view file

[sumanth2002]

The view file has a if condition where if the device is "gpu" use it else use "cpu", it should be "cuda" and not "gpu"
`from django.shortcuts import render, redirect
import torch
import torchvision
from torchvision import transforms, models
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset
import os
import numpy as np
import cv2
import matplotlib.pyplot as plt
import face_recognition
from torch.autograd import Variable
import time
import sys
from torch import nn
import json
import glob
import copy
from torchvision import models
import shutil
from PIL import Image as pImage
import time
from django.conf import settings
from .forms import VideoUploadForm

index_template_name = 'index.html'
predict_template_name = 'predict.html'
about_template_name = "about.html"

im_size = 112
mean=[0.485, 0.456, 0.406]
std=[0.229, 0.224, 0.225]
sm = nn.Softmax()
inv_normalize = transforms.Normalize(mean=-1*np.divide(mean,std),std=np.divide([1,1,1],std))
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'

train_transforms = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((im_size,im_size)),
transforms.ToTensor(),
transforms.Normalize(mean,std)])

class Model(nn.Module):

def __init__(self, num_classes,latent_dim= 2048, lstm_layers=1 , hidden_dim = 2048, bidirectional = False):
    super(Model, self).__init__()
    model = models.resnext50_32x4d(pretrained = True)
    self.model = nn.Sequential(*list(model.children())[:-2])
    self.lstm = nn.LSTM(latent_dim,hidden_dim, lstm_layers,  bidirectional)
    self.relu = nn.LeakyReLU()
    self.dp = nn.Dropout(0.4)
    self.linear1 = nn.Linear(2048,num_classes)
    self.avgpool = nn.AdaptiveAvgPool2d(1)

def forward(self, x):
    batch_size,seq_length, c, h, w = x.shape
    x = x.view(batch_size * seq_length, c, h, w)
    fmap = self.model(x)
    x = self.avgpool(fmap)
    x = x.view(batch_size,seq_length,2048)
    x_lstm,_ = self.lstm(x,None)
    return fmap,self.dp(self.linear1(x_lstm[:,-1,:]))

class validation_dataset(Dataset):
def init(self,video_names,sequence_length=60,transform = None):
self.video_names = video_names
self.transform = transform
self.count = sequence_length

def __len__(self):
    return len(self.video_names)

def __getitem__(self,idx):
    video_path = self.video_names[idx]
    frames = []
    a = int(100/self.count)
    first_frame = np.random.randint(0,a)
    for i,frame in enumerate(self.frame_extract(video_path)):
        #if(i % a == first_frame):
        faces = face_recognition.face_locations(frame)
        try:
          top,right,bottom,left = faces[0]
          frame = frame[top:bottom,left:right,:]
        except:
          pass
        frames.append(self.transform(frame))
        if(len(frames) == self.count):
            break
    """
    for i,frame in enumerate(self.frame_extract(video_path)):
        if(i % a == first_frame):
            frames.append(self.transform(frame))
    """        
    # if(len(frames)<self.count):
    #   for i in range(self.count-len(frames)):
    #         frames.append(self.transform(frame))
    #print("no of frames", self.count)
    frames = torch.stack(frames)
    frames = frames[:self.count]
    return frames.unsqueeze(0)

def frame_extract(self,path):
  vidObj = cv2.VideoCapture(path) 
  success = 1
  while success:
      success, image = vidObj.read()
      if success:
          yield image

def im_convert(tensor, video_file_name):
""" Display a tensor as an image. """
image = tensor.to("cpu").clone().detach()
image = image.squeeze()
image = inv_normalize(image)
image = image.numpy()
image = image.transpose(1,2,0)
image = image.clip(0, 1)
# This image is not used
# cv2.imwrite(os.path.join(settings.PROJECT_DIR, 'uploaded_images', video_file_name+'_convert_2.png'),image*255)
return image

def im_plot(tensor):
image = tensor.cpu().numpy().transpose(1,2,0)
b,g,r = cv2.split(image)
image = cv2.merge((r,g,b))
image = image*[0.22803, 0.22145, 0.216989] + [0.43216, 0.394666, 0.37645]
image = image*255.0
plt.imshow(image.astype('uint8'))
plt.show()

def predict(model,img,path = './', video_file_name=""):
fmap,logits = model(img.to(device))
img = im_convert(img[:,-1,:,:,:], video_file_name)
params = list(model.parameters())
weight_softmax = model.linear1.weight.detach().cpu().numpy()
logits = sm(logits)
_,prediction = torch.max(logits,1)
confidence = logits[:,int(prediction.item())].item()*100
print('confidence of prediction:',logits[:,int(prediction.item())].item()*100)
return [int(prediction.item()),confidence]

