def crop_img_by_lmk(
lmk2D_txt_path,
lmk3D_txt_path,
lmk2D_crop_txt_path,
lmk3D_crop_txt_path,
img_dir,
out_crop_dir,
orig=False,
):
if os.path.exists(out_crop_dir) is False:
os.makedirs(out_crop_dir)
fopen2D = open(lmk2D_crop_txt_path, "w")
fopen3D = open(lmk3D_crop_txt_path, "w")
landmarks2D, images_name = load_landmark(lmk2D_txt_path, 68)
landmarks3D, images_name = load_landmark(lmk3D_txt_path, 86)
count = 0
for i in range(0, len(images_name)):
img_name = images_name[i]
img = cv2.imread(os.path.join(img_dir, img_name))
(
crop_img,
prediction3D,
prediction2D,
ori_crop_img,
) = crop_image_and_process_landmark(img,
landmarks3D[i],
landmarks2D[i],
size=300,
orig=orig)
write_lmk(img_name, np.reshape(prediction3D, [86, 2]), fopen3D)
write_lmk(img_name, np.reshape(prediction2D, [68, 2]), fopen2D)
cv2.imwrite(os.path.join(out_crop_dir, img_name), crop_img)
if orig:
cv2.imwrite(
os.path.join(out_crop_dir,
img_name[:-4] + "_ori" + img_name[-4:]),
ori_crop_img,
)
count = count + 1
if (count % 100 == 0) | (count == len(images_name)):
print("has run crop_img_by_lmk: " + str(count) + " / " +
str(len(images_name)))
fopen2D.close()
fopen3D.close()
return
def detect_2Dlmk_all_imgs(graph_file, img_dir, lmk3D_txt_path, lmk2D_txt_path):
with tf.Graph().as_default():
graph_def = tf.GraphDef()
graph_file = graph_file
with open(graph_file, "rb") as f:
print("hello")
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name="")
with tf.Session() as sess:
tf.initialize_all_variables().run()
fopen = open(lmk2D_txt_path, "w")
landmarks3D, images_name = load_landmark(lmk3D_txt_path, 86)
count = 0
for i in range(0, len(images_name)):
img_name = images_name[i]
img = cv2.imread(os.path.join(img_dir, img_name))
lmk3D = landmarks3D[i]
LMK2D_batch = detect_2D_landmark.detect_2Dlmk68(
np.array([lmk3D]), np.array([img]), sess)
write_lmk(img_name, np.reshape(LMK2D_batch[0], [68, 2]), fopen)
count = count + 1
if (count % 100 == 0) | (count == len(images_name)):
print("has run 68pt lmk: " + str(count) + " / " +
str(len(images_name)))
fopen.close()
return LMK2D_batch
def face_seg(graph_file, lmk3D_crop_txt_path, out_crop_dir, seg_dir):
if os.path.exists(seg_dir) is False:
os.makedirs(seg_dir)
landmarks3D, images_name = load_landmark(lmk3D_crop_txt_path, 86)
with tf.Graph().as_default():
graph_def = tf.GraphDef()
graph_file = graph_file
with open(graph_file, "rb") as f:
print("hello")
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name="")
with tf.Session() as sess:
tf.initialize_all_variables().run()
count = 0
for i in range(0, len(images_name)):
img_name = images_name[i]
crop_img = cv2.imread(os.path.join(out_crop_dir, img_name))
lmk3D = landmarks3D[i]
SEG_batch, SEG_color_batch = face_segmentation.run_face_seg(
np.array([lmk3D]), np.array([crop_img]), sess
)
np.save(os.path.join(seg_dir, img_name[:-3] + "npy"), SEG_batch[0])
cv2.imwrite(
os.path.join(seg_dir, img_name[:-4] + "_seg.jpg"),
SEG_color_batch[0],
)
count = count + 1
if (count % 100 == 0) | (count == len(images_name)):
print(
"has run face_seg: "
+ str(count)
+ " / "
+ str(len(images_name))
)
return
