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e0c75d6 | 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 | import os
import numpy as np
import cv2
import torch
import dlib
import face_recognition
from torchvision import transforms
from tqdm import tqdm
from dataset.loader import normalize_data
from .config import load_config
from .genconvit import GenConViT
from decord import VideoReader, cpu
import glob
from PIL import Image
device = "cuda" if torch.cuda.is_available() else "cpu"
def load_genconvit(config, net, ed_weight, vae_weight, fp16):
model = GenConViT(
config,
ed= ed_weight,
vae= vae_weight,
net=net,
fp16=fp16
)
model.to(device)
model.eval()
if fp16:
model.half()
return model
def face_rec(frames):
temp_face = np.zeros((len(frames), 224, 224, 3), dtype=np.uint8)
count = 0
mod = "cnn" if dlib.DLIB_USE_CUDA else "hog"
for _, frame in tqdm(enumerate(frames), total=len(frames)):
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
face_locations = face_recognition.face_locations(
frame, number_of_times_to_upsample=0, model=mod
)
for face_location in face_locations:
if count < len(frames):
top, right, bottom, left = face_location
face_image = frame[top:bottom, left:right]
face_image = cv2.resize(
face_image, (224, 224), interpolation=cv2.INTER_AREA
)
face_image = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)
temp_face[count] = face_image
count += 1
else:
break
return ([], 0) if count == 0 else (temp_face[:count], count)
def preprocess_frame(frame):
df_tensor = torch.tensor(frame, device=device).float()
df_tensor = df_tensor.permute((0, 3, 1, 2))
for i in range(len(df_tensor)):
df_tensor[i] = normalize_data()["vid"](df_tensor[i] / 255.0)
return df_tensor
def pred_vid(df, model, net=None):
with torch.no_grad():
output = model(df, net=net).squeeze()
if len(output.shape) == 1:
output = output.unsqueeze(0)
# Apply softmax to get probabilities
probabilities = torch.softmax(output, dim=1)
return max_prediction_value(probabilities)
def max_prediction_value(y_pred):
# Finds the index and value of the maximum prediction value.
mean_val = torch.mean(y_pred, dim=0)
max_val, max_idx = torch.max(mean_val, dim=0)
return max_idx.item(), max_val.item()
def real_or_fake(prediction):
return {0: "FAKE", 1: "REAL"}[prediction]
def extract_frames(video_file, num_frames=15):
vr = VideoReader(video_file, ctx=cpu(0))
total_frames = len(vr)
if num_frames == -1:
# if -1, get all frames
indices = np.arange(total_frames).astype(int)
else:
indices = np.linspace(0, total_frames -1, num_frames, dtype=int)
return vr.get_batch(indices).asnumpy() # seek frames with step_size
def df_face_from_folder(vid, num_frames):
img_list = glob.glob(vid+"/*")
img = []
for f in img_list:
try:
im = Image.open(f).convert('RGB')
img.append(np.asarray(im))
except:
pass
face, count = face_rec(img[:num_frames])
return preprocess_frame(face) if count > 0 else []
def df_face(vid, num_frames):
img = extract_frames(vid, num_frames)
face, count = face_rec(img)
return preprocess_frame(face) if count > 0 else []
def is_video(vid):
return os.path.isfile(vid) and vid.endswith(
tuple([".avi", ".mp4", ".mpg", ".mpeg", ".mov"])
)
def is_video_folder(vid_folder):
img_list = glob.glob(vid_folder+"/*")
return len(img_list)>=1 and img_list[0].endswith(tuple(["png", "jpeg","jpg"]))
def set_result():
return {
"video": {
"name": [],
"pred": [],
"klass": [],
"pred_label": [],
"correct_label": [],
}
}
def store_result(
result, filename, y, y_val, klass, correct_label=None, compression=None
):
result["video"]["name"].append(filename)
result["video"]["pred"].append(y_val)
result["video"]["klass"].append(klass.lower())
result["video"]["pred_label"].append(real_or_fake(y))
if correct_label is not None:
result["video"]["correct_label"].append(correct_label)
if compression is not None:
result["video"]["compression"].append(compression)
return result
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