| import cv2 | |
| from albumentations import Compose, PadIfNeeded | |
| from transforms.albu import IsotropicResize | |
| import numpy as np | |
| import os | |
| import cv2 | |
| import torch | |
| from statistics import mean | |
| def transform_frame(image, image_size): | |
| transform_pipeline = Compose([ | |
| IsotropicResize(max_side=image_size, interpolation_down=cv2.INTER_LINEAR, interpolation_up=cv2.INTER_LINEAR), | |
| PadIfNeeded(min_height=image_size, min_width=image_size, border_mode=cv2.BORDER_REPLICATE) | |
| ] | |
| ) | |
| return transform_pipeline(image=image)['image'] | |
| def resize(image, image_size): | |
| try: | |
| return cv2.resize(image, dsize=(image_size, image_size)) | |
| except: | |
| return [] | |
| def custom_round(values): | |
| result = [] | |
| for value in values: | |
| if value > 0.6: | |
| result.append(1) | |
| else: | |
| result.append(0) | |
| return np.asarray(result) | |
| def get_method(video, data_path): | |
| methods = os.listdir(os.path.join(data_path, "manipulated_sequences")) | |
| methods.extend(os.listdir(os.path.join(data_path, "original_sequences"))) | |
| methods.append("DFDC") | |
| methods.append("Original") | |
| selected_method = "" | |
| for method in methods: | |
| if method in video: | |
| selected_method = method | |
| break | |
| return selected_method | |
| def shuffle_dataset(dataset): | |
| import random | |
| random.seed(4) | |
| random.shuffle(dataset) | |
| return dataset | |
| def get_n_params(model): | |
| pp=0 | |
| for p in list(model.parameters()): | |
| nn=1 | |
| for s in list(p.size()): | |
| nn = nn*s | |
| pp += nn | |
| return pp | |
| def check_correct(preds, labels): | |
| preds = preds.cpu() | |
| labels = labels.cpu() | |
| preds = [np.asarray(torch.sigmoid(pred).detach().numpy()).round() for pred in preds] | |
| correct = 0 | |
| positive_class = 0 | |
| negative_class = 0 | |
| for i in range(len(labels)): | |
| pred = int(preds[i]) | |
| if labels[i] == pred: | |
| correct += 1 | |
| if pred == 1: | |
| positive_class += 1 | |
| else: | |
| negative_class += 1 | |
| return correct, positive_class, negative_class | |
| def custom_video_round(preds): | |
| for pred_value in preds: | |
| if pred_value > 0.55: | |
| return pred_value | |
| return mean(preds) | |