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import argparse |
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import os |
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import sys |
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import torch |
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from PIL import Image |
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import torchvision.transforms as transforms |
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from models.networks.drn_seg import DRNSub |
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from utils.tools import * |
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from utils.visualize import * |
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def load_classifier(model_path, gpu_id): |
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if torch.cuda.is_available() and gpu_id != -1: |
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device = 'cuda:{}'.format(gpu_id) |
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else: |
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device = 'cpu' |
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model = DRNSub(1) |
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state_dict = torch.load(model_path, map_location='cpu') |
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model.load_state_dict(state_dict['model']) |
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model.to(device) |
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model.device = device |
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model.eval() |
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return model |
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tf = transforms.Compose([transforms.ToTensor(), |
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transforms.Normalize(mean=[0.485, 0.456, 0.406], |
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std=[0.229, 0.224, 0.225])]) |
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def classify_fake(model, img_path, no_crop=False, |
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model_file='utils/dlib_face_detector/mmod_human_face_detector.dat'): |
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im_w, im_h = Image.open(img_path).size |
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if no_crop: |
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face = Image.open(img_path).convert('RGB') |
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else: |
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faces = face_detection(img_path, verbose=False, model_file=model_file) |
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if len(faces) == 0: |
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print("no face detected by dlib, exiting") |
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sys.exit() |
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face, box = faces[0] |
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face = resize_shorter_side(face, 400)[0] |
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face_tens = tf(face).to(model.device) |
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with torch.no_grad(): |
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prob = model(face_tens.unsqueeze(0))[0].sigmoid().cpu().item() |
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return prob |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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"--input_path", required=True, help="the model input") |
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parser.add_argument( |
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"--model_path", required=True, help="path to the drn model") |
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parser.add_argument( |
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"--gpu_id", default='0', help="the id of the gpu to run model on") |
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parser.add_argument( |
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"--no_crop", |
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action="store_true", |
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help="do not use a face detector, instead run on the full input image") |
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args = parser.parse_args() |
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model = load_classifier(args.model_path, args.gpu_id) |
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prob = classify_fake(model, args.input_path, args.no_crop) |
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print("Probibility being modified by Photoshop FAL: {:.2f}%".format(prob*100)) |
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