempt = np.array(bboxes)
# Sbbox = (bbox_tempt[:, 2] - bbox_tempt[:, 0]) * (bbox_tempt[:, 3] - bbox_tempt[:, 1])
# print(Sbbox[i])
#landmark就是基于bbox做的归一化在untils。norm——landmark所以就不用求Sbbox
target.append(Slandmark)
draw_hist(target, 'acreage title', 'SL/SB', 'amount', 0, 1, 0, 3000)
for i, filename in enumerate(filenames):
img_path = os.path.join(image_dir, filename)
if target[i] > 0.8:
n = 's1'
elif target[i] > 0.75:
n = 's2'
elif target[i] > 0.7:
n = 's3'
elif target[i] > 0.64:
n = 's4'
elif target[i] > 0.6:
n = 's5'
elif target[i] > 0.54:
n = 's6'
elif target[i] > 0.5:
n = 's7'
else:
n = 's8'
id = get_id(filename)
cmd = 'ln -s %s %s/%s/%s/%s' % (img_path, root_dir, split[id], n, filename)
os.system(cmd)
root_dir = 'D:\icmedata\sendaligned\\aligned\\'
lamdmark_dir = os.path.join(root_dir, 'landmark')
image_dir = os.path.join(root_dir, 'picture')
#bbox_dir = os.path.join(root_dir, 'bbox')
filenames = os.listdir(image_dir)
for filename in filenames:
id = get_id(filename)
image_path = os.path.join(image_dir, filename)
image = cv.imread(image_path)
landmark_path = os.path.join(lamdmark_dir, filename + '.txt')
landmarks = utils.read_mat(landmark_path)
x = landmarks[0:33, 0]
y = landmarks[0:33, 1]
# img = img +255
# img.fill(255)
color = (255, 255, 255)
# print(landmarks[0,:])
# print(landmarks[1,:])
#脸部外轮廓
img = np.zeros((128, 128, 3), np.uint8)
for j in range(32):
cv.line(img, (landmarks[j, 0], landmarks[j, 1]),
(landmarks[j + 1, 0], landmarks[j + 1, 1]),
color,
bins = os.listdir(bin_dir)
file_list = []
b = bins[pose]
curr = os.path.join(bin_dir, b)
files = os.listdir(curr)
for i in files:
file_list.append(i)
for i in range(100):
img_dir = os.path.join(root_dir, 'data/picture')
landmark_dir = os.path.join(root_dir, 'data/landmark')
bbox_dir = os.path.join(root_dir, 'bbox')
images = [os.path.join(img_dir, f) for f in file_list]
landmarks = [os.path.join(landmark_dir, f + '.txt') for f in file_list]
bboxes = [os.path.join(bbox_dir, f + '.rect') for f in file_list]
img_path = images[i]
bbox_path = bboxes[i]
landmark_path = landmarks[i]
bbox = ul.read_bbox(bbox_path)
landmarks = ul.read_mat(landmark_path)
image = cv2.imread(img_path)
image, landmark, t = a(image, landmarks)
plt.imshow(image)
plt.scatter(landmark[:, 0], landmark[:, 1])
# plt.scatter(self.reference[:, 0], self.reference[:, 1])
# plt.plot(bbox[:, 0], bbox[:, 1])
plt.xlim(0, a.scale[0])
plt.ylim(a.scale[1], 0)
plt.show()
import os
from utils import pwa
from data import utils
import numpy as np
import matplotlib.pyplot as plt
filename = os.listdir("/home/zhzhong/Desktop/correctdata/train/1/")
img_dir = '/home/zhzhong/Desktop/correctdata/train/1/'
base_dir = "/home/zhzhong/Desktop/correctdata/bbox/"
new_dir = "/home/zhzhong/Desktop/newdata/1/"
bbox1 = np.array([0, 0, 128, 128])
bbox2 = np.array([0, 0, 128, 128])
dst = utils.read_mat(
'/home/zhzhong/Desktop/correctdata/data/landmark/AFW_5452623_1_5.jpg.txt')
dst = utils.norm_landmarks(dst, bbox2)
filenameforlmk = os.listdir("/home/zhzhong/Desktop/correctdata/data/landmark/")
base_diroflmk = "/home/zhzhong/Desktop/correctdata/data/landmark/"
for img in filename:
image = plt.imread(img_dir + img)
# plt.imshow(image)
# plt.show()
box_dir = "/home/zhzhong/Desktop/correctdata/bbox/" + img + '.rect'
box = utils.read_bbox(box_dir)
image = image[box[1]:box[3], box[0]:box[2]]
for lmk in filenameforlmk:
src = utils.read_mat(base_diroflmk + lmk)
src = utils.norm_landmarks(src, bbox1)
image_pwa, _ = pwa.pwa(image, src, dst, (128, 128))
#image_pwa.save(new_dir + img)
plt.imshow(image_pwa)
plt.show()
