File size: 6,616 Bytes
479504c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 |
import cv2
import dlib
import json
import glob
import os
from multiprocessing import Pool
LIP_COORDINATES_DIRECTORY = "lip_coordinates"
ERROR_DIRECTORY = "error_videos"
# path to the original GRID dataset whose videos are converted to frames
GRID_IMAGES_DIRECTORY = "lip/GRID_imgs"
train_unseen_list = "data/unseen_val.txt"
train_overlap_list = "data/overlap_train.txt"
test_unseen_list = "data/unseen_val.txt"
test_overlap_list = "data/overlap_val.txt"
def load_data_list(data_path, dictionary):
with open(data_path, "r") as f:
for line in f.readlines():
line = line.strip()
speaker = line.split("/")[-4]
vid = line.split("/")[-1]
dictionary[f"{speaker}/{vid}"] = 1
return dictionary
def extract_lip_coordinates(detector, predictor, img_path):
# used to preprocess the original image frames in the GRID dataset to extract the lip coordinates
image = cv2.imread(img_path)
image = cv2.resize(image, (600, 500))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
rects = detector(gray)
assert len(rects) == 1
for rect in rects:
# extract the coordinates of the bounding box
x1 = rect.left()
y1 = rect.top()
x2 = rect.right()
y2 = rect.bottom()
# apply the shape predictor to the face ROI
shape = predictor(gray, rect)
x = []
y = []
for n in range(48, 68):
x.append(shape.part(n).x)
y.append(shape.part(n).y)
return [x, y]
def log_error_video(video_path):
print("Error: ", video_path)
with open(ERROR_DIRECTORY + "/error_videos.txt", "a") as f:
f.write(video_path + "\n")
data_dict = {}
data_dict = load_data_list(train_unseen_list, data_dict)
data_dict = load_data_list(train_overlap_list, data_dict)
data_dict = load_data_list(test_unseen_list, data_dict)
data_dict = load_data_list(test_overlap_list, data_dict)
speakers = glob.glob(GRID_IMAGES_DIRECTORY + "/*")
print(speakers[0])
def generate_lip_coordinates(speakers):
file_path_sep = "\\"
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(
"lip_coordinate_extraction/shape_predictor_68_face_landmarks_GTX.dat"
)
for speaker in speakers:
print(speaker)
videos = glob.glob(speaker + "/*")
for video in videos:
print(video)
frames = glob.glob(video + "/*.jpg")
if len(frames) < 50: # filter out bad videos
continue
vid = {}
try:
frames = sorted(
frames,
key=lambda x: int(x.split(file_path_sep)[-1].split(".")[0]),
)
for frame in frames:
retry = 3
while retry > 0:
try:
coords = extract_lip_coordinates(detector, predictor, frame)
break
except Exception as e:
retry -= 1
print("Error: ", video)
print(e)
print("retrying...")
vid[frame.split(file_path_sep)[-1].split(".")[0]] = coords
vid_path = video.split(file_path_sep)
save_path = (
LIP_COORDINATES_DIRECTORY
+ "/"
+ vid_path[-2]
+ "/"
+ vid_path[-1]
+ ".json"
)
if not os.path.exists(LIP_COORDINATES_DIRECTORY + "/" + vid_path[-2]):
os.makedirs(LIP_COORDINATES_DIRECTORY + "/" + vid_path[-2])
with open(
save_path,
"w",
) as f:
json.dump(vid, f)
except Exception as e:
print(e)
log_error_video(video)
def generate_lip_coordinates(speakers):
file_path_sep = "\\"
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(
"lip_coordinate_extraction/shape_predictor_68_face_landmarks_GTX.dat"
)
for speaker in speakers:
print(speaker)
videos = glob.glob(speaker + "/*")
for video in videos:
# if (
# video.split(file_path_sep)[-2] + "/" + video.split(file_path_sep)[-1]
# not in data_dict
# ):
# continue
print(video)
frames = glob.glob(video + "/*.jpg")
if len(frames) < 50: # filter out bad videos
continue
vid = {}
try:
frames = sorted(
frames,
key=lambda x: int(x.split(file_path_sep)[-1].split(".")[0]),
)
for frame in frames:
retry = 3
while retry > 0:
try:
coords = extract_lip_coordinates(detector, predictor, frame)
break
except Exception as e:
retry -= 1
print("Error: ", video)
print(e)
print("retrying...")
vid[frame.split(file_path_sep)[-1].split(".")[0]] = coords
vid_path = video.split(file_path_sep)
save_path = (
LIP_COORDINATES_DIRECTORY
+ "/"
+ vid_path[-2]
+ "/"
+ vid_path[-1]
+ ".json"
)
if not os.path.exists(LIP_COORDINATES_DIRECTORY + "/" + vid_path[-2]):
os.makedirs(LIP_COORDINATES_DIRECTORY + "/" + vid_path[-2])
with open(
save_path,
"w",
) as f:
json.dump(vid, f)
except Exception as e:
print(e)
log_error_video(video)
num_processes = 8
speaker_groups = []
speaker_interval = len(speakers) // num_processes
for i in range(num_processes):
if i == 4:
speaker_groups.append(speakers[i * speaker_interval :])
else:
speaker_groups.append(
speakers[i * speaker_interval : (i + 1) * speaker_interval]
)
if __name__ == "__main__":
with Pool(num_processes) as p:
p.map(generate_lip_coordinates, speaker_groups)
|