Spaces:
Sleeping
Sleeping
File size: 4,033 Bytes
bc3753a |
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 99 100 101 102 103 104 105 106 107 |
import os
import cv2
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
from util.preprocess import align_for_lm
from shutil import move
mean_face = np.loadtxt('util/test_mean_face.txt')
mean_face = mean_face.reshape([68, 2])
def save_label(labels, save_path):
np.savetxt(save_path, labels)
def draw_landmarks(img, landmark, save_name):
landmark = landmark
lm_img = np.zeros([img.shape[0], img.shape[1], 3])
lm_img[:] = img.astype(np.float32)
landmark = np.round(landmark).astype(np.int32)
for i in range(len(landmark)):
for j in range(-1, 1):
for k in range(-1, 1):
if img.shape[0] - 1 - landmark[i, 1]+j > 0 and \
img.shape[0] - 1 - landmark[i, 1]+j < img.shape[0] and \
landmark[i, 0]+k > 0 and \
landmark[i, 0]+k < img.shape[1]:
lm_img[img.shape[0] - 1 - landmark[i, 1]+j, landmark[i, 0]+k,
:] = np.array([0, 0, 255])
lm_img = lm_img.astype(np.uint8)
cv2.imwrite(save_name, lm_img)
def load_data(img_name, txt_name):
return cv2.imread(img_name), np.loadtxt(txt_name)
# create tensorflow graph for landmark detector
def load_lm_graph(graph_filename):
with tf.gfile.GFile(graph_filename, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
tf.import_graph_def(graph_def, name='net')
img_224 = graph.get_tensor_by_name('net/input_imgs:0')
output_lm = graph.get_tensor_by_name('net/lm:0')
lm_sess = tf.Session(graph=graph)
return lm_sess,img_224,output_lm
# landmark detection
def detect_68p(img_path,sess,input_op,output_op):
print('detecting landmarks......')
names = [i for i in sorted(os.listdir(
img_path)) if 'jpg' in i or 'png' in i or 'jpeg' in i or 'PNG' in i]
vis_path = os.path.join(img_path, 'vis')
remove_path = os.path.join(img_path, 'remove')
save_path = os.path.join(img_path, 'landmarks')
if not os.path.isdir(vis_path):
os.makedirs(vis_path)
if not os.path.isdir(remove_path):
os.makedirs(remove_path)
if not os.path.isdir(save_path):
os.makedirs(save_path)
for i in range(0, len(names)):
name = names[i]
print('%05d' % (i), ' ', name)
full_image_name = os.path.join(img_path, name)
txt_name = '.'.join(name.split('.')[:-1]) + '.txt'
full_txt_name = os.path.join(img_path, 'detections', txt_name) # 5 facial landmark path for each image
# if an image does not have detected 5 facial landmarks, remove it from the training list
if not os.path.isfile(full_txt_name):
move(full_image_name, os.path.join(remove_path, name))
continue
# load data
img, five_points = load_data(full_image_name, full_txt_name)
input_img, scale, bbox = align_for_lm(img, five_points) # align for 68 landmark detection
# if the alignment fails, remove corresponding image from the training list
if scale == 0:
move(full_txt_name, os.path.join(
remove_path, txt_name))
move(full_image_name, os.path.join(remove_path, name))
continue
# detect landmarks
input_img = np.reshape(
input_img, [1, 224, 224, 3]).astype(np.float32)
landmark = sess.run(
output_op, feed_dict={input_op: input_img})
# transform back to original image coordinate
landmark = landmark.reshape([68, 2]) + mean_face
landmark[:, 1] = 223 - landmark[:, 1]
landmark = landmark / scale
landmark[:, 0] = landmark[:, 0] + bbox[0]
landmark[:, 1] = landmark[:, 1] + bbox[1]
landmark[:, 1] = img.shape[0] - 1 - landmark[:, 1]
if i % 100 == 0:
draw_landmarks(img, landmark, os.path.join(vis_path, name))
save_label(landmark, os.path.join(save_path, txt_name))
|