update

app.py

@@ -1,179 +1,3 @@
 import gradio as gr
-import cv2
-import numpy as np
-import tensorflow as tf
-import tensorflow_addons
 
-from facenet_pytorch import MTCNN
-from PIL import Image
-import moviepy.editor as mp
-import os
-import zipfile
-
-# local_zip = "FINAL-EFFICIENTNETV2-B0.zip"
-# zip_ref = zipfile.ZipFile(local_zip, 'r')
-# zip_ref.extractall('FINAL-EFFICIENTNETV2-B0')
-# zip_ref.close()
-
-# Load face detector
-mtcnn = MTCNN(margin=14, keep_all=True, factor=0.7, device='cpu')
-
-#Face Detection function, Reference: (Timesler, 2020); Source link: https://www.kaggle.com/timesler/facial-recognition-model-in-pytorch
-class DetectionPipeline:
-    """Pipeline class for detecting faces in the frames of a video file."""
-
-    def __init__(self, detector, n_frames=None, batch_size=60, resize=None):
-        """Constructor for DetectionPipeline class.
-
-        Keyword Arguments:
-            n_frames {int} -- Total number of frames to load. These will be evenly spaced
-                throughout the video. If not specified (i.e., None), all frames will be loaded.
-                (default: {None})
-            batch_size {int} -- Batch size to use with MTCNN face detector. (default: {32})
-            resize {float} -- Fraction by which to resize frames from original prior to face
-                detection. A value less than 1 results in downsampling and a value greater than
-                1 result in upsampling. (default: {None})
-        """
-        self.detector = detector
-        self.n_frames = n_frames
-        self.batch_size = batch_size
-        self.resize = resize
-
-    def __call__(self, filename):
-        """Load frames from an MP4 video and detect faces.
-
-        Arguments:
-            filename {str} -- Path to video.
-        """
-        # Create video reader and find length
-        v_cap = cv2.VideoCapture(filename)
-        v_len = int(v_cap.get(cv2.CAP_PROP_FRAME_COUNT))
-
-        # Pick 'n_frames' evenly spaced frames to sample
-        if self.n_frames is None:
-            sample = np.arange(0, v_len)
-        else:
-            sample = np.linspace(0, v_len - 1, self.n_frames).astype(int)
-
-        # Loop through frames
-        faces = []
-        frames = []
-        for j in range(v_len):
-            success = v_cap.grab()
-            if j in sample:
-                # Load frame
-                success, frame = v_cap.retrieve()
-                if not success:
-                    continue
-                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-                # frame = Image.fromarray(frame)
-
-                # Resize frame to desired size
-                if self.resize is not None:
-                    frame = frame.resize([int(d * self.resize) for d in frame.size])
-                frames.append(frame)
-
-                # When batch is full, detect faces and reset frame list
-                if len(frames) % self.batch_size == 0 or j == sample[-1]:
-
-                    boxes, probs = self.detector.detect(frames)
-
-                    for i in range(len(frames)):
-
-                        if boxes[i] is None:
-                            faces.append(face2)     #append previous face frame if no face is detected
-                            continue
-
-                        box = boxes[i][0].astype(int)
-                        frame = frames[i]
-                        face = frame[box[1]:box[3], box[0]:box[2]]
-
-                        if not face.any():
-                            faces.append(face2)     #append previous face frame if no face is detected
-                            continue
-
-                        face2 = cv2.resize(face, (224, 224))
-
-                        faces.append(face2)
-
-                    frames = []
-
-        v_cap.release()
-
-        return faces
-
-
-detection_pipeline = DetectionPipeline(detector=mtcnn,n_frames=20, batch_size=60)
-
-model = tf.keras.models.load_model("./EfficientNetV2_Deepfakes_Video_Detector/p1")
-
-
-def deepfakespredict(input_video):
-
-    faces = detection_pipeline(input_video)
-
-    total = 0
-    real = 0
-    fake = 0
-
-    for face in faces:
-
-        face2 = face/255
-        pred = model.predict(np.expand_dims(face2, axis=0))[0]
-        total+=1
-
-        pred2 = pred[1]
-
-        if pred2 > 0.5:
-          fake+=1
-        else:
-          real+=1
-
-    fake_ratio = fake/total
-
-    text =""
-    text2 = "Deepfakes Confidence: " + str(fake_ratio*100) + "%"
-
-    if fake_ratio >= 0.5:
-        text = "The video is FAKE."
-    else:
-        text = "The video is REAL."
-
-    face_frames = []
-    
-    for face in faces:
-        face_frame = Image.fromarray(face.astype('uint8'), 'RGB')
-        face_frames.append(face_frame)
-        
-    face_frames[0].save('results.gif', save_all=True, append_images=face_frames[1:], duration = 250, loop = 100 )
-    clip = mp.VideoFileClip("results.gif")
-    clip.write_videofile("video.mp4")
-
-    return text, text2, "video.mp4"
-
-
-
-title="EfficientNetV2 Deepfakes Video Detector"
-description="This is a demo implementation of EfficientNetV2 Deepfakes Image Detector by using frame-by-frame detection. \
-            To use it, simply upload your video, or click one of the examples to load them.\
-            This demo and model represent the Final Year Project titled \"Achieving Face Swapped Deepfakes Detection Using EfficientNetV2\" by a CS undergraduate Lee Sheng Yeh. \
-            The examples were extracted from Celeb-DF(V2)(Li et al, 2020) and FaceForensics++(Rossler et al., 2019). Full reference details is available in \"references.txt.\" \
-            The examples are used under fair use to demo the working of the model only. If any copyright is infringed, please contact the researcher via this email: tp054565@mail.apu.edu.my.\
-            "
-            
-examples = [              
-                ['./EfficientNetV2_Deepfakes_Video_Detector/Video1-fake-1-ff.mp4'],
-                ['./EfficientNetV2_Deepfakes_Video_Detector/Video6-real-1-ff.mp4'],
-                ['./EfficientNetV2_Deepfakes_Video_Detector/Video3-fake-3-ff.mp4'],
-                ['./EfficientNetV2_Deepfakes_Video_Detector/Video8-real-3-ff.mp4'],
-                ['./EfficientNetV2_Deepfakes_Video_Detector/real-1.mp4'],
-                ['./EfficientNetV2_Deepfakes_Video_Detector/fake-1.mp4'],
-           ]
-           
-gr.Interface(deepfakespredict,
-                     inputs = ["video"],
-                     outputs=["text","text", gr.outputs.Video(label="Detected face sequence")],
-                     title=title,
-                     description=description,
-                     examples=examples
-                     ).launch()
+gr.load("models/DaMsTaR/Detecto-DeepFake_Video_Detector").launch()