Upload extract_landmarks.py

extract_landmarks.py

@@ -0,0 +1,77 @@
+import numpy as np
+import cv2
+from landmark_utils import detect_frames_track
+
+def detect_track(video):
+    vidcap = cv2.VideoCapture(video)
+    fps = vidcap.get(cv2.CAP_PROP_FPS)
+    frames = []
+    while True:
+        success, image = vidcap.read()
+        if success:
+            frames.append(image)
+        else:
+            break
+    raw_data = detect_frames_track(frames, fps, video)
+
+    vidcap.release()
+    return np.array(raw_data)
+
+def extract_landmark(video):
+    raw_data = detect_track(video)
+    print("video=",video)
+    if len(raw_data) == 0:
+        pass
+    else:
+        np.savetxt(video + ".txt", raw_data, fmt='%1.5f')
+        path = video + ".txt"
+        print(path)
+    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)
+    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