def main():
#model = double_stream_model_12()
#model.load_weights("checkpoints/"+"doublestream"+"_model_weights.h5")
from keras.applications.mobilenet import preprocess_input
base_model = MobileNet(weights='imagenet',include_top=False,input_shape=(HEIGHT,WIDTH,3))
preprocessing_function = preprocess_input
class_list_file = "checkpoints/MobileNet_class_list.txt"
class_list = utils.load_class_list(class_list_file)
model = utils.build_finetune_model(base_model,dropout=1e-3,num_classes=len(class_list),fc_layers=[1024,1024])
model.load_weights("checkpoints/MobileNet_model_weights.h5")
path = "/Users/tangxi/Downloads/Compressed/deepfake_baselinev1_1/test_videos"
videos = getvideos(path)
predictor = dlib.shape_predictor("/Users/tangxi/Downloads/Compressed/deepfake_baselinev1_1/shape_predictor_68_face_landmarks.dat")
detector = dlib.get_frontal_face_detector()
predictions=[]
for each in videos:
p_each_video=0.0
vc = cv2.VideoCapture(each)
rval, frame = vc.read()
# 获取视频fps
fps = vc.get(cv2.CAP_PROP_FPS)
# 获取视频总帧数
frame_all = vc.get(cv2.CAP_PROP_FRAME_COUNT)
print("[INFO] 视频FPS: {}".format(fps))
print("[INFO] 视频总帧数: {}".format(frame_all))
print("[INFO] 视频时长: {}s".format(frame_all/fps))
fake_count=0
total_count=0
while True:
ret,frame = vc.read()
if ret is False:
break
total_count+=1
img = frame.copy()
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
dets = detector(gray, 0)
if len(dets)!=1:continue
d = dets[0]
x1 = d.top() if d.top() > 0 else 0
y1 = d.bottom() if d.bottom() > 0 else 0
x2 = d.left() if d.left() > 0 else 0
y2 = d.right() if d.right() > 0 else 0
face = img[x1-25:y1+10,x2-8:y2+8]
print(face.shape)
p_fake = predict_single_image(model,face)
if p_fake>0.5:
fake_count+=1
if fake_count/float(total_count)>0.5:
p_each_video = fake_count/float(total_count)
else:
p_each_video = 0.5
predictions.append(p_each_video)
submission_df = pd.DataFrame({"filename": test_videos, "label": predictions})
submission_df.to_csv("submission.csv", index=False)
os.makedirs("%s" % ("Predictions"))
# Read in your image
image = cv2.imread(args.image)
image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
image = np.array(image)
#image = Image.open(args.image)
#print(image.shape)
#image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
save_image = image
image = np.float32(cv2.resize(image, (HEIGHT, WIDTH)))
image = preprocessing_function(image.reshape(1, HEIGHT, WIDTH, 3))
class_list_file = "./checkpoints/" + args.model + "_class_list.txt"
class_list = utils.load_class_list(class_list_file)
finetune_model = utils.build_finetune_model(base_model,
num_classes=len(class_list),
dropout=args.dropout,
fc_layers=FC_LAYERS)
finetune_model.load_weights("./checkpoints/" + args.model +
"_model_weights.h5")
# Run the classifier and print results
st = time.time()
out = finetune_model.predict(image)
confidence = out[0]
ValueError("You must pass an image path when using prediction mode.")
images = []
if args.image_dir is None:
images.append(args.image)
else:
for file in next(os.walk(args.image_dir))[2]:
if not file.lower().endswith(
('.png', '.jpg', '.bmp', '.ppm', '.tif')):
continue
images.append(os.path.join(args.image_dir, file))
images.sort()
class_list = utils.load_class_list(model_name=args.model,
dataset_name=DATASET_NAME)
#final_model = load_model(FINAL_MODEL_PATH)
from keras.models import model_from_json
with open(FINAL_MODEL_PATH, "r") as json_file:
loaded_model_json = json_file.read()
final_model = model_from_json(loaded_model_json)
json_file.close()
final_model.load_weights(FINAL_WEIGHTS_PATH)
# Run the classifier and print results
st = time.time()
# Read in images
for file in images:
print("File: ", file)
