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import numpy as np
import cv2
from landmark_utils import detect_frames_track
def detect_track(video):
vidcap = cv2.VideoCapture(video)
frames = []
while True:
success, image = vidcap.read()
if success:
frames.append(image)
else:
break
raw_data = detect_frames_track(frames)
vidcap.release()
return np.array(raw_data)
def extract_landmark(video):
raw_data = detect_track(video)
if len(raw_data) == 0:
print("No face detected", video)
else:
np.savetxt(video + ".txt", raw_data, fmt='%1.5f')
path = video + ".txt"
return path
def get_data_for_test(path, fake, block): # fake:manipulated=1 original=0
file = path
x = []
x_diff = []
y = []
video_y = []
count_y = {}
sample_to_video = []
# for file in tqdm(files):
vectors = np.loadtxt(file)
print("vectors = ",vectors)
video_y.append(fake)
for i in range(0, vectors.shape[0] - block, block):
vec = vectors[i:i + block, :]
x.append(vec)
vec_next = vectors[i + 1:i + block, :]
vec_next = np.pad(vec_next, ((0, 1), (0, 0)), 'constant', constant_values=(0, 0))
vec_diff = (vec_next - vec)[:block - 1, :]
x_diff.append(vec_diff)
y.append(fake)
# Dict for counting number of samples in video
if file not in count_y:
count_y[file] = 1
else:
count_y[file] += 1
sample_to_video.append(file)
return np.array(x), np.array(x_diff), np.array(y), np.array(video_y), np.array(sample_to_video), count_y
def merge_video_prediction(mix_prediction, s2v, vc):
prediction_video = []
pre_count = {}
for p, v_label in zip(mix_prediction, s2v):
p_bi = 0
if p >= 0.5:
p_bi = 1
if v_label in pre_count:
pre_count[v_label] += p_bi
else:
pre_count[v_label] = p_bi
for key in pre_count.keys():
prediction_video.append(pre_count[key] / vc[key])
return prediction_video |