def plot_heat_map(i, model, img, path = './', video_file_name=''):
fmap,logits = model(img.to(device))
params = list(model.parameters())
weight_softmax = model.linear1.weight.detach().cpu().numpy()
logits = sm(logits)
_,prediction = torch.max(logits,1)
idx = np.argmax(logits.detach().cpu().numpy())
bz, nc, h, w = fmap.shape
#out = np.dot(fmap[-1].detach().cpu().numpy().reshape((nc, hw)).T,weight_softmax[idx,:].T)
out = np.dot(fmap[i].detach().cpu().numpy().reshape((nc, hw)).T,weight_softmax[idx,:].T)
predict = out.reshape(h,w)
predict = predict - np.min(predict)
predict_img = predict / np.max(predict)
predict_img = np.uint8(255predict_img)
out = cv2.resize(predict_img, (im_size,im_size))
heatmap = cv2.applyColorMap(out, cv2.COLORMAP_JET)
img = im_convert(img[:,-1,:,:,:], video_file_name)
result = heatmap * 0.5 + img0.8*255

Saving heatmap - Start

heatmap_name = video_file_name+"heatmap"+str(i)+".png"
image_name = os.path.join(settings.PROJECT_DIR, 'uploaded_images', heatmap_name)
cv2.imwrite(image_name,result)

Saving heatmap - End

result1 = heatmap * 0.5/255 + img*0.8
r,g,b = cv2.split(result1)
result1 = cv2.merge((r,g,b))
return image_name

Model Selection

def get_accurate_model(sequence_length):
model_name = []
sequence_model = []
final_model = ""
list_models = glob.glob(os.path.join(settings.PROJECT_DIR, "models", "*.pt"))

for model_path in list_models:
    model_name.append(os.path.basename(model_path))

for model_filename in model_name:
    try:
        seq = model_filename.split("_")[3]
        if int(seq) == sequence_length:
            sequence_model.append(model_filename)
    except IndexError:
        pass  # Handle cases where the filename format doesn't match expected

if len(sequence_model) > 1:
    accuracy = []
    for filename in sequence_model:
        acc = filename.split("_")[1]
        accuracy.append(acc)  # Convert accuracy to float for proper comparison
    max_index = accuracy.index(max(accuracy))
    final_model = os.path.join(settings.PROJECT_DIR, "models", sequence_model[max_index])
elif len(sequence_model) == 1:
    final_model = os.path.join(settings.PROJECT_DIR, "models", sequence_model[0])
else:
    print("No model found for the specified sequence length.")  # Handle no models found case

return final_model

ALLOWED_VIDEO_EXTENSIONS = set(['mp4','gif','webm','avi','3gp','wmv','flv','mkv'])

def allowed_video_file(filename):
#print("filename" ,filename.rsplit('.',1)[1].lower())
if (filename.rsplit('.',1)[1].lower() in ALLOWED_VIDEO_EXTENSIONS):
return True
else:
return False
def index(request):
if request.method == 'GET':
video_upload_form = VideoUploadForm()
if 'file_name' in request.session:
del request.session['file_name']
if 'preprocessed_images' in request.session:
del request.session['preprocessed_images']
if 'faces_cropped_images' in request.session:
del request.session['faces_cropped_images']
return render(request, index_template_name, {"form": video_upload_form})
else:
video_upload_form = VideoUploadForm(request.POST, request.FILES)
if video_upload_form.is_valid():
video_file = video_upload_form.cleaned_data['upload_video_file']
video_file_ext = video_file.name.split('.')[-1]
sequence_length = video_upload_form.cleaned_data['sequence_length']
video_content_type = video_file.content_type.split('/')[0]
if video_content_type in settings.CONTENT_TYPES:
if video_file.size > int(settings.MAX_UPLOAD_SIZE):
video_upload_form.add_error("upload_video_file", "Maximum file size 100 MB")
return render(request, index_template_name, {"form": video_upload_form})

        if sequence_length <= 0:
            video_upload_form.add_error("sequence_length", "Sequence Length must be greater than 0")
            return render(request, index_template_name, {"form": video_upload_form})
        
        if allowed_video_file(video_file.name) == False:
            video_upload_form.add_error("upload_video_file","Only video files are allowed ")
            return render(request, index_template_name, {"form": video_upload_form})
        
        saved_video_file = 'uploaded_file_'+str(int(time.time()))+"."+video_file_ext
        if settings.DEBUG:
            with open(os.path.join(settings.PROJECT_DIR, 'uploaded_videos', saved_video_file), 'wb') as vFile:
                shutil.copyfileobj(video_file, vFile)
            request.session['file_name'] = os.path.join(settings.PROJECT_DIR, 'uploaded_videos', saved_video_file)
        else:
            with open(os.path.join(settings.PROJECT_DIR, 'uploaded_videos','app','uploaded_videos', saved_video_file), 'wb') as vFile:
                shutil.copyfileobj(video_file, vFile)
            request.session['file_name'] = os.path.join(settings.PROJECT_DIR, 'uploaded_videos','app','uploaded_videos', saved_video_file)
        request.session['sequence_length'] = sequence_length
        return redirect('ml_app:predict')
    else:
        return render(request, index_template_name, {"form": video_upload_form})

def predict_page(request):
if request.method == "GET":
# Redirect to 'home' if 'file_name' is not in session
if 'file_name' not in request.session:
return redirect("ml_app:home")
if 'file_name' in request.session:
video_file = request.session['file_name']
if 'sequence_length' in request.session:
sequence_length = request.session['sequence_length']
path_to_videos = [video_file]
video_file_name = os.path.basename(video_file)
video_file_name_only = os.path.splitext(video_file_name)[0]
# Production environment adjustments
if not settings.DEBUG:
production_video_name = os.path.join('/home/app/staticfiles/', video_file_name.split('/')[3])
print("Production file name", production_video_name)
else:
production_video_name = video_file_name

    # Load validation dataset
    video_dataset = validation_dataset(path_to_videos, sequence_length=sequence_length, transform=train_transforms)

    # Load model
    if(device == "cuda"):
        model = Model(2).cuda()  # Adjust the model instantiation according to your model structure
    else:
        model = Model(2).cpu()  # Adjust the model instantiation according to your model structure
    model_name = os.path.join(settings.PROJECT_DIR, 'models', get_accurate_model(sequence_length))
    path_to_model = os.path.join(settings.PROJECT_DIR, model_name)
    model.load_state_dict(torch.load(path_to_model, map_location=torch.device('cpu')))
    model.eval()
    start_time = time.time()
    # Display preprocessing images
    print("<=== | Started Videos Splitting | ===>")
    preprocessed_images = []
    faces_cropped_images = []
    cap = cv2.VideoCapture(video_file)
    frames = []
    while cap.isOpened():
        ret, frame = cap.read()
        if ret:
            frames.append(frame)
        else:
            break
    cap.release()

    print(f"Number of frames: {len(frames)}")
    # Process each frame for preprocessing and face cropping
    padding = 40
    faces_found = 0
    for i in range(sequence_length):
        if i >= len(frames):
            break
        frame = frames[i]

        # Convert BGR to RGB
        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

        # Save preprocessed image
        image_name = f"{video_file_name_only}_preprocessed_{i+1}.png"
        image_path = os.path.join(settings.PROJECT_DIR, 'uploaded_images', image_name)
        img_rgb = pImage.fromarray(rgb_frame, 'RGB')
        img_rgb.save(image_path)
        preprocessed_images.append(image_name)

        # Face detection and cropping
        face_locations = face_recognition.face_locations(rgb_frame)
        if len(face_locations) == 0:
            continue

        top, right, bottom, left = face_locations[0]
        frame_face = frame[top - padding:bottom + padding, left - padding:right + padding]

        # Convert cropped face image to RGB and save
        rgb_face = cv2.cvtColor(frame_face, cv2.COLOR_BGR2RGB)
        img_face_rgb = pImage.fromarray(rgb_face, 'RGB')
        image_name = f"{video_file_name_only}_cropped_faces_{i+1}.png"
        image_path = os.path.join(settings.PROJECT_DIR, 'uploaded_images', image_name)
        img_face_rgb.save(image_path)
        faces_found += 1
        faces_cropped_images.append(image_name)

    print("<=== | Videos Splitting and Face Cropping Done | ===>")
    print("--- %s seconds ---" % (time.time() - start_time))

    # No face detected
    if faces_found == 0:
        return render(request, 'predict_template_name.html', {"no_faces": True})

    # Perform prediction
    try:
        heatmap_images = []
        output = ""
        confidence = 0.0

        for i in range(len(path_to_videos)):
            print("<=== | Started Prediction | ===>")
            prediction = predict(model, video_dataset[i], './', video_file_name_only)
            confidence = round(prediction[1], 1)
            output = "REAL" if prediction[0] == 1 else "FAKE"
            print("Prediction:", prediction[0], "==", output, "Confidence:", confidence)
            print("<=== | Prediction Done | ===>")
            print("--- %s seconds ---" % (time.time() - start_time))

            # Uncomment if you want to create heat map images
            # for j in range(sequence_length):
            #     heatmap_images.append(plot_heat_map(j, model, video_dataset[i], './', video_file_name_only))

        # Render results
        context = {
            'preprocessed_images': preprocessed_images,
            'faces_cropped_images': faces_cropped_images,
            'heatmap_images': heatmap_images,
            'original_video': production_video_name,
            'models_location': os.path.join(settings.PROJECT_DIR, 'models'),
            'output': output,
            'confidence': confidence
        }

        if settings.DEBUG:
            return render(request, predict_template_name, context)
        else:
            return render(request, predict_template_name, context)

    except Exception as e:
        print(f"Exception occurred during prediction: {e}")
        return render(request, 'cuda_full.html')

def about(request):
return render(request, about_template_name)

def handler404(request,exception):
return render(request, '404.html', status=404)
def cuda_full(request):
return render(request, 'cuda_full.html')
`